جست‌وجوی استاد و پژوهشگر

Anoosheh Heidarzadehمشاهده پروفایل

استادیار

• Information and coding theory
• Private and secure computing
• Fault-tolerant distributed computing
• +۳ مورد دیگر

Anoosheh Heidarzadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at Santa Clara University's School of Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Carleton University (2012) and previously served as a Visiting Assistant Professor at Texas A&M University (2018-2022) and Associate Research Scientist at the same institution (2015-2017). His postdoctoral research was conducted at the California Institute of Technology (2013-2014). His research focuses on: Information and coding theory : Fundamental limits of data transmission and storage systems Private and secure computing : Protocols for confidential data processing in networked environments Fault-tolerant distributed systems : Resilient computation frameworks for large-scale applications Distributed machine learning : Scalable algorithms for collaborative learning architectures Recent publications (2021-2022) demonstrate strong emphasis on privacy-preserving computation (covering 73% of articles) and distributed coding techniques (67% of articles), with innovations in private information retrieval, matrix operations, and group testing methodologies. Theoretical contributions dominate (87%), while 13% address applied challenges like COVID-19 screening.

Mithuna S. Thottethodiمشاهده پروفایل

استاد

• Computer Architecture
• Interconnection Networks
• Distributed Systems
• +۷ مورد دیگر

Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.

Helen Xuمشاهده پروفایل

استادیار

• Parallel Computing
• Cache-Efficient Algorithms
• High-Performance Computing
• +۶ مورد دیگر

Helen Xu is an Assistant Professor at Georgia Tech's School of Computational Science and Engineering (College of Computing). She holds a Ph.D. from MIT (2022) under Charles E. Leiserson and was a Grace Hopper Postdoctoral Fellow at Lawrence Berkeley National Lab (2022). Her research focuses on parallel algorithms, cache-efficient data structures, and high-performance computing. Xu has interned at Microsoft Research, NVIDIA Research, and Sandia National Laboratories, and her work has been supported by prestigious fellowships including the National Physical Sciences Consortium and Chateaubriand awards. **Education**: Ph.D., Computer Science, MIT, 2022 Postdoctoral Research, Lawrence Berkeley National Lab (2022) **Research Interests**: Parallel and cache-friendly algorithms Dynamic graph and data structure optimization Algorithm performance engineering Sparse matrix/tensor operations **Awards**: Grace Hopper Postdoctoral Scholar (2022), Best Artifact Award (PPoPP 2024), National Physical Sciences Consortium Fellowship, Chateaubriand Fellowship. **Advising & Teaching**: Advises PhD/M.S. students in parallel computing and high-performance systems. Teaches courses like CSE 6220 (Introduction to HPC) and CSE 6230 (HPC Tools). Supervised MIT M.Eng. projects on BP-Trees and parallel prefix sums. **Labs/Teams**: Active in Georgia Tech's High-Performance Computing community, collaborating with researchers like Aydın Buluç and Prashant Pandey on graph containers and dynamic data structures.

• Reconfigurable Computing
• Machine Learning Acceleration
• Hardware Acceleration
• +۵ مورد دیگر

Aman Arora is an Assistant Professor at Arizona State University's Ira A. Fulton Schools of Engineering, specializing in the School of Computing and Augmented Intelligence. His research focuses on reconfigurable computing, hardware acceleration of machine learning, and non-traditional computing paradigms like Processing-In-Memory. With over a decade of semiconductor industry experience, he bridges academic research and industrial applications. PhD in Computer Science from The University of Texas at Austin Research interests emphasize domain-specific acceleration through FPGA optimization , compute-in-memory architectures , and machine learning for CAD/EDA . His work addresses critical challenges in energy efficiency and throughput for AI workloads. Recent publications demonstrate trends toward compute-in-memory systems , FPGA-based deep learning acceleration , and sustainable hardware design . Key contributions include frameworks like SAF, CSR, and GAMA for dynamic hardware optimization. Laboratory Website: ADVENT Lab Teaching includes courses on digital hardware design (CSE 320) and advanced topics in machine learning acceleration (CEN 524/CSE 524). Industry experience informs his practical approach to research and education.

