Field of Study India: Computer Science

Shruti Singh

Ms. Shruti Singh is a doctoral candidate at the Computer Science and Engineering department, Indian Institute of Technology Gandhinagar, Gujarat. Her research interests lie in the field of natural language processing, specifically in learning representations of scientific articles. Her research goal is to develop tools that assist researchers at various stages of the research cycle and democratize the entry of marginalized communities into research.

Ms. Singh received her bachelor’s in information and communication technology with a minor in computational sciences from Dhirubhai Ambani Institute of Information and Communication Technology, Gujarat. Post her bachelor’s, she worked as a research engineer at Raxter and a product engineer at Sprinklr.

During her Fulbright-Nehru Doctoral Research fellowship, Ms. Singh is working with Prof. Arman Cohan at Yale University on learning aspect-based representations for scientific articles. Aspect-based representations of research articles will enable fine-grained scholarly search, increase the productivity of researchers, and expedite the process of knowledge discovery.

Debanjan Konar

Dr. Debanjan Konar earned a Bachelor of Engineering in Computer Science and Engineering (CSE) from the University of Burdwan , in 2010, an MTech in CSE from the National Institute of Technical Teachers’ Training and Research (NITTTR), Kolkata , in 2012, and a PhD from the Indian Institute of Technology Delhi in New Delhi , in 2021. He is currently working as a postdoctoral researcher at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany. Prior to this, Dr. Konar served as an Assistant Professor at Sikkim Manipal Institute of Technology, Sikkim , and SRM University-AP, Andhra Pradesh . His research interests include quantum machine learning (QML), hybrid classical-quantum neural networks, deep learning, and computer vision. He has authored several papers in prestigious computer science journals, conference proceedings, book chapters, and internationally renowned books. Dr. Konar received a National Scholarship in 2001 and a GATE Postgraduate Fellowship in 2010. He is an IEEE senior member and an ACM member. He also serves as an editor and a reviewer for several esteemed journals and international conferences.

Recently, Quantum Computing (QC) has been leveraged for machine learning with the expectation that the uncertainty inherent in QC may be used to great advantage in stochastic-based modelling , spurring new research on Noisy Intermediate-Scale Quantum (NISQ) devices. To exploit the advantages of stochastic-based modelling in QML research, Dr. Konar has proposed Spiking Quantum Neural Networks using hybrid classical-quantum algorithms with the merits of superposition states and amplitude encoding. Within this Fulbright-Nehru Postdoctoral Research Fellow ship, the proposed models will be extensively validated on various computer vision applications, including disguised facial recognition using the PennyLane Quantum Simulator with limited quantum hardware and supercomputing resources available at Purdue University, USA.

Vineeth N Balasubramanian

Dr. Vineeth N Balasubramanian is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Hyderabad (IIT-H), and currently serves as the Head of the Department of Artificial Intelligence at IIT-H. His research interests include deep learning, machine learning, and computer vision. His research has resulted in many publications in several top conferences and journals including ICML, CVPR, NeurIPS, ICCV, AAAI, TPAMI, etc. His Ph.D. dissertation at Arizona State University on the Conformal Predictions framework was nominated for the Outstanding Ph.D. Dissertation at the Department of Computer Science. His recent awards include: Best Paper Awards at CODS-COMAD 2022, CVPR 2021 workshops on Causality in Vision and Adversarial Machine Learning; Teaching Excellence Awards at IIT-H in 2017 and 2021; Google Research Scholar Award (earlier known as Google Research Faculty award) in 2020; Outstanding Reviewer Awards at ICLR 2021, CVPR 2019, ECCV 2020. For more details, please see https://iith.ac.in/~vineethnb/.

During his Fulbright-Nehru Research Fellowship, Dr. Balasubramanian aims to work towards developing trustworthy machine learning models that are implicitly imbued with causal reasoning capabilities. In particular, he plans to understand and develop methods for causal generative mechanisms in real-world data, and bring together perspectives of causality and robustness into explanations of deep neural network models.

Sathesh Mariappan

Dr. Sathesh Mariappan is currently serving as an Associate Professor in the Department of Aerospace Engineering at the Indian Institute of Technology Kanpur. He completed his Bachelors at Madras Institute of Technology, 2007 (University First Rank) and obtained his Ph.D. from Indian Institute of Technology Madras, 2012, both in Aerospace Engineering. Before joining IIT Kanpur, he worked in the German Aerospace Center, Goettingen as a Humboldt Post Doctoral fellow. He is a recipient of Young Engineer Awards from the Indian National Academy of Engineering and Institution of Engineers. He is also recognized internationally through the Humboldt Fellowship and International Exchanges award (co-applicant) from The Royal Society – London. His research focuses on understanding and mitigating combustion-driven oscillations in gas turbine engines.

During the Fulbright-Nehru Fellowship, Dr. Mariappan will specialize in applying physics informed neural network (PINN): a machine learning method, to study combustion driven oscillations in combustors of gas turbine engines. PINN is an emerging tool, having the striking advantage to synergize experimental data and physics-based models. This synergy brings a new understanding of flame-flow interactions and helps develop more accurate hybrid models, which serve for instability prognosis and mitigation. This alternative (superior) hybrid framework will model combustor dynamics more accurately (than models derived purely from theory or experiments), even in practical systems, leading to efficient/robust control of oscillations.

Nanditha Rao

Dr. Nanditha Rao is Assistant Professor at the International Institute of Information Technology (IIIT), Bengaluru in the VLSI Systems group. She received her Ph.D. in electrical engineering from IIT Bombay in 2017. Her research interests include FPGA based acceleration for machine learning, RISC-V and radiation-hardened designs. She has received the SERB Core Research Grant 2018, MITACS Globalink Research Award 2018 and SERB SUPRA research grant 2022.

