جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Visual Computing
• Image Analysis
• +۶ مورد دیگر

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.

Supratik Chakrabortyمشاهده پروفایل

استاد

• Formal Methods
• Formal Verification
• Automated Synthesis
• +۷ مورد دیگر

Supratik Chakraborty serves as the Bajaj Group Chair Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He maintains dual affiliations with the Centre for Formal Design and Verification of Software and the Centre for Liberal Education at IIT Bombay, demonstrating his cross-disciplinary engagement. Professor Chakraborty's research spans formal methods with focus on formal verification, rigorous analysis of system models, and automated synthesis of systems from specifications. His work bridges theoretical foundations with practical applications, particularly in developing mathematically provable guarantees for increasingly complex hardware, software, and intelligent systems. Current research interests include constrained counting and sampling, scalable formal verification of software and hardware systems, automated synthesis of programs and circuits, and applications of automata, logic and finite model theory to practical verification challenges. His publication trajectory shows a significant evolution from traditional hardware and software verification toward addressing verification challenges in machine learning and AI systems. Recent work increasingly focuses on interpretability of black-box models, verification of neural networks, and synthesis techniques applicable to intelligent systems. The research demonstrates strong interdisciplinary connections between formal methods, programming languages, and artificial intelligence. IIT Bombay Excellence in Thesis (CSE) Award 2011 (for Bhargav Gulavani's thesis) IIT Bombay Excellence in Thesis (CSE) Award 2017 (for Abhisekh Sankaran's thesis) Best Paper in Algorithms and Architecture track at IEEE International Conference on Computer Design: VLSI in Computers and Processors, 1998 Professor Chakraborty has successfully supervised 11 doctoral students, with research spanning formal verification techniques, Boolean functional synthesis, constrained counting, and applications to hardware and software systems. His students have gone on to positions at major institutions including Microsoft Research, TCS Research, Georgia Tech, and BARC, reflecting the strong industry and academic impact of his mentorship. Current research directions show increasing emphasis on verification challenges posed by machine learning systems and AI. His research group at IIT Bombay, while not explicitly named in the materials, appears to focus on formal methods with strong connections to the Centre for Formal Design and Verification of Software. The group maintains active collaborations with international researchers including Moshe Y. Vardi at Rice University, and has made significant contributions to verification tools like VeriAbs that bridge theoretical advances with practical applications.

Chester Rebeiroمشاهده پروفایل

دانشیار

• Hardware Security
• Applied Cryptography
• Side Channel Analysis
• +۳ مورد دیگر

Chester Rebeiro is an Associate Professor at the Department of Computer Science and Engineering within the Indian Institute of Technology Madras . His work spans hardware and software security with a focus on cryptographic implementations and microarchitectural vulnerabilities. Research interests include hardware security, applied cryptography, side channel analysis, and operating system security. He develops frameworks for automatic vulnerability detection and mitigation in cryptographic systems. Editorial Board: Associate Editor at Journal of Hardware and Systems Security (Springer, 2021-2024) Conference Leadership: General Co-Chair for SPACE 2024, Program Co-Chair for ATS 2024, and Program Co-Chair for INDOCRYPT 2023 Professional Activities: Organizer of e-CTF Embedded Capture The Flag and contributor to cybersecurity workshops across India and abroad Scientific contributions highlight two major awards: a Distinguished Paper Award at USENIX Security 2024 and a Best Paper Award at IEEE HOST 2020. His research focuses on practical security solutions for processors and cryptographic systems. Advising includes mentoring 11 PhD students and 7 MS by Research candidates, with notable co-guided projects in fault attack detection and side-channel mitigation. He actively contributes to educational initiatives through courses on Secure Processor Microarchitecture and Operating Systems.