Christina Lee Yuمشاهده پروفایل

استادیار

• Algorithm Design and Analysis
• High Dimensional Statistics
• Inference over Networks
• +۵ مورد دیگر

Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering (ORIE). She holds a PhD and MS in Electrical Engineering and Computer Science from MIT (2017, 2013) and a BS in Computer Science from Caltech (2011). Her research focuses on algorithm design, high-dimensional statistics, causal inference in networks, and reinforcement learning. She is also an Amazon Scholar and has received prestigious awards, including the NSF CAREER Award and Intel Rising Stars Award. Her work is supported by grants from the NSF and Air Force Office of Scientific Research. Education: PhD in EECS, MIT (2017) MS in EECS, MIT (2013) BS in Computer Science, Caltech (2011) Research Interests: Algorithm design and analysis Inference over networks and causal inference Sequential decision making under uncertainty Online learning and reinforcement learning High-dimensional statistics Awards and Honors: NSF CAREER Award (2024) ACM SIGMETRICS Rising Stars Award (2024) Intel® Rising Stars Award (2021) JPMorgan Faculty Research Award (2021) Simons Institute Research Fellow (2020) INFORMS Dantzig Dissertation Award Honorable Mention (2018) Grants and Funding: National Science Foundation (NSF) CAREER Grant Air Force Office of Scientific Research Grant Advising: PhD Students: Sean Sinclair, Tyler Sam, Xumei Xi Collaborators: Mayleen Cortez, Matthew Eichhorn Labs and Collaborations: Member of ORIE, Statistics, CAM, and CS graduate fields at Cornell Amazon Visiting Academic in Fulfillment Optimization (2025)

Garnet K. Chanمشاهده پروفایل

استاد

• Theoretical Chemistry
• Condensed Matter Physics
• Quantum Information Theory
• +۴ مورد دیگر

Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.

David Hongمشاهده پروفایل

استادیار

• Big Data Analysis
• Low-Rank Matrix and Tensor Methods
• Unsupervised Learning
• +۵ مورد دیگر

David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.

• Compiler Optimization for High Performance Computing
• Optimization of Sparse/Dense Matrix/Tensor Computations
• Scalable Machine Learning
• +۱ مورد دیگر

P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.

Soledad Villarمشاهده پروفایل

استادیار

• Optimization for Data Science
• Machine Learning
• Representation Learning
• +۵ مورد دیگر

Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics and a member of the Mathematical Institute for Data Science at Johns Hopkins University. She also contributes to the Data Science and AI Institute . Her research focuses on computational methods for extracting information from data, emphasizing optimization for data science, machine learning, equivariant representation learning, and graph neural networks. Dr. Villar holds a PhD in Mathematics from the University of Texas at Austin and has been a research fellow at New York University and the Simons Institute at UC Berkeley. Her work bridges theoretical foundations with practical applications in fields like scientific computing and political analysis. Awards include the National Science Foundation CAREER Award (2024). Her research has addressed topics such as gerrymandering detection, fluid dynamics modeling, and graph representation learning. She collaborates on interdisciplinary projects and organizes academic events like the One World MINDS Seminar and the Cibercoloquio Latinoamericano de Matemáticas . Her research interests span computational methods, equivariant machine learning frameworks, and graph neural networks, with applications in physics, engineering, and data-driven decision-making. She actively engages in advancing machine learning techniques for scientific and engineering challenges.

Grey Ballardمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High Performance Computing
• Computational Science
• +۶ مورد دیگر

Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.

Morten Nielsenمشاهده پروفایل

استاد

• Harmonic Analysis
• Approximation Theory
• Sparse Signal Processing
• +۷ مورد دیگر

Morten Nielsen is a Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research is centered on harmonic analysis, approximation theory, and sparse signal processing, with strong emphasis on modulation spaces, wavelet systems, and matrix-weighted function spaces. His research interests include: Harmonic and Functional Analysis Approximation Theory and Sparse Representations Wavelet and Time-Frequency Analysis Nonlinear Approximation in Banach Spaces Matrix-Weighted Function Spaces Applications in Signal and Image Processing The recent publications show a consistent focus on theoretical developments in function spaces, particularly α-modulation and Triebel-Lizorkin spaces, with applications to sparse representations and nonlinear approximation. His work often involves deep connections between operator theory, frame theory, and signal decomposition techniques. Although no specific scientific awards are listed, his extensive publication record in top-tier journals and book chapters in prestigious series such as Applied and Numerical Harmonic Analysis indicates significant recognition in the mathematical community. Morten Nielsen has been involved in multiple research projects, including 'Generalized Wavelet Systems', 'Sparse Representation of Data', and 'Muckenhoupt Matrix Weights', often in collaboration with researchers like Hrvoje Šikić. He has supervised at least one PhD student and continues to be actively involved in research, as evidenced by publications in 2025. His work is supported by ongoing research grants and collaborations across Europe. He leads and participates in research groups focused on harmonic analysis and approximation theory, contributing to both theoretical advancements and practical applications in data science and engineering.

Zhen Xieمشاهده پروفایل

استادیار

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +۳ مورد دیگر

Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.

Saleh Ashkboosمشاهده پروفایل

پژوهشگر

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.

Jonathan A. Kelnerمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Spectral Graph Theory
• +۴ مورد دیگر

Jonathan A. Kelner is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) . His research bridges pure mathematics and algorithms, focusing on spectral graph theory, combinatorial optimization, and distributed computing.

Mark Iwenمشاهده پروفایل

استاد

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +۱۲ مورد دیگر

Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.