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Bill GroppView profile

Professor

• High Performance Computing
• Parallel Computing
• Scientific Computing
• +3 more

William Gropp is the Grainger Distinguished Chair and Director of the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Computer Science from Stanford University (1982) and has contributed extensively to parallel computing, software for scientific computing, and numerical methods for PDEs. His research focuses on high performance computing, programming models, and scalable algorithms. Education: B.S. Mathematics (Case Western Reserve, 1977), M.S. Physics (University of Washington, 1978), Ph.D. Computer Science (Stanford, 1982). Research Interests: Gropp's work spans HPC, parallel computing, and numerical methods. He co-developed the MPI standard and MPICH implementation, and contributed to the PETSc library. His current projects include the Delta and DeltaAI supercomputers, Illinois Computes, and exascale initiatives. Scientific Awards: AAAS Fellow (2018), ACM/IEEE-CS Ken Kennedy Award (2016), SIAM/ACM Prize (2015), and 2024 ACM Software System Award. Member of the National Academy of Engineering. Grants & Leadership: Director of NCSA, leader of the Midwest Big Data Hub, and contributor to NSF-funded projects. His teams support AI/ML infrastructure and exascale computing. He advises on HPC policy and serves on committees like the Computing Community Consortium. Labs/Teams: NCSA, Siebel School research groups, collaborations with DOE, NSF, and industry partners. His work drives advancements in cyberinfrastructure and computational science.

Asuman OzdaglarView profile

Professor

• Optimization
• Machine Learning
• Game Theory
• +7 more

Asuman Ozdaglar is the MathWorks Professor of Electrical Engineering and Computer Science and Department Head of EECS at MIT. She also serves as Deputy Dean of Academics for the MIT Stephen A. Schwarzman College of Computing. Her research focuses on large-scale networked systems, including optimization, game theory, social networks, and distributed algorithms. Education: BS in Electrical and Electronics Engineering from Middle East Technical University (1996), SM (1998) and PhD (2003) in Electrical Engineering and Computer Science from MIT. Research emphasizes nonlinear optimization, machine learning, and network economics. She leads work on robust algorithms, misinformation dynamics, and networked systems. Affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). Her contributions span theoretical and applied domains, including distributed optimization methods, social network analysis, and privacy-preserving data mechanisms. Active in shaping academic policy through her roles in the College of Computing.

Ali GhodsiView profile

Professor

• Machine Learning
• Deep Learning
• Natural Language Processing
• +3 more

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.

Samuel W.K. WongView profile

Associate Professor

• Data Science Methodology
• Protein Structure Analysis
• Reliability Engineering
• +4 more

Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.

Andrea TapiaView profile

Associate Professor

• Information Systems
• Disaster Management
• Social Media Analysis
• +3 more

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. SmithView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +5 more

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 SüsstrunkView profile

Professor

• Computational Imaging
• Computational Photography
• Color Image Processing
• +4 more

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.

• Computational Fluid Dynamics
• Scientific Machine Learning
• Data-Driven Modeling
• +3 more

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.

Ebrahim Bedeer MohamedView profile

Assistant Professor

• Signal Processing
• Wireless Communications
• Artificial Intelligence
• +3 more

Ebrahim Bedeer Mohamed is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan. He joined in July 2019, following roles as an Assistant Professor (Lecturer) at Ulster University, UK, and postdoctoral fellowships at Carleton University and the University of British Columbia. He holds a Ph.D. (Distinction) from Memorial University of Newfoundland (2014), with expertise in signal processing and wireless communications. His research focuses on optimizing communication systems through advanced signal processing techniques, including faster-than-Nyquist signaling, IoT network design, AI integration, and energy-efficient protocols. Key areas include next-generation communication networks, non-orthogonal modulation, and MIMO systems. Notable contributions include work on channel estimation for FTN signaling, RIS-aided wireless systems, and LR-FHSS protocols in IoT. His publications span spectral efficiency, interference minimization, and energy management in 5G/6G contexts. He actively seeks Ph.D. students with strong backgrounds in signal processing fundamentals. Awards and grants are not explicitly listed in the provided texts. His work emphasizes practical applications, such as UAV trajectory optimization for IoT data collection and energy-efficient caching strategies in dynamic networks.

