جست‌وجوی استاد و پژوهشگر

Yan Chenمشاهده پروفایل

استاد

• Cybersecurity
• Networking
• Distributed Systems Security
• +۵ مورد دیگر

Yan Chen is a Professor of Computer Science at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He leads the Northwestern Lab for Internet and Security Technology (LIST) and the Center for Ultra-scale Computing and Information Security. His research focuses on cybersecurity, network measurement, and distributed systems security. Chen holds a Ph.D. from UC Berkeley (2003), M.S. from SUNY Stony Brook, and B.E. from Zhejiang University. Research interests include securing networking systems, intrusion detection, cloud-native platforms, and mobile security. Notable awards include the DOE Early CAREER Award (2005), Air Force Young Investigator Award (2007), and ACM ASPLOS'18 Most Influential Paper Award. His work has been cited over 17,000 times with an h-index of 62 (2024). Key contributions include the LIST lab's advancements in APT detection, provenance tracking in microservices, and security frameworks like FlowCog. He advises numerous Ph.D. students and has graduated over 20 researchers now in academia and industry.

Ming Linمشاهده پروفایل

استاد

• AI and Robotics
• Computer Vision
• Computer Graphics
• +۵ مورد دیگر

Ming Lin is a Distinguished University Professor at the University of Maryland, College Park, holding joint appointments in Computer Science (Department of Computer Science), the Institute for Advanced Computer Studies (UMIACS), Electrical and Computer Engineering (ECE), and the Maryland Robotics Center. She holds the Dr. Barry Mersky and Capital One E-Nnovate Endowed Professorships. Her research focuses on physically-based modeling, virtual environments, haptics, robotics, and AI applications in healthcare and urban computing. Education: Ph.D., M.S., and B.S. in Electrical Engineering & Computer Sciences from UC Berkeley. She previously spent 20 years at UNC Chapel Hill before joining UMD in 2018. Research interests include collision detection algorithms (e.g., Lin-Canny algorithm), real-time physics simulation, virtual/augmented reality systems, and medical imaging applications. Her work has led to over 2 million downloads of her group's software tools and licenses with 60+ companies. Notable contributions include the Oculus Rift-related VR technologies and Amazon's virtual try-on system. Awards: IEEE Fellow (2012), ACM Fellow (2011), NAI Fellow (2022), and Washington Academy of Sciences Distinguished Career Award (2020). Active in professional service, she serves on the CRA Board and chairs the Committee on Widening Participation in Computing Research. Advising: Supervises 12+ PhD/Master's students. Her lab (GAMMA Group) focuses on AI-driven robotics, autonomous systems, and physically-based simulations. Key projects include traffic simulation frameworks, medical VR applications, and 3D garment modeling.

Paul Wardمشاهده پروفایل

دانشیار

• Distributed Systems
• Computer Networks
• Autonomic Computing
• +۳ مورد دیگر

Paul Ward is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo and a faculty fellow at the IBM Centre for Advanced Studies. He holds a PhD (2002) and MASc (1993) from Waterloo and a BScE (1998) from the University of New Brunswick. His research focuses on distributed systems management, dependable systems, autonomic computing, wireless networks, and IoT. Key areas include fault detection in web services, service-oriented networking, and optimization of wireless mesh networks. Ward's publications span computer networks, cognitive science, and sports analytics, reflecting interdisciplinary applications of computational methods. He holds two patents in mobile web services and fault resolution.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

Andrea Tapiaمشاهده پروفایل

دانشیار

• Information Systems
• Disaster Management
• Social Media Analysis
• +۳ مورد دیگر

Andrea Tapia is an Associate Professor of Information Sciences and Technology at Pennsylvania State University. She holds a Ph.D. in Sociology from the University of New Mexico (2000). Her research focuses on the intersection of social theory, ICT, and crisis response, with a particular emphasis on leveraging social media for disaster resilience and humanitarian action. She has secured over $3.7 million in external funding, supervised 16 graduate committees (including 10 PhDs and 6 master’s theses), and authored over 40 journal articles, 60 conference papers, and 12 book chapters. Tapia is an elected leader in the American Sociological Association and the International Association for Information Systems for Crisis Response and Management. Her work directly influences UN policy, international relief organizations, and U.S. governmental initiatives. She has pioneered platforms like Aurorasaurus and contributed to frameworks for social media integration in emergency dispatch systems. Her research spans crisis informatics, crowdsourced early warning systems, and inter-organizational collaboration in humanitarian contexts. Notable achievements include developing methodologies for trust detection in social media data and analyzing disaster response coordination networks. Tapia has presented at 73 conferences, including 31 invited talks, and her scholarship emphasizes actionable insights for policymakers and practitioners. Tapia teaches 12 courses across undergraduate, Honors, and graduate levels, reflecting her commitment to education. Her current focus includes refining frameworks for social media data adoption in public safety answering points and advancing resilience analytics for cyber-physical-social systems. She leads interdisciplinary teams addressing challenges in disaster response, digital volunteering, and technology-mediated collaboration.

