# Networks Seminar

Networks Seminar, supported by the Centre for Networked Intelligence, is a technical discussion forum in topics including but not limited to computer networks, machine learning, signal processing, and information theory. The seminar series receives audience from faculty and students in the EECS division, RBCCPS, and engineering professionals working in related fields. Network Seminar series is being held since about an year (Sep. 2019).

Title: Right Buffer Sizing Matters: Some Dynamical and Statistical Studies on Compound TCP

Speaker: Dr. Debayani Ghosh, Chalmers University
Date: 12/01/2021

Abstract: Queuing delay has the potential to hurt the performance of many network applications, especially delay sensitive applications like Voice over IP (VoIP), live video streaming, and online gaming. Unfortunately, queuing delay appears to be on the rise in today’s Internet. This is caused primarily due to persistently full large router buffers, which is commonly known as bufferbloat.This provides the motivation for our work, where we aim to understand the consequences of reducing router buffer sizes on system performance. Our study focuses on Compound TCP, in the regime of small Drop-Tail buffers, where the bandwidth-delay product is large. As TCP’s congestion control mechanism constitutes a closed loop feedback system, we adopt a dynamical systems approach to understand the system dynamics. This constitutes a study of stability (using control-theoretic techniques) and nonlinear dynamics (using Hopf bifurcation analysis). In addition, we pay particular attention to the statistical properties of the queue. In our work, we systematically study three topologies.  We start with a single bottleneck scenario and then proceed towards more complex topologies with two and then three bottleneck routers. Our study reveals the importance of buffer thresholds for ensuring stability. In all cases considered, smaller buffer thresholds tend to favour stability. We also emphasise that larger buffers induce limit cycles in the system dynamics. From a practical perspective, queues with limit cycles would result in a loss of link utilisation, the losses would be bursty, and cause synchronisation among the TCP flows. These insights remain consistent across all topologies, and the traffic scenarios, considered. Hence, our study provides a strong case for why router buffers should be small in terms of reduced latency and stability. Further, such small buffers yield fairly good system performance, in terms of throughput and flow completion times. We also look at the performance of Compound TCP in conjunction with Active Queue Management (AQM) strategies and arrive at the conclusion that even with AQM strategies, the queue size dynamics still shows the emergence of limit cycles and a small threshold-based policy works better. In another thread, we are also looking at conditions under which the TCP flows synchronise, inspired by time-delayed coupled oscillators.

Speaker Bio: Debayani Ghosh obtained her M.S. (by Research) and Ph.D under the dual degree programme from the Department of Electrical Engineering at the Indian Institute of Technology Madras, Chennai, India in 2019. Further, she obtained her B. Tech. in Electronics and Communication Engineering from Cochin University of Science and Technology, Cochin, India in 2012. Currently, she is a post doctoral researcher at Chalmers University of Technology, Sweden. Her research interests lie in the broad area of Networked Control of Large-Scale Systems.

Title: Improving performance in TDMA over Wifi-PHY mesh Networks for Rural Internet Connectivity

Speaker: Dr. Vishal Sevani, DeNovo Group
Date: 22/12/2020

Abstract: The Internet has become an essential part of our daily lives. However, still a significant fraction of the population, especially in rural regions in the developing world, lack access to the Internet. The cost economics is the key reason for this digital divide, as conventional technologies for providing Internet access are not cost effective. In this talk I will highlight the use of “TDMA over WiFI-PHY” mesh networks for providing low cost Internet connectivity. Commodity WiFi hardware is available for cheap and so WiFi mesh networks are an attractive low cost option for Internet connectivity. However the disadvantage of WiFi mesh networks is that the conventional 802.11 CSMA/CA based MAC protocol does not work efficiently in mesh network scenarios. To alleviate this, we have considered the use of TDMA MAC protocol for mesh networks. TDMA MAC protocol does not make use of contention based access as in CSMA/CA MAC protocol and so can provide guaranteed QoS performance. To leverage the low cost benefit of commodity WiFi hardware, we have implemented the TDMA MAC protocol, in software, on top of commodity hardware. We term such networks as “TDMA over WiFi-PHY” mesh networks as for these, PHY layer is that which is specified by WiFi standard but MAC protocol is modified to TDMA MAC protocol. There are several challenges associated with TDMA over WiFi-PHY mesh networks, before these can be practically realized. In this talk I will highlight these different challenges and solutions that we have devised for realizing such networks for low cost Internet connectivity.

Speaker Bio: Vishal Sevani received his B.E in Electronics Engineering and M.E in Electronics and Telecommunication Engineering from Mumbai University (India) in 2002 and 2006 respectively. After completing his Masters, he worked as a Software Engineer for two years. He then joined Computer Science and Engineering Dept. of Indian Institute of Technology, Bombay (India) to pursue his PhD. After PhD, he worked for about a year as a guest researcher in NIST, USA focussing on 5G networks. For the past three years, he has been working for DeNovo Group, helping them in their goal to provide Internet connectivity to rural California.  His research interests are broadly in the area of systems and networking.

Title: Link Speed Estimation for Traffic Flow Modeling Based on Videofeeds from Monocular Cameras

Speaker: Dr. Raghu Krishnapuram
Date: 10/11/2020

Abstract: This talk describes a reliable and scalable approach for real-time estimation of link speeds (i.e., traffic speeds on specific road segments) based on video feeds coming from monocular cameras. We detect and track vehicles of specific types, identify anchor points (or keypoints) on them, compute their poses, and use this information to estimate their speeds. We use deep learning methods for vehicle detection, tracking, keypoint detection and localization, and traditional 3D pose estimation techniques for which precise mathematical solutions are available. Thus, our approach exploits the best of both worlds. The proposed approach does not require any physical measurements (extrinsics) in the road scene, making it scalable and easy to install. Our results on video feeds from Bangalore, India, show that the method is able to generalize well for cameras mounted on streetlight poles, congested traffic situations, and various lighting conditions. Thus, the solution is suitable for emerging market scenarios where traffic tends to be chaotic and dense, and mounting speed sensors or strategically located downward-facing cameras is not feasible. The code and dataset for this work have been made available. This work was carried out in collaboration with Shantam Shorewala and Prajwal Rao, and was recently presented at the 2020 IEEE Intelligent Transportation Systems Conference (ITSC)Rhodes, Greece.

Speaker Bio: Dr. Raghu Krishnapuram is a Distinguised Member of Technical Staff at RBCCPS, IISc, Bangalore. He received his B.Tech. degree from IIT Bombay in 1978, and his M.S. degree from Louisiana State University, Baton Rouge, USA, in 1984. After graduating with a PhD in Computer Engineering from Carnegie Mellon University in 1987, he worked initially at the University of Missouri, Columbia, and later at the Colorado School of Mines, Golden until the year 2000. Most recently, he served as the Head of the R&D and IP Cell, as well as a Professor in Computer Science and Engineering, at the Ramaiah Institute of Technology, Bangalore. Earlier, he spent 16 years in industry R&D. From 2000 to 2013, he held various leadership positions at IBM Research India. During the last 4 years of his tenure at IBM Research India, he served as Associate Director, where he led projects in the area of “Knowledge, Information, and Smarter Planet Solutions”, with a particular focus on emerging markets. He also served as a relationship manager for IBM’s services divisions such as IBM Global Process Services and IBM Business Services during this time. During 2014-15, he worked at the IBM T J Watson Center, Yorktown Heights, New York, where he was a technical leader for cognitive computing research. He was also the Program Manager, Financial Services, Xerox Research Centre India, during 2015-16. Dr. Raghu has published about 170 papers in journals and conferences, many with a very high citation count. He has filed over 40 patent disclosures at the US Patent Office. He has been recognized as a Master Inventor by IBM and has served on the Technology Council of the IBM Academy of Technology. He is also a Fellow of the IEEE and the Indian National Academy of Engineers (INAE).

