Cluster Structure Assessment in Big Static and Streaming Data, and their Applications for IoT and Transportation
Everyday, an abundant amount of data is generated from various sources such as Internet of Things (IoT) networks, smartphones, and social network activities. Making sense of such an unprecedented amount of data is essential for many businesses, services and almost every smart city domain such as healthcare, transportation, environment, and energy sectors. The data generated from these domains are mostly unlabeled, anomalous, spatio-temporal, streaming, and/or high-dimensional, which makes their interpretation challenging to create useful knowledge. In this talk, Dr. Punit Rathore will discuss his efficient machine learning algorithms to manage and extract actionable information from big data from various domains. Specifically, he will present his novel cluster assessment and clustering algorithm for time-efficient tracking of cluster structures in static big data and high-velocity data streams, and its application for anomaly and change point detection and vehicle trajectory prediction.
Punit Rathore, IISc Bangalore
Dr. Punit Rathore is currently an Assistant Professor at Indian Institute of Science, Bangalore in Robert Bosch Centre for Cyber physical Systems, jointly with Centre for infrastructure, Sustainable Transportation, and Urban Planning. Before joining IISc, Dr. Rathore worked as a Postdoctoral Fellow in Senseable City Lab at Massachusetts Institute of Technology (MIT), Cambridge, USA and in Grab-NUS AI Lab at National University of Singapore. Dr Rathore completed his Ph.D. from the Department of Electrical and Electronics Engineering, University of Melbourne, Australia in Jan-2019. Prior to PhD, Dr. Punit worked as a Researcher in Automation Division at Tata Steel Limited, Jamshedpur, where he developed several real-time systems based on machine learning and machine vision for manufacturing industries. His research work has also been internationally recognized with multiple best-paper awards at world-recognized IEEE conferences and best thesis prizes by IEEE System, Man, and Cybernetics Society (SMC) and Melbourne School of Engineering, the University of Melbourne. His research interests are in big data analytics, unsupervised learning, spatio-temporal data mining, and data-driven analytics IoT, transportation and autonomous systems.