Find Academic Supervisors

• Biomedical Image Analysis
• Cryogenic Electron Microscopy (Cryo-EM)
• MRI and Ultrasound Image Segmentation
• +5 more

Dr. Hemant Tagare is a Professor at Yale School of Medicine with primary appointments in the Radiology & Biomedical Imaging department and secondary appointments in Biomedical Engineering and Statistics . He is affiliated with multiple research centers, including the Bioimaging Sciences , Center for Brain & Mind Health , and Yale Biomedical Imaging Institute . Research Interests Cryogenic Electron Microscopy (Cryo-EM) for protein structure reconstruction MRI/ultrasound image segmentation using machine learning Non-rigid registration and shape theory Mathematical foundations of image analysis Articles Trends : Recent publications focus on cryo-EM particle picking consensus algorithms, T2-weighted MRI standardization, and IVIM perfusion measurement in peripheral artery disease. Earlier works emphasize segmentation techniques, non-rigid registration, and phantom validation. Scientific Awards Best Poster Award (2003) for ultrasound segmentation algorithm Francois Erbsmann Award (1993) for medical image processing National Science Talent Search Scholarship (1977) from India

Paweł TysiącView profile

Assistant Professor

• Coastal Engineering
• Remote Sensing
• Geodesy
• +3 more

Dr. Paweł Tysiąc serves as an Assistant Professor in the Department of Geodesy at the Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, where he conducts research at the intersection of geospatial technologies and environmental engineering. His work focuses on developing innovative measurement methodologies for coastal and structural applications. His primary research interests include coastal engineering, particularly low-energy coast dynamics and beach nourishment; remote sensing using UAV-LiDAR and multispectral systems for environmental monitoring; and applications of 3D printing in civil engineering with fractal-based designs. He specializes in leveraging low-cost LiDAR solutions for deformation analysis, sediment monitoring, and heritage conservation, demonstrating strong interdisciplinary collaboration across environmental science and geodesy. Recent publications reveal a consistent trend toward practical applications of emerging measurement technologies, with significant emphasis on sustainable coastal management and advanced construction techniques. His work bridges theoretical geodesy with real-world environmental challenges, particularly in the Baltic region.

Kimin LeeView profile

Assistant Professor

• Safe and capable decision-making agents
• Physical AI
• Reinforcement Learning from Human Feedback
• +9 more

Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.

Andreas BaasView profile

Professor

• Aeolian Geomorphology
• Sediment Transport
• Turbulence Physics
• +5 more

Professor Andreas Baas is a leading academic in aeolian geomorphology, holding a Professorship at King's College London's Department of Geography within the School of Global Affairs. His research focuses on dune dynamics on Earth and Mars, aeolian sand transport, and climate change impacts. He earned BSc/MSc from the University of Amsterdam and a PhD from the University of Southern California, supported by an NSF Research Award. Previously, he was an Adjunct Professor at California State University San Bernardino. Research interests include desert dune hazards, microplastics transport, and Martian dune bedforms. He has published 40+ peer-reviewed articles, 10+ book chapters, and his work has been cited over 3,500 times. His grants include funding from the Leverhulme Trust, Nuffield Foundation, and UK NERC. He supervises PhD students on topics like barchan swarms and Mars dune dynamics. His lab work includes developing tools like PyShoreVolume for shoreline change analysis. He is an associate editor for Earth Surface Dynamics and a member of the NERC Peer-Review College.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +6 more

