جست‌وجوی استاد و پژوهشگر

Palash Deyمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Parameterized Algorithms
• Approximation Algorithms
• +۷ مورد دیگر

Palash Dey is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kharagpur. His research lies at the intersection of theoretical computer science and algorithmic game theory, with a primary focus on computational social choice, voting theory, and parameterized algorithms. His research interests include: Theoretical Computer Science Parameterized Algorithms Approximation Algorithms Algorithmic Game Theory Computational Social Choice Voting Theory Algorithmic Fairness Network Games His recent publications, spanning from 2023 to 2025, demonstrate a strong and consistent research trajectory in the analysis of voting systems, manipulation, and fairness. The articles focus on complex problems such as bribery, gerrymandering, rank aggregation, and networked public goods, primarily using tools from parameterized complexity and algorithmic game theory. Key venues for his work include AAMAS, IJCAI, AAAI, and Theoretical Computer Science, indicating a high impact in the fields of AI and theoretical computer science. His professional service includes being the Newsletter and Social Media Chair of the IEEE Kharagpur Section and serving on the Senior Program Committee for AAAI (2021-2024) and the Program Committee for AAAI, IJCAI, AAMAS, and COMSOC. He has also organized significant workshops such as CALDAM 2019 and GAME-ARTS. Palash Dey actively advises Ph.D. students, including Sipra Singh, Koustav De, Ashlesha Hota, and Narayan Sharma. He teaches courses such as Algorithms II, Randomized Algorithm Design, and Algorithmic Game Theory. His email is palash.dey@cse.iitkgp.ac.in.

Animesh Mandalمشاهده پروفایل

دانشیار

• Near Surface Geophysics
• Geophysical Data Enhancement
• Subsurface Modeling
• +۷ مورد دیگر

Animesh Mandal serves as an Associate Professor in the Department of Earth Sciences at Indian Institute of Technology Kanpur (IITK) , where he has been a faculty member since April 2015. His academic journey includes a B.Sc. in Physics from University of Calcutta (2005), M.Sc. in Physics from IIT Delhi (2007), and Ph.D. in Geophysics from IIT Kharagpur (2013). Prior to joining IITK, he worked as a Project Scientist at the National Geophysical Research Institute (NGRI). Research Interests : Dr. Mandal specializes in near-surface geophysical studies , geophysical data enhancement , and subsurface modeling . His work focuses on integrated geophysical approaches to understand shallow crustal configuration and delineate natural resources, utilizing gravimeter, magnetometer, electrical, and EM equipment. He has pioneered research in machine learning-assisted interpretation of geophysical data, particularly in seismic impedance inversion, reservoir characterization, and geothermal system analysis. His recent publications demonstrate expertise in applying deep learning techniques to solve complex geophysical problems. Research Trends : Analysis of his recent publications reveals a strong focus on geothermal energy systems , particularly non-volcanic hot springs in the Eastern Ghats Mobile Belt. His work combines gravity-magnetic studies with advanced computational methods to understand crustal configuration and thermal structure. Another major trend involves machine learning applications in seismic data processing, where he develops novel deep learning architectures for impedance inversion and reservoir characterization. His research bridges traditional geophysical methods with cutting-edge AI techniques, creating innovative approaches to subsurface imaging. Scientific Awards : Supervised students who received Prime Minister's Research Fellowship (PMRF) Advisees awarded FARE Fellowship, ONGC-IGU Best Poster Presentation Award, and EAGE PACE grants Students received FULL sponsorship to attend major international conferences like IMAGE 2024 Mentored students who secured postdoctoral positions at prestigious institutions including KAUST and University College Dublin Academic Leadership : Dr. Mandal has successfully supervised multiple Ph.D. and M.Tech. students to completion, with recent graduates securing positions at institutions like University College Dublin, Queen's University, and industry roles at companies including Verisk Analytics and ONGC. His research group maintains active collaborations with international institutions and regularly presents at major geoscience conferences including EAGE, SEG, and AOGS. The group has secured competitive research funding that supports field studies, laboratory work, and student training in advanced geophysical techniques.