Title: Average age of information for irregular repetition slotted ALOHA

Speaker: Prof. Vineeth B.S., IIST Thiruvananthapuram
Date: 03/11/2020

Abstract: In this talk, we will discuss the average age of information (AAoI) performance of Irregular Repetition Slotted ALOHA (IRSA) which is a grant free access mechanism for massive machine type communication (mMTC). The mMTC paradigm has enabled the deployment of wide-area sensor networks with a very large number of sensors. Grant-free access mechanisms are useful in mMTC due to their low complexity and ability to scale to a large number of sensors. IRSA is a frame-based grant free channel access scheme which uses repeated transmissions of a data packet within a frame followed by successive interference cancellation at the base station to decode packets. The repetition of data packets in a frame is controlled by the choice of a repetition distribution. IRSA is known to achieve higher values of throughput compared to conventional random access methods. The AAoI is a relevant metric for evaluating the performance of a sensor network in which measurements about a process are transmitted over a channel to a base-station which remotely estimates the process. We consider AAoI performance of frame-based IRSA since AAoI is a more relevant metric for the use case outlined above. We present our results on the characterization of AAoI for IRSA for the cases where measurements are taken by a sensor either at the start of a frame or just before the first transmission of a packet. We also consider a successive interference cancellation scheme that is applied in every slot and characterize its AAoI performance. These characterizations of AAoI depend on the choice of the repetition distribution and we also present our results on the optimal choice of the repetition distribution to minimize the AAoI. The challenges in this optimization problem and the characterization of AAoI will also be discussed.

Speaker Bio: Vineeth is an Assistant Professor at the Department of Avionics in the Indian Institute of Space Science and Technology, Thiruvananthapuram. He received his PhD from the Department of Electrical Communication Engineering, IISc and his B.Tech from the College of Engineering, Trivandrum. His research interests lies in the development and application of models and analytical tools for performance analysis and optimization of engineering systems.

Title: Implementation of reliable protection and energy management schemes in smart grids

Speaker: Harikrishna Muda
Date: 30/10/2020

Abstract: Microgrids are receiving attention due to the increasing need to integrate distributed generations and to ensure power quality and energy surety. At the same time, the great evolution in communication and information technology is leading the transformation of microgrids into smart grids, where the continuous dialogue between all of the devices connected to the network implies improvements in the overall efficiency and quality of the system. This presentation mainly focuses on background and fundamental building blocks of smart grid with stringent emphasis on practical applications in the existing power system network. An overview of smart grid and its potential in different types of power sectors such as power generation, transmission and distribution in Metro, Urban/Semi urban and remote locations of India is described. This also emphasizes on renewable energy source integration in present grids as well as in micro, nano grids and explores its issues in protection, operation, and control. In addition to it, this further provides utility level analysis in terms of energy management, optimal setting, and operation of conventional and renewable based power plants.

Speaker Bio: Dr. Harikrishna is currently working as a Post-Doctoral Associate with an emphasis on “Integrated approach to development of India’s power sector” at National Institute of Advanced Studies, Bangalore. He is closely working with India’s regional grid operators for utility-scale feasibility studies. Dr. Harikrishna completed his Doctor of Philosophy and Master of Technology from the Indian Institute of Technology Roorkee in 2017 and 2010 respectively. His research interests include ac/dc micro-grids stability, protection, control schemes and data analysis in the energy mix. He has the systematic in-depth specialized knowledge about renewable energies, the paradigm of micro-grids, and various simulation tools MATLAB/Simulink, PSSE, PSCAD, RSCAD, MiPower, and python etc. Harikrishna completed his Bachelor of Technology from Jyothishmathi Institute of Science and Technology, Telangana. He has a total six years of experience in academic, industry and research fields. Initial phase of his career, mainly he engaged in the consulting, training, and development work on engineering services in various organizations in India. He worked as a Research Engineer at Khalifa University, Abu Dhabi. He received a POSOCO award by PGCIL, five scholarships from MHRD, Government of India, and best paper certificate in international conference, which is organized Amity Univeristy, Dubai. He received Excellence Teaching in Higher Education Award-2020, DKIRF Govt. of Tamilnadu and Best Young Researcher Award by Venus International foundation. Dr. Hari has published 4 journal articles in IEEE, IET, 14 conference papers, 2 more chapters in springer journal and assisted to guide several graduate and post graduate students. He is a Member of IEEE, IET, IET and life member of ISTE organizations. He has presented seminars in various reputed organizations in his career.

Title: Joint Communication and Signal Reconstructions over Multi-user Channels

Speaker:  Dr. Viswanathan Ramachandran, IIT-Bombay
Date: 29/10/2020

Abstract: Joint communication and signal estimation is important in several communication systems employing hybrid analog-digital radios. The goal here is to reconstruct an analog signal with reasonable fidelity, while simultaneously conveying a digital data stream. Several interesting multi-user information theoretic models with message and state communication are considered in this talk, for which optimal communication and distortion trade-offs are investigated. We further explore multi-terminal coordination problems, where the objective is to simulate joint probability distributions across distributed terminals, using rate limited links.

Speaker Bio: Viswanathan Ramachandran obtained his bachelors and  masters degrees in Electronics and Telecom Engineering from the University of Mumbai in 2011 and 2014 respectively. He recently completed his PhD in Electrical Engineering from the Indian Institute of Technology Bombay. His research interests are in information theory and wireless communications, particularly in joint source-channel coding and channel simulation problems in multi-terminal settings.

Title: Enhancing QoS in Delay/Disruption Tolerant Network

Speaker:  Sangita Dhara, IIT Kharagpur
Date: 28/10/2020

Abstract: In this talk, I will discuss two topics of study.

Maximizing Throughput for InterPlanetary networks: My current work, building around on the topic of my Ph.D. dissertation, focuses on maximizing throughput in interplanetary network. Interplanetary Network (IPN) or Deep Space Network (DSN) provides communication between stations on Earth and other remote Spacecrafts, Satellites, Orbiters, Lander and Rovers. Interplanetary communication is different from our terrestrial communication because it poses some specific characteristics like long propagation delay, intermittent connectivity, asymmetric bandwidth, etc. In the future, many more spacecraft, landers, rovers and relay orbiters will be launched in deep space and therefore, deep space network has to undergo many changes as traffic load and type of communication. Deep space data transmission, therefore, will not be a pre-scheduled time/bandwidth specific communication as it exists now. Multiple deep space nodes may transmit huge amount of data to the Earth station (destination) using limited bandwidth and multiple hops. Therefore, we have critically examined whether the limited and precious bandwidth of the network is being put to the best possible use.Enhancing QoS in in Information-Centric Vehicular Networks: With the experience of working with a network where connection are intermittent, in my post doctorate study I would like to work with vehicle to vehicle networking (VANET). This has been evolved as a core technology for ensuring safety, managing traffic, and providing comfort to the users in vehicular environments.

Speaker Bio: Sangita Dhara is currently a Ph.D. research scholar in Advanced Technology Development Centre at Indian Institute of Technology Kharagpur, India. In her research, she is focusing on maximizing throughput in interplanetary networks. She received the B.Tech degree in Information Technology from West Bengal University of Technology in 2010 and the MS (by Research) degree from Indian Institute of Technology Kharagpur in 2013. She was working in projects sponsored by SAC, Ahmedabad (ISRO) titled ‘Enhancement of transport layer protocol for interplanetary network’ from Oct 2010 to Mar 2013 and ‘Protocol development for delay tolerant deep space network’ form Jun 2013 to Mar 2017 respectively. Her main research interest is computer networks and design of algorithms. She is an IEEE Student member.

Title: Sotware-Defined Networking for Internet of Things: Rule Placement and Routing

Speaker:  Samaresh Bera
Date: 27/10/2020

Abstract: The software-defined networking (SDN) technology enables real-time programmability of network devices by decoupling control- and data-planes. Thus, the complexity involved in managing networks is reduced significantly compared to that of the traditional networks. Although different SDN-based approaches exist in the literature, they either focus on the traditional networking issues or are at their infancy to be integrated into fully programmable networks to provide differentiated service provisioning for IoT applications. In this talk, adaptive rule-placement and routing strategies to satisfy heterogeneous quality-of-service (QoS) requirements of traffic from sensors and network devices in SDN-enabled end-to-end IoT networks will be discussed. Further, the scalability issues with a single SDN controller in the presence of a large number of flows in the network will be discussed, and a dynamic controller assignment scheme will be presented to mitigate the scalability issues.

Speaker Bio: Samaresh Bera has submitted his Ph.D. thesis at the Department of Computer Science and Engineering, IIT Kharagpur, India. Currently, he is working as a Visiting Researcher at BTIRC, IISc Bangalore. Prior to joining BTIRC, he worked as a Research Engineer at INRIA Sophia Antipolis, France in a joint collaboration with Nokia Bell Labs, France. He received the M.S. degree from the School of Information Technology, IIT Kharagpur, India in 2015. He has co-authored more than 25 research papers and his publications have received more than 1000 citations in Google Scholar. He was awarded the IEEE Richard E Merwin student scholarship by the IEEE Computer Society in 2017 for his outstanding contributions to the IEEE. Further, he was also selected as ‘Young Researcher’ for attending Heidelberg Laureate Forum, Germany in 2017. His research interests include software-defined networks, 5G networks, and IoT.

