جستجوی استاد راهنمای دانشگاهی

Amit Mitraمشاهده پروفایل

استاد

• Statistical Signal Processing
• Time Series Analysis
• Financial Econometrics
• +۳ مورد دیگر

Amit Mitra is a Professor in the Department of Mathematics & Statistics at the Indian Institute of Technology Kanpur (IIT Kanpur), India. He has held academic positions at IIT Kanpur since 2005, progressing from Assistant Professor (2005-2007) to Associate Professor (2008-2012) and Professor (2012-present). Prior to this, he served as an Assistant Professor at IIT Bombay (2002-2005) and held research roles at the Reserve Bank of India (1996-2002). His international collaborations include visiting positions at Uppsala University (Sweden) and universities in Australia and Cyprus. His educational qualifications include: Ph.D. in Statistics from IIT Kanpur (1996) M.Sc. in Statistics from IIT Kanpur (1991) B.Sc. (Honors) in Statistics from the University of Calcutta (1988) Professor Mitra's research centers on statistical signal processing with emphasis on parameter estimation for nonlinear time series models, particularly chirp signals, and data mining of financial/economic time series. His work develops robust algorithms for signal model estimation using techniques like genetic algorithms, M-estimators, and wavelet filtering, with applications in radar, sonar, finance, and image processing. He has pioneered methods for 1D/2D chirp signal analysis and volatility modeling in econometrics. His 15 most recent publications (2018-2023) reveal a sustained focus on advanced parameter estimation for chirp and sinusoidal models, with increasing attention to computational efficiency and real-world applicability. Key trends include the development of robust M-periodogram approaches, asymptotic analysis of quantile estimators, and extensions to multidimensional signal processing—particularly for image applications through nonnegative matrix factorization. His scientific awards include: Excellence in Teaching Award (2019, IIT Kanpur) Distinguished Teacher Award (2010, IIT Kanpur) M. N. Murthy Award from the Indian Statistical Institute (2002) Postdoctoral award from Swedish Foundation for Strategic Research (2000) Research award from National Board for Higher Mathematics (1992) Professor Mitra has supervised 67 students including 3 PhD candidates (1 completed), 4 M.Tech students, and 59 Masters students across Mathematics, Economics, and Statistics programs. His applied work includes consultancy projects with Politis (Cyprus) on market data modeling, Retailzoom (Cyprus) on retail analytics, and QuantLink Solutions (USA) on data mining software development for financial applications. No specific research labs or teams are mentioned in the provided text, though his patent in image processing (US Patent 9,940,868) indicates collaborative work with engineers on real-time display systems.

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).

S. Akshayمشاهده پروفایل

استاد

• Formal Methods
• Automated Functional Synthesis
• Probabilistic Systems
• +۴ مورد دیگر

S. Akshay is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay . He is affiliated with the IITB Trust Lab , the Ashank Desai Centre for Policy Studies , and the Centre for Formal Design and Verification of Software Systems . His academic background includes a joint PhD from École Normale Supérieure de Cachan and Chennai Mathematical Institute (2010), postdoctoral work at IRISA/ENS Cachan Bretagne (2012) and National University of Singapore (2011), and earlier degrees from ÉNS-Cachan (2006) and Chennai Mathematical Institute (2004). Research interests focus on formal methods , particularly verification of timed, recursive, and distributed systems; automated functional synthesis; formal modeling of probabilistic and dynamical systems; and trust certification in AI models. Applications span systems biology , cyber-physical systems , and artificial intelligence . His recent publications emphasize verification techniques for Markov chains, timed automata, and Boolean functional synthesis, with algorithmic advancements in probabilistic inference, quantifier elimination, and robustness analysis. Professional activities include serving as Treasurer and Council Member of the Indian Association for Research in Computer Science (IARCS). He has co-chaired FSTTCS 2016 and ATVA 2024, and participated in program committees for major conferences like AAAI, CAV, and LICS. Teaching roles at IIT Bombay include courses on Discrete Structures , Formal Models of Concurrent Systems , and Quantitative Verification . Collaborative projects involve institutions such as University of Waterloo, IIT Delhi, and IRISA, Rennes.