R.K. Shyamasundarمشاهده پروفایل

استاد

• Real-Time Programming
• Reactive Programming
• Logic Programming
• +۷ مورد دیگر

R.K. Shyamasundar is a Professor at the Indian Institute of Technology Bombay , with a focus on Real-Time and Reactive Programming, Logic Programming, Pi-Calculus, and Parallel Programs. Research spans formal verification, concurrency, and distributed systems. Key contributions include RT-CDL semantics, Esterel language extensions, and hybrid system controller synthesis. Scientific awards include JC Bose National Fellow, Fellowships at Indian Academy of Sciences and Indian National Science Academy, and Senior Membership in IEEE. His work involves collaborations with institutions like TCS Group and researchers such as Basant Rajan, N. Raja, and Deepak Kapur.

Karthik P. N.مشاهده پروفایل

استادیار

• Probability Theory
• Detection and Estimation Theory
• Markov Decision Processes
• +۶ مورد دیگر

Karthik P. N. is an Assistant Professor in the Department of Artificial Intelligence at the Indian Institute of Technology Hyderabad (IIT Hyderabad). He previously served as a Research Fellow at the Institute of Data Science, National University of Singapore (NUS), and completed his Ph.D. and Master of Science (Engineering) at the Department of Electrical Communication Engineering, Indian Institute of Science (IISc), Bengaluru, under the guidance of Prof. Rajesh Sundaresan. Earlier, he worked as a Project Assistant in Prof. Chandra R. Murthy's lab at IISc and earned a Bachelor of Engineering in Electronics and Communications from R V College of Engineering, Bengaluru. Ph.D., Electrical Communication Engineering, IISc Bengaluru M.Sc. (Engineering), IISc Bengaluru B.E., Electronics and Communications, R V College of Engineering His research focuses on Probability Theory, Detection and Estimation Theory, Markov Decision Processes , and Multi-armed Bandits , with applications in Federated Learning, Differential Privacy, Reinforcement Learning, Information Theory , and Statistics . Recent work explores optimal strategies for best arm identification in restless bandits and privacy-preserving machine learning. He has received high instructor ratings for courses like Stochastic Processes and Programming for AI , and was honored with the Faculty Teaching Excellence Award 2025 at IIT Hyderabad. His Google Scholar articles highlight advances in 3D point cloud security, railway accident prevention, and vision-language model robustness . Scientific awards include the Faculty Teaching Excellence Award 2025 and First place in the INAE Kanpur Chapter 100 Seconds Competition (2021) . He collaborates with researchers like Prof. Vincent Y. F. Tan and Prof. Yeow Meng Chee, and has served on technical program committees for conferences such as APWDSIT 2025 and ISIT 2025 . Teaching roles at IIT Hyderabad involve graduate-level courses on Probability and Stochastic Processes , cross-listed with Electrical Engineering.

Jyothi Veduradaمشاهده پروفایل

استادیار

• Compilers
• Program Analysis
• High-Performance Computing
• +۳ مورد دیگر

Jyothi Vedurada is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). Her research focuses on compilers, program analysis, high-performance computing (HPC), and software engineering. Prior to joining IIT Hyderabad, she was a postdoctoral researcher at Microsoft Research Lab in Bangalore and worked as a Software Engineer at Hewlett Packard in Chennai. Her research spans four major areas: Compiler Optimizations : Developing global compiler optimizations for CPU-GPU heterogeneous systems to enhance performance across varied hardware environments. ML for PL : Leveraging AI to overcome scalability and semantic challenges in program analysis, with applications in bug detection, API misuse recommendation, and algorithm description generation for Jupyter notebooks. Parallelization : Designing efficient GPU algorithms for tasks like Approximate Nearest Neighbour search and subgraph isomorphism optimization, alongside developing the Tensor Transposition Library for GPUs (TTLG). Concurrency Testing : Creating systematic testing frameworks for concurrent systems, including language-agnostic libraries and mock storage systems to validate weak isolation levels in databases. Scientific Awards & Recognition : Distinguished Artifact Award at ECOOP 2025 Google Research exploreCSR Award (2021) Tata Consultancy Services PhD Fellowship ACM SIGAI, SIGPLAN, and IEEE Travel Grants Advising & Collaborations : Supervising 11 PhD and MTech students, including Soumik Kumar Basu and Karthik V, with projects spanning compiler optimizations, HPC, and AI-driven software analysis. Collaborating with institutions like Microsoft Research and IIT Madras.