Melissa C. Smithمشاهده پروفایل

استاد

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

Melissa C. Smith is a Professor of Electrical and Computer Engineering and Associate Dean for Graduate Studies at Clemson University. She holds a Ph.D. from the University of Tennessee and degrees from Florida State University. Her research focuses on machine learning, reconfigurable computing, and high-performance systems, with applications in embedded systems and interdisciplinary scientific advancements. Before joining Clemson in 2006, she was a research associate at Oak Ridge National Laboratory (ORNL), contributing to projects like the Spallation Neutron Source and PHENIX experiments. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee M.S., Electrical Engineering, Florida State University B.S., Electrical Engineering, Florida State University Research Interests: Machine Learning and AI High-Performance and Reconfigurable Computing System Performance Modeling Embedded Systems Articles Summary: Her recent work spans machine learning applications, GPU/FPGA architectures, speech enhancement, and medical systems. Key themes include optimizing heterogeneous computing for real-time and scientific workloads, and advancing interdisciplinary solutions through architecture-application co-design. Lab & Collaborations: Leads the Future Computing Technologies Lab and collaborates with ORNL and national labs on projects like GEMmaker and HPC-enabled medical systems.

Sabine Süsstrunkمشاهده پروفایل

استاد

• Computational Imaging
• Computational Photography
• Color Image Processing
• +۴ مورد دیگر

Sabine Süsstrunk is a Full Professor and Director of the Images and Visual Representation Laboratory (IVRL) at EPFL's School of Computer and Communication Sciences. She holds a BS in Scientific Photography from ETH Zürich, MS from Rochester Institute of Technology, and PhD from University of East Anglia. Her career includes positions at Hewlett-Packard Labs and Corbis Corporation. Her research explores computational imaging, computational photography, color processing, computer vision, and image quality. Key interests include near-infrared applications, multispectral imaging, and computational aesthetics. Her work bridges hardware and software solutions for imaging challenges. Publications demonstrate consistent focus on advancing generative models (diffusion models, neural cellular automata), 3D reconstruction (NeRF variants), and media integrity (DeepFake detection). Recent trends show increased emphasis on 3D vision, robustness in generative AI, and video analysis. Awards & Honors: IS&T/SPIE Electronic Imaging Scientist of the Year (2013) Raymond C. Bowman Teaching Award (2018) EPFL AGEPoly IC Polysphere Award (2020) 8 Best Paper/Demo Awards Fellowships: IEEE, IS&T, ELLIS, AIIA She leads the IVRL lab and advises PhD candidates while serving as President of the Swiss Science Council. Research is supported through competitive grants and industry collaborations.

John Bagterp Jørgensenمشاهده پروفایل

استاد

• Model Predictive Control
• Numerical Optimization
• Process Control
• +۵ مورد دیگر

John Bagterp Jørgensen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computational methods for Model Predictive Control (MPC), numerical optimization, and dynamic optimization, with applications in industrial processes, biomedical systems, and sustainable energy. He holds leadership roles in 2-control ApS, a company developing advanced control solutions for industries such as cement production and oil recovery. Education: PhD and M.Sc. in Technical Sciences from DTU (1997–2005 and 1991–1997). Professional experience includes roles as an Assistant Professor at DTU and CTO/CEO at 2-control ApS. Research interests span MPC algorithms, numerical methods for differential equations, and system identification. His work bridges academia and industry, addressing challenges in energy efficiency, vaccine manufacturing, diabetes treatment, and cement production processes. His recent articles emphasize industrial applications of control systems, including cement rotary kiln dynamics, vaccine production optimization, and dual-hormone artificial pancreas development. He has received the Nordic Energy Research Award (1994) and contributed to UN Sustainable Development Goals related to affordable energy and industrial innovation. Advising and grants: Supervises multiple PhD projects on topics like electrification of industrial processes and sustainable SCP production. Collaborates with global institutions on energy and biomedical research. Labs/teams: Leads teams in DTU’s Scientific Computing and Center for Energy Resources Engineering, with active partnerships in industry and academia.