Moncef Gabbouj is a Professor of Signal Processing at the Department of Computing Sciences, Tampere University, Finland. He holds a PhD from Purdue University and has held academic positions including Academy of Finland Professor (2011–2015) and Head of the Department of Signal Processing (2002–2007). His research focuses on artificial intelligence, machine learning, multimedia signal processing, and nonlinear signal/image processing. He has authored over 800 papers and supervised 64 doctoral and 72 master’s theses, earning accolades such as IEEE Fellow, Finnish Cultural Foundation Award, and TUT Foundation Grand Award. Education: BS (Electrical Engineering, Oklahoma State University, 1985), MS and PhD (Electrical Engineering, Purdue University, 1986–1989). Visiting roles include Hong Kong University of Science and Technology and University of Southern California. Research interests include Big Data analytics, multimedia content analysis, pattern recognition, and video coding. He leads the Artificial Intelligence Research Task Force of the Research Alliance on Autonomous Systems (RAAS) and directs the NSF IUCRC Center for Visual and Decision Informatics (CVDI). Awards highlight contributions to signal processing and AI, including IEEE Fourier Award Committee membership and leadership roles in EURASIP and IEEE. Grants and projects span EU Horizon programs, NSF, and industry collaborations.

Gaurav AryaView profile

Professor

• Viral DNA Packaging
• Polymer-Nanoparticle Composites
• Virus Assembly
• +7 more

Gaurav Arya is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, with additional appointments in the Department of Chemistry and Biomedical Engineering. His research laboratory employs physics-based computational tools to investigate biological and soft-material systems at the molecular scale. Ph.D. from University of Notre Dame (2003) B.Tech. from Indian Institute of Technology Delhi (1998) Research focuses on: Molecular modeling and simulations Statistical mechanics DNA nanotechnology Viral DNA packaging Chromatin biophysics Polymer-nanoparticle composites Recent research trends emphasize AI-driven materials discovery and programmable DNA nanostructures. Key grants include: NSF DMREF: Architecting DNA nanodevices (2023-2027) DOE Mesoscale Self-Assembly (2020-2027) UC San Diego Ligand-Nanocrystal Interlayer Studies (2024-2027) Scientific contributions: Featured in 2025 Nature Communications on DNA superstructures 2022 Science Advances on DNA nanodevice reconfiguration 2021 PNAS on viral DNA packaging motors

• Power Electronics
• Semiconductor Device Modeling
• Electromagnetic Modeling
• +2 more

Dr. Ivana Kovacevic is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. Her research focuses on power electronics, semiconductor device modeling, and electromagnetic analysis of wide bandgap devices. ETH Zürich, Department of Information Technology and Electrical Engineering Contact: kovacevic@aps.ee.ethz.ch Her research explores SiC power MOSFETs, emphasizing their dynamic performance, reliability, and optimization through advanced modeling techniques like the Partial Element Equivalent Circuit (PEEC) method. She investigates parasitic extraction, thermal behavior, and stability issues in power modules, contributing to design improvements for high-efficiency systems. Her publications highlight trends in electromagnetic modeling, device-circuit interactions, and reliability analysis under extreme conditions. Key subfields include gate resistance dynamics, frequency-dependent capacitances, and multi-chip module design. Current projects involve virtual prototyping for power electronics and mission profile-based optimization of wearable power systems.

Luke OlsonView profile

Professor

• Numerical Analysis
• High-Performance Computing
• Scientific Computing
• +4 more

Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).

Solveig BruvollView profile

Associate Professor

• Autonomous Systems
• Cybersecurity
• Military Technology
• +4 more

Solveig Bruvoll is an Associate Professor at the University of Oslo (15% position) and holds a 100% research position at the Norwegian Defence Research Establishment (FFI). She specializes in Autonomous Systems and Cybersecurity , focusing on security in military operations and software resilience. Her work bridges engineering, mathematics, and defense applications. Research interests include autonomous vehicle security, defense simulation, and mathematical modeling. She teaches the course TEK5510 and has published extensively in conferences like IEEE CNS and journals such as Journal of Defense Modeling and Simulation . Her FFI affiliation emphasizes practical military technology and operational analysis. Publications highlight contributions to path planning algorithms, command agent modeling, and cybersecurity frameworks for autonomous systems. She collaborates on NATO projects, as seen in her work on M&S support for operational tasks. No awards are explicitly listed, but her research impacts critical defense and civilian infrastructure security domains.