Title: Naturalness Assessment for Video Prediction.

Speaker:  Nagabhushan S N
Date: 20/10/2020

Abstract: Video Prediction refers to predicting future frames of a video given a few past frames. Video Prediction has found applications in video representation learning, robotics, compression, and many others. While researchers have focused on this problem of prediction, there has been very little work on the complementary problem of evaluating the predicted videos. To bridge this gap, we proposed video naturalness as an evaluation measure. In this regard, we have developed a new database of videos predicted by various models and conducted a human study to understand how humans perceive naturalness, which we call as the IISc Video Naturalness Evaluation (IISc-VINE) database. Using our database, we have developed a new naturalness measure based on deep features of videos. We show that, while popular quality assessment (QA) measures such as mean squared error (MSE) and structural similarity (SSIM) do not correlate adequately well with human perception (in this context), our algorithm achieves the state of the art performance w.r.t. correlation with human scores.

Speaker Bio: Nagabhushan S N is a PhD Student in Visual Information Processing lab in ECE Dept, working under the guidance of Prof. Rajiv Soundararajan. He obtained his B.E. degree in Electronics and Communications from P.E.S. Institute of Technology in 2016, with a gold medal. He worked as a Software Engineer in Cisco Systems India Pvt. Ltd. for 2 years (2016-18). His current research interests are in the areas of Image and Video Signal Processing, Machine Learning and Computer Vision. Nagabhushan’s personal webpage can be found at https://sites.google.com/site/nagabhushansn95.

Title: Development of an Autonomous Micro-robotic Swarm System – An overview

Speaker:  Akshatha Jagadish
Date: 15/09/2020

Abstract: Micro-robotics is an emerging field of research where the focus areas are physical actuation, system control, materials, sensor research and so on. It is an interdisciplinary field where researchers from communities such as physics, chemistry, engineering (biotech, mechanical, comp science) have a role to play. A micro-robot is a controllable machine of micron scale with application specific capabilities in addition to generic functions such as motion, sensing and control mechanism. Scaling robotic systems to micro-scale, forces us to focus on physical parameters such as surface tension, adhesion and drag instead of mass and inertia. There has been research in development of actuation mechanisms at micron scale such as magnetically actuated rigid helices, cilia and sperm-mimetic synthetic tails, chemically powered spherical particles and cannons, synthetically engineered bacteria, muscle cells, etc. Parallel research in this field studies the swarm behaviour and control of such micro-robots. In this talk we will focus on the introduction to this field of research, look at some implementations and talk about our work on the study of the effect of external control on active particle behaviour.

Speaker Bio: Akshatha Jagadish is a PhD student at RBCCPS working with Prof. Manoj Varma (CeNSE). She received her B.Tech. degree in Electronics and Communication from PES Institute of Technology, Bangalore in 2015. She worked as Associate Software Engineer for Automotive Functional Safety at Robert Bosch Engineering and Business Solutions, India during 2015-17. Her current research area is the field of micro-robotic system design and control.

Title: Mathematics of Neural Nets
Speaker:  Anirbit Mukherjee
Date: 08/09/2020

Abstract: One of the paramount mathematical mysteries of our times is to be able to explain the phenomenon of deep-learning i.e training neural nets. Neural nets can be made to paint while imitating classical art styles or play chess better than any machine or human ever and they seem to be the closest we have ever come to achieving “artificial intelligence”. But trying to reason about these successes quickly lands us into a plethora of extremely challenging mathematical questions – typically about discrete stochastic processes. Some of these questions remain unsolved for even the smallest neural nets! In this talk we will give a brief introduction to neural nets and describe two of the most recent themes of our work in this direction.

Firstly we will explain how under certain structural and mild distributional conditions our iterative algorithms like “Neuro-Tron”, which do not use a gradient oracle can often be proven to train nets using as much time/sample complexity as expected from gradient based methods but in regimes where usual algorithms like (S)GD remain unproven. Our theorems include the particularly challenging regime of non-realizable data. Next we will briefly look at our first-of-its-kind results about sufficient conditions for fast convergence of standard deep-learning algorithms like RMSProp, which use the history of gradients to decide the next step. In the second half of the talk, we will focus on the recent rise of the PAC-Bayesian technology in being able to explain the low risk of certain over-parameterized nets on standardized tests. We will present our recent results in this domain which empirically supersede some of the existing theoretical benchmarks in this field and this we achieve via our new proofs about the key property of noise resilience of nets.

This is joint work with Amitabh Basu (JHU), Ramchandran Muthukumar (JHU), Jiayao Zhang (UPenn), Dan Roy (UToronto, Vector Institute), Pushpendre Rastogi (JHU, Amazon), Soham De (DeepMind, Google), Enayat Ullah (JHU), Jun Yang (UToronto, Vector Institute) and Anup Rao (Adobe).

Speaker Bio: Anirbit Mukherjee finished his Ph.D. in applied mathematics at the Johns Hopkins University advised by Prof. Amitabh Basu. He is soon starting a post-doc at Wharton (UPenn), Statistics with Prof. Weijie Su. He specializes in deep-learning theory and has been awarded 2 fellowships from JHU for this research – the Walter L. Robb Fellowship and the inaugural Mathematical Institute for Data Science Fellowship. Earlier, he was a researcher in Quantum Field Theory, while doing his undergrad in physics at the Chennai Mathematical Institute (CMI) and masters in theoretical physics at the Tata Institute of Fundamental research (TIFR).

Title: Analysis of Network Logs
Speaker:  Dr. Mouli Chandramouli
Date: 01/09/2020

Abstract: Analysis of network logs collected from network devices is presented.  The objective is to understand and determine the important network events and infer the possible root causes of those network events.  The volume of network data is very high and often it can be quite challenging to filter out only the key important messages.  We have developed ML / NLP based techniques to extract the underlying statistical templates of the SYSLOG messages, and secondly identify anomalous patterns observed in the SYSLOG events which can be useful to recommend suitable remedial actions. The proposed solution is under evaluation by network operations.

Speaker Bio: Mouli Chandramouli is currently working as a Data Scientist at Cisco Systems, Bangalore in the area of application of Machine Learning algorithms for analytics of Network Telemetry and Network Inference. He is also a Visiting Professor at the RBCCPS, IISc. He received his M. S. and  Ph.D. from University of Arizona, Tuscon, AZ in the area of Stochastic Process and Queueing Theory.  Prior work experience at AT&T Bell Laboratories, Holmdel, NJ,  Bell Communications Research, NJ in the area of network performance modelling and Dynamicsoft, NJ a startup company focussed on VOIP products based on SIP Protocol which was acquired by Cisco Systems.  At Cisco Systems, his work has been is in the areas of MPLS networks, Energy Management for networking devices and distributed embedded network analytics algorithms.

Title: Minimizing Age in a Multihop Wireless Network
Speaker:  Ashok Krishnan K. S.
Date: 18/08/2020

Abstract: Age of Information (AoI) of packets in a network give a sense of the ‘freshness’ of the information. Applications often require packets to be delivered before they age too much. The talk will discuss a scheduling algorithm designed to transmit packets across the network while meeting age requirements of multiple flows, simultaneously. The algorithm uses a packet dropping rule and a slot wise optimization, which can also be implemented in a distributed fashion. It is seen to perform well, and brings the age close to a theoretical lower bound.

Speaker Bio: Ashok Krishnan K.S. recently completed his Ph.D. from the Department of ECE, IISc. His research interests are in the areas of wireless networks, communications and queueing.

Title: Fair Cake Division
Speaker:  Nidhi Rathi
Date: 11/08/2020

Abstract: The theory of Fair Division addresses the fundamental problem of allocating goods among agents with equal entitlements but distinct preferences. Here, the resources can be (1) divisible like water/land, (2) indivisible like courses in universities, property settlements or (3) indivisible resources with money like electronic frequency allocation. In this talk, I will, in particular focus on the classic cake-cutting problem that provides a model for addressing fair and efficient allocation of a divisible, heterogeneous resource (metaphorically, the cake) among agents with distinct preferences. I will present some of the recent results that complements the existential (and non-constructive) guarantees and various hardness results by way of developing efficient (approximation) algorithms for cake division. I will also talk about a recent result that identifies a broad class of cake division instances that essentially admits a polynomial time algorithm to compute fair and efficient allocations.

