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Ibrahim SabekView profile

Assistant Professor

• Data Management
• Quantum Computing
• Machine Learning
• +3 more

Ibrahim Sabek is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Dornsife Spatial Sciences Institute. He leads the Next-generation Data-Intensive Systems Group (NexDIG) and previously held postdoctoral positions at MIT's Data Systems Group and an NSF/CRA Computing Innovation Fellowship. He earned his PhD in computer science from the University of Minnesota, Twin Cities in 2020, with recognition for his dissertation's excellence. His research focuses on integrating machine learning and quantum computing into data management systems, emphasizing scalable systems design, algorithms, and data structures. Notable awards include the Google Systems and ML Junior Faculty Award (2025), the NSF/CRA Computing Innovation Fellowship, and the Best Demo Award at ACM SIGSPATIAL 2024. His work spans quantum-augmented database engines, learned query optimizers, and causal inference for system debugging. He actively serves on program committees for top conferences like VLDB and SIGMOD, and organizes workshops such as Q-Data. Current courses include CSCI 543 on modern data management, emphasizing prerequisites like CSCI-485/585. Key contributions include frameworks like LIMAO, TurboReg, and Flash, addressing challenges in spatial probabilistic modeling and scalable regression. His research bridges machine learning, quantum computing, and traditional database systems, with applications in spatial data analysis and system optimization.

Marc PlantevitView profile

Professor

• Data Mining
• Machine Learning
• Artificial Intelligence
• +7 more

Marc Plantevit is a Full Professor at EPITA, member of the Laboratoire LRDE (LRDE). Previously, he served as an Associate Professor at University Claude Bernard Lyon 1 (2010–2021), leading the Data Mining & Machine Learning group at LIRIS lab. He holds a PhD in Computer Science from the University of Montpellier (2008), supervised by Maguelonne Teisseire and Anne Laurent at LIRMM Lab. His research focuses on foundational data mining, graph mining, subgroup discovery, and explainable AI. He is an editorial board member of Data Mining and Knowledge Discovery Journal and has held roles such as CAPES NSI jury member and former head of the Data Mining & Machine Learning group at LIRIS. Research Interests : Data Mining, Machine Learning, Explainable AI, Graph Mining, Subgroup Discovery, Exceptional Model Mining, Constraint-based Pattern Mining, and applications in neuroscience and energy systems. His work explores interpretable AI, GNN explainability, and interdisciplinary applications like odor perception modeling and electricity price forecasting. Key Contributions : Best Paper Award at EGC'22 for work on GNN representations. Active in program committees for ECMLPKDD, IJCAI, and IEEE ICDM . Supervised PhD students working on GNN explainability, electricity forecasting, and machine learning in exposome studies. Labs & Teams : LRDE (EPITA), previously involved with LIRIS (UMR CNRS 5205) and collaborative projects with institutions like INSA Lyon and ISGlobal (Barcelona).

