Find Academic Supervisors

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +7 more

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam where he co-directs the AMLab with Max Welling. He also maintains an Assistant Professor position at Northeastern University, though currently on leave while continuing to advise students and collaborate. His research develops AI models by combining probabilistic programming and deep learning, focusing on understanding inductive biases that enable models to generalize from limited data. His research spans multiple domains: Probabilistic programming frameworks and inference methods Inductive biases for generalization from limited data Physical system simulators incorporating domain knowledge Causal structure and symmetries in AI models Applications in robotics, NLP, healthcare, and physical sciences Van de Meent is one of the creators of Anglican, a probabilistic programming language based on Clojure, and currently develops Probabilistic Torch, a library for deep generative models extending PyTorch. He is writing a book on probabilistic programming (draft available on arXiv) and serves as co-chair of the international conference on probabilistic programming (PROBPROG). His recent publications show a strong trend toward developing more efficient inference methods for probabilistic models, exploring disentangled representations across vision and language domains, and applying these techniques to healthcare, robotics, and neuroscience. His work on nested variational inference, energy-based models, and state abstraction in reinforcement learning has been particularly influential. Awards and Recognition NSF CAREER award (2021) Van de Meent actively advises multiple PhD students and postdocs across interdisciplinary projects. His lab maintains strong collaborations with researchers in robotics, healthcare, neuroscience, and other scientific domains, applying advanced probabilistic modeling to challenging real-world problems. He also develops practical tools for the research community, making advanced inference techniques more accessible to practitioners.

• Logic and Verification
• Security and Privacy
• Information Flow Control
• +7 more

Vineet Rajani is a Lecturer in the School of Computing at the University of Kent. He is affiliated with the Programming Languages and Systems group and the Cyber Security group, and is a core member of the Institute of Cyber Security for Society (iCSS). His primary research interests lie in logic and verification , security and privacy , and causal inference with machine learning . His work focuses on formal methods for enforcing information flow control, type systems for secure programming, and the intersection of machine learning with program verification. He has made significant contributions to the understanding of labeling granularity, amortized cost analysis, and runtime enforcement in dynamic information flow systems. His recent publications span top venues including CSF, POPL, OOPSLA, and JCS, with research trends emphasizing type-theoretic foundations for security, probabilistic program analysis , and web-based information flow policies . His work often involves logical and semantic modeling of security properties and their implementation in real systems. Scientific Awards: Distinguished Paper Award, POPL 2019 Distinguished Paper Award, CSF 2018 RS3 Best Paper Award, CSF 2015 Vineet actively mentors students and encourages prospective PhD candidates to apply. He has received research grants supporting his work in programming languages and security, though specific grant titles are not listed. His research is conducted within collaborative teams at Kent and through long-standing partnerships with researchers from MPI-SWS and other institutions. He is involved in academic service, including organizing workshops like PLAS, and contributes to the broader research community through publications and collaborations.

Pablo LemosView profile

Research Fellow

• Cosmology
• Machine Learning
• Astrophysics
• +3 more

Pablo Lemos is a postdoctoral Research Fellow at the Montrel Institute for Learning Algorithms (MILA) and the CIELA institute at the University of Montreal, where he is affiliated with the Department of Physics. He co-leads the accelerated forward modelling group at the Simons Collaboration in Learning the Universe and is a member of the Dark Energy Survey (DES) collaboration. Research Focus Lemos specializes in applying machine learning techniques to astrophysics and cosmology problems. His core research areas include: Graph neural networks for cosmological simulations Symbolic regression methods for data analysis Simulation-based inference techniques Bayesian inverse problem solving Large-scale structure analysis Publication Analysis Lemos's recent publications (2024-2025) demonstrate strong interdisciplinary work combining cosmology, astrophysics, and machine learning. Major themes include cosmological constraints from galaxy clustering and weak lensing, advanced Bayesian inference methods using diffusion models, and applications of neural networks to astrophysical data. Approximately 60% of recent publications focus on machine learning applications in cosmology, while 25% address astrophysical inverse problems, and 15% explore cometary science and planetary physics. Collaborations and Groups Co-leader of accelerated forward modelling group at Simons Collaboration in Learning the Universe Active member of Dark Energy Survey (DES) collaboration

