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Chia-Yi ChiuView profile

Associate Professor

• Educational Measurement
• Nonparametric Cognitive Diagnosis
• STEM Assessments
• +3 more

Chia-Yi Chiu is an Associate Professor in Applied Statistics at Teachers College, Columbia University. She specializes in educational measurement and psychometrics, focusing on nonparametric cognitive diagnosis and small data applications in STEM assessments. Her work emphasizes real-time classroom monitoring and innovative algorithms for diagnostic testing. PhD: Quantitative Methodology, Measurement, and Evaluation (University of Illinois at Urbana-Champaign) MS: Statistics (University of Illinois at Urbana-Champaign) MA: Mathematics in Teaching (University of Northern Colorado) BS: Mathematics (National Taiwan Normal University) Her research advances classroom-level assessments through methods like the restricted DINA model and nonparametric CD-CAT, addressing challenges in small-sample settings. She collaborates widely, with notable contributions to Q-matrix theory and cognitive diagnosis algorithms. Recent publications explore method selections in cognitive diagnosis and Q-matrix optimization. Awards: John P. Yackel Professorship (2022-2024), Chancellor’s Scholar Award (2017-2022), and NCME Dissertation Award (2010). Dr. Chiu advises students on topics like diagnostic testing algorithms and adaptive assessment systems. Her grants include innovations in education technology and small-data psychometric methods.

Keenan CraneView profile

Associate Professor

• Computer Science
• Mathematics
• Scientific Computing
• +7 more

Keenan Crane is the Michael B. Donohue Associate Professor of Computer Science and Robotics at Carnegie Mellon University , with membership in the Center for Nonlinear Analysis and mentorship in the Geometry Collective . His research bridges differential geometry and computer science to develop fundamental algorithms for geometric data processing. Education : BS from University of Illinois at Urbana-Champaign, PhD from Caltech Fellowships : Google PhD Fellow, NSF Mathematical Sciences Postdoctoral Fellow Research focuses on Discrete Differential Geometry , addressing PDE solutions, mesh processing, and geometric modeling through methods like: Walk on Spheres for PDEs Intrinsic Triangulations for robust geometry Repulsive Energy formulations for collision avoidance Recent publications span 2025–2021 , emphasizing grid-free algorithms , anisotropic mesh generation , and differentiable systems . Scientific accolades include Packard Fellowship and NSF CAREER Award . Students include Nicole Feng , Olga Gutan , and Zoë Marschner . During his 2024 sabbatical at Roblox , he does not accept new researchers. Key software contributions include Penrose (math diagram generation) and I♥Mesh (domain-specific language for mesh algorithms).

Diptarka ChakrabortyView profile

Assistant Professor

• Theoretical Computer Science
• Algorithms on large data sets
• Sublinear algorithms
• +5 more

Diptarka Chakraborty is an Assistant Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He obtained his PhD from the Indian Institute of Technology Kanpur in 2016 and held postdoctoral positions at Charles University, Prague, and the Weizmann Institute of Science, Israel. His educational background includes: PhD in Computer Science and Engineering, Indian Institute of Technology Kanpur, 2016 Dr. Chakraborty's research lies at the intersection of Theoretical Computer Science and Algorithms , specializing in sublinear algorithms , approximation algorithms , graph algorithms , and data structures for massive datasets. His work addresses computational hardness in edit distance, rank aggregation, and fault-tolerant graph structures, with recent emphasis on algorithmic fairness and streaming applications. Analysis of his 2020-2025 publications reveals dominant trends in fairness-constrained algorithms (e.g., fair rank aggregation) and scalable techniques for massive data (e.g., clustering permutations in streaming models), alongside foundational contributions to equivalence testing, model counting, and reachability under failures. His scientific contributions have been recognized with: Faculty Teaching Excellence Award 2024 Google South & Southeast Asia Research Award 2022 Best Paper Award at FOCS 2018 IBM Research India Outstanding Ph.D. Student Award 2015-16 Dr. Chakraborty actively mentors two PhD students and multiple research fellows while leading three major research projects: massive data handling under edit metrics, fair rank aggregation, and fault-tolerant graph structures. His Google Research Award supports investigations into computational challenges of fairness. He maintains an active research group with international collaborations and regular seminar participation across institutions including IITs, TIFR, and UC campuses.

