جست‌وجوی استاد و پژوهشگر

L. Sunil Chandranمشاهده پروفایل

استاد

• Graph Theory
• Combinatorics
• Algorithms
• +۵ مورد دیگر

L. Sunil Chandran is a Professor at the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore. His research spans graph theory, combinatorics, algorithms, and quantum computing, with extensive work on graph coloring, boxicity, rainbow connectivity, and computational complexity. He leads a large research group, advising PhD, MSc, and ME students, many of whom hold faculty positions at premier institutions like IITs, NITs, and international universities. Chandran's research focuses on combinatorial graph algorithms, structural graph theory, and applications in quantum physics. Key areas include approximation algorithms for NP-hard problems, graph representation (boxicity/cubicity), and rainbow coloring variants. His recent work explores quantum entanglement via hypergraphs and parameterized complexity for vertex deletion problems. His publications (15 most recent shown) demonstrate a consistent focus on graph-theoretic problem-solving, with innovations in algorithmic techniques for graph coloring, combinatorial optimization, and quantum-graph intersections. Trends include increased interdisciplinary work bridging computer science, physics, and discrete mathematics. Awards and Recognitions: Adjunct Professorship at NIT Calicut (2023) INSA Fellow (2022) & INAE Fellow (2015) Alexander von Humboldt Fellowship NASI-SCOPUS Young Scientist Award (2012) MSR India Outstanding Young Faculty Award (2009-2010) INSA Young Scientist Award (2007) He has secured grants from DST, UGC, SERB, and NBHM to support postdoctoral researchers and students. Current projects include quantum-graph theory applications and parameterized algorithms. His group maintains active collaborations with institutions globally.

Rushikesh K. Joshiمشاهده پروفایل

استاد

• Software Engineering
• Distributed Systems
• Program Analysis
• +۵ مورد دیگر

Rushikesh K. Joshi is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Bombay. His research focuses on computational models for programs, their structures and dynamics, including processes, interactions, concurrency, and distributed systems. He has taught advanced courses like CS 770 (Process Engineering), CS 787 (Language Engineering), and CS 757 (Design and Re-engineering). Research Interests: He specializes in software engineering, distributed systems, program analysis, and code refactoring. His work includes modular process modeling, trace language mining, and consistency models for business processes. He also explores objectification of procedural code through projects like Ox and AccessViz. Notable Projects: Anonymous Remote Computing (ARC), Filter Objects, Constore Graph Database, and Ox (objectification of Linux kernel structures). Funding sources include IBM, Microsoft, Infosys, and the Ministry of Information Technology. Scientific Contributions: His research has led to publications in IEEE Transactions, LNCS, and other venues. He has advised over 100 theses, including Ph.D. students Karnika Shivhare, Omkarendra Tiwari, and Vrinda Yadav.

• Control Engineering
• Nonlinear dynamical systems
• Control of Unmanned vehicles
• +۳ مورد دیگر

Soumya Ranjan Sahoo is an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. His academic career at IIT Kanpur began with a contract position as Assistant Professor in 2014, progressing to Assistant Professor in 2016, and currently holding the position of Associate Professor since December 2020. Prior to joining IIT Kanpur, he worked as a Scientist Fellow at the National Aerospace Laboratories in Bangalore. PhD in Systems and Control Engineering from IIT Bombay (2013) Thesis Title: Coordination problems with minimal sensing and coarse actuation Thesis Supervisors: Prof. Ravi N. Banavar and Dr. Arpita Sinha B.Tech in Electrical Engineering from University College of Engineering, Burla, Odisha (2008) Dr. Sahoo's research spans the complex intersection of control theory and practical engineering applications. His work primarily focuses on nonlinear dynamical systems, with significant contributions to the control of unmanned vehicles and coordinated control systems. His research has evolved to address modern power system challenges, particularly in the domain of AC and DC microgrids, where he has developed innovative control strategies for improved performance. His work on discontinuous systems demonstrates his ability to tackle mathematically challenging control problems with practical implications. Analysis of Dr. Sahoo's publication record reveals a clear evolution from fundamental control theory to applied research with significant practical impact. His early work focused on theoretical aspects of multi-agent systems and network control, which has progressively shifted toward power systems applications, particularly microgrids. The consistent theme across his publications is the development of robust control strategies that work effectively under realistic constraints such as communication delays, incomplete information, and system heterogeneity. His research bridges theoretical control concepts with practical engineering implementations, particularly in the domains of autonomous systems and modern power grids. Dr. Sahoo teaches several key courses in the Electrical Engineering curriculum including Introduction to Electrical Engineering, Control Systems Analysis, Basics of Modern Control Systems, Nonlinear Systems, Adaptive Control Theory, and advanced topics in Nonlinear Systems. His teaching portfolio reflects his deep expertise in control theory and its applications. His laboratory work, particularly through the DiSCo Lab (as referenced in his homepage), focuses on practical implementations of control algorithms for various applications including unmanned vehicle coordination and microgrid management. This laboratory work bridges the gap between theoretical control design and real-world implementation challenges.

