Find Academic Supervisors

• Artificial Intelligence
• Reinforcement Learning
• Evolutionary Computation
• +1 more

Tim Rocktaschel is a Professor of Artificial Intelligence in the Department of Computer Science at University College London (UCL), where he has been working since 2018. He was promoted to Professor in October 2023, having previously served as an Associate Professor (2021-2023) and Lecturer (2018-2021) at the same institution. His educational background includes a Doctorat from University College London (2017) and a Diplom Informatiker from Humboldt-Universitat Berlin (2012). Rocktaschel's research focuses on the cutting edge of artificial intelligence, with particular emphasis on reinforcement learning, evolutionary computation, and open-ended learning systems. His work explores how AI systems can learn more efficiently through better exploration strategies, environment design, and the integration of language models with reinforcement learning frameworks. His recent publications reveal a strong trend toward developing more efficient and generalizable AI systems. The research spans unsupervised environment design, prompt engineering for self-improving systems, exploration strategies in reinforcement learning, and the application of language models to enhance policy learning. His work often bridges theoretical AI concepts with practical implementations, as evidenced by tools like GriddlyJS for reinforcement learning development. Rocktaschel maintains an active presence in the AI research community with numerous publications in top venues including NeurIPS, ICML, and the Journal of Artificial Intelligence Research. His work on zero-shot generalization, pragmatic understanding in language models, and open-ended learning environments has garnered significant attention in the field. He is actively involved in developing tools and datasets for the AI community, such as the large-scale NetHack dataset, which provides a complex environment for testing reinforcement learning algorithms. His research continues to push the boundaries of what's possible in artificial intelligence, particularly in creating systems that can learn and adapt in complex, open-ended environments.

Mauricio AlvarezView profile

Senior Lecturer

• Machine Learning
• Gaussian Processes
• Bayesian Methods
• +5 more

Mauricio Alvarez is a Senior Lecturer in Machine Learning at the Department of Information Management, University of Manchester. His research focuses on Gaussian processes, Bayesian methods, and their applications in healthcare and robotics. He contributes to UN Sustainable Development Goals, particularly in advancing health innovations and AI fundamentals. He is part of the Digital Futures Institute and Christabel Pankhurst Institute. Research Interests: Gaussian Processes, Medical Imaging, Drug Discovery, Pediatric Health Analysis Collaborations: IEEE, npj Precision Oncology, and international institutions His work bridges machine learning with real-world challenges, such as cancer diagnosis and drug repositioning. He leads projects like the MCAIF Centre for AI Fundamentals and collaborates with the RAI Centre for Robotics and Artificial Intelligence. His recent articles emphasize multi-task learning and probabilistic models in healthcare.

Jonathan Ragan-KelleyView profile

Assistant Professor

• High-Efficiency Visual Computing
• Compilers
• Computer Architecture
• +3 more

Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and an Assistant Professor of EECS at UC Berkeley. He leads the Visual Computing group at CSAIL, focusing on high-efficiency visual computing, compilers, and architectures for image processing, machine learning, and 3D rendering. His research bridges systems, compilers, and hardware design, emphasizing scalable solutions for computational challenges. Education: PhD in Computer Science from MIT (2014), postdoc at Stanford University, and visiting researcher at Google. He co-created the Halide language and has developed multiple domain-specific languages (DSLs) and compiler systems. Research interests include compiler optimization, scheduling languages (e.g., Exo), and efficient computing frameworks. He has received awards such as the NSF CAREER Award and ACM SIGGRAPH’s Significant New Researcher Award. Awards: ACM SIGGRAPH Award, NSF CAREER, Intel Outstanding Researcher Award Key Contributions: Halide compiler framework, Exo scheduling language, machine learning acceleration techniques Labs/Teams: Visual Computing at MIT CSAIL

