جست‌وجوی استاد و پژوهشگر

Chorng Hwa Changمشاهده پروفایل

دانشیار

• Computer Architecture
• Parallel Processing
• Computer Networking
• +۶ مورد دیگر

Chorng Hwa Chang is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts University. He joined the faculty in 1987 and currently directs the Computer Engineering Program and the Tufts Wireless Lab (TWL). His research focuses on computer architecture, wireless communications, IoT protocols, and engineering education. Education: Ph.D., Electrical and Computer Engineering, Drexel University (1987) M.S., Computer Science, Montana State University (1983) B.S., Engineering Science, National Cheng Kung University (1977) Research Interests: Dr. Chang’s work spans wireless sensor networks, 6LoWPAN protocols, WiFi backtracking, and IoT implementations. His projects include the TWL Lab’s initiatives in smart healthcare systems and autonomous robotic networks. Recent publications highlight innovations in GPU-accelerated algorithms, cloud shape classification, and interferometric positioning systems. Grants & Advising: He advises numerous graduate and undergraduate students on projects like motion detection systems, IoT platforms, and sensor network optimization. His lab collaborates on military and civilian applications, including border surveillance and healthcare monitoring. Labs/Teams: Director of the Tufts Wireless Lab (TWL), leading cross-disciplinary research in wireless communication and embedded systems.

Arvindمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Computing
• Memory Systems
• +۳ مورد دیگر

Arvind is the Johnson Professor of Computer Science and Engineering at MIT and a member of CSAIL (Computer Science and Artificial Intelligence Laboratory). He holds a B.Tech. from IIT Kanpur (1969), M.S. and Ph.D. from the University of Minnesota (1972-1973). His research focuses on computer architecture, parallel computing, memory models, and hardware synthesis. He pioneered dataflow architectures and developed the pH programming language. Notable projects include the Monsoon dataflow machine and Sandburst, a semiconductor company for 10G-bit Ethernet routers. Arvind has received prestigious awards like the IEEE Harry H. Goode Memorial Award (2012) and ACM Fellow (2007). He co-founded Bluespec Inc. and managed collaborations like Nokia-CSAIL (2006-2010). His work spans academia and industry, emphasizing scalable systems and secure computing. Research interests include synthesis/verification of digital systems, graph algorithms, and weak memory models. Current projects explore next-gen Graph AI systems and financial security applications.

Tong Gaoمشاهده پروفایل

دانشیار

• Fluid Mechanics
• Biophysics
• Active Matter
• +۷ مورد دیگر

Dr. Tong Gao is an Associate Professor in the Department of Mechanical Engineering and Computational Mathematics, Science, and Engineering (CMSE) at Michigan State University (MSU), with a joint appointment in the College of Engineering and College of Natural Science. He is currently transitioning to Tufts University in the fall of 2023 as a faculty member in Mechanical Engineering. His research focuses on fluid mechanics, biophysics, and computational methods, particularly in active/soft matter, fluid-structure interactions, and complex fluids. Dr. Gao holds a Ph.D. from the University of Pennsylvania and bachelor’s/master’s degrees from the University of Science and Technology of China. Research Interests: Dr. Gao’s work integrates mathematical modeling and high-performance computing to study multiscale phenomena in soft materials and active systems. Key areas include: - Active matter and collective behavior in biological and synthetic systems - Numerical methods for fluid-structure interaction (e.g., immersed boundary methods) - Rheology of complex fluids and soft colloids - Bio-inspired robotics and soft material design Recipient of the NSF Career Award (2020) Principal Investigator on grants from NSF, MSU Strategic Partnership, and MSU-Henry Ford Health Advising and Labs: He advises a diverse group of graduate students and postdocs in the Complex Fluids Group (CFG), focusing on fluid dynamics, computational methods, and soft robotics. Notable past advisees include Zhaowu Lin (Postdoc), Andrew Hess (Ph.D.), and Sheng Chen (Ph.D.). Labs/Teams: The CFG leverages advanced numerical simulations and experimental collaborations to address challenges in energy conversion, biomedical applications, and materials science.