Saman RazaviView profile

Associate Professor

• Hydrology
• Water Resources Management
• Climate Change Impact Analysis
• +3 more

Dr. Saman Razavi is an Associate Professor at the University of Saskatchewan, holding dual appointments in the School of Environment and Sustainability (SENS) and the Department of Civil, Geological and Environmental Engineering in the College of Engineering. He is a member of the Global Institute for Water Security and leads the Razavi EnviroFutures Lab. His research focuses on hydrological modeling, water resources management, climate change impacts, and the integration of machine learning with environmental science. Dr. Razavi has earned a PhD in Civil and Environmental Engineering from the University of Waterloo. Education: PhD in Civil and Environmental Engineering, University of Waterloo MS in Civil and Environmental Engineering, Amirkabir University, Iran BS in Civil Engineering, Iran University of Science and Technology Research interests include hydrologic model development, optimization, uncertainty quantification, climate change analysis, and socio-hydrological modeling. He emphasizes interdisciplinary approaches to address water challenges through integrated frameworks that bridge natural science, engineering, and socio-economic factors. Award: Walter L. Huber Civil Engineering Research Prize from ASCE (2024) Advising & Grants: Dr. Razavi leads the Integrated Modelling Program for Canada under Global Water Futures, focusing on transboundary water systems. His work involves developing decision-support tools for flood/drought management and climate adaptation. Labs/Teams: The Razavi EnviroFutures Lab advances research on water-human systems, leveraging AI and big data to enhance resilience against water-related hazards. Current projects include flood-prone area mapping, drought prediction, and socio-hydrological modeling in transboundary basins.

Alan SussmanView profile

Professor

• Databases and Big Data
• High Performance Computing
• Parallel and Distributed Systems
• +4 more

Alan Sussman is a Professor and Associate Chair of Undergraduate Education in the Computer Science department at the University of Maryland. His research focuses on databases, high-performance computing, parallel systems, and educational curriculum development for computing disciplines. He holds a Ph.D. from Carnegie Mellon University (1991) and a B.S.E. from Princeton University (1982). His educational contributions include integrating parallel and distributed computing concepts into early undergraduate courses, supported by NSF-funded initiatives like the CyberTraining program. He has advised students such as Harshit Soora (Master's) and Xiaolong Tian (PhD). His research spans compiler optimizations for parallel programs, distributed data management systems, and scientific workflow frameworks like DYFLOW. He collaborates with UMIACS and contributes to interdisciplinary projects like the TASCS center. Key innovations include VeloxDFS for distributed dataset streaming, compiler techniques for irregular memory access in PGAS programs, and NetCDFaster for geospatial data optimization. His work emphasizes productivity improvements for high-performance applications and curriculum modernization to address emerging computational challenges. Awards: No individual awards explicitly listed; however, collaborator Jik-Soo Kim received a best paper award in 2006. Grants: NSF CyberTraining, TCPP Curriculum Initiative, and Center for Technology for Advanced Scientific Component Software (TASCS). Labs/Teams: Active in UMIACS and interdisciplinary collaborations, including the TASCS center and InterComm framework development.

Ben Bloem-ReddyView profile

Assistant Professor

• Machine Learning
• Causal Inference
• Bayesian Methods
• +3 more

Ben Bloem-Reddy is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. His research focuses on statistical theory and applications in machine learning, particularly in causal inference, Bayesian methods, neural networks, and probabilistic models. He advises current students Quanhan (Johnny) Xi, Kenny Chiu, and Gian Carlo Diluvi. His work bridges foundational statistical theory with practical machine learning challenges, including causal discovery, model identifiability, and uncertainty quantification. Recent research explores topics such as latent variable models, generative processes, and symmetry in data and algorithms. His contributions span interdisciplinary areas like particle physics applications and information theory-based compression techniques. Ben’s research trends emphasize advancing theoretical guarantees for modern machine learning systems while addressing real-world problems. His publications frequently intersect with algebraic topology (e.g., cocycles in causal inference) and nonparametric methods. He maintains an active lab within the Department of Statistics, fostering collaborations across UBC’s academic ecosystem. No scientific awards are explicitly listed in the provided information. His advising and grant activities focus on statistical methodology development, as evidenced by his student supervision and published work. His office is located in ESB 3168, and he can be reached at benbr@stat.ubc.ca.