• Machine Learning
• Data Mining
• Bioinformatics
• +11 more

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

Chen WangView profile

Assistant Professor

• Robotics
• Computer Vision
• Machine Learning
• +3 more

Dr. Chen Wang is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo. He holds a PhD from Nanyang Technological University and a B.Eng from the Beijing Institute of Technology. His research focuses on robotic perception, vision, and learning, emphasizing algorithm development for autonomous systems. He is affiliated with the Spatial AI and Robotics Lab (SAIR Lab) and serves as an Associate Editor for The International Journal of Robotics Research (IJRR) and IEEE Robotics and Automation Letters (RA-L) . His work spans neuro-symbolic AI, SLAM systems, and reinforcement learning for robotics. Dr. Wang's research interests include creating efficient algorithms with theoretical guarantees, open-source distribution, and real-world validation. He has contributed to areas like visual navigation, few-shot detection, and robot autonomy frameworks. His educational background in electrical engineering and robotics underscores his expertise in bridging theory and practical applications. Notable contributions include the iWalker framework for humanoid robots, AirSLAM for visual SLAM, and SuperPC for 3D point cloud processing. His editorial roles and conference service (e.g., CVPR Area Chair) reflect his leadership in the field. The SAIR Lab under his direction advances spatial AI, robotics, and autonomous systems through interdisciplinary collaboration.

• Cyber-Physical Systems
• System Safety Assurance
• Model-Based Engineering
• +2 more

Sohag Kabir is an Associate Professor in the School of Computer Science, Artificial Intelligence, and Electronics at the University of Bradford. He leads the MSc Big Data Science and Technology, MSc Artificial Intelligence and Machine Learning, and MSc Applied Computer Science and Artificial Intelligence programs. He holds a Ph.D. in Computer Science from the University of Hull (2016), an M.Sc. in Embedded Systems, and a B.Sc. in Computer Science and Engineering. Dr. Kabir's research focuses on safety, reliability, and security assurance of cyber-physical autonomous systems. His work includes model-based safety analysis, probabilistic risk assessment, dynamic reliability analysis, and stochastic modeling. Current projects address IoT security, machine learning certification for automotive systems, and dependability frameworks for complex systems. His publications demonstrate consistent focus on developing integrated frameworks for system dependability, with recent work emphasizing IoT security, autonomous vehicle safety, and AI certification challenges. Dr. Kabir has contributed to multiple EU-funded projects including DEIS (Dependability Engineering Innovation for Cyber-Physical Systems) and MAENAD (Model-based Analysis & Engineering of Novel Architectures for Dependable Electric Vehicles).

Randa HerzallahView profile

Associate Professor

• Control Systems
• Energy Systems
• Machine Learning
• +2 more

Dr. Randa Herzallah is an Associate Professor at the University of Warwick with interdisciplinary expertise spanning control systems, quantum engineering, and machine learning. Her research develops probabilistic frameworks for complex systems control. Research interests focus on probabilistic control methods applied to energy grids, quantum systems, and biomedical applications. Recent work integrates machine learning with control theory for smart grid optimization and quantum system management. Publication analysis shows consistent focus on probabilistic control frameworks, with recent expansion into quantum applications and deep learning for industrial applications. Research funding includes EPSRC and Leverhulme Trust grants supporting quantum control and energy systems projects. Leads research in probabilistic control methodologies with industrial applications.