Surender Baswanaمشاهده پروفایل

استاد

• Graph Algorithms
• Dynamic Algorithms
• Fault Tolerant Structures
• +۴ مورد دیگر

Surender Baswana is a Professor and Tapas Mishra Memorial Chair Professor in the Department of Computer Science and Engineering at IIT Kanpur. His research focuses on the design and analysis of computer algorithms, with specializations in graph algorithms, dynamic algorithms, fault-tolerant structures, and randomized algorithms. He holds a PhD (2005), M.Tech. (1999), and B.Tech. (1997) from IIT Delhi. Research Interests: His work spans fundamental algorithmic domains including minimum cuts (with recent breakthroughs in Steiner mincuts), fault-tolerant data structures (handling edge failures in DFS trees and mincuts), dynamic graph algorithms (efficient updates for DFS and matching), and approximate shortest paths. He develops theoretically optimal solutions with applications in network reliability and optimization. Awards and Honors: Alexander von Humboldt Fellowship (2018) Wagner Prize Finalist for Operations Research (2018) IIT Kanpur Distinguished Teacher Award (2017) Young Engineer Award (2009) Best Faculty Award (8-time recipient) PhD Dissertation Award (IBM, 2005) PhD Supervision: Mentored 5+ PhD students including ACM India Dissertation Award recipient Keerti Choudhary and Manas Mandal Award winner Koustav Bhanja, now faculty at IIT Delhi and Weizmann Institute postdoc respectively. Professional Journey: From software engineer at Lucent Technologies (1999), he transitioned to academia via postdoctoral research at Max Planck Institute (2003-2006), rising through ranks at IIT Kanpur from Assistant Professor (2006) to Professor (2016-present).

Soumen Chakrabartiمشاهده پروفایل

استاد

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +۳ مورد دیگر

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

Abir Deمشاهده پروفایل

استادیار

• Machine Learning
• Graph Neural Networks
• Data-efficient Learning
• +۳ مورد دیگر

Abir De is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Bombay. His research focuses on designing machine learning models for structured objects like graphs and sets, emphasizing data-efficient learning and human-machine collaboration. Academic Rank: Assistant Professor Institution: Indian Institute of Technology Bombay Department: Computer Science and Engineering Research Interests : Differentiable surrogates for combinatorial graph algorithms (e.g., subgraph isomorphism detection) Data-efficient machine learning through strategic subset selection Neural models for submodular set functions Human-in-the-loop machine learning Information diffusion with capacity constraints Scientific Awards : Qualcomm Innovation Fellowship Winner (2022) and Superwinner (2023) Indian National Academy of Engineering Young Engineer Award (2021) Prof. Krithi Ramamritham Award for Creative Research (2020) Google India PhD Fellowship (2013) Advising and Community Contributions : Co-advising PhD students Indradyumna Roy and N Lokesh Program Co-chair of IndoML 2023 Co-organizer of CSE Research Symposium (2023) and SubSetML workshop (2021) Tutorial creator at AIML Systems Conference (2021) and AAAI (2022) Serving on Senior PC at AAAI (2022-2024), PC member at NeurIPS (2016-2023), and ICLR (2018-2023)

Maunendra Sankar Desarkarمشاهده پروفایل

دانشیار

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +۱۰ مورد دیگر

Dr. Maunendra Sankar Desarkar is an Associate Professor at the Computer Science and Engineering Department of Indian Institute of Technology Hyderabad , India, with affiliations to the AI Department. His research spans Natural Language Processing , Information Retrieval , and Machine Learning , focusing on robust, responsible, and culturally-aware AI systems. Education: B.E. (University of Burdwan), M.Tech (IIT Kanpur), Ph.D. (IIT Kharagpur, 2014) Current work emphasizes zero-shot cross-lingual transfer , dialogue interpretability , and ethical NLP (non-toxicity, empathy). Recent articles explore large language model evaluation , hallucination detection , and tokenization for Indic languages . Collaborations include Microsoft and JICA-funded projects. Scientific contributions include: Microsoft India Ph.D. Fellowship Award Yahoo Key Scientific Challenges Honorable Mention (2012) Students include Suvodip Dey (Ph.D. in Dialogue Systems), Aishwarya Maheswaran , Debolena Basak , Kaushal Kumar Maurya , and Maharaj Brahma . Advises on projects involving disaster response systems , generative AI , and multilingual NLP .