Eshed Ohn-BarView profile

Assistant Professor

• Machine Learning
• Autonomous Systems
• Computer Vision
• +4 more

Eshed Ohn-Bar is an Assistant Professor in the Department of Electrical & Computer Engineering at Boston University. He leads the Human-to-Everything (H2X) Lab, focused on developing intelligent systems for assistive and autonomous technologies. His research bridges machine perception, learning, and human-computer interaction, with applications in autonomous driving and accessibility for visually impaired individuals. Educated at UCLA (BS in Mathematics, 2010; MEd, 2011) and UCSD (PhD in Electrical Engineering, 2017), he holds a Humboldt Fellowship and has received the IEEE ITS Society Best PhD Dissertation Award (2017) and the 2025 BU Early Career Excellence in Research Award. His work emphasizes robust autonomy, real-time assistance, and inclusive design, collaborating with industry partners like Motional and receiving NSF grants (e.g., IIS-2152077). Research interests include autonomous systems, computer vision, and assistive technologies. Recent trends in publications highlight advancements in decision-making frameworks, neural volumetric models, and scalable learning for navigation. His lab’s projects address challenges in accessibility, such as blind motion generation and inclusive autonomous vehicle design. Awards: Humboldt Fellowship, IEEE ITS Best Dissertation, BU Early Career Award Grants: NSF IIS-2152077 Labs/Teams: H2X Lab, collaborating on projects with industry and academic partners

Anne MagurranView profile

Professor

• Biological Diversity
• Ecology
• Community Ecology
• +5 more

Prof. Anne Magurran is a Professor (Re-engaged) at the School of Biology, University of St Andrews. Her research focuses on biological diversity, quantifying ecological patterns, and understanding processes influencing biodiversity change over space and time to inform conservation efforts. She holds academic qualifications including a D.Phil. from the University of Ulster and an honorary doctorate from the University of Bergen. She is a Fellow of the Royal Society of Edinburgh (FRSE) and an honorary life member of the Fisheries Society of the British Isles. Her work spans global ecosystems and involves collaborations across five continents. Key projects include the ERC-funded BioTIME initiative, analyzing biodiversity time series, and NERC-supported studies integrating taxonomic, phylogenetic, and functional diversity metrics. She has supervised PhD students such as Cher Chow, Amber Cowans, and Volodymyr Hudgins. Prof. Magurran has received prestigious awards like the Beverton Medal (2006), ASAB Medal (2021), and the CBE (2022). Her research also addresses UN SDGs related to life on land and below water. She leads interdisciplinary teams at the Centre for Biological Diversity and contributes to institutions like the Marine Alliance for Science & Technology Scotland.

Guy-Vincent JourdanView profile

Professor

• Software Security
• Cybersecurity
• Distributed Systems
• +6 more

Professor Guy-Vincent Jourdan is affiliated with the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Ph.D. from Université de Rennes/INRIA (France, 1995) focusing on distributed systems analysis. Prior to academia, he served as CTO and CEO of Decision Academic Graphics, an Ottawa-based firm. His research interests span software security, cybersecurity (including cybercrime prevention), distributed systems modeling, formal methods, mobile applications, and rich internet applications. Specific technical emphases include phishing detection systems, blockchain fraud analysis, and adversarial machine learning. Professor Jourdan has pioneered tools like D-ForenRIA for reconstructing user interactions in Rich Internet Applications and contributed to cybersecurity frameworks such as HEART for log anomaly detection. His work integrates machine learning techniques with domain-specific challenges in network security and software verification. His publications (2023-2025) reflect advancements in AI-driven vulnerability analysis, blockchain fraud detection, and automated phishing detection systems. Notable projects include SV-TrustEval-C for source code vulnerability analysis and Intellitweet for social media threat detection. While no scientific awards are explicitly listed, his prolific publication record and industry-academia transition highlight sustained contributions to computer science and cybersecurity domains.