Dr. Rao believes in encouraging students in technology and leadership roles. She took up administrative roles such as General Secretary of a women’s hostel in IIT Bombay for which she was awarded the Institute Organizational Citation. She is currently the associate warden of women’s hostel in IIIT Bangalore. She worked as a hardware design engineer at Intel for five years prior to her Ph.D. Her work at Intel involved signal integrity simulations of PCIe, LVDS, DisplayPort and HDMI interfaces. She received 13 Intel Spontaneous Recognition Awards and one Intel Divisional Recognition Award.

During her Fulbright-Nehru Academic and Professional Excellence fellowship, Dr. Rao is working on improving the performance of hardware accelerators for convolutional neural networks (CNN). CNNs are most commonly used today in computer vision, and image and video processing. The CNN accelerator implemented using field-programmable gate arrays (FPGA) enables significant performance improvement and power efficiency compared to GPU implementations. However, to improve the performance on the FPGA further, it is important to explore the appropriate mapping of the accelerator architecture onto optimal FPGA resources, which is what Dr. Rao is focusing on during this fellowship.

Sarvesh Pandey

Dr. Sarvesh Pandey is an assistant professor of computer science at Banaras Hindu University since November 2020. At BHU, he has been actively involved in teaching, research, and administration activities. Dr. Pandey obtained his MTech and Ph.D. degrees from the Computer Science and Engineering Department of Madan Mohan Malaviya University of Technology, Gorakhpur. His broad research areas include blockchains, cloud computing, and database systems. In 2014, he secured 33rd rank in the CSIR-NET examination for engineering sciences. Under the CSIR scheme, he worked as a Junior Research Fellow (JRF) and subsequently as a Senior Research Fellow (SRF) during his Ph.D. He has also qualified for the GATE entrance examination in computer science and information technology.

As a Fulbright-Nehru Postdoctoral Research fellow, Dr. Pandey will explore two crucial research directions: efficient data management utilizing blockchain technology, and blockchain application in crowdsourcing. As the current decentralized data processing and retrieval landscape is transitioning, his plan involves optimizing blockchain performance, specifically addressing query retrieval efficiency and facilitating rich queries. Additionally, he aims to incorporate access control mechanisms within the blockchain framework. The research outcomes will be seamlessly integrated into existing crowdsourcing applications, capitalizing on the strengths of both domains

Saptarshi Saha

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.

Sriparna Saha

Dr. Sriparna Saha is currently serving as an Associate Professor in the Department of Computer Science and Engineering, IIT Patna, India. She has authored or co-authored more than 400 papers. Her current research interests include machine-learning, deep-learning, natural-language-processing, and biomedical-information-extraction. She is the recipient of Google-India-Women-in-Engineering-Award-2008, NASI-Young-Scientist-Platinum-Jubilee-Award-2016, BIRD Award-2016, IEI-Young-Engineers’-Award-2016, SERB-Women-in-Excellence-Award-2018, Pattern-Recognition-Letters-Editor-Award-2023, prestigious “Young-Faculty-Research-Fellowship” under Visvesvaraya-PhD-Scheme for Electronics-&-IT for-5-years (Jan 2019-Jan 2024), Humboldt-Research-Fellowship, Indo-U.S.-Fellowship-for-Women-in-STEMM-2018. She won the best paper awards in ICONIP 2023, CLINICAL-NLP workshop of COLING 2016, and Area-chair-award (Information Extraction) at IJCNLP-AACL 2023.

With her Fulbright fellowship, at University of South Carolina, Sriparna is working towards developing some unified large language models (LLM) for low-resource settings. In general, it has been shown in the recent literature that the existing LLMs are not performing well for low resource Indian languages like Bengali. This disparity raises concerns about the fairness of LLMs, as it may lead to biased outcomes and unequal access to information and resources for speakers of low-resource languages. In this project Sriparna aims to develop some LLMs for low-resource language setting by developing a scalable training approach using reinforcement learning from human feedback.

Ujjwal Maulik

Prof. Ujjwal Maulik is a full Professor in the Department of Computer Science and Engineering, Jadavpur University since 2004. He was also the former head of the same department. He has worked in many universities and research laboratories in Australia, China, France, Germany, Hungary, Italy, Slovenia and U.S. and also delivered lectures in many more countries. He is the Fellow of India (INAE), India, National Academy of Science India (NASI), International Association for Pattern Recognition (IAPR), US, The Institute of Electrical and Electronics Engineers (IEEE), U.S., Asia-Pacific Artificial Intelligence Association (AAIA), Singapore and Distinguish Member of the Association for Computing Machinery (ACM). He is a Distinguish Speaker of IEEE as well as ACM. His research interests include machine learning, pattern analysis, data science, bioinformatics and computational biology, multi-objective optimization, social networking, IoT and autonomous car. In these areas he has published ten books, more than three hundred fifty papers, mentored several start-ups, filed several patents and already guided twenty five doctoral students. His other interests include outdoor sports and classical music.

During his tenure as Fulbright-Nehru Academic and Professional Excellence Fellowship Prof. Maulik is working for the better understanding of newly developed NicE-seq technology for chromatin accessibility through the application of artificial intelligence (AI) methods. This research has the potential to uncover novel regulatory mechanisms and advance our understanding of the functional genomics landscape. The AI-driven approaches can expedite and enhance chromatin accessibility studies, leading to advancements in various fields, including gene regulation, disease mechanisms, and therapeutic development.