Yukun Liمشاهده پروفایل

دانشیار

• Finite Element Methods
• Numerical Solutions of Stochastic Differential Equations
• Adaptive Methods
• +۳ مورد دیگر

Yukun Li is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research focuses on numerical analysis, stochastic partial differential equations, and computational finance. He holds a Ph.D. in Mathematics from the University of Tennessee, Knoxville (2010-2015), followed by postdoctoral roles at Penn State (2015-2016) and The Ohio State University (2016-2019). He has secured grants including NSF REU funding (2023-2026) and led an NSF-funded project on stochastic phase field models (2021-2025). Research interests include: Continuous/Discontinuous Finite Element Methods Numerical Solutions of Stochastic ODEs/PDEs Adaptive Algorithms and Fast Solvers Computational Finance Models Recent publications emphasize stochastic wave equations, phase field models, and financial mathematics. His work spans theoretical analysis and numerical methods for complex systems. Notable recognition includes the 2015 Achievement Award from the University of Tennessee's Mathematics Department. Teaching highlights include advanced graduate courses like Computational Methods for Financial Mathematics and Numerical Linear Algebra, alongside contributions to undergraduate mathematics education. He is proficient in computational tools including MATLAB, Python, FEniCS, and MPI.

Vlatko Vedralمشاهده پروفایل

استاد

• Quantum Information Science
• Quantum Physics
• Quantum Gravity
• +۴ مورد دیگر

Prof. Vlatko Vedral is a Professor of Quantum Information Science in the Department of Physics at the University of Oxford, affiliated with the Clarendon Laboratory. He leads research in the Frontiers of Quantum Physics group. His work focuses on quantum entanglement, quantum gravity, quantum foundations, and quantum thermodynamics, with applications to biological systems and quantum technologies. Notable contributions include theoretical frameworks for quantum gravity experiments and quantum causal inference protocols. Research interests span quantum information science, quantum gravity, atomic and laser physics, and the philosophical interpretation of quantum mechanics. Recent work explores emergent geometry from quantum correlations, quantum refrigeration with indefinite causal order, and experimental probes of quantum effects in macroscopic systems. Publications highlight interdisciplinary approaches, such as testing quantum gravity via entanglement and analyzing non-classicality in photosynthetic systems. His research often bridges theoretical physics with experimental feasibility, leveraging quantum simulators and NMR systems.

• Computational Fluid Dynamics
• Scientific Machine Learning
• Data-Driven Modeling
• +۳ مورد دیگر

Shaowu Pan is an Assistant Professor of Aerospace Engineering at Rensselaer Polytechnic Institute (RPI), affiliated with the Future of Computing Institute (FOCI) and the Scientific Computation Research Center (SCOREC). He holds a Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan and completed a postdoctoral fellowship at the University of Washington’s AI Institute in Dynamic Systems. Education: Ph.D., University of Michigan, 2021 M.S., University of Michigan, 2015 B.E. & B.S., Beihang University, 2013 Research Interests: His work focuses on the intersection of computational fluid dynamics, data-driven modeling, and scientific machine learning. Key areas include operator-theoretic modeling of fluid flows, generative AI for physical systems, and physics-informed neural networks. He develops novel algorithms for reduced-order modeling and stability-preserving surrogate models, with applications in turbulence, plasma physics, and aerodynamics. Key Contributions: Developed PyKoopman , an open-source Python package for Koopman operator approximation. Pioneered mesh-agnostic representation methods like Neural Implicit Flow for spatio-temporal data. Advanced physics-informed neural networks for solving Grad-Shafranov equations and plasma equilibrium problems. Awards & Recognition: John Tichy Junior Faculty Travel Grant (2024) Chinese Outstanding Student Abroad Award (2021) Richard and Eleanor Towner Prize Nominee (2019) Teaching & Mentorship: He teaches courses like MANE 2110: Numerical Methods and Programming for Engineers and mentors multiple Ph.D., master’s, and undergraduate students. His doctoral committee involvement spans interdisciplinary projects in fluid dynamics and AI. Grants & Software: Lead PI for NSF-funded projects on neural representation learning for turbulent flows. Developed software tools like spKDMD and Warp-DG for dynamics analysis and CFD simulations. Labs & Collaborations: Collaborates with institutions like Los Alamos National Laboratory and actively participates in conferences (e.g., AIAA SciTech, SIAM). His research bridges computational science, machine learning, and fluid dynamics to address complex nonlinear systems.