• Biodiversity Conservation
• Invasive Species Management
• Endangered Species Recovery
• +4 more

Alison Copeland is a Research Postgraduate (PhD) in the Department of Biosciences at Durham University. Her research focuses on Bermuda’s vegetation communities, particularly invasive plant species and their interactions with indigenous flora. Previously, she served as Biodiversity Officer for Bermuda’s Government, leading projects like the IUCN Red Listing of endemic plants and the Governor Laffan’s Fern recovery initiative. She holds an MSc in Geography from Memorial University of Newfoundland (MUN), where her thesis involved seabed habitat mapping in Newman Sound fjord using multibeam sonar and field sampling. Her expertise spans marine and terrestrial conservation, with a focus on endangered species recovery, habitat management, and translating science into policy. Key affiliations include MUN’s Marine Habitat Mapping Group and the Royal Botanic Gardens Kew. Awards include the Durham Doctoral Studentship (Faculty of Science) and a Bermuda Public Service Education Award. Her recent work highlights invasive species dynamics in island ecosystems, biodiversity assessments, and policy frameworks for conservation. She has contributed to over 15 peer-reviewed articles, emphasizing habitat mapping methodologies, invasive species management, and endangered species recovery strategies. Awards: $3000 Garden Club of Bermuda Grant (PhD support) Fellow of MUN’s School of Graduate Studies Grants: Durham Doctoral Studentship (2022–present) Her PhD project investigates the ecological origins and interactions of invasive plants in Bermuda, bridging field research with policy recommendations to enhance biodiversity protection.

Vernet LasradoView profile

Lecturer

• Operations Research
• Manufacturing Systems
• Industrial Engineering
• +3 more

Vernet Lasrado is a Lecturer at the University of Central Florida (UCF), affiliated with the Department of Industrial Engineering and Management Systems (IEMS) within the College of Engineering and Computer Science. His primary research focuses on manufacturing systems optimization, technology transfer, and economic policy analysis related to building codes. He holds a Ph.D. in Industrial Engineering and Management Systems from UCF. Education: PhD in Industrial Engineering and Management Systems, University of Central Florida (2000s). Research interests include operations research applications in facility layout design, conveyor system optimization, semiconductor manufacturing automation, and policy impacts of building regulations. His work bridges theoretical models (e.g., queueing theory, genetic algorithms) with real-world industrial challenges. Recent studies examine university incubator effectiveness and economic consequences of delayed building code implementations. His publications span topics from material handling system design to phase transition simulation methods. Notable contributions address closed-loop conveyor optimization using genetic algorithms and crossover placement strategies in semiconductor fabs. No awards or grants are listed in the provided materials.

• Artificial Intelligence
• Computer Vision
• Digital Agriculture
• +3 more

Ferdous Sohel is a Professor of Information Technology at Murdoch University and inaugural lead of the Agricultural Technologies program. His research spans AI, computer vision, and digital agriculture, with applications in medical imaging and environmental monitoring. He received the Mollie Holman Doctoral Medal and Vice Chancellor's Early Career Research Award. Research Impact: Developed innovative AI models for aquaculture oxygen prediction, 3D object tracking, quantum neural networks, and prohibited item detection. His work advances precision agriculture through hyperspectral classification frameworks and irrigation decision systems. Professional Service: Associate Editor for IEEE Transactions on Multimedia and senior IEEE member. Current projects include adversarial robustness for LiDAR systems and lightweight dormitory security networks.

Amanda ProrokView profile

Professor

• Multi-Agent Systems
• Robotics
• Collective Intelligence
• +6 more

Professor Amanda Prorok leads the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, focusing on multi-agent and multi-robot systems. Her work integrates machine learning, planning, and control to coordinate intelligent agents in shared environments, with applications in transport, environmental monitoring, and search-and-rescue. She is a Fellow of Pembroke College and holds editorial roles at IEEE Robotics and Automation Letters and Autonomous Robots. Education: Ph.D., EPFL (Switzerland); Postdoctoral Research, University of Pennsylvania (USA). Research Interests: The lab pioneers methods like differentiable communication between learning agents and develops decentralized algorithms for navigation, coverage, and coordination. Key themes include neural diversity in collective learning, resilient swarm systems, and environment-aware control. Notable Achievements: ERC Starting Grant, Amazon Research Award, EPSRC New Investigator Award ABB Prize for Best Thesis in Computer Science (EPFL) Teaching: Leads Computing for Collective Intelligence (MPhil/Part III) modules. Lab & Infrastructure: The Prorok Lab operates the Cambridge RoboMaster platform and develops testbeds for connected vehicles and robot swarms. Recent work emphasizes scalable reinforcement learning and graph neural networks for decentralized decision-making.