Speaker Bio: Nidhi Rathi is an Integrated PhD student at the Department of Mathematics, Indian Institute of Science (IISc). She started her research as a PhD scholar under the guidance of Prof. Siddharth Barman (Dept. of Computer Science and Automation, IISc) and Prof. Mrinal K. Ghosh (Department of Mathematics, IISc). She is a recipient of the prestigious IBM PhD fellowship 2020. Her main area of research is Algorithmic Game theory. In particular, she is interested in exploring the computability of equilibria and fair resource allocations under various settings, and hence, developing algorithms with provable fairness guarantees. She was one of the invited speakers in ACM summer school on Algorithmic Game theory held at IIT Gandhinagar in the summer of 2019.

Title: Support Recovery from Linear Sketches.
Speaker:  Lekshmi Ramesh
Date: 04/08/2020

Abstract: In this talk, I will describe the problem of support recovery, where samples sharing a common unknown support are observed through low dimensional projections or ”linear sketches”, and the goal is to recover the common support. This problem has been well-studied in the single sample setting, and it is known that for certain classes of random projections, the projection dimension only needs to scale linearly in the support size and logarithmically in the dimension of the samples for guaranteeing support recovery. For the multiple sample setting, a natural question to ask is if we can make do with a smaller projection dimension per sample at the cost of a larger number of samples, and to characterize the tradeoff arising between support size, projection dimension, and the number of samples. We will see that the nature of this tradeoff differs depending on whether the projection dimension is larger or smaller relative to the support size. I will mention some results from the literature, which has mostly focused on characterizing this tradeoff in the large projection dimension regime, followed by our results in the case when the projection dimension is small. We will see some commonly used algorithms for this problem, and some lower bounds results that are used to show optimality of the algorithms.

Speaker Bio:Lekshmi Ramesh is a PhD student in the Department of ECE, working with Prof. Chandra R. Murthy and Prof. Himanshu Tyagi.

Title: Metastability phenomenon: large deviations in the stationary regime.                                             Speaker: Sarath AY
Date: 28/07/2020

Abstract: A perturbed dynamical system is said to exhibit the metastability phenomenon when it behaves very differently over different time scales. Many networked systems such as load balancing networks, WiFi networks, etc. exhibit such phenomenon when there are multiple stable operating points in the system. One approach to quantify the metastability phenomenon is to study large deviations of this perturbed dynamics in the stationary regime. This talk will provide an overview of various techniques (in the existing literature) to obtain large deviations in the stationary regime from process-level large deviations.

Speaker Bio: Sarath is a PhD student in ECE department working with Prof. Rajesh Sundaresan.

Title: Latency of cellular communication                                                                                                         Speaker: Prof. Himanshu Tyagi
Date: 14/07/2020

Abstract: Emerging applications of cellular communication such as control over wireless links require low latency communication. LTE provides an approximately 15ms latency link, which is supposed to suffice for roughly 150-350ms end-to-end latency requirements. The emerging 5G standard will push this envelop further to even lower latency. But standards just pose a challenge which the technology strives to address. In this talk, we will see what are the typical latency requirements for various applications, what are the main factors that add up to LTE latency, and how 5G is trying to circumvent these bottlenecks of latency. It is a summary of our learnings over the past year as a part of the 5G project where we have been trying to understand the standards, study the limitations of typical implementations, and identify the main bottlenecks for low latency communication in practice. This talk is based on weekly discussions with SVR Anand, Aditya Gopalan, Parimal Parag, and other brave soldiers of the 5G V2X team at the ECE department.

Speaker Bio: Himanshu Tyagi is an Assistant Professor at the Electrical Communication Engineering department at the Indian Institute of Science. He is a co-convenor of the Centre for Networked Intelligence. He received the Bachelor of Technology degree in electrical engineering and the Master of Technology degree in communication and information technology, both from IIT Delhi in 2007. He received the Ph.D. degree in Electrical and Computer Engineering from the University of Maryland, College Park. From 2013 to 2014, he was a postdoctoral researcher at the Information Theory and Applications (ITA) Center, UCSD.

Title: CORNET: A Co-Simulation Middleware for Robot Networks                                                                Speaker: Srikrishna Acharya B.
Date: 30/06/2020

Abstract: CORNET is a co-simulation middleware for applications involving multi-robot systems like a network of Unmanned Aerial Vehicle (UAV) systems. Design of such systems requires knowledge of the flight dynamics of UAVs and the communication links connecting UAVs with each other or with the ground control station. Besides, UAV networks are dynamic and distinctive from other ad-hoc networks and require protocols that can adapt to high-mobility, dynamic topology and changing link quality in power constrained resource platforms. Therefore, it is necessary to co-design the UAV path planning algorithms and the communication protocols. The proposed co-simulation framework integrates existing tools to simulate flight dynamics and network related aspects. Gazebo with robot operating system (ROS) is used as a physical system UAV simulator and NS-3 is used as a network simulator, to jointly capture the cyber-physical system (CPS) aspects of the multi- UAV systems. A particular aspect we address is on synchronizing time and position across the two simulation environments, and we provide APIs to allow easy migration of the algorithms to real platforms.

Speaker Bio:  Srikrishna has a Bachelor degree in Electronics from G. Pulla Reddy College of Engineering & Technology, Kurnool and a Master degree in VLSI and Embedded Systems from the Indraprastha Institute of Information Technology, Delhi.  His current research interest are software and simulation frameworks for connected drones.

Title: The IISc-TIFR agent-based city simulator for the study of COVID-19 spread                                   Speaker: Prof. Prahladh Harsha (TIFR)
Date: 23/06/2020

Abstract: Over the last few months, since the spread of the COVID-19 pandemic, several epidemiological models have been proposed to understand the spread of the infection in the population. In this talk,
we will provide an overview of the agent-based models and compare their pros-and-cons against other models. We will argue that agent-based models let one capture detailed interactions at a granular level and can thus be useful in comparing the impact of various non-pharmaceutical interventions against each other. We will then present the IISc-TIFR agent-based city simulator developed for studying the spread of the COVID-19 infection in the cities of Mumbai and Bengaluru under various “unlocking” strategies. We will
conclude with a presentation of the simulator’s estimates for the infection-spread in Mumbai under different lockdown-relaxation scenarios; containment strategies, phased opening of workplaces, gradual
resumption of train services, importance of compliance.

Speaker Bio: Prahladh Harsha is an Associate Professor at the School of Technology and Computer Science (STCS) at the Tata Institute of Fundamental Research (TIFR), Mumbai. He obtained his Bachelors degree from IIT Madras in 1998 and his Ph.D. from MIT in 2004. After MIT, he was a post-doctoral researcher at Microsoft Research, Silicon Valley, a research assistant professor at the Toyota Technological Institute at Chicago, (2005), a visiting scientist at the University of Texas at Austin and at the Technion, Israel Institute of Technology and has been at the faculty at TIFR since Dec 2009.

Title: Bellman meets Shannon: On the Applications of Dynamic Programming in Capacity Computation           Speaker: V. Arvind Rameshwar
Date: 16/06/2020

Abstract: In this talk, we shall go over dynamic programming-based (or DP-based) methods for computing the capacity of finite-state channels (FSCs) or channels with memory, with and without feedback. First, we consider the setting of FSCs without feedback and derive lower bounds on the capacity for the broad class of input-driven channels, where the current channel state is a time-invariant deterministic function of the previous state and the current input. The lower bounds are based on a DP characterization of a bound on the maximum reverse directed information rate. We show that one can explicitly solve the said DP problem, and in the process, obtain useful achievable rates for the runlength-limited input-constrained binary erasure and binary symmetric channels. This is based on joint work with Prof. Navin Kashyap.

We then move on to the setting of a class of FSCs with feedback, where the feedback capacity expression is amenable to formulation as a DP problem. In particular, we shall consider the inter-cell interference (ICI) channel in NAND flash memories and provide numerical evaluations of the feedback capacity. We also discuss an interesting scenario where a simple constrained code achieves the capacity, with and without feedback. The results are from joint work with Aryabhatt M.R. and Prof. Navin Kashyap.

Speaker Bio:  V. Arvind Rameshwar is a PhD student at the Code Design and Analysis Lab, in the Department of ECE, working under the guidance of Prof. Navin Kashyap. A goldmedallist from BITS Pilani, Hyderabad Campus, he graduated with a B.E. (Hons.) in ECE, in 2018. His research interests lie in the information theory of finite-state channels.