Rogers Mathewمشاهده پروفایل

دانشیار

• Graph Theory
• Combinatorics
• Graph Algorithms
• +۷ مورد دیگر

Rogers Mathew is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Hyderabad. His research focuses on graph theory, combinatorics, and theoretical computer science, with applications in algorithm design and discrete mathematics. Ph.D. from Indian Institute of Science, Bengaluru Postdoctoral experience at University of Haifa (Israel) and Dalhousie University (Canada) His research interests include: Graph theory and combinatorics Algorithm design for discrete structures Extremal set theory and linear algebra methods Probabilistic and polynomial techniques in theoretical computer science Recent publications span topics in graph theory, combinatorics, and algorithm analysis, with keywords including computer science, mathematics, and theoretical computer science. His work often bridges discrete mathematics with computational complexity. Scientific awards include: VATAT Postdoctoral Fellowship AARMS Postdoctoral Fellowship Rogers Mathew has supervised doctoral students such as Tapas Kumar Mishra, Atrayee Majumder, Seshikanth Varma, and Shiwali. He has taught courses in computational complexity theory, advanced graph theory, combinatorics, and data structures.

Sutanu Gayenمشاهده پروفایل

استادیار

• Theory of Machine Learning
• Algorithms
• Bayesian Networks
• +۳ مورد دیگر

Sutanu Gayen is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on theoretical aspects of machine learning, algorithms, and computational statistics. Designing algorithms for Bayesian networks and causality Complexity analysis of distribution learning Applications in high-dimensional statistical modeling His recent publications explore challenges in total variation distance computation, Gaussian tree models, and interventional distribution learning. Courses taught include advanced algorithms for machine learning and data structures. Research connects fundamental computational limits with practical statistical learning tasks.

• Algorithms & Data Structures
• Discrete & Computational Geometry
• Combinatorial Optimization
• +۱ مورد دیگر

Dr. Sujoy Bhore is a faculty member at the Indian Institute of Technology (IIT) Bombay in the Department of Computer Science & Engineering. He is also associated with the Center for Machine Intelligence and Data Science (C-MInDS) and the IIT Bombay Trust Lab. Additionally, he serves as a visiting fellow in the Department of Mathematics at the London School of Economics. Affiliation: IIT Bombay (Department of Computer Science & Engineering) Associated Labs: Center for Machine Intelligence and Data Science (C-MInDS), IIT Bombay Trust Lab Visiting Position: London School of Economics (Department of Mathematics) Research Interests: Algorithms & Data Structures Discrete & Computational Geometry Combinatorial Optimization Theoretical Machine Learning Dr. Bhore welcomes postdoctoral researchers and highly motivated Ph.D. students with strong mathematical backgrounds. He also pursues creative interests in Painting, Photography, and Poetry, reflecting his holistic approach to academia.

• Rough Set Theory and its Applications
• Modal Logics
• Mathematical Logic
• +۱ مورد دیگر

Mohua Banerjee is a Professor in the Department of Mathematics & Statistics at Indian Institute of Technology Kanpur. Her research focuses on Rough Set Theory , Modal Logics , and their applications in computer science and mathematical logic. Education : PhD (University of Calcutta) Contact : mohua@iitk.ac.in , +91-512-259-7634 Her work bridges foundational logic with computational methods, particularly in reasoning under uncertainty and feature selection for biological data. Recent publications highlight interdisciplinary applications in gene expression analysis and algebraic structures for rough sets. Scientific Awards : INSA Young Scientist Medal (1995)

Sashikumaar Ganesanمشاهده پروفایل

استاد

• Scientific Machine Learning
• Physics-Informed Neural Networks
• High-performance computing
• +۷ مورد دیگر

