Find Academic Supervisors

Pierre F BaldiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +10 more

Pierre Baldi is a Distinguished Professor at the University of California, Irvine (UCI), holding appointments in the School of Information and Computer Sciences (ICS), the Institute for Genomics and Bioinformatics (IGB), and the Center for Machine Learning and Intelligent Systems (CML). His research focuses on AI, machine learning, and their applications in natural sciences, including physics, chemistry, and biology. He leads projects on neural networks, probabilistic modeling, and bioinformatics. Key roles include Director of the IGB and AI in Science Institute. Awards include the Dennis Gabor Award and Fellowships from AAAI, AAAS, and IEEE. His work spans interdisciplinary collaborations, such as the DUNE neutrino experiment and AI-driven drug discovery. Teaches courses like Probabilistic Modeling of Biological Data and Neural Networks. Active in conferences, including workshops on deep learning and its implications in science.

Pooya DavariView profile

Professor

• Harmonic and EMI analysis and mitigation in grid-tied converters
• Electromagnetic interference and compatibility
• High power density power electronic systems
• +2 more

Pooya Davari is a Professor and Head of the Section for Applied Power Electronic Systems at Aalborg University , Denmark. He leads the EMI/EMC in Power Electronics Research Group and serves as Vice Chair of the Energy Efficiency Mission. His research focuses on electromagnetic interference (EMI) and harmonic mitigation in power electronic systems, with over 200 publications and significant contributions to renewable energy integration. Education: B.Sc. and M.Sc. in Electronic Engineering (2004, 2008), Ph.D. in Power Electronics from Queensland University of Technology (2013) Prior Roles: Lecturer at QUT (2013–2014), Postdoc at AAU (2014) Research Interests: Harmonic and EMI analysis in grid-tied converters High power density converter design Signal processing for converter modeling Reliability of power electronic systems Article Trends: Recent work emphasizes EMI/EMC in renewable energy systems, wide bandgap semiconductors (SiC/GaN), and reliability modeling for EVs and hydrogen production via electrolysis. Sub-fields include converter topologies, grid integration challenges, and AI-driven diagnostics. Scientific Awards: Equinor 2022 Prize (Denmark’s oldest engineering award) IEEE EMC Society Young Professional Award (2020) World’s Top 2% Highly Cited Scientist (Stanford, 2021–2025) Multiple best paper awards (IEEE, Applied Sciences, etc.) Grants & Editorial Roles: Recipient of grants from Innovation Fund Denmark (Supra-EMC project), Horizon Europe (SOLARIS), and industry partnerships. Serves as Area Editor for IEEE Transactions on Transportation Electrification , Associate Editor for IEEE Transactions on Power Electronics , and Editor-in-Chief of Circuit World Journal (2020–2025). Labs & Standards: Coordinator of the EMC Laboratory at Aalborg University. Member of IEC standardization Working Groups 6 and 8 (TC77A), focusing on EMC strategies for power grids.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

• Mathematical Logic
• Formal Methods
• Automated Reasoning
• +4 more

Dr. Sasha Rubin is a Senior Lecturer and leader of the Computational Logic for AI (LOGIC-AI) group at the School of Computer Science, The University of Sydney. He holds a PhD in Mathematics and Computer Science from the University of Auckland and previously worked at the University of Naples Federico II. His research focuses on logic foundations of AI, including synthesis, planning, formal methods, and multi-agent systems. He teaches courses like Models of Computation and supervises students in topics like probabilistic systems and reinforcement learning. Research Interests: Mathematical Logic, Formal Verification, Temporal Logic Synthesis, Automated Reasoning, and Multi-Agent Systems. He has published extensively in top venues like IJCAI, AAAI, and ACM Transactions. His work includes verification of agent navigation, strategy logic, and planning under uncertain environments. Awards: Recognized as an Australian Research Field Leader in Theoretical Computer Science (2020). He serves on editorial boards for JAIR and conferences like KR, and organizes events such as the Australasian Association for Logic Conference (2024). Supervision and Grants: Current students include Ethan HIRSCHOWITZ and Kunal OSTWAL. Past supervision spans MPhil/PhD projects on probabilistic systems, ML classifier fairness, and symbolic automata. His grants include studies on logic and robots in anonymous graphs. Professional Activities: Member of EATCS, ACM, and mentor for the Sydney Summer Innovation Programme. He leads the LOGIC-AI lab and collaborates internationally, notably with Giuseppe De Giacomo at Sapienza University of Rome.