Title: Metastability phenomenon: large deviations in the stationary regime                                              Speaker: Sarath A. Y.
Date: 17/03/2020

Abstract: A perturbed dynamical system is said to exhibit the metastability phenomenon when it behaves very differently over different time scales. Many networked systems such as load balancing networks, WiFi networks, etc. exhibit such phenomenon when there are multiple stable operating points in the system. One approach to quantify the metastability phenomenon is to study large deviations of this perturbed dynamics in the stationary regime. This talk will provide an overview of various techniques (in the existing literature) to obtain large deviations in the stationary regime from process-level large deviations.

Speaker Bio:  Sarath is a PhD student in ECE department working with Prof. Rajesh Sundaresan

Title: Visual Search with a Trembling Hand: An Analysis of Odd Arm Identification in Restless Multi-armed Bandits                                                                                                                                                                    Speaker: Karthik P. N
Date: 10/03/2020

Abstract: This work is motivated by a visual search experiment in which a human subject is shown a number of drifting-dots images. The direction of drift in one of these images (the odd image) is different from the common direction of drift in rest of the images. The goal of the human subject is to identify the location of the odd drifting-dots image in the shortest possible time while keeping his probability of decision error small. Our interest is in understanding the relation between (a) the amount of time taken by the human subject to identify the odd image, and (b) the “closeness” of the odd and the non-odd images used in the experiment. It is often the case that the human subjects participating in such visual search experiments tend to sample image locations uniformly at random in an attempt to complete the given task as soon as possible. In this work, we model the above visual search experiment as a problem of odd arm identification in a multi-armed bandit in which (a) each arm yields Markov observations, and (b) the arms are restless. Further, we model the tendency of human subjects to sample image locations randomly as a “trembling hand” for the human subject, and come up with a metric that captures the notion of “closeness” between the odd and the non-odd images. Our results generalize all the previously known results for odd arm identification in multi-armed bandits.

Speaker Bio:   Karthik is a PhD student in the Wireless Information Systems Lab, Department of ECE, working under the supervision of Prof. Rajesh Sundaresan. Prior to joining for PhD, he served as a project assistant in the Signal Processing for Communications Lab of the Dept of ECE, where he worked with Prof. Chandra R. Murthy. Karthik holds a Bachelor’s degree in Electronics and Communications from RV College of Engineering, Bangalore, where he graduated from in 2014.

Title: The four levels of the fixed-point analysis
Speaker: Prof. Rajesh Sundaresan
Date: 03/03/2020

Abstract: The talk will be on the mean-field limit of an interacting particle system, for e.g., a WiFi system. The fixed-point analysis is a useful technique that helps the analyst understand the system’s equilibrium behaviour. One can identify four levels of fixed points that fix (1) the relationship between certain macroscopic observables of the system, (2) the equilibrium distribution over particle states, (3) the evolution of the mean-field over time, and (4) the law associated with the limiting Markovian evolution of a particle. The talk will highlight these four levels and how they are related to each other.

Speaker Bio:   Prof. Rajesh Sundaresan received the B.Tech. degree in electronics and communication from IIT Madras, India, and the M.A. and Ph.D. degrees in electrical engineering from Princeton University, Princeton, NJ, USA, in 1996 and 1999, respectively. From 1999 to 2005, he worked with Qualcomm Inc., where he was involved in the design of communication algorithms for wireless modems. Since 2005, he has been with the Indian Institute of Science, Bengaluru, India, where he is currently a Professor with the Department of Electrical Communication Engineering and an Associate Faculty with the Robert Bosch Centre for Cyber-Physical Systems. His interests include the areas of communication, computation, and control over networks.

Title: Tracking an Autoregressive process with limited communication
Speaker: Rooji Jinan
Date: 11/02/2020

Abstract: Samples from a high-dimensional AR[1] process are quantized and sent over a communication channel of finite capacity. The receiver seeks to form an estimate of the process in real-time. We consider a time-slotted communication model in slow-sampling regime where multiple communication slots occur between two sampling instants. We propose a successive update scheme which uses communication between sampling instants to refine estimates of the latest sample. We study the following question: Is it better to form refined estimates and send them over multiple communication slots, making the receiver wait more for an update, or to be fast but loose and send new information in every communication opportunity? We show that the fast but loose successive update scheme with spherical codes is universally optimal asymptotically for large dimension. However, most practical quantization codes do not meet the ideal performance required for this optimality, and will typically will have a fixed additive error. Interestingly, our analysis shows that in presence of such an error optimal choice is not fast but loose, but a judiciously chosen frequency of updates is needed.

Speaker Bio:   Rooji Jinan is a 3rd year PhD student at Robert Bosch Centre for Cyber Physical Systems, IISc. She is guided by Dr. Parimal Parag, Department of Electrical Communication Engineering, IISc. Her current research area is timely updates for cyber-physical systems. Rooji received her B.Tech. degree in Electronics and Communication and M.Tech. in Communication Engineering and Signal Processing from Calicut University, Kerala.

Title: Stochastic optimization with compressed gradients
Speaker: Prathamesh Mayekar
Date: 28/01/2020

Abstract: We consider stochastic optimization over $\ell_p$ spaces using access to a first-order oracle. In the first part of the talk, we ask: What is the minimum precision required for oracle outputs to retain the unrestricted convergence rates? We characterize this precision for every $p\geq 1$ by deriving information theoretic lower bounds and by providing quantizers that (almost) achieve these lower bounds. In the second part of the talk, we completely characterize the precision-convergence trade-off for the Euclidean case. Interestingly, the quantizer designed for this setting, RATQ, (almost) achieves the rate-distortion bounds universally for the well-studied Gaussian rate-distortion problem. This talked is based on joint work with Himanshu Tyagi.

Speaker Bio:   Prathamesh Mayekar is a fourth year Ph.D. candidate in the Department of Electrical Communication Engineering at the Indian Institute of Science, Bengaluru. He is advised Dr. Himanshu Tyagi. He received his Master’s degree in Industrial Engineering and Operation Research from the Indian Institute of Technology Bombay in 2015 and a Bachelor’s degree in Electronics and Telecom. Engineering from the University of Mumbai in 2013. Broadly, his research interests lie at the intersection of information theory and optimization. He is a recipient of Jack Keil Wolf ISIT Student Paper Award and Wipro PhD fellowship.

Title: Multi-agent Reinforcement Learning and its applications to Smart Grids
Speaker: Raghuram Bharadwaj
Date: 20/01/2020

Abstract: Reinforcement Learning (RL) deals with the algorithms that a single agent can apply to learn its optimal behavior from the environment. However, in the modern world, we encounter many applications where there are multiple agents involved instead of a single agent. It has many advantages over a single agent learning. For example, consider the task of moving a heavy object from one place to another. It may be impossible for a single agent to complete the task within defined time constraints. We can employ multiple agents and successfully finish the task. The key idea here is that the learning task can be shared among the agents. In this talk, I shall discuss algorithms for multi-agent RL under the settings of constrained cooperative stochastic games and two-player zero-sum games. One of the practical applications of multi-agent learning is Smart Grids. Smart Grid is a concept of developing a power grid that can intelligently make use of electricity. In this context, I shall also be discussing a novel stochastic game framework for energy management in microgrids networks and present the advantages of our proposed framework.

Speaker Bio:   Raghuram is a Ph.D. student in the department of CSA under the guidance of Prof. Shalabh Bhatnagar. His research interests include developing convergent algorithms for multi-agent and off-policy learning in the context of Reinforcement Learning, application of multi-agent reinforcement learning algorithms to smart grids. He is currently working on developing dynamic pricing solutions for energy management in microgrid networks.

Title: Sequential addition of coded tasks for straggler mitigation
Speaker:  Ajay Badita
Date: 14/01/2020

Abstract: Given the unpredictable nature of the nodes in distributed computing systems, some of the tasks can be significantly delayed. Such delayed tasks are called stragglers. In order to mitigate stragglers, redundancy in computation is often employed by encoding k tasks to n tasks such that any k of them can help ascertain the completion of the tasks. Two important metrics of interest are service completion time of the k tasks, and server utilization cost which is sum of time each server spends working on the tasks. Even though starting all n jobs at the start (t = 0) leads to lower mean service completion time, it leads to higher mean server utilization cost. We consider a proactive straggler mitigation strategy where n0 <= n tasks are started at t = 0 while the remaining n − n0 tasks are launched when l0 <= min(n0, k) tasks finish. The tasks are halted when k tasks finish. This gives a flexible forking strategy with multiple parameters. We analyze the mean of two performance metrics for the proposed forking strategy when the random task completion time at each server is independent and distributed as a shifted exponential. This talk demonstrates an effective algorithm to find the tradeoff between the two performance metrics mean server utilization cost and mean service completion time so as to choose efficient choice of parameters. This work has been accepted at INFOCOM-2020 conference.