Gabriele LigaView profile

Assistant Professor

• Digital Communications
• Information Theory
• Optical Communications
• +5 more

Gabriele Liga is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems (SPS) Group. He holds a Marie Curie Eurotech Fellowship focusing on signal shaping techniques for nonlinear optical fiber channels. His academic journey includes a Ph.D. in optical communications from University College London, followed by postdoctoral research in digital signal processing and nonlinearity compensation. Education: B.Sc. in Telecommunications Engineering from Università degli Studi di Palermo (2005), M.Sc. in Telecommunications Engineering from Politecnico di Milano (2011), and a Ph.D. in Optical Communications from University College London (2017). Research Interests: Digital communications, information theory, fiber-optic systems, nonlinearity compensation, channel coding, and multi-user optical communication theory. His work emphasizes achieving transmission limits through signal shaping and advanced signal processing techniques. Projects: Active roles in NESTOR (Next-gen optical networks), QuNEST (quantum communication security), Fun-NOTCH (nonlinear optical channel fundamentals), and SSTOC (signal shaping tailored to optical channels). Collaborations span institutions globally, focusing on optical fiber communication challenges. Awards: 2023 ACP/POEM Best Student Paper Award and 2019 OECC Best Paper Award. Serves as a reviewer for IEEE journals and OSA publications. Labs/Teams: Core member of the SPS Group and involved in interdisciplinary projects blending theory and experimental validation.

Fotios PetropoulosView profile

Professor

• Time Series Forecasting
• Judgmental Forecasting
• Statistical Model Selection
• +3 more

Fotios Petropoulos is a Professor at the University of Bath, holding the Management Chair in Management Science within the School of Management's Information, Decisions & Operations department. He also served as the Spyros Makridakis Chair in Forecasting at the University of Nicosia (2023–2023). His research focuses on time series forecasting, judgmental approaches, and integrating statistical and human judgment in decision-making processes. He has contributed to improving forecasting accuracy through temporal aggregation and hierarchical methods. Petropoulos holds a Doctor of Engineering (2012) and Bachelor of Engineering (2007) from the National Technical University of Athens. Editor of the International Journal of Forecasting (2020–present) Associate Editor of Foresight: The International Journal of Applied Forecasting (2015–2022) Director of the International Institute of Forecasters (2016–2018) His research interests emphasize forecasting processes, model selection, and the role of judgment in statistical models. Key areas include temporal aggregation, forecast reconciliation, and behavioral operations analytics. He has published over 100 peer-reviewed articles, focusing on topics like computational cost optimization, probabilistic forecasting, and scalable reconciliation methods. His work contributes to Sustainable Development Goals related to education and innovation. Recent articles highlight advancements in univariate forecasting efficiency, forecast selection criteria, and dynamic reconciliation. Petropoulos is a member of the Smart Warehousing and Logistics Systems group and actively participates in editorial boards of leading forecasting journals. His academic and professional roles bridge theoretical research and practical applications in operational decision-making.

Max FathiView profile

Professor

• Optimal Transport
• Probability Theory
• Statistical Physics
• +9 more

Max Fathi is a Professor of Mathematics at Université Paris Cité, affiliated with the Laboratoire Jacques-Louis Lions (LJLL) and Laboratoire de Probabilités, Statistique et Modélisation (LPSM). He concurrently holds a part-time teaching position at the Department of Mathematics and Applications (DMA) at École Normale Supérieure (ENS). Since 2023, he has been a member of the Institut Universitaire de France (IUF), a prestigious national research fellowship in France. He completed his PhD in 2013 at Université Pierre et Marie Curie under Cédric Villani, followed by a postdoctoral position at the University of California, Berkeley with Lawrence C. Evans and Fraydoun Rezakhanlou. Previously, he was a CNRS researcher at the Institut de Mathématiques de Toulouse before joining Université Paris Cité. His habilitation thesis (2019) focuses on optimal transport applications in analysis and probability. Fathi's research centers on optimal transport theory, particularly its applications to analysis, probability, and statistical physics. Key topics include interacting particle systems, functional inequalities (e.g., Poincaré, log-Sobolev), high-dimensional phenomena, Ricci curvature in discrete/continuous spaces, Stein's method, concentration of measure, and numerical methods for stochastic dynamics. His work is supported by the ANR project 'Conviviality.' He has delivered courses on functional analysis at ENS and participated in summer schools, including an MSRI course on functional inequalities and localization techniques. His teaching materials include lecture notes on optimal transport and stochastic processes. His contributions have been recognized through awards such as the IUF membership. Notable research collaborations include work with Thomas Courtade, Matthias Erbar, and Gabriel Stoltz on topics ranging from stability estimates of inequalities to hypocoercivity and numerical analysis of stochastic systems.

