Computer Science Archives

Ashish Tiwari

Ashish Tiwari is currently a Ph.D. candidate in electrical engineering at IIT Gandhinagar. He obtained his MTech in electrical engineering from IIT Gandhinagar in 2020. He is the recipient of the Prime Minister’s Research Fellowship (PMRF) 2020-2024. His research interest lies at the intersection of computer vision, computer graphics, and deep learning with a primary focus on inferring the 3D world from image(s) through photometric methods such as photometric stereo, Shape from Polarization (SfP), and photo-polarimetric stereo. He was awarded the Qualcomm Innovation Fellowship (QIF) 2023-2024 for his project proposal, “Photometric Stereo for Refractive Objects.” He was a part of the core organizing team of ICVGIP 2022, held at IIT Gandhinagar. He was also a part of the Google Research Week 2023.

As a Fulbright-Nehru Doctoral Research fellow at Rice University, Houston, TX, Ashish is investigating a scene’s geometry, material, and lighting through a sparse set of images captured through hand-held acquisition devices such as smartphones. Ashish enjoys teaching and has delivered plenty of invited talks on his research on photometric stereo. He likes singing, sketching, playing outdoor sports, especially cricket, and long-hour endurance runs. He also enjoys interacting with people from different regions and cultures and involves himself in community services.

Rishiraj Adhikary

Mr. Rishiraj Adhikary is a Ph.D. student at the Computer Science Department, Indian Institute of Technology (IIT) Gandhinagar, Gujarat. His research interest is in human-computer interaction, ubiquitous computing and sensor-enabled embedded systems that can impact healthcare delivery or pave the way towards making healthcare more accessible. His current research focuses on retrofitting consumer-grade masks with sensors to detect lung health. His prior work has also studied the perception of people around air pollution to aid in risk communication.

During his Fulbright-Nehru Fellowship, he will study how contexts like human activity can be leveraged to implement opportunistic sensing techniques in smart masks. Successful research on context sensing will pave the way to preserve privacy and reduce the energy consumption of a smart face mask.

Mr. Adhikary received his B.Tech. (Electronics and Communication Engineering) from Gauhati University, Assam, where his capstone project was recognised as the best project. He has successfully conducted events targeting school children in the past where he has demonstrated prototyping tools to help students understand the basics of electronics. He also takes a keen interest in teaching undergraduate and school students.

Harish Sankar Aghila

Harish S A is a Ph.D. candidate and teaching assistant at the Department of Computer Science and Engineering, Indian Institute of Technology Hyderabad. The broader domains encompassing his research interests include networks, systems, and security. His current research explores the security implications of in-network systems that leverage cutting-edge technologies like software-defined networks and programmable data planes.

Harish is a recipient of the prestigious Prime Minister’s Research Fellowship (PMRF) awarded by the Ministry of Education, Government of India. As a part of his doctoral research, he actively engaged in a security project alongside ASEAN countries and participated in their student exchange program. He published research papers and presented his work at reputed international venues. Additionally, he has received the notable SIGCOMM travel grant award, among many others.

He earned a bachelor’s degree in computer science and engineering from the National Institute of Technology Puducherry, during which time he interned with the UMR TETIS Joint Research Unit in Montpellier, France. He holds a master’s degree in computer engineering (cyber security) from the National Institute of Technology Kurukshetra.

As a Fulbright-Nehru Doctoral Research fellow at the University of Texas, Austin, TX, Harish is examining to secure data-driven, in-network systems built on high-speed programmable data planes against adversarial inputs. His vision is to bolster the resilience of next-generation computer networks against security threats. Harish teaches undergraduate students about network security. He enjoys road trips and chess.

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.