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)

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.

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.

• Databases
• Algorithms

Sumit Ganguly is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), where he has established himself as a leading researcher in database systems and algorithms. Education: PhD in Computer Science, University of Texas, Austin (1992) MS in Computer Science, University of Texas, Austin (1989) B.Tech in Computer Science, IIT Kanpur (1987) Professor Ganguly's research primarily focuses on database systems with special emphasis on algorithms for data streams. His work explores the theoretical foundations and practical implementations of database technologies, particularly in the context of streaming data where traditional database approaches face significant challenges. His research has contributed to developing efficient algorithms for frequency estimation, sampling techniques, and processing hybrid frequency moments in data streams, addressing critical challenges in big data analytics. His publications demonstrate a consistent focus on data stream algorithms, with significant contributions spanning from 2008 to 2012. These works explore connections between expander graphs and data stream processing, deterministic data structures, and methods for estimating various statistical properties of streaming data. His research bridges theoretical computer science with practical database applications. Professor Ganguly maintains an active research program within the Department of Computer Science and Engineering at IIT Kanpur, contributing to the institution's reputation for excellence in theoretical computer science and database systems research.

• Optimization algorithms in communications
• Dynamic network measurement and cartography
• Network Localization
• +۲ مورد دیگر

Ketan Rajawat is an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), specializing in signal processing in communications and networks. His research and teaching focus on optimization algorithms, network measurement, localization, and network optimization within communication systems. He maintains an active research program with publications in top IEEE journals. Dr. Rajawat's research interests span optimization algorithms in communications, dynamic network measurement and cartography, network localization, and network optimization. His work bridges theoretical foundations with practical applications in wireless communications and networked systems. He has developed novel approaches for distributed estimation, matrix completion, and multidimensional scaling in network environments, contributing significantly to the field of signal processing for communication networks. His recent publications demonstrate a consistent research trajectory in distributed optimization techniques for communication networks, with a focus on wireless sensor networks, matrix completion, and asynchronous optimization methods. These works primarily appear in high-impact IEEE journals including IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing, reflecting the quality and relevance of his research contributions to the field. Dr. Rajawat teaches courses in Convex Optimization, Networks, and Signals and Systems, providing students with both theoretical foundations and practical applications in electrical engineering. His office is located in ACES 305C at IIT Kanpur, where he conducts his research and academic activities.

Vaskar Sarkarمشاهده پروفایل

استاد

• Power Systems
• Renewable Energy Integration
• Photovoltaic Systems
• +۳ مورد دیگر

Vaskar Sarkar is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad), where he has held academic positions since 2009, progressing from Visiting Assistant Professor to full Professor (since October 2023). His research focuses on power systems stability, renewable energy integration, photovoltaic systems optimization, and microgrid control. Education includes: B.E. from Bardhaman University (2002) M.E. from Bengal Engineering and Science University (2004) Ph.D. from IIT Bombay (2009) Research Interests: Dr. Sarkar's work spans flexible power point tracking in PV systems, grid integration of renewables, microgrid stability, power system optimization, and development of PV-based EV charging stations. His research emphasizes interdisciplinary solutions with theoretical development and experimental validation for real-world engineering problems. Publication Trends: His recent articles focus on renewable energy integration (especially photovoltaic systems), power market mechanisms, and advanced control strategies for grid stability. Emerging themes include AI-assisted PV optimization, wide-area monitoring systems, and HVDC grid integration. Awards: Excellence in Teaching Award from IIT Hyderabad (2021) Advising and Grants: Currently advises 4 PhD students and 1 M.Tech student. Secured research grants including: ₹33.79L from DST-SERB for PV power regulation under partial shading (2022-ongoing) Multiple projects from Hitachi India Limited (2012-2013, completed) Laboratory: Leads the SPARC Lab equipped with real-time simulators (eMEGASIM), solar PV emulators, power stacks (SEMIKRON), and control systems (CompactRIO, PXI) for power electronics and grid integration research.