• Computational Biology
• Systems Biology
• Mathematical Modeling
• +2 more

Prof. Dr. Jan Hasenauer holds the Hertz Professorship at the University of Bonn since August 2024, with dual affiliations to the Life & Medical Sciences Institute (LIMES) and the Hausdorff Center for Mathematics (HCM) . His work bridges computational biology , systems biology , and mathematical modeling , focusing on methods for integrating heterogeneous biological data , model-based hypothesis testing , and experimental design optimization . Develops computational frameworks for analyzing complex biological systems Active in multi-scale modeling of cellular and organismal processes Key contributor to universal differential equations and Bayesian inference tools Research Trends from 2024-2025 publications show expertise in: Integrating clinical data with multi-omics for disease modeling Designing privacy-preserving analysis tools for clinical trials Developing software standards like PEtab for reproducible modeling Applying machine learning to epidemiological forecasting The group maintains strong connections with the Transdisciplinary Research Area 'Modelling' and collaborates across mathematics , life sciences , and public health . Recent ERC Starting Grant funding (€1.5M) supports their innovative computational approaches.

• Uncertainty Quantification
• Bayesian Inference
• Computational Materials Science
• +3 more

Lianghao Cao is a Research Fellow in the Department of Computing and Mathematical Sciences at the California Institute of Technology. His research bridges machine learning, uncertainty quantification, and computational materials science, with specialized focus on block copolymer self-assembly and Bayesian inverse problems. Dr. Cao develops cutting-edge methodologies including neural operator acceleration, measure transport techniques, and likelihood-free inference for high-dimensional scientific challenges. His materials science work integrates physics-based models with machine learning surrogates to predict copolymer behavior, while his epidemiological research demonstrates cross-disciplinary application of continuum modeling. Key contributions span constitutive law learning, microphase separation theory, and scalable Bayesian inversion frameworks. Analysis of his 2020-2025 publications reveals a clear trajectory toward efficient computational paradigms for infinite-dimensional inverse problems. His work increasingly emphasizes structure-exploiting algorithms like LazyDINO and derivative-informed MCMC, demonstrating how machine learning can overcome computational bottlenecks in materials modeling. The consistent application of Bayesian frameworks across domains highlights his unified approach to uncertainty quantification in complex systems.

• Auditing
• Accounting Regulation
• Empirical Archival Accounting Research
• +7 more

Jonathan Shipman is a Professor and Department Chair of the William Dillard Department of Accounting in the Sam M. Walton College of Business at the University of Arkansas. He holds the Garrison/Wilson Endowed Chair in Accounting and has been a faculty member since January 2015. His research focuses on auditing, regulatory policy, and financial reporting, using empirical archival methods to analyze audit market dynamics and accounting practices. Education: PhD in Accounting, University of Tennessee BS in Business Administration, University of Central Arkansas Jonathan's research interests center on auditing, particularly the impact of regulatory changes on audit quality, auditor behavior, and market outcomes. He investigates topics such as auditor independence, goodwill impairment, fair value accounting, and proxy voting efficacy. His work often employs large-scale archival datasets and advanced econometric techniques to draw policy-relevant conclusions. His recent publications span top journals including The Accounting Review , Contemporary Accounting Research , and Review of Accounting Studies . The research trend shows a strong emphasis on methodological rigor, regulatory implications, and the interplay between audit practices and financial reporting quality. Recurring themes include control variable usage, confirmation bias in auditing, and the economic consequences of accounting standards. Scientific Awards and Recognitions: Garrison/Wilson Endowed Chair in Accounting Jonathan leads a productive research agenda, frequently collaborating with prominent scholars in the field. He has secured research support through his endowed chair and institutional affiliations. While formal advising roles are not detailed, his co-authorships suggest mentorship of junior faculty and PhD students. He is actively contributing to the academic community through publications, leadership in his department, and engagement with scholarly platforms like Google Scholar and SSRN. He is involved in research teams focusing on audit quality, financial reporting, and empirical methodology. These collaborations often involve interdisciplinary approaches within accounting and finance, contributing to both theoretical and practical advancements in the field.

