Find Academic Supervisors

Joshua SpeagleView profile

Assistant Professor

• Astrostatistics
• Data Science
• Machine Learning
• +7 more

Joshua Speagle is an Assistant Professor jointly appointed in the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. He is also an Associate Member of the Dunlap Institute for Astronomy & Astrophysics and a Member of the Data Sciences Institute. His research lies at the intersection of statistics, astronomy, and computer science, focusing on astrostatistics and data-intensive astrophysics. His research interests include astrostatistics, data science, machine learning, statistical inference, and Bayesian methods. He develops novel statistical learning techniques to extract insights from large, complex datasets, particularly from astronomical surveys. His work emphasizes interpretability, robust inference, and computational efficiency, with applications to galaxy formation, stellar photometry, and 3D dust mapping. The trends in his recent publications reflect a strong focus on interdisciplinary methodologies, particularly in Bayesian inference, nested sampling, and machine learning applied to astrophysical problems. His work consistently bridges theoretical statistics with practical applications in astronomy, emphasizing open-source software and reproducible research. Banting Postdoctoral Fellowship Dunlap Fellowship Joshua Speagle is deeply committed to mentorship and collaboration. He co-leads the Astrostatistics Research Team (ART) with Gwen Eadie, mentoring students and postdocs across disciplines. He is involved in graduate and undergraduate research programs, including the Astronomy & Astrophysics Summer Undergraduate Research Program (SURP). He teaches courses in statistics and astronomy and serves on committees within the University of Toronto and professional societies such as the AAS, ASA-AIG, and SSC-DSA. He co-leads the interdisciplinary Astrostatistics Research Team (ART), which fosters a collaborative, inclusive environment focused on cutting-edge research at the intersection of statistics and AI. The team emphasizes open and accessible science, releasing open-source tools like dynesty and brutus , and mentoring the next generation of data scientists.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +7 more

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

Tingting LiView profile

Lecturer

• AI for Cyber Security
• Automated Cyber Defence
• Diversification and Deception Strategies
• +8 more

Dr. Tingting Li is a Lecturer (Assistant Professor) in Cyber Security at Cardiff University's School of Computing and Informatics and a member of the Centre for Cyber Security Research. She also holds an Honorary Research Fellow position at Imperial College London, reflecting her ongoing research collaboration. Her work bridges artificial intelligence and cybersecurity, with a focus on protecting critical systems such as cyber-physical systems (CPS), industrial control systems (ICS/SCADA), and autonomous vehicles. BEng (Hons) in Information Security, Xidian University, China MSc in Computing, Imperial College London PhD in Artificial Intelligence, University of Bath Dr. Li’s research interests lie at the intersection of AI and cybersecurity, particularly in automated cyber defense , diversification/deception strategies , and symbolic AI for knowledge representation . She investigates how AI can enhance the resilience of critical infrastructures through intelligent, adaptive defense mechanisms. Her recent work explores quantum-inspired reinforcement learning and machine learning models for proactive threat mitigation in complex systems. Her recent publications demonstrate a strong trend in applying advanced AI techniques—especially deep reinforcement learning, LSTM networks, and Bayesian models—to cybersecurity challenges in industrial and autonomous systems. These works span domains like IoT malware suppression, network diversity for ICS resilience, and automated compliance checking, reflecting a multidisciplinary approach combining security, AI, and systems engineering. Dr. Li has secured significant research funding, including a grant from the Alan Turing Institute (2024–2025) on AI safety in autonomous cyber defense, a RITICS/NCSC-funded project on diversity-based cybersecurity (2021–2022), and an EPSRC-funded project on metric-driven cybersecurity frameworks for critical national infrastructure (2021–2023). She actively supervises PhD students in areas including cybersecurity for autonomous vehicles, moving target defense, and cyber-physical system security. Her team includes Iryna Bernyk, Sanyam Vyas, Victoria Marcinkiewicz, Ellis Doran, Stephen Morris, and Sam Braithwaite. She also leads teaching modules on databases (CM6125/CM6625) and cybersecurity (CM6224/CM6724). Dr. Li’s research is conducted within the Centre for Cyber Security Research at Cardiff University, where she collaborates with experts in AI, security, and critical infrastructure protection. Her work is highly interdisciplinary, involving partnerships with institutions like Imperial College London and funding bodies such as EPSRC, NCSC, and the Alan Turing Institute.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

Gábor Hofer-SzabóView profile

Research Fellow

• Foundations of Quantum Theory
• Probabilistic Causality
• Noncommutative Causality
• +3 more

