جستجوی استاد راهنمای دانشگاهی

Lars Kristiansenمشاهده پروفایل

استاد

• Mathematical Logic
• Computability Theory
• Complexity Theory
• +۴ مورد دیگر

Lars Kristiansen is Professor II (part-time Professor) at the Department of Mathematics, University of Oslo . His research centres on the intersection of mathematical logic, computability theory, and computational complexity, with recent emphasis on computable analysis, weak first-order theories, subrecursive degree structures, and implicit computational complexity. Research Interests Computable Analysis: Representations and computational complexity of irrational and real numbers. Weak First-Order Theories: Decidability, interpretability and fragments of concatenation theories. Subrecursive Degree Theory: Fine structure of honest subrecursive degrees and the Grzegorczyk hierarchy. Implicit Computational Complexity: Reversible computing, type systems that capture complexity classes, and resource-bounded program analysis. Across more than 50 refereed publications since 1996, a clear trend emerges: an early focus on subrecursive hierarchies and honest degrees evolved into an intensive study of the computational content of real number representations and the logical strength of weak arithmetics. Recent work (2023-2025) deepens this agenda, analysing the complexity of converting between alternative representations of reals and the degree structures that these induce. Scientific Output While no named awards are listed, Kristiansen’s contributions are disseminated in top venues such as Bulletin of Symbolic Logic , Annals of Pure and Applied Logic , Archive for Mathematical Logic , Science of Computer Programming , and leading LNCS conferences. His work is frequently co-authored with Amir Ben-Amram, Jakob Grue Simonsen, Juvenal Murwanashyaka, Ivan Georgiev, and Neil D. Jones, indicating active collaborative networks in both logic and theoretical computer science. Research Groups & Collaborations Logic research group at the University of Oslo Data and Knowledge Management (DKM) group

Charles Gillanمشاهده پروفایل

مدرس ارشد

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +۴ مورد دیگر

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

Bernhard Schröderمشاهده پروفایل

استاد

• Computational Linguistics
• Cognitive Science
• Corpus Linguistics
• +۳ مورد دیگر

Prof. Dr. Bernhard Schröder holds the full professorship (W3) of German Studies/Linguistics at the University of Duisburg-Essen , where he leads research at the Institute of German Studies (Faculty of Humanities). His academic career includes a Magister in Linguistics from University of Bonn (1991), PhD in Philosophical Linguistics (1997), and Habilitation in Language and Communication Sciences (2003). Education: Magisterabschluss (1991), University of Bonn Dr. phil. (1997), University of Bonn Habilitation (2003), University of Bonn His research bridges computational linguistics with cognitive science, focusing on prosodic analysis in mathematical proof texts, autism spectrum disorder communication studies, and corpus-based linguistic resource development. He directs the Naproche Project for computational proof checking and the AEET institute for early modern text digitization. Key projects include: Mathematical proof text modeling with frame semantics Prosody perception in autism through speech synthesis Corpus portal korpora.org development German-Namibia institute partnership Media-intensive linguistics training modules (MiLCA) He collaborates across institutions like University Hospital Freiburg, TU Dresden, and University of Bonn, with leadership roles in the European Society for Philosophy and Psychology and German Society for Computational Linguistics . His work involves speech synthesis uncertainty modeling, Kant text digitization, and Frege's arithmetic analysis.

