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Sebastian KurtekView profile

Professor

• Shape Analysis
• Functional Data Analysis
• Image Analysis
• +4 more

Sebastian Kurtek is a Professor of Statistics at The Ohio State University, affiliated with the College of Arts and Sciences. He holds roles in the Interdisciplinary PhD Program in Biostatistics and Translational Data Analytics. His research focuses on statistical shape analysis, functional data analysis, and Bayesian statistics, addressing challenges in medical imaging, computer vision, and biometrics. Education: PhD in Biostatistics, Florida State University (2012) M.S. in Biostatistics, Florida State University (2009) B.S. in Mathematics, Tulane University (2007) Research Interests: Development of statistical models for complex functional data, including shapes and images Applications in medical imaging, biology, and computer vision Computational tools for Riemannian manifold analysis Publications & Trends: Recent work emphasizes geometric frameworks for analyzing eye tracking heat maps, radiogenomics in tumors, and Bayesian approaches for functional data registration. His articles highlight interdisciplinary applications, combining statistics with fields like computer vision and medical imaging. Awards: Best Paper Awards in computer vision, pattern recognition, and medical imaging NSF funding (multiple grants since 2016) IMS and AMS Travel Awards Grants & Collaborations: NSF DMS-1613054 (2016-2020) NIH R37-CA214955 (2017-2024) Collaborations on endometrial tissue modeling and fossil classification Labs/Teams: Involved in CBMS Conference on Elastic Functional and Shape Data Analysis (EFSDA), advancing methodologies for shape and functional data analysis.

Risi KondorView profile

Associate Professor

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +6 more

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

Zhizhong HanView profile

Assistant Professor

• 3D Computer Vision
• Digital Geometry Processing
• Artificial Intelligence
• +1 more

Zhizhong Han serves as an Assistant Professor in the Department of Computer Science at Wayne State University, specializing in 3D computer vision and artificial intelligence. Prior to his current position, he completed a three-year postdoctoral fellowship in the Department of Computer Science at the University of Maryland, College Park. His academic credentials include: Bachelor of Engineering (B.E.) in Pattern Recognition and Intelligent Systems from Northwestern Polytechnical University (2009) Master of Engineering (M.E.) in Pattern Recognition and Intelligent Systems from Northwestern Polytechnical University (2012) Doctor of Philosophy (Ph.D.) in Pattern Recognition and Intelligent Systems from Northwestern Polytechnical University (2017) Dr. Han's research centers on 3D Computer Vision , Digital Geometry Processing , and 3D Deep Learning , with emphasis on interpreting 3D structures through point clouds, meshes, and voxel grids. His work tackles fundamental challenges in shape recognition, segmentation, and reconstruction, often leveraging unsupervised learning frameworks to model complex 3D geometries without extensive labeled data. His publication record reveals a concentrated research trajectory in geometric deep learning, evidenced by 10+ papers at premier venues including CVPR, ICCV, and NeurIPS. Key innovations include differentiable rendering techniques, multi-view representation learning, and novel architectures for 3D shape completion—demonstrating consistent contributions to advancing 3D scene understanding capabilities. No scientific awards were documented in the source material. Teaching responsibilities include graduate and undergraduate courses such as Introduction to Deep Learning (CSC4760) and specialized topics in 3D shape analysis (CSC6991), with upcoming instruction in Computer Vision (CSC4860) for Fall 2025. No student advising details or grant funding information were provided. Research activities appear centered on algorithmic development for 3D vision tasks, though specific laboratory affiliations or collaborative teams were not mentioned in the available text.

Benjamin RaichelView profile

Associate Professor

• Computational Geometry
• Algorithms
• Theoretical Computer Science
• +3 more

Benjamin Raichel is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on computational geometry and geometric approximation algorithms, with contributions to areas such as Voronoi diagrams, Fréchet distance, clustering, and algorithmic efficiency. Dr. Raichel holds a PhD from the University of Illinois Urbana-Champaign under the supervision of Sariel Har-Peled. He currently teaches Computational Geometry (CS 6319) and has advised multiple PhD students, including Md. Billal Hossain and Jonathan James Perry. His work is supported by NSF grants such as 'Shape Matching in a Messy World Using Fréchet Distance' and 'Metric Violation Distance: Hardness and Approximation.' He is a member of the Algorithms and Theory Group at UT Dallas.