Prof. Sashikumaar Ganesan is a Professor at the Indian Institute of Science (IISc), Bangalore, where he leads the AI for Research and Engineering eXcellence (AiREX) Lab. He also serves as the Founder and Chief AI Officer of Zenteiq AiTech Innovations and has held leadership positions including Chairman of the CDS Department (2018-2024) and Programme Director for Advanced Programs in Gen AI, Computational Data Science and AI, and AI & MLOps since 2020. His research interests include: Scientific Machine Learning (SciML) Physics-Informed Neural Networks (PINNs) High-performance computing Hardware-aware numerical methods Machine Learning Operations (MLOps) Computational Science Prof. Ganesan's work focuses on integrating scientific computing with machine learning to advance AI applications in scientific and engineering domains. His research spans AI for aerospace and defense applications, scientific machine learning, MLOps frameworks, and computational science including finite element analysis and fluid dynamics modeling. He has developed science-native Foundation Models through his company Zenteiq.ai to enhance AI-driven R&D in aerospace and defense sectors. His significant projects include the Indo-German Partnership Program (2020-2024) focused on hardware-aware numerical schemes for multi-GPU supercomputers and the Shell India project on Large Scale Simulation augmented by AI for wind turbine farms. He has mentored over 25 students and published more than 60 works, including the book 'Finite Elements: Theory and Algorithms' (Cambridge University Press, 2017). Prof. Ganesan teaches courses including 'Generative and Agentic AI in Practice' (2025), 'AI and Machine Learning' (2024), and 'High-Performance Computing and Data Science,' while also contributing to societal discourse through COVID-19 projections featured in major Indian media outlets.

Sourav Chakrabortyمشاهده پروفایل

استاد

• Theoretical Computer Science
• Boolean Functions
• Quantum Complexity
• +۴ مورد دیگر

Sourav Chakraborty is a Professor in the Advanced Computing and Microelectronics Unit (ACMU) of the Computer and Communication Sciences Division at the Indian Statistical Institute (ISI), Kolkata, India. He joined ISI in July 2018 after serving as faculty at Chennai Mathematical Institute from 2010-2018. Previously, he held postdoctoral positions at Centrum Wiskunde & Informatica (CWI) in Amsterdam and Technion in Israel. Education: Ph.D. in Computer Science, University of Chicago (2008) M.S. in Computer Science, University of Chicago (2005) B.Sc. in Mathematics, Chennai Mathematical Institute (2003) Research Focus: Professor Chakraborty specializes in Theoretical Computer Science with emphasis on classical and quantum complexity of Boolean functions, including sensitivity analysis, property testing, and quantum database search. His work extends to graph algorithms, electronic commerce mechanisms, and coding theory. His research explores fundamental questions in computational complexity through innovative mathematical frameworks. Publication Trends: Recent work demonstrates a strong focus on property testing, sampling algorithms, and complexity theory, with significant contributions to streaming algorithms, Boolean function analysis, and quantum query complexity. His publications frequently appear in top theoretical computer science venues and exhibit consistent innovation in algorithm design and complexity boundaries. Awards & Honors: Praise from Donald E. Knuth for streaming algorithms research Inclusion in Oded Goldreich's 'my choices' list for Conditional Sampling and Huge-Object Model work Chakraborty's function named in his honor for Sensitivity Conjecture contributions Academic Service: Teaches courses in discrete mathematics and theoretical computer science, with detailed course materials available through institutional pages. Organized workshops including the 2020 Workshop on Sensitivity and Query Complexity at ISI.

Chethan Kamathمشاهده پروفایل

استادیار

• Cryptography
• Theoretical Computer Science
• Foundations of Cryptography
• +۵ مورد دیگر