Tsan-sheng Hsuمشاهده پروفایل

استاد پژوهش

• Graph Theory
• Algorithm Design
• Data Privacy Protection
• +۳ مورد دیگر

Tsan-sheng Hsu is a Research Professor at the Institute of Information Science (IIS), Academia Sinica in Taiwan. He has been a tenured full research fellow since June 2003, after serving as an assistant research fellow starting in 1993 and associate research fellow from 1997-2003. He also served as Deputy Director of IIS from 2002-2004 and Director of Academia Sinica Computing Center from 2008-2010. Since 2019, he has been Editor in Chief for the Journal of Information Science and Engineering. Dr. Hsu's educational background includes: Bachelor of Science in Computer Sciences from National Taiwan University (1981-1985), graduating first in class Master of Science in Computer Sciences from University of Texas at Austin (1990) Ph.D. in Computer Sciences from University of Texas at Austin (1993) Dr. Hsu's primary research interests focus on graph theory and its applications , where he investigates fundamental properties of graphs and develops algorithms for connectivity augmentation problems. His work in algorithm design, analysis, implementation and performance evaluation spans sequential, parallel, and distributed algorithms. In data-intensive computing , he explores data privacy protection, large-scale social network analysis, and computer Chinese chess. His research often bridges theoretical breakthroughs with practical applications in network reliability, statistical data security, and efficient computation. Dr. Hsu's publication record demonstrates a consistent focus on graph theory problems, particularly connectivity augmentation, graph searching, and distance-hereditary graphs. His work shows progression from theoretical foundations to practical applications, with an emphasis on developing efficient algorithms with provable performance guarantees. The majority of his publications appear in top theoretical computer science venues, reflecting his strong contributions to fundamental algorithmic research. Dr. Hsu has received several prestigious awards and honors: Bert Kay Dissertation Award from University of Texas at Austin (1993) Academia Sinica Research Award for Junior Research Investigators (2003) Phi Tau Phi Scholastic Honor Society membership (1985) MCD fellowship and IBM graduate fellowship during doctoral studies Dr. Hsu leads the Massive Data Computation and Management Lab at Academia Sinica, where he directs research on graph theory applications, algorithm design, and data privacy. His research has been supported by various projects including theoretical foundation work on graph augmentation and searching problems, as well as applied research in inference control, computer Chinese chess, and parallel computation. He has collaborated extensively with researchers from institutions including Northwestern University, National Tsing Hua University, and National Taiwan University.

Oskar Mencerمشاهده پروفایل

استاد

• Numerical methods and simulations
• Imaging
• Discretization of space, time and value
• +۷ مورد دیگر

Oskar Mencer is a Professor in the Department of Computing at Imperial College London , where he has been a member of academic staff since 2000. He was also a Consulting Professor at Center for Computational Earth and Environmental Science , Stanford University (2009-2010). His research focuses on Multiscale Dataflow Computing , exploring the interaction of hardware and software systems through domain-specific representations, parallel programming, VLSI design, and compiler methodologies. He has contributed to fields including high-performance computing, FPGA optimization, and computational finance. Dr. Mencer's work has been recognized with a Special Award from Com.sult (2012), Imperial College Research Excellence Award (2007), and a Top EPSRC Advanced Fellowship (2001). He has received multiple best paper awards, including at ICFPT'08 and ASAP 2008, and his 2000 paper on stream architectures was recognized as historically significant in 2015. His publications demonstrate a strong emphasis on FPGA acceleration , custom architectures , and parallel processing across geophysics, finance, and neuroscience applications. Special Award for Dataflow Innovation (2012) Best Paper Award - ICFPT'08 Best Paper Award - ASAP 2008 He has served on technical committees for conferences such as FPL , DATE , and FPT , and leads the Computer Architecture Research Group (currently inactive).

Heike Jagodeمشاهده پروفایل

دانشگاهی

• 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 .

• Image Processing
• Computer Vision
• Signal Processing
• +۴ مورد دیگر

Sos S. Agaian is a Professor affiliated with City University of New York (CUNY), USA, with a former position at the University of Texas at San Antonio's Department of Electrical and Computer Engineering. His research focuses on advanced image and signal processing techniques, including thermal imaging, medical imaging, neural networks, and computer vision applications. He has authored numerous publications in top-tier journals and conferences, contributing to fields like adversarial attack defenses, solar panel segmentation, and quantum-inspired algorithms. His work emphasizes practical solutions for image enhancement, deblurring, and segmentation, with applications in healthcare, energy systems, and cybersecurity. Research Interests: Image Processing and Computer Vision Signal Processing and Machine Learning Medical and Thermal Imaging Neural Networks and Deep Learning Quantum Computing Applications in Imaging Recent Trends in Articles: Recent work highlights advancements in quaternion-based neural networks for weather removal, lightweight networks for solar panel fault detection, and novel entropy models for thermal imaging. Collaborations span medical diagnostics, cybersecurity, and energy systems, reflecting interdisciplinary impact. Grants and Collaborations: While specific grants are not detailed, his extensive publication record suggests active research funding in areas like computer vision and quantum information processing. Collaborators include institutions globally, emphasizing translational research. Labs/Teams: Not explicitly mentioned, but his work implies involvement in imaging and machine learning research groups focused on practical solutions for real-world challenges.