Boris LandaView profile

Assistant Professor

• Statistical Signal Processing
• Geometric Data Analysis
• Manifold Learning
• +5 more

Boris Landa is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on statistical signal processing and geometric data analysis, developing theoretical and computational tools for analyzing large, complex datasets. He holds a Ph.D. and M.S. from Tel Aviv University, Israel, and a B.S. from the Technion - Israel Institute of Technology. His work bridges computational methods with applications in molecular biology, cryo-electron microscopy, and high-dimensional data analysis. Notable contributions include robust inference of manifold geometry, doubly stochastic scaling techniques, and multi-reference factor analysis for alignment problems. Recent research explores optimal transport metrics for single-cell data and noise stabilization in signal recovery. Landa's publications span prestigious journals such as SIAM Journal on Mathematics of Data Science and Information and Inference. His methodologies address challenges in manifold learning, graph Laplacian normalization, and biological dataset integration. Active areas include developing adaptive algorithms for low-rank signal detection and robust statistical techniques for heterogeneous data. His educational background includes advanced studies in applied mathematics and engineering, with a strong emphasis on interdisciplinary applications. Ongoing projects involve geometric approaches to omics data analysis and scalable solutions for large scientific imaging datasets.

Per BerglundView profile

Professor

• Quantum Mechanics
• String Theory
• Theoretical Physics
• +3 more

Per Berglund is Professor and Chair of the Department of Physics & Astronomy at the University of New Hampshire's College of Engineering and Physical Sciences. He holds a Ph.D. in Physics from the University of Texas at Austin and a B.S. from Lund University. Berglund's research focuses on theoretical physics, particularly string theory, quantum gravity, and Calabi-Yau manifolds. His work bridges mathematics and physics to explore fundamental questions about quantum mechanics and spacetime structure. Recent publications examine precision predictions in string phenomenology and geometric approaches to quantum gravity. His publications demonstrate sustained investigation into string theory applications across cosmology, quantum mechanics, and differential geometry. Recent work shows increasing focus on computational approaches to Calabi-Yau metrics and curvature properties.

Ibrahima BahView profile

Associate Professor

• Theoretical High-Energy Physics
• Cosmology
• String Theory
• +5 more

Ibrahima Bah is an Associate Professor in the Department of Physics & Astronomy at Johns Hopkins University (JHU), affiliated with the Krieger School of Arts and Sciences. His research focuses on theoretical high-energy physics, cosmology, and string theory, particularly exploring holography and the interplay between quantum field theories, gravity, and black holes. He joined JHU in 2017 after completing a PhD at the University of Michigan (2012), followed by postdoctoral positions at the University of Southern California, the Institut de Physique Théorique in France, and the University of California, San Diego. Education: PhD in Physics & Astronomy, University of Michigan, Ann Arbor (2012) Bachelor's/Master's degrees (not explicitly stated in text) His research interests include supergravity, non-invertible symmetries, brane dynamics, and the geometric resolution of black hole singularities. He actively investigates quantum gravity via holographic duals of superconformal field theories (SCFTs) and the role of black holes in fundamental physics. Recent articles highlight work on higher condensation defects, geometric resolutions of Schwarzschild horizons, and non-BPS bubbling geometries. His contributions bridge string theory and gravitational phenomena, with implications for understanding quantum entanglement and spacetime structure. Bah has no listed advising grants or scientific awards in the provided texts. His work often involves collaborations on AdS/CFT correspondence and M5-brane configurations, contributing to the broader field of physical mathematics.