Han ZhaoView profile

Assistant Professor

• Trustworthy Machine Learning
• Transfer Learning
• Algorithmic Fairness
• +3 more

Han Zhao is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Department of Electrical and Computer Engineering. He is also an Amazon Scholar at Amazon AI and Search Science. Prior to UIUC, he was a machine learning researcher at D.E. Shaw & Co. Zhao holds a Ph.D. from Carnegie Mellon University's Machine Learning Department, an MMath from the University of Waterloo, and a BEng from Tsinghua University's Computer Science Department. His research focuses on trustworthy machine learning, emphasizing transfer learning (domain adaptation, generalization, multitask/meta-learning), algorithmic fairness, and probabilistic circuits. Applications span natural language processing, signal processing, and quantitative finance. He aims to develop robust, fair, and interpretable ML systems. Recent work includes advancements in domain adaptation theory, multi-task learning optimization, and fair classification post-processing. He advises numerous PhD and master’s students across CS and ECE, co-advising some with colleagues like Hari Sundaram and Ilan Shomorony. Courses taught include CS 442 (Trustworthy ML) and CS 446 (Machine Learning). Key contributions include the MDAN framework for multi-source domain adaptation and theoretical analyses of invariant representation learning. His work balances foundational theory with practical applications, addressing challenges like hyperparameter sensitivity and scalable influence functions.

Benjamin Bloem-ReddyView profile

Assistant Professor

• Statistics
• Machine Learning
• Probabilistic Modeling
• +4 more

Benjamin Bloem-Reddy is an Assistant Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on probabilistic approaches in statistics and machine learning, emphasizing symmetry, causality, and model-driven scientific knowledge acquisition. Prior to UBC, he completed his PhD under Peter Orbanz at Columbia University and a postdoc with Yee Whye Teh at the University of Oxford. He holds a physics background from Stanford and Northwestern Universities. Education: PhD in Statistics, Columbia University Postdoctoral Research, CSML Group, University of Oxford Physics degrees from Stanford and Northwestern Universities Research Interests: Bloem-Reddy explores symmetry in modeling and inference, causal discovery, and integrating scientific models with statistical frameworks. His work includes developing hypothesis tests for symmetry, causal inference via cocycles, and leveraging invariance properties in neural networks. He collaborates with scientists to apply statistical methods to domain-specific problems. Awards: Best Student Poster Award at NeurIPS 2014 Workshop on Networks Teaching & Advising: He teaches courses like STAT 460/560 (Statistical Inference) and advises a vibrant research group. His students include Boyan Beronov, Kenny Chiu, and Johnny Xi. He emphasizes recruiting curious, mathematically skilled students aligned with his research themes. Grants & Funding: Supported by NSERC, CANSSI, and UBC, with computational resources from ARC at UBC.

• AI Ethics
• Epistemic Logic
• Philosophy of Language
• +3 more

Simon David Goldstein is an Associate Professor at the Dianoia Institute of Philosophy within the Faculty of Theology and Philosophy. His research focuses on epistemic logic, formal epistemology, philosophy of language, and AI ethics. He explores topics such as knowledge norms, epistemic modalities, and ethical implications of AI systems. Research Interests: AI Ethics: Examining AI safety, deception, and existential risks Epistemic Logic: Investigating knowledge fragility, contextualism, and modal credence Philosophy of Language: Analyzing dynamic semantics and attitude reports Formal Epistemology: Probability and modal reasoning in epistemic contexts Recent Work Trends: Recent publications emphasize interdisciplinary approaches to AI ethics, integrating technical AI challenges with philosophical frameworks. His work on epistemic modals and contextualism bridges semantics and epistemology. Advising & Grants: No specific grants or advisees listed. Active in collaborative research with institutions like the Dianoia Institute. Labs/Teams: Affiliated with the Dianoia Institute's research groups on ethics and epistemology.