Hyeongrak Choiمشاهده پروفایل

استادیار

• Quantum Information Science
• Quantum Computing
• Quantum Photonics
• +۳ مورد دیگر

Hyeongrak Choi is an Assistant Professor in the Department of Electrical and Computer Engineering at Stony Brook University. His research focuses on quantum information science, quantum computing, quantum photonics, and quantum networks. He is affiliated with the Light Engineering research group and specializes in developing scalable quantum systems through innovations in spin-photon interfaces, photonic integrated circuits, and entanglement protocols. His work emphasizes practical applications of quantum technologies, including congestion-free quantum networks, heterogeneous integration with CMOS platforms, and reinforcement learning-driven gate synthesis. Key contributions include designs for optical interconnects using 2D atomic emitters, cavity-based light-matter interactions, and entanglement distribution protocols leveraging percolation-based architectures. His research also intersects with nanofabrication techniques and superconducting materials for enhanced magnetic field control. Research Highlights Developed scalable quantum computing architectures using optical interconnects Pioneered congestion-free hierarchical entanglement routing protocols Advanced photonic integrated circuits for spin-photon coupling in CMOS platforms Designed ultrastrong magnetic light-matter interaction systems via cavity engineering Grants & Advising No specific grants or advisees are currently listed in the provided information. Labs & Teams His work is conducted in collaboration with the Light Engineering research group at Stony Brook, focusing on experimental and theoretical advancements in quantum photonics and integrated quantum systems.

Julien Tiernyمشاهده پروفایل

استاد

• Topological Data Analysis
• Scientific Visualization
• Data Compression
• +۲ مورد دیگر

Julien Tierny is a Professor at Ecole Polytechnique and a Senior Researcher (DR2) at CNRS within the Computer Science Department (LIP6) at Sorbonne Université, part of the APR team. His research focuses on Topological Data Analysis and Visualization, with a particular emphasis on developing efficient algorithms for scientific data processing and visualization. He leads the ERC-funded project TORI, which aims to reduce scientific data through in-situ topological methods. Education & Affiliations: PhD in Computer Science (2008), Université de Lille Habilitation Thesis (HDR, 2016), Sorbonne Université Principal Investigator of ERC Project TORI Research Interests: Topological Data Analysis, Scientific Visualization, High-Performance Computing, and Algorithm Design. His work bridges theoretical foundations with practical tools like the Topology ToolKit (TTK), widely used in visualization and data science. Awards & Recognition: ERC Starting Grant (2020) Best Paper Honorable Mention (IEEE VIS 2019) Best Paper Award (IEEE LDAV 2018) Teaching: Leads courses on Scientific Visualization, Topological Data Analysis, and Computer Graphics at Ecole Polytechnique and Sorbonne Université. Popular courses include 'Introduction to Topological Data Analysis' and 'Advanced Visualization Techniques'. Labs & Contributions: Co-developer of the Topology ToolKit (TTK), a widely adopted open-source library for topological data analysis. Active in IEEE Visualization conferences, publishing award-winning papers on distributed computing and topological simplification.

Kinshuk Pandaمشاهده پروفایل

پژوهشگر

• Clean energy systems modeling at scale
• Uncertainty quantification
• Multidisciplinary design analysis and optimization
• +۳ مورد دیگر

Kinshuk Panda is a Researcher II in the Computational Sciences division at the National Renewable Energy Laboratory (NREL) . His work focuses on applying computational methods to clean energy systems, particularly in grid operations, wind farm design, and water treatment technologies under uncertainty. He utilizes high-performance computing (HPC) systems, including NREL’s Eagle supercomputer, to develop scalable software solutions. Education: Bachelor of Mechanical Engineering, Manipal Institute of Technology PhD in Mechanical Engineering, Rensselaer Polytechnic Institute Panda’s research spans uncertainty quantification , multi-fidelity optimization , and resilient grid operations . He contributes to projects like the Exascale Computing Project , enhancing modeling under extreme weather events, and develops tools for water treatment performance analysis using HPC. His recent publications highlight advancements in co-simulation frameworks , emergency asset positioning , and parameter sweep analyses , emphasizing scalability and efficiency. Collaborations include work with the IEEE Power and Energy Society and SIAM , focusing on energy systems and computational methods. Panda’s professional engagement includes memberships in IEEE and Society for Industrial and Applied Mathematics , supporting interdisciplinary approaches to energy and environmental challenges.