CHENG Shih-FenView profile

Associate Professor

• Artificial Intelligence
• Data Science
• Decision Making & Optimization
• +5 more

CHENG Shih-Fen is an Associate Professor of Computer Science at Singapore Management University (SMU) and a Principal Research Scientist at Amazon. He holds a PhD in Industrial and Operations Engineering from the University of Michigan and a BSE in Mechanical Engineering from National Taiwan University. His research focuses on modeling and optimization of complex systems in urban computing, decision-making, and transportation, with notable contributions to taxi fleet management, ride-hailing systems, and sustainable logistics. Research interests include Artificial Intelligence , Decision Optimization , Machine Learning , and Urban Sustainability . Notable achievements include prestigious awards from CIKM, AAMAS, and INFORMS. He has advised students such as Qian Shao and Pang Jin Tan, who received SMU Presidential Doctoral Fellowships. Key contributions include the Driver Guidance System (DGS) for taxis and patented taxi demand prediction models. Publications span top venues like IJCAI, AAAI, and Transportation Science. He is a Senior Editor of Electronic Commerce Research and Applications and actively contributes to professional communities like INFORMS and AAAI.

George DasoulasView profile

Researcher

• Graph Machine Learning
• Structured Data Analysis
• Biomedical Informatics
• +3 more

George Dasoulas is a Postdoctoral Researcher at Harvard University's Department of Biomedical Informatics, affiliated with the Zitnik Lab. He holds a PhD in Computer Science from École polytechnique in Paris, France, and previously worked at Huawei Technologies France. His research focuses on graph machine learning, particularly in biomedical applications and telecommunications, with contributions to graph neural networks (GNNs), attention mechanisms, and topological deep learning. Education: Ph.D., Computer Science (DaSciM group, LIX, École polytechnique); Diploma in Electrical & Computer Engineering (National Technical University of Athens). His work includes developing Lipschitz-normalized attention layers, parametrized graph shift operators, and modularity-aware graph autoencoders. He has been recognized with the 2022 Wojcicki and Troper Fellowship from Harvard's Data Science Initiative. Key Research Themes: Graph Representation Learning, Topological Neural Networks, Equivariant Learning, Multimodal Learning Applications: Biomedical Informatics, Telecommunications, Sustainable AI His articles emphasize scalable GNN architectures, graph-based unlearning strategies, and multimodal protein phenotyping. He has contributed to open-source projects like LipschitzNorm and PGSO, and actively publishes in top conferences (ICML, ICLR, NeurIPS).

Hanan SametView profile

Professor

• Spatial Data Structures
• Geographic Information Systems (GIS)
• Computer Vision
• +3 more

Hanan Samet is a Distinguished University Professor in the Computer Science Department at the University of Maryland, College Park. He holds affiliations with the Center for Automation Research and the Institute for Advanced Computer Studies (UMIACS). His academic journey includes a PhD from Stanford University (1975) in Computer Science, following degrees in Engineering (UCLA) and Operations Research/Computer Science (Stanford). Affiliations: University of Maryland, College Park (since 1975) Roles: Professor, Founding Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems, Founder of ACM SIGSPATIAL Samet's research focuses on spatial data structures, spatial databases, GIS, computer vision, and information retrieval. His seminal work includes the Foundations of Multidimensional and Metric Data Structures , an award-winning book addressing spatial indexing and query optimization. He pioneered frameworks like NewsStand for map-based news exploration and Coronaviz for pandemic visualization. Key contributions span spatial synonyms for approximate search, SAND spatial browser for digital government, and trajectory analysis systems for aviation safety and urban mobility. His work bridges theory and practice, influencing databases, graphics, and geographic systems. Education: B.S. Engineering, UCLA M.S. Operations Research, Stanford M.S./Ph.D. Computer Science, Stanford Samet has advised numerous students and led NSF-funded projects on spatio-textual data, similarity search, and spreadsheet analysis. His honors include the ACM Paris Kanellakis Award (2011), IEEE Wallace McDowell Award (2014), and UCGIS Research Award (2009). His labs and teams focus on spatial algorithms, visualization, and GIS applications. Notable projects include VASCO (spatial index demo), MARCO (image databases), and CHOLERA (disease tracking).