Chengzhou TangView profile

Assistant Professor

• 3D Computer Vision
• Deep Learning
• Robotics
• +3 more

Chengzhou Tang is an Assistant Professor in the Department of Computer Science at the University of Manitoba. His research focuses on 3D computer vision, deep learning, robotics, and embodied artificial intelligence. He leads the Spatial Intelligent System (SPINS) Lab, exploring topics such as 3D object reconstruction, camera localization, and video processing techniques. His work spans applications in robotics, virtual reality, and computational imaging. Dr. Tang's research interests include developing novel algorithms for multi-view diffusion models, deep recurrent optimizers, and subspace methods. He has contributed to advancements in video depth estimation, point cloud processing, and stabilization of 360-degree videos. His recent work emphasizes generative models and embodied AI, addressing challenges in sparse-view 3D reconstruction and scene-agnostic camera localization. His publications reflect a trend toward integrating deep learning with traditional computer vision problems, such as structure-from-motion and SLAM (Simultaneous Localization and Mapping). The SPINS Lab collaborates on projects involving global bundle adjustment networks and initialization-robust monocular SLAM systems. While no awards or grants are explicitly listed, his active publication record indicates sustained research engagement in these areas. Dr. Tang’s academic profile highlights his contributions to foundational techniques in computer vision and their practical applications in robotics and AI systems. His work bridges theoretical advancements with real-world challenges in imaging and spatial intelligence.

Aakriti UpadhyayView profile

Research Fellow

• Robotics
• Motion Planning
• Topological Data Analysis
• +3 more

Aakriti Upadhyay is a Research Fellow at the Colorado School of Mines (CSM), working in the Dynamic Automata Lab (DyALab) under Dr. Neil T. Dantam. She holds a Ph.D. in Computer Science from the University at Albany, SUNY, where she was part of the Robotics Algorithms and Computable Systems (RACS) lab under Dr. Chinwe P. Ekenna. Her research integrates Computational Geometry, Applied Mathematics, Machine Learning, and Topological Data Analysis (TDA) to advance motion planning for robotics and computational biology applications. Her work focuses on optimizing robot motion paths using topological methods, with extensions to identifying protein interaction sites. Key areas include robust path planning, fault tolerance in robotic systems, and topological modeling of biomolecular surfaces. Her research trends emphasize interdisciplinary applications of TDA, bridging robotics and computational biology. Collaborations include developing tools for Intrinsically Disordered Proteins (IDPs) analysis and enhancing path planning algorithms through geometric and topological insights. Aakriti has contributed to academic initiatives like the Student Activities Committee and advocated for women in engineering during the pandemic. Her work reflects a commitment to advancing both technical and community-oriented academic goals.

Ioannis SgouralisView profile

Assistant Professor

• Data Science
• Applied Mathematics
• Mathematical Biology
• +7 more

Ioannis Sgouralis is an Assistant Professor in the Department of Mathematics at The University of Tennessee, Knoxville. He holds a Ph.D. from Duke University and specializes in Data Science and Applied Mathematics with a focus on interdisciplinary research in Physics, Chemistry, Biology, and Medicine. His research group integrates computational methods with experimental data to address challenges in biophysics, biochemistry, and biomedicine. His educational background includes a Ph.D. in a relevant field (specific details not provided). His research interests span Bayesian nonparametrics, computational statistics, mathematical biology, and multiscale modeling. He leads projects on single-molecule biophysics, image analysis, and cardiovascular system modeling. Key research areas include: Single molecule experiments and analysis Super-resolution microscopy and multi-particle tracking Biomedical systems modeling (cardiovascular/urinary) Statistical methods for noisy experimental data Ioannis actively recruits graduate/undergraduate students for projects in Data Science, Mathematical Biology, and Applied Mathematics. His group uses advanced computing techniques such as Hamiltonian Monte Carlo and topological data analysis. Labs/Teams: Sgouralis Research Group focuses on cross-disciplinary computational methods for biological systems. Their work involves collaborations across departments and institutions.