Gábor Hofer-Szabó is a Senior Research Fellow and Deputy Head at the Institute of Philosophy within the Research Centre for the Humanities at the Hungarian Academy of Sciences in Budapest. As a Friedrich Wilhelm Bessel Research Award Winner of the Alexander von Humboldt Foundation (2021-22), he spent the 2021/22 academic year at the Munich Center for Mathematical Philosophy (MCMP) at Ludwig-Maximilians-Universität München. His work bridges foundational quantum theory, probabilistic causality, and interdisciplinary applications of contextuality. Education: DSc in Philosophy of Science (Hungarian Academy of Sciences, 2020) Habilitation in Philosophy of Science (Eötvös Loránd University Budapest, 2012) PhD in Philosophy of Science (Budapest University of Technology and Economics, 2001) MA in Physics (Eötvös Loránd University Budapest, 1993) Research Interests: Hofer-Szabó's primary focus is on the foundations of quantum theory and probabilistic causality , with specific projects in noncommutative causality, dynamical systems approaches to causation, emergence in statistical physics, and contextuality in physical, social, and psychological sciences. His work integrates formal methods with philosophical analysis to address fundamental questions in quantum mechanics and causality. Publications: His recent articles explore quantum contextuality, noncommutative causality, and Bell-type inequalities, demonstrating a consistent focus on the interplay between quantum foundations, probabilistic models, and causal inference. Key themes include operational equivalence, Kochen-Specker arguments, and macrostates in statistical physics. Awards and Grants: Friedrich Wilhelm Bessel Research Award, Alexander von Humboldt Foundation (2021-22) János Bolyai Research Scholarship (2008-2011, 2003-2006) Teacher-Researcher Award, Hungarian Academy of Sciences (2004) Fulbright Research Grant (2011-2012) Principal Investigator for multiple OTKA grants including 'Rethinking probability, causality, and contextuality' (2020-2024) Collaborations: Hofer-Szabó leads research teams at the Institute of Philosophy and collaborates internationally, including projects at the Edelstein Center (Jerusalem), MCMP (Munich), and partnerships across European institutions. His work involves interdisciplinary applications of quantum contextuality in social and psychological sciences.

Panagiotis HadjidoukasView profile

Associate Professor

• Parallel Computing
• Distributed Systems
• High Performance Computing
• +3 more

Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.

• Causal Inference
• Machine Learning
• Natural Language Processing
• +3 more

Dhanya Sridhar is an Assistant Professor at the Department of Computer Science and Operations Research (DIRO) of the University of Montreal and a core academic member of Mila - Quebec Artificial Intelligence Institute. She holds a Canada CIFAR AI Chair and co-leads the IVADO R3AI working group on safe and aligned AI. PhD from University of California Santa Cruz Postdoctoral research at Columbia University Data Science Institute Her research focuses on integrating causality and machine learning to build AI systems that are robust to distribution shifts, capable of efficient task adaptation, and aligned with human knowledge. Recent work includes causal effects of social interactions on US election participation and causal modeling in geriatric-oncology patient outcomes. Current research themes: Causal representation learning Temporal causal inference Counterfactual modeling Causal abstraction in large models Responsible AI development Scientific awards: Canada CIFAR AI Chair Major grants: NSERC Discovery Grant (PVX20965-RGP) 2023-2029 NSERC DGECR Grant 2023-2025 Multiple MITACS Acceleration Quebec grants Advising: PhD students: Philippe Brouillard, Shruti Joshi, Mizu Nishikawa-Toomey, Tom Marty, Cristian Manta

R. Iris BaharView profile

Professor

• Computer Architecture
• Low Power Design
• Machine Learning
• +5 more

R. Iris Bahar is a Professor of Computer Science and Engineering at Colorado School of Mines, where she serves as Department Head of Computer Science since 2022. Previously, she held dual appointments as Professor of Engineering and Computer Science at Brown University for 26 years. Her research focuses on energy-efficient and reliable computing across system-level to device-level domains, with applications in near-data processing, robust machine learning for robotics, and noise-immune nanoscale circuit design. Research Interests include computer architecture, low-power design, and robotic system optimization. She pioneers hybrid generative-discriminative inference techniques for robot scene perception, achieving 40% accuracy improvements in occluded environments through FPGA-based Monte-Carlo sampling implementations. Her work on subthreshold CMOS noise modeling provides 1000x simulation speedups over SPICE-based methods. Near-Data Processing Architectures Approximate Computing Hardware/Software Co-design Nanoscale Noise Modeling Robust AI for Robotics Scientific Awards include the 2024 IEEE Undergraduate Teaching Award, 2019 Marie R. Pistilli Women in Engineering Achievement Award, and NSF CAREER award. She is an IEEE Fellow and ACM Distinguished Scientist. Funded Research spans NSF grants on thermal noise effects ($360k), durable data structures ($500k), and Brown SEED grants for autonomous robotics. Her lab trains 12+ students in hardware acceleration and probabilistic computing.