Martin RajmanView profile

Researcher

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +7 more

Martin Rajman is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL) with multiple affiliations across the institution. He holds positions in the School of Computer and Communication Sciences (SIN - Teaching, SCI IC MR Group, SSC - Teaching) as well as in the Vice Presidency for Strategic Development (VPS Artificial Intelligence) and the Vice Presidency for Academic Affairs (SNAI Administration). He serves as the Executive Director of Nano-tera.ch, a large Swiss Research Program funding collaborative multi-disciplinary projects in Health and the Environment. Rajman's research spans the intersection of artificial intelligence, natural language processing, and information retrieval. His work demonstrates a consistent focus on developing practical applications of computational linguistics and machine learning techniques. Early in his career, he contributed significantly to syntactic parsing, stochastic language models, and vector space representations for text. More recently, his research has expanded into deep learning applications for 3D reconstruction, empathetic conversational agents, and distributed analytics systems. His publications reveal a trajectory from foundational NLP research toward increasingly applied and interdisciplinary work connecting AI with healthcare, environmental monitoring, and human-computer interaction. Analysis of his recent publications (2015-2024) shows a clear evolution toward more applied AI research with strong interdisciplinary connections. While maintaining his core expertise in natural language processing and information retrieval, his work has expanded into computer vision, healthcare applications, and sustainable computing. The publications demonstrate increasing collaboration across disciplines, with applications in medical imaging, mental health support systems, environmental monitoring, and human-centered AI. His leadership role in the Nano-tera.ch program reflects this interdisciplinary approach, connecting computing research with real-world challenges in health and environmental contexts. Rajman has mentored several PhD students including Ailomaa Marita, Eckard Emmanuel, Melichar Miroslav, and Veselý Martin. His research has been supported through the Nano-tera.ch program, which has funded more than 100 research projects with over 95 million CHF in public funding. He has also managed more than 20 European projects during his tenure as Director of the EPFL Global Computing Center. As Executive Director of Nano-tera.ch, Rajman leads a significant research initiative connecting EPFL with national and international partners. His work bridges academic research with industry applications, notably through collaborations with eBay on product ranking technology and with Elsevier on article recommendation systems. His leadership extends to managing large-scale research programs while maintaining an active research agenda and mentoring the next generation of computer scientists.

Bei JiangView profile

Associate Professor

• Biostatistics
• Functional Data Analysis
• Statistical Machine Learning
• +6 more

Dr. Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta , Canada. She holds the Canada CIFAR AI Chair and is affiliated with the Alberta Machine Intelligence Institute (Amii) . Her academic journey includes a PhD in Biostatistics (2014) from the University of Michigan, MS (2008) and BS (2004) from the University of Alberta and Beijing University of Technology, respectively. Current Positions : 2021–Present (Associate Professor), 2022–Present (CIFAR AI Chair) Past Appointments : Assistant Professor (2015–2021), Postdoctoral Fellow at Columbia University (2014–2015), Research Assistant at University of Michigan (2009–2013) Research Interests : Dr. Jiang specializes in methods for joint modeling of longitudinal and health outcome data , Bayesian hierarchical modeling , functional and imaging data analysis , and statistical machine learning . Her work integrates kernel machine regression , differential privacy , and synthetic data generation to address challenges in heterogeneous health data and neuroimaging. Scientific Awards : Highlights include the 2015 SAMSI New Research Fellow , multiple Rackham Conference Travel Awards (2013, 2012), and prestigious NSERC scholarships (2009–2012). She has also received the J Gordin Kaplan Graduate Award (2008) and Statistical Society of Canada Travel Award (2008). Grants : $375,000 (CIFAR AI Chairs, 2022–2027), $480,000 (MITACS Accelerate, 2022–2025), and $210,000 (Canadian Statistical Sciences Institute, 2022–2025) Students and Postdocs : She mentors numerous PhD , MSc , and Postdoctoral Fellows , including Junxi Zhang (2023–Present), Enze Shi (2022–Present), and former advisees like Wenxing Guo (now Lecturer at University of Essex) and Yafei Wang (Assistant Professor at University of Alberta).

Jason Bennett ThatcherView profile

Professor

• Information Systems
• Cybersecurity
• Strategic Alignment
• +6 more

Dr. Jason Bennett Thatcher is a Professor at Temple University in the Department of Management Information Systems at the Fox School of Business. He holds additional faculty appointments at the Technical University of Munich, Information Technology University-Copenhagen, and Hong Kong Polytechnic University. His research bridges human behavior and information technology, focusing on cybersecurity, strategic alignment, and digital innovation. 20-year track record in top FT50 journals Top 35 active IS researcher by productivity Senior Editor roles at MIS Quarterly , Information Systems Research , and Journal of the AIS Research spans three pathways: strategic IT decisions (firm performance, governance), IT workforce management (job satisfaction, turnover), and post-adoption IT innovation (technostress, IT identity). 2022 publications emphasize digital commerce, social media ethics, and technostress mitigation. Awards include: Clemson's 2008 Undergraduate Teaching Award KPMG Foundation Circle of Compadres Top Associate Editor recognition by Information Systems Research Multiple productivity rankings Teaching spans undergraduate to Ph.D. levels with global mentorship experience. Editorial leadership roles include Senior Editor positions and former editorial board memberships. Productivity metrics highlight 12,000+ Google Scholar citations and consistent publication in FT50 journals since 2002.