Dustin Richmondمشاهده پروفایل

استادیار

• Computer Architecture
• FPGA
• RISC-V
• +۴ مورد دیگر

Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.

Xiaoyi Luمشاهده پروفایل

دانشیار

• Parallel and Distributed Computing
• High-Performance Communication and I/O Technologies
• Big Data Analytics
• +۴ مورد دیگر

Dr. Xiaoyi Lu is an Associate Professor in the Department of Computer Science & Engineering at the University of California, Merced (UC Merced), where he founded and directs the Parallel and Distributed Systems Laboratory (PADSYS Lab). He is affiliated with the AgAID Institute since 2023 and has authored over 170 publications, including ten Best Paper Awards or Nominations (e.g., SC 2019, IPDPS 2024). His research outcomes like OpenDOTA and MVAPICH2-Virt are used by hundreds of organizations globally. Research Interests: He focuses on scalable parallel systems for HPC, Big Data, AI, Cloud, and Edge Computing, leveraging advanced technologies like RDMA/PMEM/NVMe/GPU/DPU. His work bridges high-performance computing with applications in precision agriculture, biostatistics, and digital twin technology. Article Trends: Recent publications emphasize DPU offloading, compression-optimized collective communication, error detection in HPC, and scalable Bayesian group testing. Topics span GPU clusters, NVMe-over-Fabrics, and adaptive networks for LLM training, reflecting his expertise in heterogeneous architectures and distributed systems. Scientific Awards: NSF CAREER Award (2024) Amazon Research Award (2023) Google Research Award (2022) Meta Faculty Research Award (2022) Multiple Best Paper Nominations Professional Activities: He serves as Associate Editor for Frontiers in High Performance Computing and organizes tracks at SCAsia and HiPC. His leadership in PADSYS Lab drives innovation in systems for social good.

• Computational Complexity Theory
• Machine Learning
• Parallel Computation
• +۵ مورد دیگر

Leslie Valiant serves as the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences, where he has been faculty since 1982. Previously, he held positions at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His academic journey began with education at King's College Cambridge, Imperial College London, and Warwick University, where he earned his PhD in computer science in 1974. Valiant's research spans theoretical computer science with primary focus areas including computational complexity theory, machine learning foundations, parallel computation systems, computational neuroscience, and evolutionary computation. His work bridges artificial and natural computational phenomena, addressing fundamental limitations in both engineered systems and biological processes. Key contributions include the development of the PAC (Probably Approximately Correct) learning model, holographic algorithms, robust logics for reconciling reasoning and learning, and theoretical frameworks for understanding cortical computation and evolvability. His publication record demonstrates sustained impact across decades, with recent work focusing on cortical computation primitives, multi-core algorithm design, and evolutionary dynamics with drifting targets. These publications reveal a consistent trajectory toward understanding computational principles in both artificial systems and biological cognition. Nevanlinna Prize (1986) Knuth Award (1997) EATCS Award (2008) A.M. Turing Award (2010) Fellow of the Royal Society Member of the National Academy of Sciences Valiant's research program integrates theoretical rigor with profound questions about natural computation, maintaining active engagement with both computer systems design and fundamental neuroscience questions. His work continues to influence multiple disciplines through formal frameworks that address computational limitations in learning, evolution, and neural processing.

Michael J. Lindseyمشاهده پروفایل

استادیار

• Computational Methods
• Numerical Linear Algebra
• Optimization
• +۱۹ مورد دیگر