Eun Jeong ChaView profile

Associate Professor

• Structural Engineering
• Societal Risk & Hazard Mitigation
• Infrastructure Systems Risk Management
• +3 more

Eun Jeong Cha is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. She holds a Ph.D. (2012) and M.S. (2009) from Georgia Institute of Technology, and a B.S. (2006) from Seoul National University. Her research focuses on risk-informed decision-making for infrastructure resilience under natural hazards, including hurricane risk assessment under climate change, interdependent infrastructure systems analysis, and seismic risk mitigation. Education: Ph.D. in Civil Engineering, Georgia Tech (2012) M.S. in Civil Engineering, Georgia Tech (2009) B.S. in Architectural Engineering, Seoul National University (2006) Her research interests span structural reliability, disaster risk management, and the integration of climate change impacts into infrastructure design. Key areas include hurricane risk modeling, interdependent infrastructure recovery, and socially-aware retrofit prioritization. She has received awards such as the ASCE/EMI Probabilistic Methods Committee Student Paper Award (2012) and is a Fellow of the Next Generation of Hazards and Disasters Researchers (2015). Dr. Cha leads the R4 Group, advancing methodologies for resilient infrastructure systems. She actively contributes to professional societies like ASCE, serving on committees for load combinations and climate adaptation. Her work bridges engineering, risk analysis, and policy to enhance community resilience against extreme events.

Garg VikasView profile

Assistant Professor

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +8 more

Professor Garg Vikas holds the position of Assistant Professor in the Department of Computer Science at the School of Science. His research spans quantum computing, artificial intelligence, and machine learning with applications in computational biology, healthcare, and drug design. He leads the HEALED/Garg project focused on human-steered machine learning for drug discovery and collaborates with institutions like MIT and industry partners. He co-founded YaiYai Oy, providing AI/ML solutions to global sectors. Education: PhD from MIT CSAIL under Tommi Jaakkola, with postdoctoral and industry experience at Amazon, Microsoft, and IBM. His work aligns with UN SDGs, particularly in healthcare and sustainable energy. Research interests include graph neural networks, generative models, and quantum AI. Recent projects involve climate modeling via physics-informed neural ODEs and optimizing quantum circuits using graph autoencoders. Key collaborations include MIT’s MLPDS Consortium and the Finnish Center for Artificial Intelligence. He supervises doctoral researchers like Yogesh Verma and postdocs such as Kogkalidis.

David CockView profile

Senior Lecturer

• Formal Verification
• Operating Systems
• Hardware-Software Co-Design
• +3 more

Dr. David Cock is a Senior Lecturer and Senior Researcher at ETH Zürich's Department of Computer Science, affiliated with the Systems Group. He holds a PhD from UNSW (2014) and a B.Sc. (hons) from UNSW (2004). His research focuses on formal verification, trustworthy systems, and hardware-software co-design, with notable contributions to projects like Enzian (a CPU/FPGA platform) and seL4 (formally verified kernel). He teaches Advanced Operating Systems and Informal Methods courses. Key achievements include the ACM Software System Award (2022) for seL4 and leadership in projects addressing hardware complexity and security. Research interests include formal methods for hardware modeling (Sockeye project), runtime verification, and mitigating timing channels. His work bridges theoretical foundations with practical systems, emphasizing secure and reliable computing platforms. Projects like Trustworthy BMC aim to enhance baseboard management systems' assurance. Collaborations span academia and industry, with open-source contributions to hardware designs and formal tools. Publications span formal verification, hardware modeling, and secure systems, with recent focus on heterogeneous computing and declarative hardware specifications. Teaching emphasizes practical formal techniques and OS design, leveraging real-world hardware (e.g., Barrelfish). His lab, the Systems Group, explores cutting-edge challenges in systems software and architecture.