Jordan CragoView profile

Assistant Professor

• Aquatic Toxicology
• Environmental Toxicology
• Ecotoxicology
• +5 more

Dr. Jordan Crago is an Assistant Professor in the Department of Environmental Toxicology at Texas Tech University and affiliated with the STEM CORE program. His research specializes in aquatic toxicology, investigating how environmental chemicals impact development and reproductive success in fish and invertebrates through integrated field-laboratory approaches. Education: Ph.D. in Biological Sciences, University of Wisconsin, Milwaukee Bachelor of Science, Ohio University Dr. Crago's research employs field observations to guide laboratory experiments focused on adverse outcome pathways across diverse contaminants. His work spans pesticides, microplastics, perfluorinated compounds, and emerging pollutants, emphasizing multi-pathway effects on development and reproduction. He prioritizes identifying high-risk environmental chemicals through mechanistic studies using model organisms like zebrafish and fathead minnow. Analysis of his 2019-2025 publications reveals consistent focus on molecular, physiological, and behavioral endpoints in aquatic toxicology. Key themes include developmental/reproductive toxicity, transgenerational effects, and advanced methodologies like predictive molecular signatures and benchmark dose modeling. His work frequently addresses pesticide impacts, microplastic fate in food webs, and PFAS alternatives using zebrafish as a primary model system. Dr. Crago actively mentors undergraduate researchers, integrating them into data collection, analysis, and publication. Three mentees have co-authored peer-reviewed publications and conference presentations. He contributes to STEM education through Texas Tech's STEM CORE program and volunteers with Lubbock's Boys and Girls Club for after-school programs.

Justin ThalerView profile

Associate Professor

• Algorithms
• Computational Complexity
• Zero-Knowledge Proofs
• +3 more

Justin Thaler is an Associate Professor in the Department of Computer Science at Georgetown University, researching algorithms and computational complexity with focus on probabilistic proof systems, verifiable computation, and streaming algorithms. Education: PhD Computer Science, Harvard University BS Computer Science and Mathematics, Yale University Research Interests: Develops protocols for verifying computations (including zero-knowledge proofs), analyzes the power of low-degree polynomials, and designs efficient streaming/sketching algorithms for large datasets. Publications: Research advances theoretical foundations of proof systems, with recent work on SNARKs, lookup arguments, and Fiat-Shamir security. Authored the monograph 'Proofs, Arguments, and Zero-Knowledge'. Advising & Labs: Advises PhD students in theoretical computer science. Contributes to open-source projects including DataSketches library of streaming algorithms. Currently on leave at a16z crypto research.

Mojgan A. JadidiView profile

Associate Professor

• Geospatial Visual Analytics
• Building Information Modeling (BIM)
• 3D GIS Integration
• +5 more

