Prof. Raghunathan Rengaswamy

Professor

Prof. Raghunathan Rengaswamy is a professor at the Department of Chemical Engineering, IIT Madras. He is also Dean Global Engagement and a founding member of Robert Bosch Center for Data Science and AI. Prior to this, he was a Professor, Chemical Engineering and co-director of the Process Control and Optimization Consortium (PCOC) at Texas Tech University, Lubbock, TX USA, Associate and full Professor at Clarkson University, Potsdam, NY, and Assistant Professor at IIT Bombay, Mumbai, India. Rengaswamy’s major research interests are in the areas of Data Science and AI, Energy systems, Droplet Microfluidics and Systems Biology.

Prof. Shankar Narasimhan

Professor

Prof. Shankar Narasimhan is currently a professor in the department of chemical engineeringat IIT Madras. His major research interests are in the areas of Data mining, Process Design and Optimization, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control. He has co-authored several important papers and a book which has received critical appreciation in India and abroad. Together with Prof. Raghunathan Rengasamy, he has a course on DataScience for Engineers in NPTEL, for which more than forty-five thousand students have enrolled in three offerings.

Prof. Arun K. Tangirala

Professor

Prof. Arun K. Tangirala is a Professor at the Department of Chemical Engineering, IIT Madras since 2004. He obtained his Bachelor’s degree in Chemical Engineering from IIT Madras and Doctoral degree in Process Control & Monitoring from the University of Alberta, Canada. Dr. Tangirala’s research interests span the fields of process systems engineering, process control and monitoring, system identification, applied signal processing and fuel cell systems. He has been teaching several full-term and short-term courses on process control, system identification, theory and applications of wavelet transforms, random processes and fuel cell systems. He is the author of a comprehensive classroom text on System Identification titled “Principles of System Identification: Theory and Practice”, published by CRC Press.

Prof. Mani Bhushan

Professor

Prof. Mani Bhushan is currently a Professor in the Department of Chemical Engineering, IIT Bombay. He has a Doctoral degree from IIT Bombay. He was a Post-Doctoral scholar at Purdue University. Subsequently, he worked as an Assistant Professor at University of Alberta, Canada. He was also a visiting researcher at Carnegie Mellon University. His main areas of interest are Nonlinear, Constrained State Estimation, Sensor Network Design, Optimal Alarm Management, and Process Data Analysis.

Prof. Balaraman Ravindran

Professor

Prof. Balaraman Ravindran has a doctoral degree from University of Massachusetts, Amherst. He is currently a professor in the department of computer science at IIT Madras. He has been working in the domain of complex data analytics for nearly a decade and in the broader area of machine learning for nearly two. He has been recently leading investigations into learning with networked data, where both the input and output space has significant structure in them. His work has been supported by Yahoo! labs, General Motors R&D, DST, Adobe Research, and Ericsson Research. With Ericsson research there has been active collaboration for the past 5 years on investigating algorithms for telecom call analysis, social computing in general and distributed computing abstractions for data analytics. He is also an advisor to several startups in the area of data analytics.

Dr. Nirav Bhatt

Assistant Professor

Dr. Nirav Bhatt is a Professor at the Indian Institute of Technology Madras in the Department of Chemical Engineering. He earned his MTech in Chemical Engineering from IIT Madras and PhD in systems and data analysis of chemical processes from EPFL, Switzerland. He worked with Prof. J B Joshi at HBNI, Mumbai on analysis of reactive dissolution processes using experimental data before joining IIT Madras. His research interests include model-based analysis of experimental data, machine learning methods and data analytics for engineering and finance applications.