Farida CherietView profile

Professor

• Biomedical technology
• Artificial intelligence
• Medical imaging
• +4 more

Dr. Farida Cheriet is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal . She serves as Director of the 4D Imaging and Vision Laboratory (LIV4D) and holds membership in the Institute of Biomedical Engineering and the Institute for Data Valorization (IVADO) . Her research spans medical imaging , biomedical technology , artificial vision , and 3D movement estimation . Ph.D. (Montreal), DEA (France), Ing. (Algeria) Her work focuses on interpretable AI models for retinal pathology diagnosis, 3D anatomical reconstruction from medical imaging, and automated segmentation of vascular and spinal structures. She leads innovations in deep learning applications for diabetic retinopathy screening, coronary artery analysis , and spine surgery planning . Recent publications highlight her leadership in computer vision for biomedical applications, with 15 featured articles covering ICU readmission prediction, retinal vessel analysis, dental crown design, and spinal ultrasound processing. She received CFI funding (2021) for cutting-edge facilities and collaborates on pan-Canadian ophthalmic image databases (2018). Dr. Cheriet has supervised 28 Ph.D. students and 46 Master's students in biomedical computing, including projects on scoliosis analysis, retinal imaging, and cardiac MRI. Her teaching includes digital communications and computer graphics courses.

Tselil SchrammView profile

Assistant Professor

• Algorithms
• Optimization
• Computational Complexity
• +3 more

Tselil Schramm is an Assistant Professor at Stanford University in the Department of Statistics, with courtesy appointments in Computer Science and Mathematics. Her work bridges theoretical computer science and statistics, focusing on algorithm design and information-computation tradeoffs. Research interests include: Theoretical power of sum-of-squares semidefinite programs Fast spectral methods for high-dimensional estimation Characterizing computational complexity in statistical problems Interplay between different computation models Teaching: She has taught courses like Intro to Statistics , Machine Learning Theory , and The Sum-of-Squares Algorithmic Paradigm in Statistics since joining Stanford in 2022. Selected publications highlight: Robust algorithmic paradigms for high-dimensional data, computational barriers in low-degree polynomial estimation, and theoretical foundations of statistical query models. Scientific honors: NSF CAREER Award (2020s) COLT 2021 Best Paper Runner-up (Statistical query algorithms paper)

• AI & Neuroscience
• Biomedical Engineering
• Neurotechnology
• +7 more

Aldo Faisal is a Professor of AI & Neuroscience at Imperial College London's Department of Bioengineering and Faculty of Engineering. He is also the Founding Director of the UKRI Centre for Doctoral Training in AI for Healthcare (£20M), leading the Faisal Lab. His roles include associate investigator at the MRC London Institute of Medical Sciences and affiliation with the Gatsby Computational Neuroscience Unit (UCL). His research bridges AI, neuroscience, and healthcare, focusing on neurotechnology, human behavior analysis, and clinical applications. Education: Studied Computer Science and Physics in Germany, followed by Biology at the University of Cambridge (Emmanuel College). Earned a PhD in Neuroscience under Simon Laughlin, and postdoctoral work with Daniel Wolpert on sensorimotor control. Prior professional experience includes roles at McKinsey & Co. and Credit Suisse. Research Interests: Combines cross-disciplinary approaches to study brain-behavior relationships, developing technologies for neurological disorders and amputees. Key labs include the Brain & Behaviour Lab (neurotechnology) and Behaviour Analytics Lab (behavioral data science). Techniques include machine learning, robotics, and neuroimaging (EEG/fNIRS). Publications: Over 100+ peer-reviewed articles, spanning AI in healthcare, motor learning, and neurotechnology. Recent work emphasizes safety of AI in critical care, wearable biomarkers for neuromuscular diseases, and human-AI collaboration. Awards: UKRI Turing AI Fellowship, Toyota Mobility Prize ($50k), Rosetree Interdisciplinary Award (£300k), and fellowships from German National Merit Foundation and Böhringer-Ingelheim Foundation. Elected to Global Futures Council (WEF, 2016). Grants & Labs: Leads £20M CDT in AI for Healthcare, £2M Turing Fellowship project, and manages labs at Imperial and University of Bayreuth (Germany). Collaborates with institutions like CRUK Convergence Science Centre and Data Science Institute.