Alexandre PougetView profile

Professor

• Neural Circuits
• Probabilistic Computation
• Decision Making
• +4 more

Prof. Alexandre Pouget is a leading Professor at the University of Geneva , affiliated with the Faculty of Medicine and Department of Basic Neuroscience . His research focuses on uncovering general principles of representation and computation in neural circuits , particularly how the brain handles uncertainty through probabilistic inference in domains like Decision Making Multisensory Integration Number Representation Visual Processing Perceptual Learning His work applies Bayesian inference to neural coding, with notable contributions to understanding odor demixing and confidence vs. certainty in cognitive tasks. He leads a dynamic research group at CMU (C08.1538.A) with postdocs and graduate students, and his collaborations span institutions like University College London Simons Foundation Human Frontiers Science Programme Swiss National Science Foundation While no specific scientific awards are listed, his publications in Nature Neuroscience and Nature highlight his impact. His lab emphasizes interdisciplinary approaches combining computational modeling , neurophysiology , and behavioral analysis .

Salar FattahiView profile

Assistant Professor

• Optimization
• Machine Learning
• Robust Matrix Recovery
• +4 more

Salar Fattahi is an Assistant Professor at the University of Michigan, affiliated with the College of Engineering’s Department of Industrial and Operations Engineering. He holds additional appointments with the Michigan Institute for Computational Discovery and Engineering (MICDE), Michigan Institute for Data Science (MIDAS), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). PhD in Industrial Engineering and Operations Research from UC Berkeley M.Sc. in Electrical Engineering from Columbia University B.Sc. in Electrical Engineering from Sharif University of Technology Research Focus: Developing scalable computational methods for structured optimization and machine learning problems by exploiting sparsity, low-rankness, and benign landscape properties. Applications span gene regulatory networks, power systems, and brain connectivity modeling. 2025: Parametric algorithms for MIQPs over trees 2024: Triple Component Matrix Factorization for global/local/noise separation 2023: Robust subspace recovery and dictionary learning Scientific Recognition: NSF CAREER Award (2023) INFORMS Best Paper Awards (2023, 2024) Dean’s MLK Spirit Award (2024) MICDE Catalyst Grant (2021) Academic Service: Associate Editor for INFORMS Journal on Data Science; Area Chair for NeurIPS, ICML, and ICLR. Mentored students including Jianhao Ma (now Tsinghua University), Geyu Liang (Amazon), and Aaresh Bhathena. Research supported by NSF, ONR, MICDE, MIDAS, START, and DEI Faculty grants.

Herke van HoofView profile

Associate Professor

• Reinforcement Learning
• Modular Reinforcement Learning
• Hierarchical Structures
• +5 more

Herke van Hoof serves as Associate Professor at the University of Amsterdam within the Informatics Institute, where he leads research in the AMLab at Science Park (Lab 42, L4.05). His academic journey spans from AI degrees at Groningen University through doctoral studies at TU Darmstadt under Professor Jan Peters to postdoctoral research at McGill University with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. PhD: TU Darmstadt (2016), supervised by Prof. Jan Peters Postdoc: McGill University, Montreal AI Bachelor & Master: University of Groningen Van Hoof's research focuses on overcoming data inefficiency in reinforcement learning through modular and hierarchical approaches. His work explores how structured representations can enable knowledge transfer between tasks, improve learning efficiency, and facilitate applications in domains with complex state and action spaces. The lab investigates symmetry exploitation in multi-agent systems, gradient estimation techniques for discrete variables, and applications to combinatorial problem solving. His publication record shows consistent contributions to top AI venues including ICML, ICLR, NeurIPS, and JMLR over the past seven years. The research trajectory demonstrates evolution from foundational policy search methods toward increasingly sophisticated modular architectures, with recent emphasis on knowledge-assisted AI for critical infrastructure applications through the AI4REALNET project. Third place in ARC prize (Paper award) Van Hoof actively supervises PhD candidates and postdoctoral researchers, with current projects including human-robot collaboration using brain-computer interfaces (with Maryam Alimardani at VU) and interactive robot learning with flexible human input (through The Hybrid Intelligence Centre). His research receives support from multiple funding initiatives including the AI4REALNET project which focuses on applying AI to critical infrastructure systems. As co-organizer of the BeNeRL 2024 workshop and participant in the AI4REALNET consortium, van Hoof maintains strong connections with the broader European AI research community while directing the AMLab's research on modular reinforcement learning approaches.

