جست‌وجوی استاد و پژوهشگر

Heike Jagodeمشاهده پروفایل

عضو هیئت علمی

• High Performance Computing
• Computer Architectures
• Performance Analysis
• +۷ مورد دیگر

Heike Jagode is a Research Associate Professor at the Innovative Computing Laboratory (ICL), part of the Tickle College of Engineering at the University of Tennessee, Knoxville. She joined ICL in 2008 and became Lead of the ICL Performance Group in 2013. Her research focuses on High Performance Computing (HPC), advanced computer architectures, performance analysis, and dataflow programming paradigms for scientific applications. PhD in Computer Science (2017), University of Tennessee, Knoxville MSc in High-Performance Computing (2006), University of Edinburgh MSc in Applied Techno-Mathematics (2001), University of Applied Sciences Mittweida BSc in Applied Mathematics (2001), University of Applied Sciences Mittweida Her work centers on developing tools for performance analysis, tuning, and energy efficiency in parallel scientific applications. She has contributed to PAPI (Performance Application Programming Interface) and task scheduling systems like StarPU and PaRSEC. Her current projects include MINCER (Monitoring Infrastructure for Network and Computing Environment Research), SPADE (Scalable Performance and Accuracy analysis for Distributed and Extreme-scale systems), and STEP (Software Tools Ecosystem Project) under DOE and NSF grants. Scientific awards include: Best Research Poster Finalist, Supercomputing Conference (SC17), 2017 Best Research Paper Finalist, IEEE High Performance Extreme Computing Conference (HPEC '17), 2017 Her publications and community roles span conferences like ISC, SC, IPDPS, HIPS, and journals such as IJHPCA and CCPE. She has served on technical program committees and steering committees for major HPC conferences.

Weiming Xiangمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Safe AI
• Formal Methods
• +۶ مورد دیگر

Dr. Weiming Xiang is an Associate Professor at the School of Computer and Cyber Sciences of Augusta University , Georgia. He transitioned from Assistant Professor (2019–2023) to his current role (2023–present). His academic journey includes postdoctoral positions at Vanderbilt University and University of Texas at Arlington , and a research associate role at the University of Hong Kong . He earned his Ph.D. (2014) in hybrid transportation systems from Southwest Jiaotong University , an M.Sc. (2007) in automation from Nanjing University of Science and Technology , and a B.E. (2005) in electrical engineering from East China Jiaotong University . Research Interests: Dr. Xiang focuses on formal synthesis and verification techniques for cyber-physical systems (CPS) , particularly addressing safety, security, and reliability in learning-enabled CPS . His work spans hybrid systems , neural network verification , control theory , and data-driven modeling , with applications in autonomous vehicles , power systems , and transportation . He develops scalable verification frameworks using reachable set computation , interval arithmetic , and polyhedral methods . Scientific Awards: NSF CAREER Award (2022) IEEE Senior Member (2017–present) Outstanding Reviewer awards from journals like IEEE Transactions on Automatic Control, Neurocomputing, and Journal of the Franklin Institute Top 1% Reviewers in Engineering, Publons (2018) Grants & Collaborations: Dr. Xiang leads multiple NSF-funded projects, including a $498,985 CAREER Award (2022–2027) for machine-learning-intensive CPS upgrades, a $499,000 CPS Program grant with Jason Orlosky (2022–2025) for XR-assisted h-CPS modeling, and a $270,913 NSF grant with Hoang-Dung Tran (2023–2026) for safety assessment of learning-enabled systems. He also contributes to Augusta University's CyberCorps Scholarships for Service program. Labs & Teams: Dr. Xiang leads the AI-CPS Lab , which explores intersections between artificial intelligence , formal verification , and cyber-physical systems . His lab collaborates on projects involving runtime safety monitoring , neural network compression , and hybrid control architectures .

Anthony Danalisمشاهده پروفایل

عضو هیئت علمی

• High-Performance Computing (HPC)
• Performance Analysis and Optimization
• System Benchmarking
• +۴ مورد دیگر

Anthony Danalis is a Research Assistant Professor at the University of Tennessee's Innovative Computing Laboratory (ICL), affiliated with the Department of Electrical Engineering and Computer Science. His research focuses on high-performance computing (HPC), including performance analysis, system benchmarking, compilers, MPI, and accelerators. He holds a Ph.D. and two M.S. degrees in Computer Science from the University of Delaware and the University of Crete, along with a B.S. in Physics from the University of Crete. Education: Ph.D. in Computer Science, University of Delaware M.S. in Computer Science, University of Delaware M.S. in Computer Science, University of Crete B.S. in Physics, University of Crete Research Interests: High-Performance Computing (HPC) Performance Analysis and Optimization System Benchmarking Compilers and Runtime Systems Parallel Programming Models (e.g., MPI) Accelerator Technologies Publications Highlight Trends in: Advancements in the PAPI library for exascale computing Dataflow-based execution for scientific applications Hardware performance counter analysis Compiler-driven optimizations Scientific Awards: Best Research Poster Finalist at SC 2017 Advising and Grants: No formal advisees listed Contributions to ICL's leadership and collaborative projects Labs and Teams: Innovative Computing Laboratory (ICL) Participation in projects like DAGuE, DPLASMA, and SHOC