Michael J. Lindsey is an Assistant Professor in the Department of Mathematics at the University of California, Berkeley, and a Faculty Scientist at Lawrence Berkeley National Laboratory. His research focuses on computational methods driven by Numerical Linear Algebra , Optimization , and Randomization , particularly for High-Dimensional Scientific Computing in quantum many-body problems and applied probability. University : UC Berkeley (Assistant Professor since 2022) Lab Affiliation : Mathematics Group at Lawrence Berkeley National Laboratory Email : lindsey@berkeley.edu His work includes Semidefinite Relaxation for quantum and classical problems, Monte Carlo Sampling techniques, and Tensor Networks for high-dimensional functions. He has pioneered Variational Embedding theory with guaranteed energy bounds and scalable solvers for quantum systems. Recent publications span Quantum Chemistry , Machine Learning , and High-Dimensional Probability , with applications to Electronic Structure , Molecular Dynamics , and Optimal Transport . He received the 2024 Hellman Fellowship and the 2019 SIAM Student Paper Prize . Teaching includes graduate and undergraduate courses in numerical analysis and applied mathematics at UC Berkeley and New York University. He also organizes the HDSC Seminar on high-dimensional scientific computing.

Bradford L. Chamberlainمشاهده پروفایل

عضو هیئت علمی

• Parallel programming languages
• Compilers for high-performance computing
• Scalable parallel algorithms
• +۳ مورد دیگر

Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He is the founding technical lead of the open-source Chapel programming language, which targets productive parallel computing from laptops to supercomputers. Education: Ph.D. in Computer Science & Engineering, University of Washington, 2001 M.S. in Computer Science & Engineering, University of Washington, 1995 B.S. (with honors) in Computer Science, Stanford University, 1992 Research interests center on user productivity for high-performance computing, spanning programming languages, compilers, algorithms, and applications. His work emphasizes scalable parallel computation and the design of language abstractions—such as regions, distributions, and iterators—that enable both high productivity and high performance. Over the past two decades he has led the Chapel project from a small research effort to a thriving open-source language with nearly twenty full-time developers at HPE and numerous external collaborators. His recent publications explore locality-aware compiler optimizations, fabric-attached memory, and the expression of sophisticated parallel patterns like stencil tiling and data re-allocation. Teaching & service: He created and regularly teaches UW’s graduate parallel computation course (CSEP 524), has advised graduate students, and serves on departmental committees, fostering tight ties between the Allen School and HPE/Cray.

Andrew Lumsdaineمشاهده پروفایل

عضو هیئت علمی

• High Performance Computing
• Scalable Graph Algorithms
• Computational Photography
• +۵ مورد دیگر

Andrew Lumsdaine is the Chief Scientist at the Northwest Institute for Advanced Computing , a dual appointee between the University of Washington and the Pacific Northwest National Laboratory (PNNL). He holds the title of Affiliate Professor in the Paul G. Allen School of Computer Science and Engineering at UW and serves as a Laboratory Fellow in PNNL's Applied Mathematics, Computing, and Data Division. His research spans foundational and applied aspects of High Performance Computing , focusing on scalable graph algorithms, computational photography, and runtime systems for distributed-memory architectures. Education : Not explicitly stated in the provided text. Lumsdaine's work addresses critical challenges in parallel and distributed computing , including synchronization avoidance, communication optimization, and domain-specific language design for graph analytics. He has led projects like GraphPack (NSF-sponsored) and contributed to DARPA's HIVE program through the HAGGLE software development kit. His publications highlight innovations in light field imaging , GPU programming models , and graph algorithm abstractions . Notable collaborations include the GraphBLAS standardization effort and development of tools for checkpoint/restart fault tolerance. His research has been presented at leading conferences like SC , IPDPS , and Eurographics . Lumsdaine actively seeks collaborators and advises students/postdocs through projects listed on his research page.

CNV Krishnanمشاهده پروفایل

استاد

• Empirical Corporate Finance
• Law and Finance
• Financial Intermediation
• +۲ مورد دیگر

CNV Krishnan is a Professor in the Banking and Finance Department at the Weatherhead School of Management, Case Western Reserve University. He holds the Henry Payne McIntosh Fellowship and has served as Department Chair and Finance Programs Director since 2001. His expertise lies in empirical corporate finance, focusing on how financial intermediaries create or destroy value. Education: PhD (University of Wisconsin-Madison, 2001), MBA (Indian Institute of Management-Ahmedabad) Research Interests : His work bridges corporate finance and law, analyzing banks, regulators, courts, and investment advisors. He has over 25 publications in top journals like the Journal of Finance, with research cited by the U.S. SEC and discussed in the Wall Street Journal and Reuters.com. Teaching & Awards : Krishnan has been recognized for teaching excellence at Weatherhead School of Management and through the Wolstein Society's 'Amazing Mentor and Guide Award'. He trains master's students in academic research and has been nominated for teaching awards repeatedly (2004, 2007, 2009, 2010, 2011, 2013, 2015, 2024). He is a top 10% author on Social Science Research Network (SSRN) by downloads. 2004-2024: Teaching Excellence Award Nominations 2024: Case Western Reserve University Pedagogical Innovation Award 2017-2023: Top 10% SSRN Author by Downloads