Dr. Mojgan A. Jadidi serves as Associate Professor in the Teaching Stream and Director of Common Engineering & BSc Science within the Department of Civil Engineering at York University's Lassonde School of Engineering. A Professional Engineer (P.Eng) and founder of the GeoVA Lab, she leads research at the intersection of geospatial analytics, digital infrastructure, and innovative engineering education aligned with UN Sustainable Development Goals. Education: PhD in Geomatics, Université Laval (2014) MSc in Earthquake and Seismology Engineering, ROSE Center (Italy) & Université Joseph Fourier (France) BSc in Civil-Survey Engineering, Iranian University of Science and Technology Research Focus: Her pioneering work in Geospatial Visual Analytics spans 2D/3D environments, Building Information Modeling (BIM) and 3D GIS integration, and Spatial Quantum Computing applications for smart cities. She develops Infrastructure Digital Twins using sensor data fusion while revolutionizing engineering education through gamification and augmented/virtual reality pedagogies that transform complex spatial concepts into immersive learning experiences. Research Trends: Recent publications (2021-2023) demonstrate convergent innovation across three domains: (1) Building energy optimization through BIM-graph analytics, (2) Transportation safety via AI-driven situational awareness, and (3) Educational technology using VR sandboxes and visual-verbal comics. These works consistently integrate quantum computing principles and UN SDG frameworks to solve urban sustainability challenges. Scientific Recognition: ASEE Zone III Best Paper Award (2023) ASEE Saint Lawrence Best Research Paper & Poster (2022) 3D GeoInfo Conference Best Paper (2018) NSERC Postdoctoral Fellowship (2016) ESRI Student Award (2011) Erasmus Mundus Scholarship (2006) Research Leadership: As Associate Director of York's ESRI Center of Excellence, she manages multi-source funding from NSERC, Mitacs, and York University internal grants. Her professional service spans global organizations including ISPRS Commission IV (Secretary), IEEE Women in Engineering (Member), PEO Etobicoke (Chair), and buildingSMART Canada (Committee Member), driving standards for BIM and digital twin implementation in urban infrastructure. Lab Innovation: The GeoVA Lab develops cutting-edge tools including the TopoSurvey Game for immersive surveying education, PAN-Lassonde XR Sandbox for virtual lab experiences, and quantum computing frameworks for bike-sharing optimization, establishing new paradigms in spatial data interaction and engineering pedagogy.

Han WangView profile

Assistant Professor

• Computer Science
• Data Privacy
• Cybersecurity
• +2 more

Han Wang is an Assistant Professor in the Electrical Engineering and Computer Science department at the University of Kansas School of Engineering. His research develops privacy-preserving frameworks for distributed systems, machine learning, and data analytics. Core research areas include differential privacy implementations for federated learning environments, privacy-preserving outsourcing of anomaly detection, and adversarial attack mitigation. Recent work focuses on developing staircase randomized response mechanisms that enhance privacy without compromising data utility in location services and video analytics. Publications demonstrate consistent innovation in privacy engineering with applications spanning vehicle trajectory protection, energy trading systems, and video recognition security. Methodological approaches combine theoretical privacy guarantees with practical system implementations.

• Machine Learning
• Data Mining
• Computer Vision
• +3 more

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.

Arno OnkenView profile

Assistant Professor

• Data Science
• Machine Learning
• Computational Neuroscience
• +7 more

Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.

Yihan SunView profile

Assistant Professor

• Parallel Computing
• Algorithms
• Data Structures
• +12 more

Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.

Julian ShunView profile

Associate Professor

• Parallel Computing
• High-Performance Computing
• Graph Analytics
• +7 more

Julian Shun is an Associate Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS) and a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Previously, he was a Miller Research Fellow at UC Berkeley and earned his Ph.D. from Carnegie Mellon University under Guy Blelloch. His research focuses on parallel and high-performance computing, with emphasis on graph analytics, spatial/graph clustering, and dynamic algorithms. He designs algorithms with theoretical guarantees and empirical efficiency, along with high-level programming frameworks to simplify parallel code development. His work spans cache-oblivious, external-memory, and streaming graph algorithms, addressing scalability and performance across diverse computational architectures. Julien's recent publications highlight advancements in parallel graph traversal, dynamic connectivity, and approximation algorithms for centrality metrics. His research also explores domain-specific languages like GraphIt for graph analytics and frameworks such as Julienne for work-efficient bucketing. Scientific Awards: Miller Research Fellow at UC Berkeley He has taught graduate-level courses at MIT, including 6.506 (Algorithm Engineering) and 6.886 (Graph Analytics), emphasizing theoretical foundations, experimental analysis, and open-ended research projects.

