University at Buffalo Archives

Dr. Prabakar Krishna Murthy is a scientist at Materials Science Group, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam. He obtained his Ph.D. in physics from Osmania University, Hyderabad in the year 2007 and joined IGCAR in the same year through the prestigious Dr. K. S. Krishnan Research Associate scheme.

His current research interests include microfabrication, microcantilever-based sensors, and semiconductor neutron detectors. He has extensively worked on the design, fabrication, and characterization of surface-enhanced SiO2 microcantilevers for ultrafast and ultrasensitive relative humidity (RH) sensing applications. Using this sensor, his group could demonstrate real-time monitoring of RH variation during human breath cycles. Dr. Krishna Murthy has also studied the photo-induced deflection in Au/Si microcantilevers for ultrasensitive temperature sensing applications and capacitive micro-machined ultrasonic transducers for NDE applications. He has published 38 research articles in national and international journals. He is also Assistant Professor in physics at Homi Bhabha National Institute, Mumbai and has guided two Ph.D. students and several graduate/postgraduate students.

During his Fulbright-Nehru Academic and Professional Excellence fellowship, Dr. Krishna Murthy is developing a standoff (remote), ultrasensitive and extremely selective detection method for lung cancer by sensing the volatile organic compounds (VOCs) released in the human exhaled breath using photothermal cantilever deflection spectroscopy.

Saptarshi Saha is a Ph.D. candidate at the Indian Statistical Institute, Kolkata. His doctoral research is focused on integrating causality into deep learning frameworks to enhance their utility. Beyond his immediate thesis goals, Saptarshi envisions a broader research trajectory aimed at utilizing deep learning for a deeper understanding of cause-and-effect relationships. His aim is to address various challenges such as improving the robustness, explainability, and interpretability of models, addressing issues with limited control over generative models, enhancing generalization performance under varying data distributions, dealing with learning using limited labelled data, promoting fairness in decision-making systems, and more. Saptarshi’s scholarly contributions extend to renowned journals such as TMLR and prominent conferences like ICLR. He has showcased his work at various research fora, such as Amazon Research Day 2023 and the Machine Learning Summer School in Okinawa, 2024.

Saptarshi holds a BS-MS dual degree in mathematics from IISER Kolkata. Throughout his BS-MS studies (2015–2020), he was a recipient of the INSPIRE fellowship from DST, Government of India.

As a Fulbright-Nehru Doctoral Research fellow at the University of Buffalo, Buffalo, NY, Saptarshi is trying to utilize causal knowledge and principles to assess data quality and make informed decisions (in the context of learning with not enough data) regarding samples that need to be labelled (from the large unlabelled dataset) rather than selecting them randomly. He is primarily working on the challenge of efficiently selecting the most relevant samples for labelling while considering budget constraints. This challenge holds excellent relevance not only in academic research but also within the AI industry. Saptarshi is an avid nature photographer and finds solace in the wilderness. His interests extend to culinary adventures, globetrotting, and engaging with diverse cultures. His leisure activities also include playing football and cricket.