Gennady SamorodnitskyView profile

Professor

• Stochastic Modeling
• Heavy Tailed Distributions
• Long-Range Dependence
• +6 more

Gennady Samorodnitsky is a Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. He holds a B.S. from the Moscow Steel and Alloys Institute (1978), M.S. from Technion – Israel Institute of Technology (1983), and a D.Sc. from Technion (1986). He joined Cornell in 1988 and has held visiting positions at the University of North Carolina at Chapel Hill and Boston University. His research focuses on stochastic processes, particularly heavy-tailed distributions, long-range dependence, and extreme value theory, with applications in finance, teletraffic, and climate modeling. Education: B.S., Moscow Steel and Alloys Institute, USSR, 1978 M.S., Technion – Israel Institute of Technology, 1983 D.Sc., Technion – Israel Institute of Technology, 1986 Samorodnitsky’s research interests span stochastic modeling, including heavy-tailed processes, self-similar processes, and extreme value analysis. He examines the behavior of financial and telecommunication systems under long memory and non-Gaussian conditions. Key areas include the statistical analysis of extremes in climate data and the theoretical foundations of stable and infinitely divisible processes. His work bridges probability theory with applications in risk management, network traffic analysis, and climate science. His publications explore topics such as high-level excursion sets in random fields, tail inference, and the interplay between ergodic theory and stochastic processes. He has contributed to books like Stochastic Processes and Long Range Dependence and authored numerous technical reports on topics like ruin probabilities and multivariate extremes. Samorodnitsky teaches advanced courses, including ORIE 7590: Martingales in Discrete and Continuous Time , and maintains an active role in academic conferences and collaborations. His research group investigates cutting-edge problems in high-dimensional extremes, privacy-aware learning, and topological data analysis.

Min SunView profile

Professor

• Educational Policy
• Teacher Education & Research
• AI/ML in Education
• +8 more

Dr. Min Sun is a Professor in the Department of Educational Policy, Organization and Leadership at the University of Washington's College of Education. Her research focuses on teacher learning, AI/ML integration in education, and policy-driven educational reforms. She leads interdisciplinary teams developing AI tools like the NSF-funded Colleague lesson planning platform and the IES-funded AmplifyGAIN Center. Her work addresses inequities in education through policy analysis and partnerships with K-12 schools and EdTech industries. Dr. Sun holds a Ph.D. in Educational Policy and Measurement from Michigan State University. She teaches courses such as EDLPS 302: Intro to Educational Policy and EDLPS 564: Economics of Education. Her research spans four key areas: AI/ML method development, AI-powered educational tools, data science training programs, and policy research with multi-sector collaborations. Notable grants include a $10 million IES grant for the AmplifyGAIN Center and a $1.5 million NSF grant for AI-driven math lesson planning. Her policy work emphasizes equitable education access and data-driven solutions. She directs the Education Policy Analytics Lab (EPAL) and collaborates with stakeholders to translate research into actionable strategies.