Ilse C.F. Ipsenمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +۸ مورد دیگر

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

• Digital Signal Processing
• Arithmetic Intensive Computing
• Communications
• +۳ مورد دیگر

Adly T. Fam is a Professor in the Department of Electrical Engineering at the University at Buffalo, School of Engineering and Applied Sciences. He has been a faculty member since 1977 and attained the rank of Professor in 1987. His research spans digital signal processing, arithmetic-intensive computing, communications, and radar systems. His educational qualifications include: PhD in Electrical Engineering from the University of California, Irvine (1977) MS in Electrical Engineering from the University of California, Irvine (1975) BS in Electrical Engineering from Cairo University (1968) Professor Fam's primary research interests are in digital signal processing with applications in radar and communications. He has pioneered work in logarithmic frequency domain waveform design for radar and communications, as well as efficient hardware implementations for arithmetic circuits. His research integrates theoretical system design with practical engineering solutions to address challenges in signal processing and communications. Analysis of his recent publications (2018-2023) reveals a strong focus on radar waveform design using logarithmic frequency domain techniques and innovations in digital circuit design for arithmetic operations. Key themes include multicarrier logarithmic warped frequency domain code waveforms for joint radar-communications systems and novel approaches to binary counters and multipliers that improve computational efficiency and reduce hardware complexity. Professor Fam has authored over 120 journal and conference publications and holds four patents in the areas of system theory, digital signal processing, arithmetic-intensive computing, computer arithmetic, radar, and communications codes.

Christopher Roseمشاهده پروفایل

استاد

• Communication Theory
• Information Theory
• Molecular Communication
• +۴ مورد دیگر

Christopher Rose is a Professor of Engineering and Director of STEMJazz Programs at Brown University. He holds an IEEE Fellowship and has served on the Army Science Board. His research spans communication theory applications in molecular systems, wireless networks, and cosmic communication. Rose earned degrees from MIT and has held roles at AT&T Bell Labs and Rutgers University. Education: S.B., S.M., Ph.D. in Electrical Engineering from MIT (1979–1985) Research interests include molecular informatics, communication theory in biological systems, and adversarial information games. He pioneered studies on inscribed matter communication and cosmic signals. Notable achievements include the 2003 Marconi Prize and 2022 IEEE Teaching Award. Collaborates with chemists and computer scientists on projects like molecular memory and chemical computing. Leads STEM initiatives and teaches communication systems courses. Awards: Extensive recognition for teaching and research, including Brown’s ONYX Society Award. Active in editorial roles for IEEE JSAC and Entropy journal.

• Digital Signal Processing
• Arithmetic-Intensive Computing
• Communications
• +۳ مورد دیگر

Adly Fam is a Professor in the Department of Electrical Engineering at the University at Buffalo, affiliated with the School of Engineering and Applied Sciences. He has been a faculty member since 1977 and achieved the rank of Professor in 1987. B.S.E.E., Cairo University (1968) M.S., Electrical Engineering, University of California, Irvine (1975) Ph.D., Electrical Engineering, University of California, Irvine (1977) His research focuses on Digital Signal Processing , Arithmetic-Intensive Computing , Communications Codes , and Radar Signal Processing . He has contributed to system theory, computer arithmetic, and radar systems, with over 120 publications and four patents.

Li Fangمشاهده پروفایل

استادیار

• Atomic Physics
• Molecular Physics
• Plasma Physics
• +۵ مورد دیگر

Dr. Li Fang is an Assistant Professor in the Department of Physics at the University of Central Florida (UCF), affiliated with the College of Sciences. His research focuses on ultrafast and attosecond spectroscopy, plasma physics, and the interaction of matter with high-intensity laser fields. He leads the Ultrafast Atomic, Molecular & Plasma Research Group, which explores electronic dynamics in atomic and molecular systems using advanced laser technologies. Key research areas include nanoplasma energy dynamics, free-electron-laser-based experiments, and the development of high-power ultrafast laser systems. His group operates state-of-the-art labs like the Atto Lab and UFAST Lab, equipped with femtosecond lasers and advanced spectroscopy tools. Dr. Fang has secured significant funding, including a 2024 NSF CAREER Award and participation in UCF's $5M Jump Start initiative. His publications span photonics, optical amplification, and biomedical applications, reflecting interdisciplinary expertise. Collaborations include international facilities like Japan's SACLA free-electron laser. Current opportunities exist for graduate and undergraduate students in lab-based research.