Anthony Danalisمشاهده پروفایل

دانشگاهی

• 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

• Computer Architecture
• Systems & Networking
• Approximate Computing
• +۶ مورد دیگر

Weng Fai WONG is an Associate Professor and Deputy Head of the Department of Computer Science at the School of Computing, National University of Singapore (NUS). With over three decades of academic experience at NUS, he has established himself as a leading researcher in computer systems, with particular expertise in the interface between hardware and software stacks. Dr. Wong received his B.Sc. (First Class Honors) and M.Sc. from the National University of Singapore in 1989 and 1991 respectively, followed by a Dr.Eng.Sc. from the University of Tsukuba in 1993. His academic journey began at NUS (then DISCS) in 1985, where he progressed from student to Senior Tutor in 1989, and later returned from Japan as a Lecturer in 1993. Dr. Wong's research focuses on systems and networking, with special emphasis on hardware-software co-optimization. His current research interests include approximate computing , neuromorphic computing , and hardware acceleration for deep learning. His work spans computer architecture , embedded systems , compilers and runtime systems , and programming languages . He has made significant contributions to optimizing software for novel hardware including FPGAs, GPUs, and non-volatile memory technologies. His recent publications (2023-2025) demonstrate a strong focus on energy-efficient AI computing, with particular emphasis on spiking neural networks, large language model acceleration, and FPGA-based solutions for graph processing and machine learning workloads. His research shows a clear trajectory toward green AI through hardware-software co-design that minimizes energy consumption while maintaining computational effectiveness. Dr. Wong is a Member of ACM and a Senior Member of IEEE. His paper "Exploiting half precision arithmetic in Nvidia GPUs" was a Best Paper Finalist at the IEEE High Performance Extreme Computing Conference (HPEC 2017). As Deputy Head of the Department of Computer Science at NUS, Dr. Wong plays a key leadership role in academic administration while maintaining an active research program. His work has been supported by numerous research grants, though specific details are not provided in the available information. Dr. Wong leads research in the Systems & Networking area at NUS, with particular focus on the Hardware-Software Interface Laboratory. His team explores innovative approaches to bridge the gap between theoretical computer science and practical hardware implementation, with applications spanning from edge computing to large-scale data centers.

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.

• Applied Mathematics
• Interval Analysis
• Modal Interval Analysis
• +۷ مورد دیگر

Remedy Calm Puig is an Associate Professor in the Department of Computer Science, Applied Mathematics and Statistics at the University of Girona, where she has been a faculty member since 1992. She is affiliated with the Institute of Computing and Applications and leads research in interval analysis and its applications in engineering and biomedical systems. She is a member of the Modeling and Intelligent Control Engineering (MiceLab) research group and collaborates with the Girona Biomedical Research Institute (IdIBGi). BSc in Mathematics, Universitat Autònoma de Barcelona (UAB), 1987 PhD in Mathematics, Universitat Politècnica de Catalunya (UPC), 2005 Her research focuses on modal interval analysis , uncertainty modeling , and biomedical simulation , particularly in glucose-insulin dynamics for diabetes management. She develops mathematical tools for reliable computing and applies them to physiological systems and control engineering. Her work bridges applied mathematics with real-world biomedical and industrial applications. The recent publications highlight a strong trend in interdisciplinary research combining applied mathematics , biomedical engineering , and clinical technology . Key themes include uncertainty quantification, virtual patient modeling, and glucose monitoring validation. Her work appears in specialized journals like Reliable Computing and Journal of Diabetes Science and Technology , reflecting both theoretical and translational impact. She has co-directed PhD research and contributed to national and institutional research projects. Her technology transfer efforts include collaborations with National Instruments and Medtronic , focusing on virtual patients and glucose monitor evaluation. Organizing Committee, Qualitative Systems and Diagnosis Conferences Organizer, XLV Spanish Mathematical Olympiad (2009) Participant, 'Innovem Junts' – ICT Research for Business Innovation She is actively involved in the MiceLab research group, which specializes in interval-based modeling and control systems, and continues to advance methods for handling uncertainty in complex systems.

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.