• 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)

Pradeesha Ashokمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithms
• Graph Theory
• +۳ مورد دیگر

Dr. Pradeesha Ashok is an Associate Professor and Controller of Examinations at IIIT Bangalore. She holds a Ph.D. from the Indian Institute of Science, Bangalore, and previously worked as a Postdoctoral Fellow at the Institute of Mathematical Sciences, Chennai. Her research focuses on Theoretical Computer Science, with specializations in Algorithms, Graph Theory, Combinatorics, and Parameterized Complexity. Her research interests include geometric problems such as polygon guarding, covering, and packing, as well as conflict-free coloring in graphs and hypergraphs. She has contributed to developing exact and parameterized algorithms for these problems. Dr. Ashok has also taught courses like Exact and Parameterized Algorithms, Graph Theory, and Design and Analysis of Algorithms. Her publications span conferences like IWOCA, CSR, and COCOON, as well as journals such as Discrete Applied Mathematics and SIAM Journal on Discrete Mathematics. Her work emphasizes algorithmic efficiency and combinatorial optimization in geometric and graph-theoretic contexts. Dr. Ashok has advised several students, including PhD candidates and MTech thesis students, focusing on topics like the chromatic art gallery problem and parameterized complexity of coloring problems. She is affiliated with the Department of Computer Science & Engineering at IIIT Bangalore and contributes to the institute's academic governance through her role as Controller of Examinations. Her research also extends to geometric separability, bichromatic covering problems, and experimental studies of the Steiner Tree problem. She collaborates on projects involving theoretical and applied aspects of computational geometry and algorithms.

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.

• 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.

Subrahmanya Swamy Peruruمشاهده پروفایل

استادیار

• Wireless Networks
• Artificial Intelligence
• Probabilistic Graphical Models
• +۲ مورد دیگر

Subrahmanya Swamy Peruru is an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). His research focuses on wireless networks, artificial intelligence, and probabilistic graphical models, contributing significantly to the field of network optimization and machine learning applications in communication systems. Dr. Peruru completed his educational journey with excellence: PhD in Electrical Engineering from Indian Institute of Technology Madras (2018) M.S. (by Research) from Indian Institute of Technology Madras (2018) B.Tech in Electronics and Communication Engineering from Sastra University (2011) Dr. Peruru's research interests lie at the intersection of wireless communications and artificial intelligence. His work primarily focuses on developing efficient algorithms for wireless network scheduling, leveraging reinforcement learning and graph neural networks to optimize network throughput and minimize delays. He has made significant contributions to CSMA (Carrier Sense Multiple Access) protocols, particularly in adapting them to SINR (Signal-to-Interference-plus-Noise Ratio) models using advanced approximation techniques from statistical physics. His research on probabilistic graphical models has enabled more efficient network resource allocation and has potential applications in 5G and beyond wireless systems. Dr. Peruru's interdisciplinary approach bridges theoretical computer science, electrical engineering, and machine learning to solve practical challenges in modern communication networks. His publication record demonstrates a consistent focus on wireless network optimization, with a recent shift toward incorporating machine learning techniques. Starting with foundational work on CSMA protocols and regional approximations (2015-2018), his research has evolved to include reinforcement learning (2023) and graph neural networks (2021) for network scheduling. This progression reflects the broader trend in networking research toward data-driven and AI-enhanced approaches. His work spans both theoretical contributions (approximation algorithms, convergence analysis) and practical implementations (scheduling algorithms with throughput guarantees). Dr. Peruru previously served as a Senior Research Engineer in the Machine Learning R&D team at Pramati Technologies, Chennai (2018-2020), where he likely applied his academic expertise to industry challenges before joining IIT Kanpur as faculty.