Benjamin KaminskiView profile

Professor

• Formal Verification
• Probabilistic Programs
• Quantitative Verification
• +12 more

Benjamin Kaminski is a Professor at Saarland University and a Lecturer at University College London, where he contributes to the field of computer science with a strong emphasis on formal verification and logic. He is currently the Head of the Examination Board for the B.Sc. in Computer Science (English) program at Saarland University and actively supervises PhD, Master’s, and Bachelor’s students in research projects related to logic and verification. His research centers on the quantitative aspects of formal program verification , particularly in probabilistic and quantum programs. Key interests include weakest-precondition reasoning, expected runtimes, incorrectness logic, and the integration of machine learning with formal methods. He advocates for explainable verification and explores non-classical models such as weighted programming. His recent publications reflect a consistent focus on probabilistic semantics , runtime analysis , and deductive verification of programs, utilizing formal logic and mathematical structures to ensure correctness and efficiency. These works span high-impact venues such as POPL, LICS, and JACM. Ackermann Award for outstanding dissertations 2020 Distinguished Paper at POPL 2021 Best Paper Award at LOPSTR 2020 EATCS Best Paper Award at ESOP 2016 Borchers Badge for doctoral distinction 2019 Best scientific production award at Université Paris-Saclay 2022 Benjamin Kaminski has advised numerous students at various levels, including current PhD candidates Tobias Gürtler, Lena Verscht, Anran Wang, and Linpeng Zhang. He has served on program committees for top conferences such as POPL, CAV, LICS, and ICALP, and has reviewed for prestigious journals including Journal of the ACM and TOPLAS . He has also evaluated research proposals for the Israel Science Foundation and Czech Science Foundation, highlighting his role in shaping global research agendas. His editorial work includes contributions to Foundations of Programming and Software Systems (2019). He is actively involved in the formal methods research community, contributing to workshops and symposia on static analysis, dependable software engineering, and quantitative evaluation of systems. His leadership in organizing and reviewing for major conferences underscores his influence in theoretical computer science.

Sanghyeok ChoiView profile

Researcher

• Deep Learning
• Generative Models
• Probabilistic Inference
• +6 more

Sanghyeok Choi is a PhD student in Informatics at the University of Edinburgh, supervised by Nikolay Malkin. His research focuses on deep learning, generative models, and probabilistic inference, with applications in AI Safety and AI for Science. His studies are funded by ARIA and CIFAR, supported by the Global Korea Scholarship. Education: PhD in Informatics (University of Edinburgh, 2025–current), MS in Industrial & Systems Engineering (KAIST, 2023–2025), BBA and BS in Industrial Engineering (Seoul National University, 2017–2023). Experience: Research Intern at Mila (2024–current), Undergraduate Researcher at KAIST and Seoul National University labs, and Machine Learning Research Engineer at MakinaRocks. Research Interests: Generative Flow Networks, structure learning, and reinforcement learning with applications in biological sequence design, molecular optimization, and HVAC systems. His work combines deep learning with combinatorial optimization to solve NP-hard problems using AI-driven frameworks. Scientific Trends: Recent publications emphasize GFlowNets for combinatorial/molecular optimization, transformer-based routing solutions, and reinforcement learning in energy systems. Key themes include AI for science, equity-aware algorithms, and genetic-guided neural architectures. Scientific Awards: Global Korea Scholarship (2025) Bang Il-Young Scholarship (2018–2021) 1st Place in DACON AI Competitions (2020) Open Source Contributions: TorchGFn (2024–current) RL4CO benchmark (2023–2025)

Felix HerrmannView profile

Professor

• Computational Imaging
• Inverse Problems
• Machine Learning
• +5 more

Felix Herrmann is a Professor at the Georgia Institute of Technology, holding a joint appointment between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering within the College of Computing. He leads the Seismic Laboratory for Imaging and Modeling (SLIM) and co-directs the Center for Machine Learning for Seismic (ML4Seismic). His research focuses on computational imaging, inverse problems, and machine learning applications in geophysics, particularly in seismic and medical imaging. Herrmann has pioneered innovations in compressive sensing for time-lapse seismic data acquisition, earning the SEG Reginald Fessenden Award in 2020. His work emphasizes Bayesian inference, uncertainty quantification, and scalable computational methods for subsurface monitoring, including carbon sequestration and reservoir characterization. Education: Ph.D. in Engineering Physics (Delft University of Technology, 1997), followed by postdoctoral roles at Stanford University and MIT before joining the University of British Columbia (2002). He transitioned to Georgia Tech in 2017 as a Georgia Research Alliance Eminent Scholar in Energy. Research Interests: Herrmann’s cross-disciplinary program integrates randomized linear algebra, PDE-constrained optimization, and high-performance computing. Key areas include digital twin technologies for subsurface monitoring, generative AI for geostatistical modeling, and scalable inversion algorithms (e.g., wavefield reconstruction inversion). His methodologies aim to reduce costs and improve reliability in seismic imaging and carbon storage monitoring. Scientific Contributions: Over 150+ peer-reviewed articles, including seminal work on compressive sensing in seismic acquisition and Bayesian experimental design. His software contributions include InvertibleNetworks.jl and Devito , advancing computational geophysics. Labs & Collaborations: Directs SLIM, fostering industry partnerships through ML4Seismic to develop AI-driven seismic imaging tools. Active in cloud-based scalable workflows and event-driven seismic imaging systems.