Niklas LindeView profile

Professor

• Geophysics
• Environmental Geophysics
• Probabilistic Inversion
• +3 more

Niklas Linde is a full professor at the University of Lausanne's Faculty of Geosciences and Environment, leading the Department of Earth Sciences. He holds a PhD in Geophysics from Uppsala University (2005) and has held roles including Assistant Professor (2008), Associate Professor (2013), and Full Professor (2019). His research focuses on transforming geophysical signals into realistic hydrogeological models with rigorous uncertainty quantification. Key areas include probabilistic inversion, Bayesian methods, and geostatistical modeling applied to environmental and subsurface processes. Education: PhD in Geophysics (Uppsala University, 2005), postdoctoral positions at Lawrence Berkeley National Lab (USA), CNRS-CEREGE (France), and ETH Zurich (Switzerland). He joined UNIL in 2008 as an Assistant Professor in Environmental Geophysics. Research interests span geophysical inversion techniques, subsurface heterogeneity characterization, and the integration of geophysical and hydrological data. Current projects emphasize Bayesian approaches for model selection and rare event estimation, supported by grants from the European Commission and Swiss National Science Foundation. Collaborations involve international teams addressing challenges in hydrogeology, rock fracture dynamics, and 4D hydrogeology. Publications reflect advancements in inverse problem solving, stochastic simulation, and machine learning applications. His work bridges theory and practice, with field studies in alpine environments, fractured media, and environmental monitoring. Students under his supervision have explored topics like deep generative networks and Bayesian hydrogeological inversion. Advising: Supervised over a dozen PhD students, including recent works on variational Bayesian methods and geophysical data fusion. Grants include projects on uncertainty quantification and experimental design. Active in scientific societies and editorial roles, contributing to methodological advancements in Earth sciences.

Hazem U. AbdelhadyView profile

Research Fellow

• Coastal Processes
• Hydrodynamic Modeling
• Remote Sensing
• +4 more

Hazem U. Abdelhady is a Postdoctoral Research Fellow at the University of Michigan's School for Environment and Sustainability (SEAS), jointly appointed with the Cooperative Institute for Great Lakes Research (CIGLR). He will join Texas A&M University's Department of Geography as an Assistant Professor in Fall 2025. His research focuses on coastal and hydrodynamic processes, leveraging machine learning, remote sensing, and physics-based modeling to address climate-driven challenges in lakes and coastal systems. Dr. Abdelhady holds a PhD from Purdue University in Hydraulics and Hydrology Engineering and Computational Engineering (2024), an MS in Irrigation and Hydraulics Engineering from Cairo University (2020), and a BS in Civil Engineering (2018). His postgraduate certificate in Geospatial Information Sciences underscores his expertise in spatial data analysis. His research interests include coastal hydrodynamics, physical limnology, and AI-driven environmental modeling. Key projects involve understanding shoreline changes in Lake Michigan, predicting ice cover dynamics, and developing tools for climate adaptation. The Aggie CIS Lab , launching at Texas A&M in Fall 2025, will further advance these efforts, bridging disciplines to enhance coastal resilience. Recent work highlights include studies on climate impacts on lake temperatures, machine learning applications for wave modeling, and automated shoreline detection algorithms. He actively recruits PhD students for Fall/Spring 2026 to investigate Great Lakes dynamics and resilience strategies. Collaborations span academic and governmental institutions, emphasizing interdisciplinary approaches to environmental challenges. His publications span high-impact journals and conferences, reflecting contributions to both theoretical and applied aspects of water resources engineering.

David WanikView profile

Assistant Professor

• Data Science
• Natural Hazards
• Remote Sensing
• +3 more

David Wanik is an Assistant Professor in the Department of Operations and Information Management and Associated Faculty in the Department of Civil and Environmental Engineering at the University of Connecticut. He serves as Academic Director for Business Data Analytics at the Stamford campus and conducts research in the Eversource Energy Center, focusing on data science, natural hazards, remote sensing, and IoT applications in utility systems. PhD, MS, and BS in Environmental Engineering from University of Connecticut His research bridges natural hazard prediction, power grid resilience, and environmental data science. Key themes include: Machine learning for power outage prediction Climate change impact on energy demand Remote sensing for population and environmental monitoring IoT-enabled infrastructure hardening Recent publications emphasize deep learning for nighttime light imagery analysis, hybrid physics-data-driven models for grid resilience, and climate-integrated demand forecasting. His work integrates satellite data, LiDAR, and utility infrastructure records for predictive analytics. Teaching includes courses in business analytics, Python-based data science, and deep learning for the MS Business Analytics and Project Management program.