K. V. Raghavanمشاهده پروفایل

دانشیار

• Programming Languages
• Program Analysis
• Software Engineering
• +۵ مورد دیگر

K. V. Raghavan is an Associate Professor at the Department of Computer Science and Automation in Indian Institute of Science, Bangalore . His research focuses on programming languages , program analysis , and software engineering , with an emphasis on automated tools for program verification and transformation. Teaching: Courses include Program Analysis and Verification (Aug-Dec 2024) and Principles of Distributed Software (Jan-Apr 2025), covering topics like abstract interpretation , dataflow analysis , lambda calculus , Hoare logic , Docker , and Kubernetes . Service: Served as PC co-chair for the Int. Symposium on Automated Technology for Verification and Analysis (ATVA 2025) . Research: Active opportunities for Project Assistant/Associate and Post Doc positions in his group.

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.

Ashish Mishraمشاهده پروفایل

استادیار

• Program Verification
• Program Synthesis
• Programming Languages
• +۱ مورد دیگر

Ashish Mishra is an Assistant Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad) , located in Kandi, Sangareddy, Telangana, India. Education: Ph.D. from Indian Institute of Science (IISc), Bengaluru Research Interests: His research spans foundational and applied areas in computer science, focusing on: Program Verification – ensuring correctness of software systems through formal methods. Program Synthesis – automatically generating programs from high-level specifications. Programming Languages – design, semantics, and implementation of new language constructs. Neurosymbolic Programming – integrating neural networks with symbolic reasoning for smarter program synthesis and verification. Current Affiliation: Room CS-606, Computer Science Building, IIT Hyderabad Contact: No public email provided; contact form available on institute page.

Ashutosh Guptaمشاهده پروفایل

دانشیار

• Formal Verification
• Software Verification
• Model Checking
• +۷ مورد دیگر

Ashutosh Gupta is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay, where he has been a faculty member since 2018. His research focuses on formal methods for software verification, particularly in the areas of model checking, constraint solving, and automated reasoning for both sequential and concurrent programs. He teaches advanced courses including Automated Reasoning (CS433), Analysis of Concurrent Programs (CS766), and Logic for Computer Science (CS228). Dr. Gupta received his Ph.D. in Computer Science from Technical University of Munich (TUM) in 2011, with affiliations during his doctoral studies at TUM, Max Planck Institute for Software Systems (MPI-SWS), and École Polytechnique Fédérale de Lausanne (EPFL). Prior to joining IIT Bombay, he served as a faculty member at Tata Institute of Fundamental Research (TIFR) in Mumbai and completed post-doctoral research in the Henzinger group at IST Austria. His research interests span formal verification of sequential and concurrent software, modeling of biological systems, and constraint solving including constraint logic programming, decision procedures, and automated theorem proving. He has developed several verification tools including VAJRA, HSF, and InvGen. His work bridges theoretical foundations with practical applications, particularly in verifying safety-critical systems and biological processes. Dr. Gupta's publication record shows a consistent trajectory of high-impact research in top venues like POPL, CAV, TACAS, and AAAI. His recent work has expanded into neural network verification, reinforcement learning verification for medical devices, and applying formal methods to biological systems, demonstrating both depth in core verification techniques and breadth in application domains. Best Paper Award at TACAS 2015 Best Paper Award at TACAS 2009 Dr. Gupta actively mentors numerous students through course projects, research presentations, and specialized workshops like SATfest. He has supervised students working on SAT/SMT solvers, verification of concurrent programs, and applications of formal methods to biological systems. His teaching philosophy emphasizes hands-on experience with verification tools and encourages students to engage with cutting-edge research through reading and presenting recent conference papers. He maintains active research collaborations with institutions including TUM, MPI-SWS, EPFL, IST Austria, and TIFR, reflecting his continued integration into the international formal methods research community.