Speaker Bio: Ajay Badita is a PhD student in the department of ECE, IISc-Bengaluru, working under the supervision of Prof. Parimal Parag.

Title: Large deviations for Cox processes and Cox/G/infinity queues, with a biological application
Speaker: Ayalvadi Ganesh, University of Bristol
Date: 10/01/2020

Abstract: We show that a sequence of Cox processes on a Polish space E satisfy a large deviation principle (LDP), provided their directing measures do so on the space of finite measures on E equipped with the weak topology. Next, we consider a sequence of infinite server queue with general iid service times, where the arrivals constitute Cox processes with translation invariant directing measures assumed to satisfy an LDP. We show that the corresponding sequence of queue occupancy measures also satisfy an LDP. These questions were motivated by the problem of describing fluctuations of molecule numbers in biochemical reaction networks within cells. Joint work with Justin Dean and Edward Cran.

Speaker Bio: Ayalvadi Ganesh received his BTech in EE from IIT Madras in 1988, MS and PhD in EE from Cornell University in 1991 and 1995 respectively. His Ph.D. thesis was on the use of large deviation techniques in queueing theory. He was with Edinburgh University, Birkbeck College, London, U.K., and Hewlett-Packards Basic Research Institute in Mathematical Sciences (BRIMS) and Microsoft Research before joining the Mathematics Department of Bristol University. He was also a Fellow of Kings College, Cambridge, from 2000 to 2004. He has published extensively on Queueing Theory and Large Deviations, Bayes’ Asymptotics, Economics of Communication Networks, Peer-to-peer Systems and Algorithms, Random graphs and stochastic processes on graphs, and Computer Viruses and Worms. He is the coauthor, with Neil O’Connell and Damon Wischik, of the Springer Book “Big Queues” published in 2004. His research interests are in the mathematical modelling of communication and computer networks, and in decentralised algorithms for such networks. Specific interests include large deviations and applications to queueing theory and statistics, random graph models and stochastic processes on graphs, and decentralised algorithms for resource allocation in the Internet and in wireless networks.

Title: Acquisition Games with Partial-Asymmetric Information
Speaker: Prof. Veeraruna Kavitha, Centre for Industrial Engineering and Operations Research, IIT Bombay
Date: 17/12/2019

Abstract: We consider an example of stochastic games with partial, asymmetric and  non-classical information. We obtain relevant equilibrium policies using  a new approach which allows managing the belief updates in a structured  manner. Agents have access only to partial information updates, and our
approach is to consider optimal open loop control until the information  update. The agents continuously control the rates of their Poisson  search clocks to acquire the locks, the agent to get all the locks before others would get reward one. However, the agents have no information about the acquisition status of others and will incur a cost proportional to their rate process. We solved the problem for the case with two agents and many locks and conjectured the results for N-agents. We showed that a pair of (partial) state dependent time-threshold policies form a Nash equilibrium. We further obtained good structural properties of the thresholds.

Speaker Bio: Dr. Veeraruna Kavitha is an Assistant Professor at the Centre for Industrial Engineering and Operations Research (IEOR), Indian Institute Technology Bombay, Mumbai, India, since May 2012. Before joining IITB, she was a Principal Research Scientist at Mymo Wireless, Bangalore and SRM Research Institute, Bangalore, India from December 2011 to May 2012. She was a Post Doctoral Fellow at MAESTRO, INRIA and LIA, University Avignon, France from 2008 to 2011 and a Post-Doctoral Fellow at Tata Institute of Fundamental Research, Bangalore, India from 2007 to 2008. She obtained a Ph.D. degree from Indian Institute of Science, Bangalore, India in 2007 and a M.Sc (Engg) in 2002. Her research interests are broadly in Stochastic processes, Performance Analysis, Queuing Theory, Polling systems, Optimal control, Game theory, Stochastic approximation, and Wireless communications.

Title: Completely Uncoupled Algorithms for Network Utility Maximization
Speaker: Ramakrishnan.S
Date: 17/12/2019

Abstract: The recent advances in wireless systems demands addressing the following resource allocation problems, viz channel selection, user association and power control. The solution to these problems should address the following objectives: (i) Network throughput optimality be ensured (ii) Users get a fair share of the network throughput. Also in a heterogeneous network, where multiple radio technologies coexists a distributed solution is preferable.

In this talk, we present two fully distributed algorithms which provide solutions to the above problems with the stated objectives. We assume that the node’s decisions are based only on their past actions and payoffs which is popularly known as completely uncoupled. Prior work in this setup has focused mainly in maximizing the sum-rate. An important attribute to consider in radio resource allocation is fairness among nodes, i.e. every node should get a fair share of the network throughput. Fairness is taken care of by introducing a utility function of the average rate. Our first algorithm, which we call General Network Utility Maximization (G-NUM), maximizes general non-concave utilities. We show that G-NUM induces a perturbed Markov chain (perturbed by ε), whose stochastically stable states are the set of actions that maximize the network utility. Our second algorithm is motivated by adaptive CSMA algorithms based on Gibbs sampling, where we present an approximate sub-gradient algorithm for concave utilities, which we call Concave Network Utility Maximization (C-NUM). C-NUM is considerably faster and requires lesser memory. Our main contribution is the expansion of the achievable rate region, which the prior works incompletely uncoupled setup has ignored to consider. This expansion aids in allocating a fair share of resources to the nodes.

Speaker Bio: Ramakrishnan received the Bachelors degree in electronics and communication engineering from SCSVMV University, Kanchipuram, India, in 2012.

Title: Operation of water distribution networks
Speaker: Prof. Sridharakumar Narasimhan, Dept. of Chemical Engineering, IIT Madras
Date: 10/12/2019

Abstract: Urban water distribution networks (WDNs) are large scale, complex systems with limited instrumentation. The nexus between water and energy reveals that energy production consumes significant quantities of of water while transporting water for end use is a highly energy intensive operation. Hence, it is important to minimize energy consumption while meeting consumer demands at required pressures On the other hand, if the available water is insufficient or inadequate to meet consumer demands at the required pressures, equitable distribution of the available resource is of primary importance.

The system we consider consists of pumps delivering water to different reservoirs  in a network, with each reservoir catering to time varying demand.  Pumps and ON/OFF valves are used as manipulated variables to control the flow and  pressure. The decision variables  are the number of pumps to be turned on and the state of the valves in the network over a given horizon and the objective  is to minimize energy consumption while meeting the time varying demand.  Given the nonlinear nature of the pump operating curve and the hydraulics, this results in a Mixed Integer NonLinear Program (MINLP). We propose to solve by decomposing it into  series of sub-problems that can be solved efficiently.  Application of these ideas to distribution networks reveals potential significant savings in energy or improvement in supply. Experimental results will be shared followed by our field implementations.

Speaker Bio: Sridharakumar Narasimhan obtained his M.Tech(integrated) and PhD in chemical engineering from IIT Maras and Clarkson University, USA in 1998 and 2006 respectively. He is currently Professor at the Department of Chemical Engineering,IIT Madras, India. His background is in process systems engineering and my interests are broadly in optimal experiment and measurement system design, water distribution networks and continuous manufacturing.

Title: An optimization approach to drift detection and clustering in time-series: Application to air quality data in India
Speaker: Dr. Alexandre Reiffers
Date: 26/11/2019

Abstract: Recent developments in low-cost sensors, wireless network communication, and computational tools have paved the way for applications like monitoring with the high spatial and temporal resolution for example in the context of air quality. However, the reduced quality of sensing units necessitates robust drift detection and calibration schemes. The few existing methods are variants of outlier detection algorithms. We presented an optimization-based clustering algorithm that first smooths the data and then performs clustering for drift detection. We present the detection efficiency of the algorithm with a simulated dataset where the proposed algorithm detects sensor failures like random walks, reduced sensitivity and changes in bias.

Speaker Bio: Alexandre Reiffers is a post-doctoral fellow at Robert Bosch Centre for Cyber-Physical Systems. He received the B.Sc. degree in mathematics (2010) from the University of Marseille, the master degree in applied mathematics (2012) from the University of Pierre et Marie CURIE and the Ph.D. degree in computer science (January 2015) from the INRIA (National research institute in computer science and control) and the University of Avignon. His supervisors were Eitan Altman and Yezekael Hayel. From July 2016 to December 2017, Alexandre Reiffers was a researcher at SafranTech where he was working on comparison of maintenance strategies. Most of his research projects concern the application of mathematical tools (game theory, optimization, stochastic process and machine learning) for a better understanding of real-world problems. The different issues that he studies touch topics such as social networks, speech between human and computer, economy and manufacturing.

