جست‌وجوی استاد و پژوهشگر

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.

• Algorithms
• Parallel Processing
• VLSI
• +۲ مورد دیگر

Sanjeev Saxena serves as Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur, where he has established himself as a prominent researcher in algorithm design and parallel computing. His research spans five key domains: Algorithms - specializing in efficient computational methods and theoretical foundations Parallel Processing - developing architectures for multi-processor computation VLSI - exploring algorithm-hardware integration for circuit design Data Structures - innovating in information organization techniques Heuristics - creating practical solutions for complex computational problems Professor Saxena's publication timeline reveals a consistent research trajectory focused on computational efficiency, with early work on VLSI parallel algorithms evolving into broader applications in data structures and scientific computing. His most recent publications demonstrate expertise in translating theoretical computer science into practical implementations for scientific and engineering applications. His educational contributions include authoring the textbook "Java for Scientists and Engineers" (2008), which bridges programming knowledge with scientific applications, reflecting his commitment to computer science education.

Arnab Chakrabortyمشاهده پروفایل

استاد

• Multivariate Statistical Analysis
• Probability Theory
• Machine Learning
• +۵ مورد دیگر

Arnab Chakraborty is a Professor at the Indian Statistical Institute (ISI), where he teaches advanced courses in statistics and probability. His teaching portfolio includes Probability I & II (scheduled for 2025), Statistical Methods, Linear Statistical Models, Numerical Analysis, and specialized courses in Multivariate Statistics and Categorical Data Analysis. Dr. Chakraborty's research expertise spans several key areas of modern statistics: Multivariate Statistical Analysis with focus on high-dimensional data Copula and Vine modeling for complex dependence structures Machine Learning algorithms including SVM, CART, and k-NN methods Statistical pattern recognition and classification techniques Data mining methodologies and applications His scholarly contributions include comprehensive educational materials on advanced statistical topics, particularly detailed expositions on regular vines and their applications in statistical modeling. Dr. Chakraborty has supervised multiple graduate students on research projects involving printed digit recognition, handwritten letter classification, and kernel methods. Dr. Chakraborty emphasizes practical application of statistical methods, incorporating real-world data examples and computational implementations using R. His teaching philosophy values presentation skills development, often replacing traditional examinations with student presentations on advanced topics. He has also contributed to statistical education through YouTube content and various tutorials on computational statistics.