side Raghunathan Rengaswamy

Raghunathan Rengaswamy

Marti Mannariah Gurunath Chair in Department of Chemical Engineering, IIT Madras

Professor Dept of Chemical Engineering, IIT Madras

Adjunct Professor Department of Chemical Engineering, Texas Tech University

Adjunct Professor Department of Chemical and Biomolecular Engineering, Clarkson University

Founding Director Gyan Data Pvt Ltd

Prof. Raghunathan Rengaswamy is a Professor at the Department of Chemical Engineering, IIT Madras. 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. He was also a visiting professor at Purdue University, USA (2001), and spent summers as visiting researcher in other universities such as University of Delaware, USA and University of Alberta, Canada. Rengaswamy’s major research interests are in the areas of Fault Detection and Diagnosis (FDD), development of Sensor Placement (SP) algorithms for FDD, and Fuel Cells. Rengaswamy has worked in these areas for more than fifteen years and has published extensively. A series of review articles on condition monitoring and fault detection and diagnosis that Rengaswamy published with co-authors has been cited more than 1400 times (Scopus database). Rengaswamy was the recipient of the Young Engineer Award for the year 2000 awarded by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32. He guided a B.Tech project “Qualitative Simulation in Process Engineering” that won an award as one of the most innovative thesis at Bachelor’s level (all disciplines) awarded by INAE. His paper on fault diagnosis was awarded the CAST Directors’ Award for the Best Poster Presentation at the AIChE Annual meeting in Los Angeles, Nov 2000. A paper that he co-authored was chosen by the International Federation of Automatic Control (IFAC) for the Best Paper Prize, for the years 2002-2005, in Engineering Applications of Artificial Intelligence Journal in the category - Application-oriented paper on Symbolic AI Approaches. His research has been funded by federal and state agencies such as the NSF, DOE, ACS-PRF, NYSERDA and companies such as Honeywell, Nanodynamics and KBR. In recent years, Rengaswamy’s group also works in the area of computational microfluidics. Research work in this area has led to the formation of a start-up, SysEng LLC, in the US funded through a NSF STTR grant. With Prof. Shankar Narasimhan of IIT Madras, Rengaswamy co-founded Gyan Data Pvt. Ltd (GDPL), a high technology service and product development company located in IIT Madras Research Park. GDPL has been in existence from July 2011 and more details can be found in GyanData.


Jul 18, 2022
Towards Personalized Cancer treatment
As per the National Cancer Registry Programme Report 2022, over 13 lakhs people in India suffer from cancer each year making India rank third nation-wise in the number of cancer cases across the world. The figures are not good globally as well! As per the WHO, cancer is a leading cause of death worldwide and accounted for nearly one in six deaths in the year 2020. The disease not only torments the patient but is physically, emotionally and financially draining for the family as well.

2 min read

Jan 20, 2021
Data Science and IoT for addressing ambient air sanctity
Sathish Swaminathan , Raghunathan Rengaswamy
Ambient air quality is a dynamic parameter that shows a high degree of spatiotemporal variation. It is known to impact human life and health. Obtaining meaningful estimates of human exposure to air pollutants is key to mitigating its ill effects. Current air quality monitoring regime consists of expensive, static monitoring stations that are sparsely distributed across a city which in turn results in poor spatial resolution. This particular work, accomplished under project Kaatru, demonstrates the efficacy of low-cost sensor based, vehicle mounted, mobile monitoring paradigm in hyperlocal air quality assessment.

7 min read