• Formal Verification
• Concurrent Systems
• Reactive Systems
• +۴ مورد دیگر

S. Ramesh serves as a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology, Bombay. His research spans formal methods, verification systems, and brain-computer interfaces with notable projects including CFDVS (Center for Formal Design and Verification of Software), UML-based specification systems, and concurrent program analysis. His research interests focus on Formal Verification , Concurrent Systems , and Reactive Applications , with significant contributions to slicing techniques for concurrent Java programs, FPGA testing, and synchronous language applications. The work demonstrates strong interdisciplinary connections between computer science and neuroscience, particularly in event-related potential analysis. Analysis of his publication history reveals dual research trajectories: computer science projects in verification systems and neuroscience publications concerning unconscious processing, ERP analysis, and brain-computer interfaces. This unusual combination suggests cross-disciplinary collaboration between IIT Bombay's computer science department and neuroscience research groups. Ramesh has supervised four PhD students including Ambar Gadkari (working on GALS verification), M.G. Nanda (slicing concurrent Java programs), Sridhar Iyer (reachability analysis of concurrent OO programs), and A. Sowmya (autonomous robot motion specification). His current projects include the CFDVS initiative, Design and Verification of Distributed Applications, Slicing Concurrent Programs, UML-based Specification Systems, and FPGA Testing, demonstrating sustained research activity in formal methods and verification technologies.

Praveen Tammanaمشاهده پروفایل

استادیار

• Systems and Networking
• Network Security
• Software-Defined Networking
• +۷ مورد دیگر

Dr. Praveen Tammana is an Assistant Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Hyderabad , where he leads the NetX lab focusing on systems, networks, and security research. He completed his PhD from University of Edinburgh (2013-2018) and holds a Masters from IIT Madras (2009-2011). Education: Ph.D in Computer Science, University of Edinburgh, UK (2013-2018) Masters in Computer Science, IIT-Madras, India (2009-2011) B.E. in Computer Science, Vasavi College of Engineering, Osmania University (2005-2009) Research Focus: His research spans systems and networking , network security , software-defined networking (SDN) , machine learning for networks , wireless communication , high performance computing , and cybersecurity . His lab specializes in programmable data planes using technologies like P4, eBPF, DPUs, and PISA switches. Research Applications: His recent work demonstrates strong focus on AI infrastructure systems , including distributed AI learning and serving, networked robotics, GPU-centric packet processing, and edge cloud applications for autonomous systems. His research bridges the gap between networking infrastructure and AI workloads. Recent Publications Trend: His 2021-2025 publications show significant contributions to network security , programmable switches , edge computing , and AI systems integration . Key themes include securing traffic control systems, AI workload optimization, malicious traffic detection, and real-time applications for autonomous vehicles. Current Funding: Actively funded by industry partners including Mondee (systems for AI workloads), Marvell Technology (data acceleration offload), and ZF India (efficient load-balancer for edge applications). Lab and Team: Leads NetX lab at IIT Hyderabad, actively recruiting full-time research candidates for exciting projects in AI infrastructure, networked systems, and programmable networks. The lab focuses on building next-generation systems for AI workloads and secure network architectures.

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.