April Z. GuView profile

Professor

• Environmental Engineering
• Biotechnology
• Water Quality Monitoring
• +3 more

April Z. Gu is a Professor of Civil and Environmental Engineering at Cornell University, joining the faculty in 2018. She holds a B.S. from Tsinghua University and a Ph.D. from the University of Washington. Previously, she served as a full professor and program leader at Northeastern University's Department of Environmental Sciences. Her research focuses on applying microbial agents to mitigate environmental pollutants, with emphasis on water quality monitoring, bioremediation, and sustainable wastewater treatment. She is a WEF Fellow (2017) and served on the AEESP Board (2017). Her research interests include microbial processes in phosphorus cycling, enhanced biological phosphorus removal, and next-generation biosensors. Current projects address climate change impacts on phosphorus utilization, energy-efficient wastewater treatment, and mechanistic toxicity assessment platforms. She has authored over 100 peer-reviewed articles and led interdisciplinary collaborations on water sustainability. Education: B.S., Environmental Engineering and Science, Tsinghua University (China) Ph.D., Civil and Environmental Engineering (with Microbiology), University of Washington (USA) Awards: WEF Fellow, AEESP Board Member Grants/Projects: Includes studies on side-stream EBPR systems, ENRe biotechnology for manure management, and hurricane-impacted water quality. Her teaching philosophy emphasizes integrative problem-solving and mentors students in water engineering and biotechnology. She leads a research group advancing translational technologies for sustainable water systems.

Terri S. HogueView profile

Professor

• Hydrology
• Water Resources Management
• Land Surface Processes
• +9 more

Terri S. Hogue is a Professor in the Department of Civil and Environmental Engineering at the Colorado School of Mines, where she also serves as Dean of Earth and Society Programs and Director of the Center for a Sustainable WE 2 ST. Her work bridges engineering, hydrology, and environmental sustainability, with a strong focus on semi-arid regions. Education: PhD in Hydrology and Water Resources, University of Arizona, 2003 MS in Hydrology and Water Resources, University of Arizona, 1998 BS in Geology, University of Wisconsin-Eau Claire, 1995 Her research focuses on hydrologic and land surface processes, particularly in the context of water resource management, climate variability, urbanization, and natural hazards. She integrates field observations, remote sensing, and modeling to study catchment responses to wildfires, urban development, and climate change. Her work emphasizes improving hydrologic forecasts and understanding human-environment interactions in water-limited systems. The 15 most recent publications highlight consistent themes in hydrologic modeling, remote sensing applications, evapotranspiration estimation, post-wildfire hydrology, and urban water systems. Her research spans both observational and computational methods, with strong applications to real-world water management challenges in the western U.S. Scientific Awards and Honors: NSF Faculty Early Career Development (CAREER) Award, 2009 NSF Fellowship Trainee Award (multiple years) NASA Earth Observing System (EOS) Graduate Fellowship, 2001–2002 AMS Journal of Hydrometeorology Editor’s Award, 2011 UCLA Professor of the Year (2004, 2007, 2010) Appointment to National Academy of Sciences Board on Atmospheric Sciences and Climate, 2013 AMS Science and Policy Colloquium Fellowship, 2002 Multitude of scholarships and fellowships from NASA, NSF, and professional societies Dr. Hogue has been actively involved in advising and research leadership through her Hogue Research Group and the Center for a Sustainable WE 2 ST. She has secured significant grants, including the NSF CAREER Award, and contributes to national science policy through service on the AGU Hydrology Section and the National Academy of Sciences. Her work emphasizes interdisciplinary collaboration, data-driven modeling, and sustainable solutions for water-stressed regions. She leads the Hogue Research Group and the Center for a Sustainable WE 2 ST, both of which focus on integrated water, energy, and environmental systems. These teams bring together engineers, hydrologists, and environmental scientists to tackle pressing sustainability challenges through research, education, and policy engagement.