Title: A unifying product form framework for queueing models
Speaker: Dr. Tejas Bodas
Date: 19/11/2019

Abstract: The discovery of queueing systems with product form stationary distribution is probably one of the fundamental contributions in queueing theory. Recent years have witnessed a surge of interest in parallel server models with multi-class jobs. In two recent studies by Visschers et al. (Multi-type job and server model, Queueing Systems 2012) and Krzesinski (Order Independent queues, Queueing Networks, 2011), sufficient conditions have been obtained for a multi-server system to have a product form. These two results differ in their Markovian descriptor for the underlying system and have led to two separate streams of research, where each approach covers applications that are not covered by the other. A natural question that arises is whether the two approaches can be generalized while preserving product-form.

In this talk, we will see that the answer to this question is in the affirmative. I will introduce a token based central queue framework that not only offers a unifying product form analysis for the above two models, but also covers applications that are not subsumed by them. We will also see an application of this new framework to redundancy based queueing systems that have become increasingly popular in recent times. This talk is based on a joint work with U. Ayesta and Maaike Verloop from the University of Toulouse and J.L. Dorsman from the University of Amsterdam.

Speaker Bio: Tejas is currently a Raman postdoc in the ECE department. Prior to this, he was a short-term postdoc at the University of Antwerp in Belgium, a postdoc at LAAS, CNRS Toulouse in France and a visiting fellow at  TIFR, Mumbai. He received his M.Tech and Ph.D (dual degree) in Electrical Engineering from IIT Bombay in 2016.

Title: On Detecting An Anomalous Arm in Multi-armed Bandits with Markov Observations
Speaker: Karthik P N
Date: 13/11/2019

Abstract: In statistical learning theory, multi-armed bandits constitute a popular model for a set of alternatives competing for a fixed amount of resources. The classical multi-armed bandit problem deals with the notion of “maximising rewards” or equivalently “minimising regret” over a finite or infinite time horizon. However, in this talk, I shall discuss the notion of “optimal stopping” in the context of multi-armed bandits, where the central objective is to identify a certain feature of the multi-armed bandit as quickly as possible. The particular feature I shall focus on throughout the talk is that of all but one of the arms (known as anomalous arm) being probabilistically identical, with a goal of identifying the anomalous arm as quickly as possible while ensuring that the probability of error is below a maximum acceptable tolerance. In the literature, this problem is well-studied under the name of “odd arm identification”, for the case when each arm yields iid observations. In this talk, I shall present the first known results for the problem of odd arm identification when each arm yields Markov observations.This is ongoing work with Prof. Rajesh Sundaresan. A part of this work was presented at the 2019 IEEE International Symposium on information Theory (ISIT), a detailed manuscript of which is available at https://arxiv.org/abs/1904.11361.

Speaker Bio: Karthik is a PhD student in the Wireless Information Systems Lab, Department of ECE, working under the supervision of Prof. Rajesh Sundaresan. Prior to joining for PhD, he served as a project assistant in the Signal Processing for Communications Lab of the Dept of ECE, where he worked with Prof. Chandra R. Murthy. Karthik holds a Bachelor’s degree in Electronics and Communications from RV College of Engineering, Bangalore, where he graduated from in 2014.

Title: Probabilistic forwarding of coded packets on networks.
Speaker: Vinay Kumar B R
Date: 06/11/2019

Abstract: Motivated by applications in sensor networks and the Internet of Things (IoT), we look into energy efficient broadcast mechanisms on distributed networks. We consider a scenario of broadcasting information over a network of nodes connected by noiseless communication links. A source node in the network has $k$ data packets to broadcast. The source encodes the $k$ data packets into $n \ge k$ coded packets. This encoding is such that any node in the network which receives at least some $k$ out of the $n$ coded packets will be able to retrieve the original $k$ data packets. The source transmits the $n$ coded packets to its one-hop neighbours. Every other node in the network follows a probabilistic forwarding protocol, in which it forwards a previously unreceived packet to all its neighbours with a certain probability $p$. In our formulation, it suffices that a large fraction of the network nodes receives the broadcast. We say that a “near-broadcast” occurs, when the expected fraction of nodes that receive at least $k$ of the $n$ coded packets is close to $1$. The forwarding probability $p$ is chosen so as to minimize the expected total number of transmissions needed for a near-broadcast. We initially examine, how this minimum forwarding probability, and correspondingly, the expected total number of packet transmissions varies with the number of coded packets $n$. We further analyze the probabilistic forwarding of coded packets on two specific network topologies: binary trees and square grids. For trees, our analysis shows that for fixed $k$, the expected total number of transmissions increases with $n$. This indicates that there is no advantage of coding along with probabilistic forwarding on trees in terms of the total number of transmissions in the network. On the other hand, on grids, a judicious choice of $n$ significantly reduces the expected total number of transmissions needed for a near-broadcast. The theory of site percolation is used to explain this behaviour on the grid. Other well-connected network topologies also exhibit similar behaviour as that of the grid which will also be discussed.

Speaker Bio: Vinay Kumar B.R. is a PhD student in the ECE Department, IISc. He is a recipient of the CISCO-IISc PhD research fellowship from 2015-2020. He finished his B.E. in Electrical Engineering and M.Sc. in Mathematics from BITS-Pilani in 2014. His research interests include distributed computation and communication on networks, percolation and random graphs.

Title: Optimizing multi-linear Product form networks using Dinkelbach’s algorithm.
Speaker: Rahul R
Date: 30/10/2019

Abstract: We aim to obtain analytical solutions to the optimal rate setting problem for product form queueing networks. We model the network as a multi-linear product form Markov network and use Lagrange dual optimization technique which enables us to decompose the global optimization problem into a local optimization problem at each of the transmitter buffers. We then use Dinkelbach algorithm and gradient-based optimization to obtain analytical solutions for the rate setting problem. We have already established the existence of strong duality for the case of M/M/1 queuing systems which established the feasibility of minimizing the Lagrange dual function. We extend the same solution approach to solve the average cost minimization problem for a cache-enabled wireless system modeled as a two-phase birth-death process. Since optimizing this system in the original form is intractable, we modify the network by introducing auxiliary transitions which reduces the complexity of the system and helps us obtain analytical solutions for optimal transmission rates. Our method has the advantage of approximating the original two-phase system asymptotically. In addition, due to the structure of the two phase network, the solutions obtained from the system with auxiliary transitions can be shown to be the optimal solutions for the original system for vanishing values of the auxiliary admission rate parameters. We then extend the analysis for the rate setting problem for G-network, which are multilinear networks with non-linear traffi c equations. We will conclude by introducing applications of G-networks for content discovery in P2P networks and Energy Sensor networks.

Speaker Bio: Rahul R is a PhD student at the Department of ECE, IISc. He is a member of the Network Engineering Lab under the guidance of Prof. Utpal Mukherji. His research interest is on optimal resource allocation in communication networks.

Title: Behaviour of critically loaded systems with randomized shortest queue routing.
Speaker: Prof. Ravi R. Mazumdar
Date: 25/10/2019

Abstract: Recently there has been a great interest in the analytic understanding of the behavior of large server systems with randomized routing. The work of Vvedenskaya and Dobrushin in Russia and Mitzenmacher in the USA first brought to light the fact that randomized routing to large number of parallel servers based on the shortest of d sampled servers achieves delay performance that is close to the optimal delay performance when Join the Shortest Queue (JSQ) routing is used. These results have been extended to other models of interest such as processor sharing and loss models in the heterogeneous setting by Mukhopadhyay and Mazumdar, Yi and Srikant, Mukherjee and Borst, etc. The approach is via a mean-field analysis. In recent work with Vasantam we showed that the insensitivity properties of processor sharing and loss models continues to hold for the fixed points of the mean field.

In this talk I will discuss the behavior of randomized routing when loads are close to critical, in the so-called Halfin-Whitt regime. For loss models this regime is of interest due to a phase change in blocking behavior and so it is important to understand the blocking behavior with heavy loads. We approach this issue by studying the fluctuations of the empirical distributions around the mean-field. We obtain FCLT results for both the transient and stationary behaviour. This allows us not only to obtain approximations for nite sized systems but yields information on the convergence rates of the empirical distribution to the fixed point of the mean-field.This work with Thirupathiah Vasantam (Waterloo).