Ziyang Liمشاهده پروفایل

استادیار

• Neurosymbolic Programming
• Machine Learning
• Programming Languages
• +۵ مورد دیگر

Ziyang Li is an Assistant Professor of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. He holds a Ph.D. in Computer Science from the University of Pennsylvania (2025) and dual bachelor's degrees in Computer Science and Mathematics from UCSD (2019). Research Areas: Neurosymbolic Programming, AI4Code His research bridges programming languages and machine learning, focusing on neurosymbolic methods that combine symbolic reasoning with learning-based techniques. Applications span software security, computer vision, natural language processing, bioinformatics, and clinical decision-making. He developed Scallop , a neurosymbolic programming language, and Lobster , a GPU-accelerated framework for neurosymbolic applications, with impacts in cybersecurity and biomedical domains. Recent publications highlight neurosymbolic approaches for RNA structure prediction, Long COVID modeling, and safety-critical systems. His work emphasizes data-efficient learning, weak supervision, and hybrid AI for scalable reasoning. Scientific Awards : AWS Fellowship (2023) KPCB Fellows, Engineering (2018) NIH L3C Honorable Mention Award Li has mentored students including Jason Liu, Felix Zhu, and Eric Zhao, and served as Teaching Assistant for courses at UPenn and UCSD. He co-organized the TACPS Workshop and reviewed for NeurIPS, ICLR, and ICML.

Shravan Veerapaneniمشاهده پروفایل

استاد

• Scientific computing
• Fast algorithms
• Potential theory
• +۱۰ مورد دیگر

Shravan Veerapaneni is a Professor in the Department of Mathematics at the University of Michigan, within the College of Literature, Science, and the Arts. His research focuses on developing large-scale computational tools for solving differential and integral equations on complex moving geometries that arise in engineering and biophysics. His work spans multiple interdisciplinary areas connecting mathematics, computational science, and applied physics. Education: B.S. from Indian Institute of Technology (2003), Ph.D. from University of Pennsylvania (2008) Previous Position: Research Scientist at Courant Institute of Mathematical Sciences (NYU), 2008-2011 Teaching: Courses include Math 671, Math 371 (Numerical Methods for Engineers), and Math 156 (Applied Honors Calculus II) Professor Veerapaneni's research interests encompass scientific computing, fast algorithms, potential theory, complex fluids, microfluidics, soft-matter, and biomechanics. His core application areas include biomembrane mechanics, blood flow modeling, cilia-driven flows, and microfluidic-chip design. More recently, he has expanded his research to include scalable solvers and machine learning techniques for autonomous vehicle mobility in off-road settings. His work demonstrates a strong emphasis on developing high-order accurate numerical methods with practical applications in biomedical engineering and fluid dynamics. His publications reveal a consistent focus on boundary integral methods, Stokes flow simulations, and optimization problems in complex geometries. The research shows progression from fundamental mathematical methods to increasingly complex applications in biophysics and engineering. His work on vesicle dynamics, microswimmers, and particulate suspensions demonstrates expertise in computational fluid dynamics at microscales. NSF CAREER Award (2015) for project 'Fast Algorithms for Particulate Flows' Professor Veerapaneni has developed computational frameworks for simulating complex fluid-structure interactions, with applications ranging from biological systems (vesicles, cilia) to engineering problems (microfluidic chips, autonomous vehicles). His group has produced significant software contributions including visualization tools for fluid dynamics simulations, as evidenced by the animations of vesicle flows on his website. His collaborative work spans mathematics, engineering, and computer science departments, reflecting the interdisciplinary nature of his research.