Auroop Ratan GangulyView profile

Professor

• Climate Extremes
• Water Sustainability
• Infrastructure Resilience
• +13 more

Auroop Ratan Ganguly is a Professor of Civil and Environmental Engineering at Northeastern University's College of Engineering , with affiliate appointments in the Khoury College of Computer Science and School of Public Policy and Urban Affairs. He serves as Director of the Sustainability and Data Sciences (SDS) Laboratory and Co-Director of the Global Resilience Institute at Northeastern, while holding a joint appointment as Chief Scientist at Pacific Northwest National Laboratory (PNNL). PhD in Civil and Environmental Engineering, MIT (2002) MS in Civil Engineering, University of Toledo (1997) B.Tech (Hons.) in Civil Engineering, IIT Kharagpur (1993) His research bridges Climate Extremes & Water Sustainability with Infrastructure Resilience and Hybrid Physics-AI Systems . Key contributions include novel methods for climate extremes under global warming, network science applications to infrastructure resilience, and physics-integrated machine learning for weather and hydrology. His work has been cited in UN and US National Climate Assessments, with media coverage in New York Times , Nature , and AGU publications. Recent articles focus on AI for Climate Risk (2025), Nonlinear Dynamics (2025), and Carbon Cycle Extremes (2025). His lab develops Explainable AI tools for climate adaptation and environmental justice. Fellow, American Society of Civil Engineers Distinguished Member, ACM (2023) Outstanding Mentoring Awards, Oak Ridge National Laboratory Best Paper Awards, SIAM Data Mining (2011) and IEEE (2017) As an advisor, he has mentored PhD students like Evan Kodra and Kate Duffy , who co-founded climate startups including risQ (acquired by ICE/NYSE) and Zeus AI . His leadership roles include co-chairing NCAR's Societal Dimensions Working Group and serving on the editorial boards of Scientific Reports and Frontiers in Water .

Michael WilliamsView profile

Research Fellow

• Gravitational Wave Physics
• Neutron Stars
• Black Holes
• +3 more

Dr. Michael Williams is a Research Fellow at the Institute of Cosmology & Gravitation under the Faculty of Technology , University of Portsmouth. His work focuses on gravitational wave physics, neutron star dynamics, and black hole binaries through collaborations with LIGO, Virgo, and KAGRA detector networks. Active in gravitational wave detection and multi-messenger astronomy Developing AI algorithms for telescope targeting and signal processing Recipient of the IOP Trusted Reviewer status (2025) His recent research includes premerger characterization of black hole binaries , continuous wave searches from pulsars , and gravitational wave-FRB counterpart detection . He contributes to open-source tools like nessai and bayesbeat , with 25 datasets released. Williams participates in conferences like the European AI for Fundamental Physics Conference and workshops on milli-hertz gravitational wave astrophysics. Key collaborations: LIGO, Virgo, KAGRA, GEO600 Technical expertise: matched filtering , Bayesian inference , normalizing flows

Jonathan SchafferView profile

Professor

• Metaphysics
• Epistemology
• Philosophy of Language
• +3 more

Jonathan Schaffer is Distinguished Professor in the Department of Philosophy at Rutgers University, New Brunswick, where he also serves as Director of Graduate Placement. He received his Ph.D. from Rutgers in 1999 and has held academic positions at the University of Houston, University of Massachusetts-Amherst, and Australian National University before returning to Rutgers in 2011. His primary research focuses on metaphysics, particularly the concept of fundamentality and reviving classical monism—the view that the cosmos as a whole is fundamental. His secondary research spans epistemology (contrastive knowledge), philosophy of language (contextual semantics), and philosophy of science (laws of nature). Recent work explores connections between grounding theory and cognitive science. Schaffer's publications demonstrate consistent focus on metaphysical foundations with emerging interdisciplinary approaches. His articles from 2015-2020 show developing interests in: Structural equation modeling applied to metaphysical explanation Empirical studies of folk metaphysics Social ontology and feminist metaphysics Quantum holism and fundamentality Interactions between grounding and causation His scholarly recognition includes: APA Article Prize (2008) AJP Best Paper Award (2008) Young Epistemologist Prize (2002) Philosophy of Science Recent Ph.D. Essay Contest (2001) Selection among ten best philosophy articles (Philosopher's Annual 2009) As Director of Graduate Placement, Schaffer oversees doctoral career development but specific advising relationships and grant activities aren't detailed in available sources.

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +6 more

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).