Speaker Bio: The speaker was educated at the Indian Institute of Technology, Bombay (B.Tech, 1977), Imperial College, London (MSc, DIC, 1978) and obtained his PhD in Control Theory under A. V. Balakrishnan at UCLA in 1983. He is currently a University Research Chair Professor in the Dept. of ECE at the University of Waterloo, Ont., Canada where he has been since September 2004. Prior to this he was Professor of ECE at Purdue University, West Lafayette, USA. Since 2012 he is a D.J. Gandhi Distinguished Visiting Professor at the Indian Institute of Technology, Bombay, India. He is a Fellow of the IEEE and the Royal Statistical Society. He is a recipient of the INFOCOM 2006 Best Paper Award, the ITC-27 2015 Best Paper Award, the Performance 2015 Best Paper Award and was runner-up for the Best Paper Award at INFOCOM 1998. Since May 2019 he is an Adjunct Professor at TIFR, Mumbai. His research interests are in stochastic modelling and analysis applied to complex networks and systems and in issues of network science.

Title: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs.
Speaker: Naganand Yadati
Date: 16/10/2019

Abstract: In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible and natural modeling tool to model such complex relationships. The obvious existence of such complex relationships in many real-world networks naturaly motivates the problem of learning with hypergraphs. A popular learning paradigm is hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph. Motivated by the fact that a graph convolutional network (GCN) has been effective for graph-based SSL, we propose HyperGCN, a novel GCN for SSL on attributed hypergraphs. Additionally, we show how HyperGCN can be used as a learning-based approach for combinatorial optimisation on NP-hard hypergraph problems. We demonstrate HyperGCN’s effectiveness through detailed experimentation on real-world hypergraphs.

Speaker Bio:  https://naganandy.github.io/cv.pdf

Title: Neural Attribution for Semantic Bug-Localization in Student Programs
Speaker: Rahul Gupta
Date: 16/10/2019

Abstract: Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback. These systems usually rely on test results to quantify the programs’ functional correctness. They return failing tests to students as feedback. However, the students may find it difficult to debug their programs if they receive no hints about where the bug is and how to fix it. In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program. At the heart of our technique is a novel tree convolutional neural network which is trained to predict whether a program passes or fails a given test. To localize the bugs, we analyze the trained network using a state-of-the-art neural prediction attribution technique and see which lines of the programs make it predict the test outcomes. Our experiments show that NeuralBugLocator is generally more accurate than two state-of-the-art program-spectrum based and one syntactic difference based bug-localization baselines.

Speaker Bio: Rahul Gupta is a Ph.D. candidate at the Department of Computer Science and Automation, IISc Bangalore. He is a member of the Software Engineering and Analysis lab, led by Prof. Aditya Kanade. His research interests lie broadly in developing deep learning techniques to solve problems in software engineering.

Title: Decentralized decision making with strategic users.
Speaker: Dr. Deepanshu Vasal
Date: 09/10/2019

Abstract: Many real-world dynamic decision-making problems consist of multiple decision-makers with asymmetric information. Some examples include Markets, social learning, traffic management, autonomous vehicles, cyber-physical systems, internet of things and many more. In these systems, there are multiple decision-makers (DMs) who make some common and private observations of the ‘state’ of the systems with the goal to minimize their own cost (dynamic games) or total cost incurred by everybody (dynamic teams).

In this talk, I will present a general sequential decomposition framework to study such problems. This framework extends currently known results in decentralized stochastic control for team problems. For strategic users, it presents a novel methodology to compute (Markovian) Perfect Bayesian equilibria (PBE), which was an open problem in the theory of dynamic games. I present a running public-goods example to study its PBE. In general, our results extend the ideas of dynamic programming to general multi-agent dynamic optimization problems to study ‘signaling’ behavior i.e. how players’ actions reveal their private information in the system which affects other users’ utilities.

Speaker Bio:  https://sites.google.com/view/dvasal/home

Title: Bayesian Optimization under Heavy-tailed Payoffs.
Speaker: Sayak Ray Chowdhury
Date: 25/09/2019

Abstract: We consider black box optimization of an unknown function in the nonparametric Gaussian process setting when the noise in the observed function values can be heavy tailed. This is in contrast to existing literature that typically assumes sub-Gaussian noise distributions for queries. Under the assumption that the unknown function belongs to the Reproducing Kernel Hilbert Space (RKHS) induced by a kernel, we first show that an adaptation of the well-known GP-UCB algorithm with reward truncation enjoys sublinear $\tilde{O}(T^{\frac{2 + \alpha}{2(1+\alpha)}})$ regret even with only the $(1+\alpha)$-th moments, $\alpha \in (0,1]$, of the reward distribution being bounded ($\tilde{O}$ hides logarithmic factors). However, for the common squared exponential (SE) and Mat\'{e}rn kernels, this is seen to be significantly larger than a fundamental $\Omega(T^{\frac{1}{1+\alpha}})$ lower bound on regret. We resolve this gap by developing novel Bayesian optimization algorithms, based on kernel approximation techniques, with regret bounds matching the lower bound in order for the SE kernel. We numerically benchmark the algorithms on environments based on both synthetic models and real-world data sets.

Speaker Bio:  Sayak Ray Chowdhury is a Google PhD fellow at the Electrical Communication Engineering department, IISc Bangalore working with Prof. Aditya Gopalan. His research interests include reinforcement learning and multi-armed bandit problems with applications in recommendation systems, sensor networks etc. Previously, He did his M.E. in System Science and Automation from IISc, Bangalore and B.E. in Electrical Engineering from Jadavpur University, Kolkata.

Title: Nonzero-sum Adversarial Hypothesis Testing Games.
Speaker: Sarath A Y
Date: 25/09/2019

Abstract:  We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks. We first show that these games admit mixed strategy Nash equilibria, and then we examine some interesting concentration phenomena of these equilibria. Our main results are on the exponential rates of convergence of classification errors at equilibrium, which are analogous to the well-known Chernoff-Stein lemma and Chernoff information that describe the error exponents in the classical binary hypothesis testing problem, but with parameters derived from the adversarial model. The results are validated through numerical experiments. This is a joint work with Patrick Loiseau.

Speaker Bio:  Sarath is a PhD student in ECE Department working with Prof. Rajesh Sundaresan. He is broadly interested in applied probability. He obtained his ME in Telecommunications from ECE, IISc and BTech from NIT Calicut.

Title: Data-driven propagation modeling for a class of IEEE 802.15.4 wireless devices in an indoor environment.
Speaker: Anitha Varghese
Date: 18/09/2019

Abstract:  We consider the problem of deployment of static indoor wireless networks for connecting sensors to a data collection station, in the context of Internet of Things (IoT) applications. The deployment of such indoor wireless networks requires the ability to predict the quality of the wireless link between any desired pair of points in the indoor environment. Modelling the channel propagation environment in factories and buildings is, however, challenging. We adopt the methodology of spatial sampling of a large number of links, exchanging packets between actual devices placed at the ends of these links, and then using the collected RSSI (received signal strength) data to develop a predictive link model. We highlight three issues in such a data-driven modelling approach. First, the limited range of link lengths over which data are collected can affect the accuracy of the estimated channel parameters. Second, due to device characteristics, packet error rate (PER) variation with an average signal to noise ratio (SNR) may be significantly different from that predicted by theory. Third, RSSI estimates based on successfully received packets suffer from success bias. Our proposed methodology overcomes these three issues via targeted sampling of link lengths, characterization of PER versus RSSI via controlled measurements on the transceiver devices, and an EM-like (expectation-maximization) framework to handle lost packets via suitable imputation of RSSI on lost packets. We validate our methodology on a generative model and then test it on data collected from field experiments, to quantify the gains coming from the EM framework. Even though our indoor environment with over three hundred links has intercepting walls of different types and numbers, doors and windows of different sizes, a linear path-loss model, superposed with a suitable Nakagami-m fading model atop log-normal shadowing, provides a good fit to the experimental data. By using ten-fold cross validation over our sample of over 500 links, we also report on the efficacy of our model in predicting the packet error rates on links.

Speaker Bio:  Anitha Varghese is working as a senior scientist in ABB corporate research, Bangalore. She is currently pursuing her Ph.D in ECE Department, IISc, Bangalore under the guidance of Prof. Anurag Kumar. She received her ME in Telecommunications engineering from the ECE Department, IISc in 2006, and her B.Tech in electronics and communication engineering from Kerala University in 2004. She worked as a researcher in General Motors India Science lab, from 2006-2010. Her research interests include design and analysis of wireless communication networks, and communication security in the context of industrial automation.