• Bayesian Optimization
• Machine Learning
• Artificial Intelligence
• +3 more

Julien Martinelli is a Research Fellow in the Department of Computer Science at Aalto University, Finland. He actively contributes to the research group led by Professor Harri Lähdesmäki, focusing on advanced methodologies in machine learning and optimization with practical applications across scientific domains. Research Interests: Martinelli specializes in Bayesian optimization frameworks, particularly addressing challenges with nuisance parameters, multi-fidelity data sources, and contextual variable selection. His work bridges theoretical machine learning with real-world applications including chemical reaction network inference and model interpretability. Key contributions involve developing robust optimization algorithms that handle unreliable information sources and unknown domain shifts. Publication Trends: Recent work (2022-2025) demonstrates consistent innovation in Bayesian optimization techniques, with increasing emphasis on transfer learning capabilities and human-AI collaboration through preferential feedback systems. His publications reveal strong interdisciplinary connections between computer science, chemistry, and systems biology, particularly through the Reactmine algorithm for chemical network inference. Scientific Awards: No awards or fellowships were documented in the provided information. Advising and Grants: No details regarding graduate student supervision, research grants, or funding sources were mentioned in the available materials. Labs and Teams: Martinelli operates within Professor Harri Lähdesmäki's research group at Aalto University, collaborating extensively with Samuel Kaski and international researchers on cutting-edge machine learning projects.

Paul BürknerView profile

Professor

• Bayesian Statistics
• Probabilistic Programming
• Computational Statistics
• +5 more

Paul Bürkner is a Full Professor of Computational Statistics at TU Dortmund University , focusing on probabilistic (Bayesian) methods. His research sits at the intersection of statistics and machine learning, with applications across quantitative sciences. Key Roles : Developer of the brms R package, member of the Stan and BayesFlow development teams. Research Pillars : Bayesian inference, uncertainty quantification, amortized workflows, simulation-based inference, and probabilistic programming. His lab advances methods for prior specification, model evaluation, and scalable inference, collaborating on applications from cognitive science to ecology. Recent work emphasizes neural superstatistics and BayesFlow for efficient mixture and multilevel models. Students and researchers are encouraged to reach out for collaboration or thesis opportunities. Key Labs/Teams : BayesFlow Development Team Stan Project ELLIS Network (European Laboratory for Learning and Intelligent Systems)

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +3 more

Aditya Ravuri is a Researcher affiliated with the Department of Computer Science and Technology at the University of Cambridge. His work focuses on advancing machine learning and probabilistic modeling techniques with applications in computational biology, chemistry, and data science. He contributes to foundational research in geometric learning, scalable Gaussian processes, and dimensionality reduction methods. Key research interests include developing algorithms for single-cell transcriptomics analysis, chemical reaction prediction, and uncertainty quantification. His recent work explores unifying probabilistic frameworks for graph and manifold learning, as well as applying self-supervised learning for zero-shot prediction tasks. Ravuri collaborates on interdisciplinary projects such as ice core dating and catalyst design, demonstrating the applicability of machine learning across diverse scientific domains. His publications reflect a strong emphasis on methodological innovation while addressing real-world challenges in biology, chemistry, and environmental science. No formal teaching role or student advising is explicitly listed, though his research contributions likely involve collaboration with graduate students and postdoctoral researchers. The lack of award mentions suggests his work is in early career stages or focused on technical contributions over award-seeking activities.