Prawesh Shankarمشاهده پروفایل

دانشیار

• Social Media
• Agent Based Simulation
• Fairness in Big Data

Prawesh Shankar is an Associate Professor in the Department of Management Sciences at the Indian Institute of Technology Kanpur. He holds a PhD from the University of South Florida, College of Business, and completed his M.Sc. (Integrated 5-year program) in Mathematics and Scientific Computing at IIT Kanpur. PhD: University of South Florida, College of Business (2009-2013) M.Sc.: IIT Kanpur, Mathematics and Scientific Computing (2004-2009) Dr. Shankar's research focuses on Social Media, Agent Based Simulation, and Fairness in Big Data. His work bridges the gap between computational methods and business applications, particularly in the domain of recommender systems and social network analysis. He has developed innovative approaches to news recommendation systems with emphasis on manipulation resistance and count amplification issues. His publication record shows a consistent focus on recommender systems, with numerous conference presentations at prestigious venues including RecSys (the premier conference in Recommender Systems), ICIS, WITS, and INFORMS. His research has evolved from foundational work on manipulation resistance in news recommenders to more recent applications in social media analytics and network analysis. College of Business Outstanding Doctoral Student Research Award 2012 University of South Florida, Graduate Fellowship 2009 – 2010 Dr. Shankar has delivered several invited talks on Social Media Analytics and Topological Data Analysis at IIT Kanpur events. His ongoing research projects include Simulation based Crowd Management for Disaster Prevention in collaboration with Indanil S. Dalal and Anurag Tripathi, and Text mining Approach to Measure Customer Satisfaction with Amit Trivedi. He maintains active collaborations with researchers including Balaji Padmanabhan and has contributed to the academic community through presentations at major conferences in business intelligence and information systems.

• Distributed Computing
• Network Algorithms
• Mobile Robotics
• +۴ مورد دیگر

Anisur Rahaman Molla is an Associate Professor at the Indian Statistical Institute (ISI) in Kolkata, India, affiliated with the Cryptology and Security Research Unit (CSRU) and the R. C. Bose Centre for Cryptology and Security . His work spans distributed computing, network algorithms, and mobile robotics, with a focus on fault tolerance and Byzantine agreement. Education : PhD in Computer Science from Nanyang Technological University, Singapore (2014) M.Tech in Computer Science from ISI Kolkata (2010) M.Sc in Pure Mathematics from Calcutta University (2007) B.Sc in Mathematics (Honors) from Calcutta University (2005) Research Trends : His recent work emphasizes mobile agent algorithms for tasks like dispersion, leader election, and graph traversal, with applications in fault-tolerant systems and dynamic networks. Papers frequently address message complexity, Byzantine resilience, and optimization in unoriented/oriented grids. Scientific Awards : Best Contributor Award (PhD Forum), ICDCN 2013 Advising : Supervised Prabhat Kumar Chand (current PhD student at ISI) and graduated Manish Kumar (PhD, ISI 2023). Co-supervised master's and bachelor's theses at the University of Freiburg and ISI. Labs & Collaborations : Works within the CSRU and R. C. Bose Centre at ISI. Collaborates with institutions like IIT Madras, University of Freiburg, and ETH Zürich.