Joel S. Emerمشاهده پروفایل

استاد

• Accelerator Architectures for Sparse Computation
• Accelerator Architectures for Deep Learning
• Spatial Processing Architectures
• +۵ مورد دیگر

Joel S. Emer is a Professor of the Practice in MIT's Department of Electrical Engineering and Computer Science (EECS) and a Senior Distinguished Research Scientist at NVIDIA. His research focuses on computer architecture, processor micro-architecture, and performance modeling. He has contributed to advancements in simultaneous multithreading, cache optimization, and reliability analysis. Emer holds over 25 patents and has published over 60 papers, earning awards like the IEEE Rau Award and induction into the National Academy of Engineering. Education: Ph.D., Electrical Engineering, University of Illinois Urbana-Champaign, 1979 M.S., Electrical Engineering, Purdue University, 1975 B.S., Electrical Engineering, Purdue University, 1974 (highest honors) Research interests include accelerator architectures for sparse computation and deep learning, spatial processing, memory hierarchy design, and reliability analysis. His work on Eyeriss and other accelerators has shaped energy-efficient neural network hardware. Recent projects explore hierarchical structured sparsity (HSS) and compute-in-memory (CIM) techniques. Key awards include the ISCA Best Paper Session (2024), IEEE Micro Top Picks (2024), and the SIGMICRO Test of Time Award (2022). He co-advises students with Prof. Vivienne Sze, focusing on sparse tensor acceleration and energy-efficient designs. Awards: 2023 IEEE Rau Award 2022 IASED Lifetime Achievement Award 2020 National Academy of Engineering Membership 2009 Eckert-Mauchly Award Grants and collaborations span industry partnerships (e.g., NVIDIA) and academic initiatives. Emer leads the Emze Group, exploring hardware-software co-design for emerging architectures. Current work includes sparse tensor accelerators (e.g., HighLight, Tailors) and modeling tools like Sparseloop and Accelergy.

Dor Abrahamsonمشاهده پروفایل

استاد

• Mathematical cognition
• Design-based research
• Embodied interaction
• +۷ مورد دیگر

Dor Abrahamson is a Professor of Learning Sciences and Human Development in the Graduate School of Education at the University of California Berkeley. He directs the Embodied Design Research Laboratory (EDRL) and has established himself as a leading design-based researcher in the field of mathematics education. His work bridges cognitive science, socio-cultural theory, and embodiment paradigms to create innovative learning environments that transform how students understand mathematical concepts. Abrahamson's research focuses on the relationship between physical action and conceptual learning, with particular emphasis on how embodied interaction facilitates mathematical understanding. His work spans intensive quantities like ratio, likelihood, and slope, as well as early algebra concepts. He develops experimental technological materials and activities that have potential for scale-up, while simultaneously refining theoretical constructs in the learning sciences and developing frameworks for educational design. His current projects include developing embodied-interaction technological systems for the guided reinvention of mathematical concepts, with recent work involving computer-embedded animated avatars that incorporate naturalistic gesture in face-to-face mathematics tutoring. Analysis of Abrahamson's recent publications reveals a strong focus on multimodal learning approaches, with significant attention to how gesture, eye-tracking, and physical manipulation contribute to mathematical understanding. His work increasingly integrates learning analytics, complex systems theory, and inclusive design principles, particularly in developing educational technologies for diverse learners. The research demonstrates a consistent trajectory toward understanding the micro-dynamics of learning through embodied interaction while creating practical educational interventions. National Academy of Education/Spencer Postdoctoral Fellowship for Seeing Chance Co-recipient of NSF grants for developing naturalistically gesturing interactive pedagogical avatars Co-recipient of NSF grants for fostering children's productive dispositions toward failure when programming Best Submission—Dr. Arthur I. Karshmer Award for Assistive Technology Research Finalist, Best Paper, IJCCI 2021 Abrahamson has secured significant research funding including NSF grants for developing gesturing avatars and for understanding how children approach programming failures. His work emphasizes collaborative, interdisciplinary research that brings together experts from education, cognitive science, movement science, and computer science. He has mentored numerous doctoral students and collaborators who have gone on to contribute significantly to the field of educational design. The Embodied Design Research Laboratory operates as a hub for innovative research that combines theoretical rigor with practical educational applications. The Embodied Design Research Laboratory (EDRL) represents a multi-disciplinary approach to educational design, inspired by the belief that all students can deeply understand mathematics. EDRL projects involve creating mixed-media materials that are iteratively refined based on empirical studies of student multimodal behaviors including speech, gesture, and eye-gaze. The lab has produced numerous technological innovations including motion sensor applications, touch screen interfaces, and agent-based simulations that support embodied mathematical learning.