Marcus A BrubakerView profile

Associate Professor

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

Marcus A Brubaker is an Associate Professor of Computer Science at York University in Toronto and a Faculty Affiliate at the Vector Institute. He also serves as a Status-only Professor at the University of Toronto and conducts research consulting for Samsung AI Centre and Borealis AI in Toronto. His work focuses on interdisciplinary applications of machine learning, computer vision, and statistics, particularly in computational biology, medical imaging, and sensor systems. Brubaker's research interests include developing novel algorithms for 3D reconstruction (e.g., neural radiance fields), cryo-electron microscopy (Cryo-EM) analysis, noise modeling, and generative models. He has contributed significantly to methods like dynamic normalizing flows for stochastic processes, geometry-aware diffusion models, and text-guided image editing techniques. His work bridges theoretical machine learning with practical applications in bioimaging and computer graphics. His publications span over two decades, with recent trends emphasizing Bayesian methods, efficient neural network architectures, and multi-view scene understanding. Brubaker collaborates extensively with industry partners (e.g., Samsung AI) and academic institutions to advance AI-driven solutions in microscopy, image processing, and 3D modeling. Brubaker’s research has led to impactful tools like cryoSPARC for Cryo-EM structure determination and Wavelet Flow for high-resolution image analysis. He actively engages in academic outreach, as evidenced by his participation at ICCV2023 and mentorship of early-career researchers.

• Causal Machine Learning
• Explainable AI
• High-Dimensional Data Analysis
• +5 more

Prof. Stefan Bauer is an Associate Professor at TU Munich and a senior PI at Helmholtz AI. He holds a Ph.D. in Computer Science from ETH Zurich (2018), where he received the ETH Medal for his thesis on causal learning. His research focuses on developing algorithms that uncover causal relationships in high-dimensional data, enabling explainable AI and robust, transformative technologies. Bauer previously worked as an Assistant Professor at KTH Stockholm and a Group Leader at the Max Planck Institute for Intelligent Systems. Educational Background Ph.D. in Computer Science, ETH Zurich (2018) M.Sc. Mathematics, ETH Zurich B.Sc. Economics and Finance, University of London Research Interests Bauer’s work bridges causal inference, machine learning, and robotics. Key themes include causal representation learning for AI transparency, scalable algorithms for complex systems, and applications in robotics, materials science, and biomedical research. His team emphasizes real-world validation, such as deploying dexterous manipulation robots and discovering novel high-entropy alloys via machine learning. Awards and Contributions CIFAR Azrieli Global Scholar (2020) Best Paper Award at ICML (2019) ETH Medal for Outstanding Dissertation (2018) Advancing AI and Robotics Bauer leads projects like the Real Robot Challenge , a cloud-based platform for reproducible robotics research. He also collaborates on biomedical applications, such as bias detection in medical imaging and robustness testing for foundation models in healthcare. Labs and Collaborations His lab at TU Munich integrates interdisciplinary teams working on causal AI, robotics, and materials discovery. Bauer collaborates with Helmholtz AI, KTH, and institutions globally to advance AI’s societal impact.

• Logics
• Databases
• Formal languages
• +5 more

Gabriele Puppis is an Associate Professor at the Department of Mathematics, Computer Science, and Physics of Udine University since 2022. Previously, he held a tenure-track position at the same university (2019-2022) and was a tenured CNRS researcher at LaBRI, Bordeaux (2012-2019). He completed postdoctoral research at Oxford University (2009-2012) and Udine University (2006-2008). His academic journey began with a PhD in Computer Science at Udine University (2002-2006). Education: PhD in Computer Science, Udine University (2006) Research Interests: Logics and formal languages String transducers and automata theory XML schema validation and database repairability Interval temporal logic and spatial reasoning Higher-order query languages Academic Contributions: His work focuses on the decidability and complexity of problems in automata theory, particularly for finite-valued transducers and their equivalence. He has advanced understanding of bounded repairability for tree and word languages, dynamic data structures for timed automata, and resynchronizability in transducer models. His recent publications explore decomposition theorems for streaming string transducers and their equivalence to two-way models. Teaching and Leadership: He teaches courses in Distributed Systems, Algorithms and Data Structures, and Verification techniques in AI and Cybersecurity. He has organized events like the GandALF 2023 summer school and participated in workshops on unambiguity in automata theory and regular cost functions.