Albert Chengمشاهده پروفایل

استاد

• Real-Time Systems
• Cyber-Physical Systems
• Internet of Things (IoT)
• +۳ مورد دیگر

Professor Albert Cheng is a faculty member in the Department of Computer Science at the University of Houston. His research focuses on real-time systems, cyber-physical systems, smart cities, and embedded systems with societal impacts. He has authored over 270 publications and a textbook on real-time systems. Cheng holds roles as an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering and ACM Computing Surveys. His research interests span real-time scheduling, machine learning applications, and systems optimization. Recent work includes vehicular traffic modeling for epidemiological risk reduction, quantum computing response time analysis, and satellite mission planning. Awards include Fulbright Specialist, Distinguished ACM membership, and IEEE Senior Member status. Cheng’s articles demonstrate expertise in real-time scheduling algorithms, cyber-physical systems development, and smart city infrastructure. His contributions bridge theoretical computer science with practical implementations in transportation, healthcare, and aerospace domains. Ongoing efforts include fault-tolerant systems, energy-efficient scheduling, and CPS education initiatives. Awards: Fulbright Specialist, ACM Distinguished Member, IEEE Senior Member, Institute of Physics Fellow Labs/Teams: Hewlett Packard Enterprise Data Science Institute (HPE DSI), Research Computing Data Core (RCDC)

• Design Science Research
• Enterprise Architecture Management
• Governance of Digital Platforms
• +۵ مورد دیگر

Robert Winter is a Full Professor of Business IT at the University of St. Gallen (HSG), Switzerland, and Director of the Institute of Information Management (IWI-HSG). He serves as Founding Director of HSG’s Executive MBA in Business Engineering and has led the School of Management’s Doctoral Program. His roles include Vice Editor-in-Chief of Business & Information Systems Engineering and current editorial board membership at MIS Quarterly Executive. Education M.Sc. Business Administration (1984), Goethe University, Frankfurt M.Sc. Business Education (1986), Goethe University, Frankfurt Ph.D. in Social Sciences (1989), Goethe University, Frankfurt Venia legendi (1995), Goethe University, Frankfurt Research Interests Focuses on Design Science Research, Enterprise Architecture Management, and governance of digital platforms. His work explores methodologies for collaborative innovation, data-driven transformation, and complexity management in large-scale systems. Key areas include enterprise transformation steering, data mesh adoption, and agile methodologies in public-sector contexts. Key Contributions Developed frameworks for enterprise architecture governance and digital platform ecosystems Authored influential papers on design science methodology and simulation-based research Recipient of AIS Senior Scholars’ Global Best Paper Award (2017) and Herbert A. Simon Award (2022) Professional Roles President of the School of Management Doctoral Program Member of editorial boards for leading journals (e.g., European Journal of Information Systems) Leadership in professional associations: VHB Board (2011–2014), AIS Swiss Chapter President (2012–2016)

Alvin J. K. Chuaمشاهده پروفایل

استادیار

• Gravitational-Wave Astronomy
• General Relativity
• Machine Learning for Science
• +۳ مورد دیگر

Dr. Alvin J. K. Chua is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship. He holds a joint appointment in the Department of Mathematics and a courtesy appointment in the Department of Statistics and Data Science. His research focuses on gravitational-wave astronomy , particularly extreme mass ratio inspirals (EMRIs) as key sources for the LISA mission. He develops computational and statistical methods for modeling GW sources and analyzing detector data, with recent work on machine learning and Bayesian inference techniques. His selected publications highlight advancements in modeling beyond-vacuum-GR effects in EMRIs non-local parameter degeneracies rapid relativistic waveform generation neural networks for GW inference statistical sampling on manifolds These works span gravitational-wave astrophysics , computational relativity , and applied statistics . Scientific recognition includes the NUS Presidential Young Professorship. He collaborates with the LISA Consortium and the North American Nanohertz Observatory for Gravitational Waves (NANOGrav).

Lena Simineمشاهده پروفایل

دانشیار

• Theoretical Chemistry
• Chemical Physics
• Computational Materials Science
• +۳ مورد دیگر

Lena Simine is an Associate Professor in the Department of Chemistry at McGill University, affiliated with the Faculty of Science. She holds a B.Sc. (2009) and Ph.D. (2015) from the University of Toronto, followed by a postdoctoral fellowship at Rice University (2015–2019). Her laboratory is located in P&P 118A, focusing on developing computational approaches for modeling molecular phenomena in theoretical chemistry and chemical physics. Her research interests center on computational materials design, quantum dynamics, and the application of machine learning to chemistry. Specific areas include simulating amorphous materials, aptamer design, and quantum systems modeling. She teaches CHEM 365 (Statistical Thermodynamics) and CHEM 593 (Statistical Mechanics and Machine Learning for Chemistry). Her work explores interdisciplinary frontiers, such as path-integral simulations, GFlowNets for molecular design, and the physical principles underlying deep learning in materials science. Recent studies highlight innovations like DeltaGzip for binding affinity prediction and the MAP protocol for 3D disordered matter simulations. Her lab’s contributions span computational methods, material innovation, and quantum phenomena, with a focus on advancing both theoretical frameworks and practical applications in chemistry and materials science.