Ran RazView profile

Professor

• Complexity Theory
• Quantum Computation
• Circuit Complexity
• +2 more

Professor Ran Raz specializes in complexity theory at Princeton University's Department of Computer Science. His research targets lower bounds in computational models, quantum communication, and derandomization. Current investigations include quantum logspace verifiability and memory-sample tradeoffs in learning algorithms. Research pillars: Boolean/arithmetic circuit complexity Quantum computation and communication Probabilistically checkable proofs Publications reflect a focus on quantum-classical hybrid systems and foundational limits of computation. Lab affiliations: Quantum research group at Princeton CS.

Hakan TüreciView profile

Professor

• Quantum optics
• Quantum information
• Superconducting circuits
• +3 more

Hakan Türeci is Professor of Electrical and Computer Engineering at Princeton University, with joint affiliation at the Princeton Materials Institute. A theoretical physicist by training, his research explores quantum optics, quantum information science, and superconducting circuits for quantum computing applications. Education: Ph.D. from Yale University (2003) M.S. in Physics from Bilkent University, Turkey (1996) B.S. in Physics from Bilkent University, Turkey (1994) Research Interests: Focuses on non-equilibrium quantum phenomena in optical and microwave platforms, quantum simulation, quantum error correction, and the development of near-term quantum devices for computation and machine learning. Publication Trends: Recent work emphasizes quantum system modeling, quantum measurement techniques, Josephson junction physics, and applications of reservoir computing in quantum information processing. Academic Leadership: Advises a large group of graduate students and researchers in quantum engineering projects, collaborating across physics and engineering disciplines to advance quantum technologies.

• Machine Learning
• Reinforcement Learning
• Sample-efficient Generalization
• +5 more

Dr. Yangchen Pan is a Departmental Lecturer in Machine Learning at the University of Oxford's Department of Engineering Science. His research focuses on achieving sample-efficient generalization in machine learning, particularly in settings involving distribution shifts (e.g., adversarial learning, domain adaptation) and adaptive capabilities like offline/online reinforcement learning and continual learning. He has contributed to foundational work in reinforcement learning, robustness, and risk-averse optimization. Education and Academic Background: While specific degree details are not explicitly listed, his academic trajectory includes roles at leading institutions such as the University of Alberta (PhD, 2017-2020), where he collaborated with prominent researchers like Martha White and Amir-massoud Farahmand. Additional affiliations include teaching roles at the University of Waterloo, University of Toronto, and Indiana University. Research Interests: Pan's work spans machine learning theory and applications, with emphasis on scalable algorithms for complex decision-making systems. Key areas include adversarial robustness, offline reinforcement learning, and mitigating distribution shifts in real-world deployments. His recent contributions explore risk-aware policy optimization and novel approaches to sample efficiency. Professional Service: Pan serves on the program committees of top conferences like NeurIPS, ICML, and ICLR. He has reviewed for journals including the Journal of Machine Learning Research and Transactions on Machine Learning Research. Teaching: Pan teaches advanced courses in optimization, machine learning, and AI at the University of Oxford, including C25 Optimization and AI/ML with Python. He has also taught at the University of Alberta and Indiana University. Labs/Teams: While no specific lab name is mentioned, his research is closely tied to Oxford's Engineering Science department and collaborative projects with institutions like the ZERO Institute (as seen in event leadership at IMAD2025).

Tianhao WangView profile

Assistant Professor

• Differential Privacy
• Machine Learning Privacy
• Data Security
• +3 more

Tianhao Wang is an Assistant Professor in the Department of Computer Science at the University of Virginia School of Engineering and Applied Science. His work focuses on advancing differential privacy and machine learning privacy, with particular expertise in privacy-preserving technologies for data synthesis, adversarial machine learning, and secure AI systems. His research interests span differential privacy mechanisms, secure data sharing, and mitigating privacy risks in modern AI systems. He explores how to protect sensitive information in machine learning models, synthetic data generation, and network analysis while maintaining utility. Recent work highlights include developing benchmarks for private image synthesis (DPImageBench), safeguarding text data from misuse (ExpShield), and analyzing privacy threats in pre-trained language models. His publications reflect a strong emphasis on both theoretical foundations and practical applications of privacy-preserving techniques. Dr. Wang's contributions address cutting-edge challenges in AI ethics, secure machine learning, and privacy engineering, with implications for healthcare, cybersecurity, and data-driven decision-making systems.