Chethan Kamath is an Assistant Professor in the Department of Computer Science and Engineering at IIT Bombay, where he is a member of the Theory Group and Trust Lab. His primary research focus is on cryptography, particularly its foundations, with broader interests extending to theoretical computer science. His educational journey includes: PhD from IST Austria (2014-2020) under Krzysztof Pietrzak, with thesis titled "On the Average-Case Hardness of Total Search Problems" Master's in CS from IISc Bangalore (2010-2013) under Sanjit Chatterjee, with thesis titled "Constructing Provably Secure Identity-Based Signature Schemes" Bachelor's in CS from University of Kerala (2005-2009) at TKM College of Engineering, Kollam Dr. Kamath's research interests span the theoretical foundations of cryptography, with particular focus on secure computation, complexity theory, and cryptographic hardness assumptions. His work often bridges theoretical computer science with practical cryptographic applications, exploring the boundaries of what can be efficiently computed while maintaining security guarantees. His research frequently addresses fundamental questions about the relationship between cryptographic primitives and complexity classes, especially the PPAD and TFNP complexity classes. His recent publications demonstrate a consistent focus on foundational aspects of cryptography, with particular emphasis on secure computation (garbled circuits, Yao's protocol), proofs systems (proofs of work, proofs of exponentiation), and complexity-theoretic aspects of cryptographic primitives. A notable trend is his exploration of the connections between complexity classes like PPAD and cryptographic assumptions, as well as his work on verifiable delay functions and their underlying number-theoretic assumptions. His research often employs tools from algorithmic graph theory (treewidth, separators) to analyze cryptographic protocols. His notable scientific achievement includes: Azrieli Fellowship during his post-doc at Tel Aviv University Dr. Kamath actively mentors students and researchers, currently advising several PhD and MS students at IIT Bombay, often in collaboration with Sruthi Sekar. His service to the academic community includes extensive program committee memberships for major conferences including Crypto, Eurocrypt, and TCC, demonstrating his standing in the cryptographic research community. He has co-organized educational events like the "Introduction to Cryptography" school as part of the ACM India Summer School 2025 and the "Theoretical Foundations of Cryptography" school as part of the ACM India Summer School 2024. He leads research activities within the Trust Lab at IIT Bombay, which focuses on theoretical and applied aspects of cryptography and security. The lab actively recruits MS/PhD students and post-docs, with ongoing research in foundational cryptography and its applications to secure computation, verifiable delay functions, and complexity-theoretic aspects of cryptographic security.

• Algorithms
• Combinatorics
• Complexity Theory
• +۳ مورد دیگر

Sundar Vishwanathan is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay (IIT Bombay), where he has established himself as a leading researcher in theoretical computer science. His academic career spans over three decades with consistent contributions to algorithms, combinatorics, and complexity theory. His research interests form a cohesive body of work focused on theoretical foundations of computing: Algorithms (particularly approximation algorithms, online algorithms, and randomized algorithms) Combinatorics and extremal set theory Complexity theory and circuit lower bounds Graph theory and graph algorithms Combinatorial optimization problems Vishwanathan's publication record demonstrates remarkable consistency and depth, with publications spanning from 1990 to 2024. His recent work focuses on graph algorithms (particularly maximum matching problems), approximation algorithms for combinatorial optimization, and circuit complexity. He has developed innovative techniques using linear algebra, combinatorial methods, and probabilistic analysis to solve challenging theoretical problems. His approach often bridges theoretical insights with practical algorithmic considerations. His collaborative work spans multiple research groups, with frequent co-authorships with researchers such as Ashish Chiplunkar, Sumedh Tirodkar, and Sreyash Kenkre. His research has evolved from foundational work in online graph coloring in the 1990s to more specialized topics in combinatorics and recently to circuit lower bounds, showing both depth in core areas and adaptability to evolving research landscapes.

Rakesh Venkatمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Approximation Algorithms
• Hardness of Approximation
• +۱ مورد دیگر

Rakesh Venkat is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. His research focuses on Theoretical Computer Science, including approximation algorithms, hardness of approximation, and communication complexity. Education : Ph.D., Tata Institute of Fundamental Research (TIFR), Mumbai. Research Trends : His work addresses fundamental challenges in algorithm design, such as optimizing cache misses, improving clustering algorithms, analyzing graph expansion, and exploring embedding techniques. Publications span top-tier conferences like APPROX, FSTTCS, ICALP, and ITCS, with collaborations at institutions including HUJI, TIFR, and IIT-Bombay. Teaching : Courses taught include Approximation Algorithms, Advanced Data Structures, Discrete Mathematics, and Spectral Graph Theory.