Zoe LiView profile

Associate Professor

• Uncertainty Analysis
• Risk Management
• Stochastic Modelling
• +7 more

Dr. Zoe Li is an Associate Professor in the Department of Civil Engineering at McMaster University, with an additional role as an Associate Member in the Department of Computing and Software. She specializes in developing modeling and decision-support tools to address challenges in water resources management, environmental systems analysis, and climate change impacts. Her research focuses on hydrological modeling, probabilistic forecasting, risk analysis, and environmental systems optimization. She holds a B.Eng. from China and M.A.Sc./Ph.D. degrees from the University of Regina. Her work spans interdisciplinary collaborations, including studies on dam safety under climate change, wastewater treatment plant optimization, and AI-driven environmental monitoring. She has been actively involved in projects such as CityDNA, a digital tool to model urban environments for decision-making during crises like the pandemic. Dr. Li teaches courses in environmental systems engineering and principles of environmental engineering, emphasizing uncertainty quantification and sustainable practices. Her research publications (15+ recent articles) address topics like machine learning applications in environmental systems, climate change impacts on water resources, and infrastructure resilience. She advises graduate students in environmental engineering and systems optimization. Notable grants include funding from Roche Canada for pandemic-related urban modeling. Her work is frequently published in high-impact journals like Journal of Environmental Management and Water Resources Research .

• Computer Vision
• Image Processing
• Deep Learning
• +3 more

Radu Timofte is an academic researcher specializing in computer vision and image processing. He completed his PhD in 2013 at Katholieke Universiteit Leuven, Belgium, with a thesis on sparse and collaborative representations for computer vision. His work focuses on advancing techniques such as image super-resolution, denoising, object detection, and deep learning-based image restoration. He has collaborated extensively with institutions like ETH Zurich and co-authored seminal papers in top-tier journals and conferences. His research bridges theoretical advancements with practical applications in areas like medical imaging, aerial scene analysis, and real-time visual tracking. Timofte’s contributions include developing efficient deep learning architectures for tasks like lightweight object detection (e.g., CH-YOLO-Lite), diffusion models for image-to-image translation (DiffI2I), and calibration-free raw image denoising. He has also contributed to the development of video restoration transformers (VRT) and frameworks for unsupervised real-time video enhancement. His work often emphasizes practicality and efficiency, addressing challenges such as small object detection in aerial imagery and underwater image super-resolution. Timofte’s collaborations span academia and industry, with notable co-authors including Luc Van Gool (ETH Zurich) and Kai Zhang (Nanjing University of Science and Technology). His research has been published in venues like IEEE Transactions on Pattern Analysis and Machine Intelligence, CVPR, ECCV, and the International Journal of Computer Vision.

Andrea SantilliView profile

Researcher

• Natural Language Processing
• Representation Learning
• Machine Intelligence
• +10 more

Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.

• Power Systems
• Electrical Engineering
• Renewable Energy Integration
• +6 more

Pavlos S. Georgilakis is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in modern techniques for power system analysis, optimization, and renewable energy integration. He holds a Diploma (1990) and PhD (2000) in Electrical Engineering from NTUA. His career includes roles as Lecturer (2009) and Associate Professor (2018–2023) at NTUA, and Assistant Professor at the Technical University of Crete (2004–2009). Research focuses on power transmission/distribution systems, transformer design, and applying AI/optimization for grid efficiency. He led 10 research projects, including Horizon 2020 initiatives SHAR-Q, WiseGRID, and NobelGrid. He authored 3 books and over 230 publications (SCOPUS citations: >5,500). Editor of IET Smart Grid, Energies, and Electricity journals; senior IEEE member. He supervised 4 doctoral, 9 master’s, and 76 diploma theses. Awards include the 2013 Best Reviewer Award from Electric Power Systems Research. Active in energy storage, smart grids, and decentralized energy resource integration.

Chenang LiuView profile

Associate Professor

• Smart Manufacturing
• Data Analytics
• Machine Learning
• +2 more

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)