Ashutosh Trivediمشاهده پروفایل

دانشیار

• Formal Methods
• Cyber-Physical Systems
• Reinforcement Learning
• +۵ مورد دیگر

Ashutosh Trivedi is an Associate Professor of Computer Science at the University of Colorado Boulder, currently on leave from his position as Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay. He is affiliated with multiple research initiatives including the Centre for Formal Design and Verification of Software (CFDVS) at IIT Bombay, Free and Open Source Software for Education (FOSSEE), and the Indo-French project on Algorithmic Verification of Real-Time Systems (AVeRTS). At CU Boulder, he leads the Programming Languages and Verification (CUPLV) research group focusing on trustworthy AI systems. Trivedi's research centers on bridging formal methods with artificial intelligence to create more trustworthy systems. His work spans formal verification of cyber-physical systems, reinforcement learning with formal guarantees, and developing techniques for ensuring software fairness and accountability. He specializes in using formal languages, automata, and logic to transform vague natural-language instructions into precise specifications for AI systems. His recent projects include developing reinforcement learning algorithms for cardiac pacemaker design based on formal safety requirements, using SAT solvers to ground large language model outputs in logical reasoning, and encoding state representations in reinforcement learning using formal languages. His publication trends reveal a strong focus on neurosymbolic approaches that combine neural networks with symbolic reasoning, particularly for safety-critical applications. Recent work demonstrates increasing integration of formal methods with reinforcement learning, with applications spanning medical devices, tax preparation software, and puzzle-solving AI. His research shows a clear trajectory toward making AI systems more explainable, accountable, and verifiable through principled mathematical frameworks. Distinguished Paper Award at CAV for Regular Reinforcement Learning (2024) NeuS 2025 Disruptive Idea Award for Stochastic Neural Simulation Relations for Transferring Control under Uncertainty ACM Senior Member recognition (2024) Royal Society Wolfson Visiting Fellowship (2024) Trivedi has successfully advised multiple PhD students to completion, including Shadi Tasdighi Kalat (2025), Mateo Perez (2025), John Komp (2024), Vishnu Murali (2024), and Taylor Dohmen (2024). His teaching portfolio includes foundational courses in automata theory, digital logic design, and cyber-physical systems at both IIT Bombay and CU Boulder. He has served on program committees for major conferences including FSTTCS, HSCC, and FORMATS, and organized workshops such as ICLA 2015 and ALC 2015. As leader of the CUPLV research group, Trivedi directs projects focused on formal verification of AI systems, reinforcement learning with safety guarantees, and software fairness. His group collaborates with medical researchers on cardiac device verification and with legal scholars on tax software accountability, reflecting his commitment to applying formal methods to real-world problems with significant societal impact.

Swarnendu Biswasمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Runtime Systems
• +۷ مورد دیگر

Swarnendu Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He teaches courses including Programming for Performance (CS 610), Analysis of Concurrent Programs (CS 636), and Compiler Design (CS 335), demonstrating his expertise across multiple areas of computer systems. His research interests center on Programming Languages, Compilers, Runtime Systems, and Parallel Software Systems. He leads the PROSPAR (Programming Languages and PARallel Systems) research group, which focuses on developing techniques to build efficient and correct parallel software through program analysis, compiler optimizations, and runtime systems. His recent publications reveal a strong trend in addressing fundamental challenges in parallel computing, with work spanning cache coherence, false sharing detection, data race analysis for GPUs, verification of neural networks, and thermal-aware management of heterogeneous systems. His research bridges theory and practice with significant contributions to both hardware and software aspects of parallel systems. His scientific achievements have been recognized through multiple prestigious awards: Google India Research Award 2021 Google Explore CSR 2022 Research Grant from Intel Corporation SERB Start-up Research Grant 2019 Google Cloud Platform Research Credits (2019, 2020) IITK Initiation Grant 2019 As an advisor, he mentors several PhD and MTech students working on cutting-edge research in parallel systems. His PROSPAR group has secured significant funding from industry and government sources, supporting innovative research in programming languages and parallel systems. The group actively collaborates with industry partners including Google and Intel, addressing real-world challenges in parallel computing. He leads the PROSPAR research group at IIT Kanpur, which brings together faculty, PhD students, and MTech researchers to tackle challenging problems at the intersection of programming languages, compilers, and parallel systems. The group maintains strong industry connections and focuses on practical solutions that can be deployed in real systems.