• Machine Learning
• Bayesian Statistics
• Latent Variable Models
• +۶ مورد دیگر

Piyush Rai is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He also holds an Adjunct Assistant Professor position in Electrical and Computer Engineering at Duke University. His academic journey includes postdoctoral research at Duke University and the University of Texas at Austin, following his PhD from the University of Utah. His research interests focus on Machine Learning and Bayesian Statistics, with specializations in Latent Variable Models, Probabilistic Modeling, Approximate Inference, and Nonparametric Bayesian Methods. His work bridges theoretical foundations with practical applications in artificial intelligence and data science. Dr. Rai's publication record shows a consistent focus on tensor factorization, Bayesian nonparametrics, and scalable algorithms for large datasets. His work spans conferences including NIPS, ICML, UAI, and AISTATS, demonstrating strong contributions to both theoretical and applied machine learning. Best Student Paper Award at ECML-PKDD (2015) National Science Foundation (USA) EAGER Award (2015) Dr. Deep Singh and Daljeet Kaur Faculty Fellowship at IIT Kanpur (2015) NIPS 2013 Reviewer Award Sheldon Ekland-Olson Postdoctoral Fellowship (2012) He teaches advanced courses in Machine Learning and Probabilistic Machine Learning at IIT Kanpur, mentoring the next generation of researchers in statistical machine learning techniques. His collaborative work with Lawrence Carin and other researchers demonstrates strong interdisciplinary connections between institutions.

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.

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 .

Ramakrishna Upadrastaمشاهده پروفایل

دانشیار

• Domain Specific Programming Languages for Parallelization
• LLVM Optimizations
• Polyhedral Compilation
• +۶ مورد دیگر

Ramakrishna Upadrasta is an Associate Professor in the Department of Computer Science & Engineering and Heritage Science & Technology at Indian Institute of Technology Hyderabad. He earned his PhD from University of Paris-Sud and INRIA, Paris, with a focus on improving scalability in polyhedral compilation tools. His academic journey includes an M.S. from Colorado State University, M.Tech from Indian Institute of Science (IISc), and B.E. in Electrical and Electronics Engineering from Andhra University. His research expertise includes: Domain Specific Programming Languages for Parallelization LLVM Optimizations Polyhedral Compilation Abstract Interpretation Sub-polyhedral Approximations High-Performance Computing (HPC) He leads the IITH-LLVM group and collaborates with industries on compiler technologies. His work addresses scalability vs. precision trade-offs in static analysis tools and extends polyhedral compilation frameworks like Pluto and Polly. Scientific Recognition : HiPEAC paper award for his POPL-2013 publication As an educator, he has taught advanced compiler courses (e.g., CS6250, CS5260) and coordinated interdisciplinary initiatives, including Sanskrit language courses via Samskrita Bharati. His students span PhD, M.Tech, and B.Tech levels, focusing on compiler design and parallelization.

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.

Sobhan Babuمشاهده پروفایل

دانشیار

• Big Data Analytics
• Graph Theory
• Algorithms
• +۴ مورد دیگر

Dr. Sobhan Babu is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). His research focuses on Big Data Analytics, Graph Theory, Algorithms, and Applied Machine Learning. He obtained his Ph.D. and MTech from IIT Bombay, and his BE from the University of Madras. Education Ph.D. in Computer Science, IIT Bombay MTech in Computer Science, IIT Bombay BE in Computer Science, University of Madras Diploma in Computer Engineering, AANM&VVRSR Polytechnic Research Interests His work bridges theoretical and applied Computer Science. In Graph Theory, he investigates structural graph properties, subdivisions, and directed graph algorithms. In AI/ML, he explores optimization techniques for VLSI design and power-efficient testing. His Big Data Analytics focus includes developing algorithms for efficient data processing and modeling. Publications His publications span VLSI optimization, algorithmic game theory, and graph theory. Key themes include power minimization in scan testing, market equilibrium algorithms, and graph structural conditions. Recent accepted works extend existing research on cycles and unicyclic graphs, indicating sustained theoretical contributions. Contact Email: sobhan@iith.ac.in Office: Room CS-307, Computer Science Building, IIT Hyderabad, Telangana, India.

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.