Wenzhuo ZhouView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +16 more

Wenzhuo Zhou is an Assistant Professor in the Department of Statistics at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. His research bridges machine learning theory and practice, focusing on reinforcement learning, deep representation learning, and large language models. He emphasizes developing efficient, reliable AI algorithms to address challenges in healthcare, finance, and robotics. Research Interests: Statistical foundations of learning algorithms Sample efficiency and model generalization Alignment of AI models with human preferences Applications in healthcare (e.g., cancer, diabetes, Alzheimer’s), finance, and robotics Collaborations involve domain experts in medical research, semantic search, and financial systems. His team adapts existing methods and develops new pipelines for real-world problem-solving. No scientific awards or grants are explicitly listed in the provided text. Advising details are mentioned but without specific student names. The Center for Statistical Consulting is part of his professional environment.

James BellinghamView profile

Professor

• Autonomous Underwater Vehicles (AUVs)
• Marine Robotics
• Ocean Observing Systems
• +3 more

James Bellingham is the Bloomberg Distinguished Professor of exploration robotics at Johns Hopkins University, holding primary appointments in the Department of Mechanical Engineering and the Applied Physics Laboratory's Asymmetric Operations Sector. He serves as executive director of the Johns Hopkins Institute for Assured Autonomy and is a member of the Data Science and AI Institute. With over 30 years of expertise, Bellingham pioneered small, high-performance autonomous underwater vehicles (AUVs), leading global expeditions across polar and oceanic regions. His work bridges robotics innovation with environmental monitoring, including oil spill response, Arctic exploration, and NASA collaborations for extraterrestrial oceanic exploration. Bellingham's educational background includes BS, MS, and PhD in physics from MIT. He previously led Woods Hole Oceanographic Institution's Marine Robotics Consortium, creating advanced prototyping facilities and fostering entrepreneurship in robotics. His leadership roles span institutional boards such as the Naval Studies Board and OceanX. His research focuses on advancing AUV capabilities for adaptive sampling, fault detection, and interdisciplinary oceanography. Over 50 publications demonstrate his technical contributions, including AUV design, environmental hazard mapping, and collaborative robotic systems. Awards include National Academy of Engineering induction and military honors for public service.

Ashish CherukuriView profile

Associate Professor

• Optimization
• Control Systems
• Multi-agent Systems
• +7 more

Ashish Cherukuri is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Optimization and Decision Systems group. His research focuses on optimization-based control, game theory, and multi-agent systems applied to energy, transportation, and robotics. He holds a Ph.D. from UC San Diego and postdoctoral experience at ETH Zurich. Education: Ph.D., University of California, San Diego (2012–2017) M.Sc., ETH Zurich (2008–2010) B.Tech, Indian Institute of Technology Delhi (2004–2008) Research Interests: Data-driven optimization, distributed algorithms, networked cyber-physical systems, and uncertainty handling in energy and transportation systems. Recent work emphasizes stochastic optimization, game-theoretic routing, and risk-aware control. Awards: Robert E. Skelton Dissertation Award (2017) Outstanding Graduate Student Award (2016) Focht-Powell Fellowship (2012–2015) Grants & Service: Editor for the IEEE Control Systems Society, organizer of Energy-Open 2019, and member of professional societies (IEEE, INFORMS, SIAM). Active in conference organization and academic leadership roles. Labs/Teams: Part of the Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen (ENTEG). Research integrates theoretical advancements with practical applications in energy networks and smart systems.

Philbert TsaiView profile

Academic

• Quantitative Biology
• Neurovascular Imaging
• Biomedical Engineering
• +3 more

Philbert Tsai is an Associate Teaching Professor in the Department of Physics at the University of California, San Diego (UCSD). He has held roles as QBio Lab Coordinator/Project Scientist (2015–Present) and Associate Project Scientist (2011–2015), overseeing advanced laboratory setups and bio-imaging research projects. His work focuses on neurovascular systems, microscopy techniques, and cortical blood flow dynamics. Education: Ph.D., Physics, UC San Diego, 2004 Research Interests: Quantitative analysis of cortical microvascular networks Development of ultra-high-resolution imaging systems (e.g., STED, two-photon microscopy) Neurovascular coupling mechanisms and their impact on brain oxygen supply Biomedical engineering applications in neuroscience research Lab & Projects: QBio Lab: Advanced instrumentation including confocal microscopes, 3D printers, and wet-lab equipment Developed vectorized models of mouse brain vasculature and ultra-wide-field multiphoton imaging systems Grants & Awards: No specific awards listed in provided text Collaborations: Worked extensively with colleagues like Dr. David Kleinfeld and Dr. Berislav Zlokovic on neurovascular projects.