Ding ZhouView profile

Assistant Professor

• Industrial Design
• STEM Education
• Human-Computer Interaction
• +3 more

Dr. Ding Zhou is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech). He holds a PhD in Industrial Design from Queensland University of Technology (QUT), Australia (commenced 2018), and prior academic roles include Associate Professor at Nanjing University of the Arts (2009–2018). His research focuses on cross-disciplinary innovation at the intersection of Industrial Design and STEM Education, emphasizing human-centric technology integration. He has conducted visiting research at the University of Virginia (2012) and Victoria University of Wellington (2014–2015). Education: PhD in Industrial Design, Queensland University of Technology (QUT), Australia (ongoing since 2018) M.A. and B.A. in Industrial Design (exact institutions unspecified) Research Interests: Dr. Zhou’s work bridges design science and education, prioritizing how technology enhances human experiences. Key areas include 3D printing applications in STEM education, exoskeleton robotics, computational design aesthetics, and user-centric design processes. He advocates for pedagogical frameworks that cultivate students’ transdisciplinary problem-solving skills through integrated STEM tasks. Articles Trends (2022–2025): Recent publications highlight advancements in 3D printing for education and manufacturing, human-robot interaction, and smart classroom technologies. Notable themes include scalable computer vision systems for posture detection, topology optimization in additive manufacturing, and BIM-enabled construction processes. Labs & Teams: He leads the Design Science and Educational Innovation Lab (DSEIL), focusing on translating theoretical research into practical educational tools and technologies.

Didong LiView profile

Assistant Professor

• Biostatistics
• Machine Learning
• Geometric Data Analysis
• +7 more

Dr. Didong Li is an Assistant Professor in the Department of Biostatistics at the Gillings School of Global Public Health , University of North Carolina at Chapel Hill. His research focuses on statistical methods for complex data, including manifold learning, nonparametric Bayesian inference, and spatial statistics, with applications in healthcare, genomics, and environmental health. He holds a PhD in Mathematics from Duke University (2020), and degrees from Beijing Institute of Technology (BS 2012, MS 2015). His research interests include dimension reduction, Gaussian processes, geometric data analysis, and network analysis. He has developed novel methods for analyzing electronic health records, single-cell RNA sequencing data, and spatial omics datasets. Notable contributions include the spherelet manifold approximation framework and the STimage-1K4M dataset. Dr. Li has received the Inaugural IMS Lawrence D. Brown PhD Student Award (2019) . He serves on the editorial board of the Journal of Machine Learning Research and organized sessions at JSM 2021-2022 on geometric Bayesian methods. His lab supports interdisciplinary collaborations, with ongoing projects in spatial transcriptomics, healthcare access analysis, and AI-driven medical informatics. Advising highlights include guiding students through successful PhD defenses (e.g., Dr. Sam Hawke, Dr. Jiawen Chen) and postdoctoral placements. His team secured a NVIDIA Academic Grant (2024) for multi-modal AI in spatial transcriptomics. The Li Lab maintains active partnerships with institutions worldwide, advancing both statistical theory and real-world applications.

James B D JoshiView profile

Professor

• Cybersecurity
• Privacy-Preserving Technologies
• Access Control
• +5 more

James B D Joshi is a Professor in the Department of Informatics and Networked Systems (DINS) at the University of Pittsburgh’s School of Computing and Information (SCI). He joined Pitt in 2003 after earning his PhD from Purdue University. From 2019 to 2023, he served as a Program Director for the NSF’s Secure and Trustworthy Cyberspace (SaTC) program. Currently, he is an intermittent Expert in NSF’s TIP Directorate and led the NSF PDaSP program. His research focuses on cybersecurity and privacy, including access control, AI/ML security, cloud/edge security, and privacy-preserving techniques. Education: PhD, Computer Engineering, Purdue University (2003) MS, Computer Science, Purdue University (1998) BE, Computer Science & Engineering, Motilal Nehru NIT, India (1993) Research Interests: Privacy, security, trust, attribute-based encryption, cloud security, insider threat detection, and privacy-preserving AI/ML. His work emphasizes trustworthy systems and secure distributed computing environments. Recent Contributions: His articles span privacy-preserving federated learning, insider threat mitigation, and secure edge computing. Recent projects include PPFL-RDSN (2025), TAPFed (2024), and blockchain-based transparency frameworks (2022). Awards & Honors: NSF Director’s Award (2023), IEEE Fellow (2023), ACM Distinguished Member (2017), NSF CAREER Award (2006), and SIRI Research Leadership Award (2018). Service & Leadership: Founding director of LERSAIS lab, co-chair of IEEE conferences (TPS, CIC, CogMI), and former Editor-in-Chief of IEEE Transactions on Services Computing. Active in NSF and NITRD policy initiatives, including privacy and digital assets R&D strategies.