Amir GiladView profile

Assistant Professor

• Responsible Data Science
• Causal Inference
• Differential Privacy
• +5 more

Amir Gilad is a Scharf-Ullman endowed Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem’s School of Computer Science and Engineering. His research focuses on responsible data science, including causal inference, differential privacy, fairness in data, and tools for data analysis. He holds a Ph.D. in Computer Science from Tel Aviv University, where he was advised by Prof. Daniel Deutch. Prior to this, he was a postdoctoral researcher at Duke University, mentored by Prof. Sudeepa Roy, Prof. Ashwin Machanavajjhala, and Prof. Jun Yang. Education: Ph.D. in Computer Science, Tel Aviv University (Advisor: Daniel Deutch) MSc in Computer Science, Tel Aviv University BSc in Mathematics and Computer Science, Tel Aviv University His research interests span data quality assessment, private and fair data generation, and causal inference applications . He has received notable awards, including the 2024 Alon Scholarship and the 2019 Google Ph.D. Fellowship. Recent Projects: Developing algorithms for data quality repair and assessing bias in datasets Generating differentially private data that satisfies fairness constraints Applying causal inference to enhance data analysis tools Awards and Honors: 2024 Alon Scholarship for Outstanding Faculty Integration 2019 Google Ph.D. Fellowship in Structured Data 2018 SIGMOD Research Highlight Award 2017 VLDB Best Paper Award Teaching: Courses include “Topics in Responsible Data Science” and “Seminar on Causal Inference in Data Analysis” at Hebrew University, and “Extended Introduction to Computer Science” at Tel Aviv University. He has also led workshops on Google Technologies. Labs & Teams: His work is centered around the School of Computer Science and Engineering’s database group, focusing on foundational and applied aspects of privacy-aware data systems.

FENG MenglingView profile

Associate Professor

• Biostatistics
• Healthcare Data Analytics
• Medical AI
• +3 more

**FENG Mengling** is an Associate Professor at the National University of Singapore (NUS) and holds primary affiliation with the Saw Swee Hock School of Public Health. She serves as the Domain Leader for the Biostatistics, Modelling, AI and Data Analytics (B.MAD) Domain and Director of the AI for Public Health (AI4PH) Program. Her academic credentials include a Senior Post-doc from Harvard-MIT Health Science Technology Division, a PhD from Nanyang Technological University (2009), and a Bachelor's degree (2003) from NTU. Research & Teaching: Her research focuses on causal inference for evidence-based medicine, generative models for medical time-series analysis, and healthcare data analytics. She teaches courses on big data technologies for healthcare problems and healthcare data analytics. Professional Roles & Awards: She has led the Biomedical and Healthcare Analytics Lab at the Institute for Infocomm Research (2014–2015) and currently serves as an Affiliate Scientist at Harvard-MIT. Notable accolades include the MIT Teaching & Learning Laboratory Kaufman Teaching Certificate and recognition as a finalist in MIT’s 2013 Innovation Showcase. Her work has been featured in prominent media outlets like The Straits Times and Channel NewsAsia, highlighting breakthroughs such as AI nurses and Singlish-speaking healthcare assistants. Publications & Impact: Over 50 peer-reviewed publications span AI-driven clinical decision support, medical imaging analysis, and predictive modeling in critical care. Key contributions include frameworks like MedDreamer (reinforcement learning for EHR analysis) and DivScore (LLM-generated text detection). Her research bridges causal inference, generative AI, and scalable healthcare solutions. Labs & Initiatives: As a leader in NUS’s Public Health AI Innovation Center (launching early 2025), she drives initiatives like FxMammo (AI for breast cancer screening) and the Biomedical and Healthcare Analytics Lab. Her work emphasizes ethical AI deployment and cross-disciplinary collaboration in healthcare.