Find Academic Supervisors

• Probabilistic Machine Learning
• Bayesian Models
• Statistical Inference
• +12 more

Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.

Jan PetersView profile

Professor

• Robot Learning
• Reinforcement Learning
• Imitation Learning
• +5 more

Jan Peters is a full professor (W3) at the Computer Science Department of Technische Universität Darmstadt and serves as the department head of the Systems AI for Robot Learning (SAIROL) at the German Research Center for AI (DFKI) . He is also a founding faculty member of the Hessian Centre for Artificial Intelligence . Peters holds a Ph.D. in Computer Science from the University of Southern California (2007) and dual master’s degrees in Computer Science and Electrical Engineering from USC and TU Munich respectively. Research Themes : Robot Learning, Reinforcement Learning, Imitation Learning, Tactile Sensing, Human-Robot Interaction, and Safe AI. Recent Article Trends : Focus on deep reinforcement learning (Iterated Q-Networks, Adaptive Q-Networks), safe robot foundation models , tactile-enhanced imitation learning , and physics-informed machine learning . Scientific Recognition : Recipient of the Dick Volz Best PhD Thesis Award , ERC Starting Grant , IEEE Fellow , and Amazon Research Award . Leadership : Founder of the IEEE RAS Technical Committee on Robot Learning and editor for journals including Autonomous Robots and IEEE Transactions on Robotics .

Olaf RonnebergerView profile

Associate Professor

• Deep Learning
• Computer Vision
• Biomedical Image Segmentation
• +4 more

Olaf Ronneberger is an associate professor at the Albert-Ludwigs-Universität Freiburg and works at Google DeepMind . His research focuses on deep learning architectures , AI applications to scientific problems , and protein structure prediction . He leads seminars on deep learning and 3D image analysis, emphasizing vision-language integration and generative models. His publications include foundational work on U-Net architectures for biomedical image segmentation, AlphaFold 3 for biomolecular interaction prediction, and Gemini models for multimodal AI systems. Key subfields span medical imaging , protein folding , and vision-language models . Co-developer of U-Net , a widely used biomedical image segmentation framework. Contributor to AlphaFold 3 for structural biology. Research on Gemini 1.5/2.5 models for multimodal reasoning.

• Stochastic Processes
• Random Environments
• Percolation Theory
• +2 more

Noam Berger Steiger is a Professor of Stochastic Processes at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His office is located at Parkring 11, Garching bei München, and he can be contacted at noam.berger@tum.de. His research focuses on stochastic processes in random environments, percolation theory, and random walks. Key contributions include asymptotic analysis of preferential attachment graphs, quenched invariance principles for non-elliptic random walks, and slowdown phenomena in ballistic random motion. His work bridges theoretical probability with applications in complex systems. Analysis of his 2012-2014 publications reveals consistent focus on random walk dynamics in disordered media, with significant results on ballisticity conditions, trail detection in random scenery, and distributional limits. His research employs advanced probabilistic techniques published in top-tier journals including Annals of Probability and Probability Theory and Related Fields . Professor Berger has supervised 11 theses: 5 bachelor's theses at TUM covering Brownian motion properties and investment strategies for risk-averse investors, and 6 master's theses (3 at TUM, 3 at Hebrew University) on topics including return times for random walks, mass transport principles, and spin-glass percolation. His current teaching includes Markov Chains, Probability on Graphs, and Brownian Motion seminars. He is an active member of TUM's Probability Theory research group, which participates in the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. The group collaborates on quantum science initiatives while maintaining strong foundations in classical probability theory and stochastic analysis.

Muhammad Ali GulzarView profile

Assistant Professor

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +5 more

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

• Observational cosmology
• Galactic astronomy
• Exoplanet science
• +5 more

David W. Hogg is Professor of Physics and Data Science in the Center for Cosmology and Particle Physics in the Department of Physics at New York University. He serves as Senior Research Scientist in the Astronomical Data Group in the Center for Computational Astrophysics of the Flatiron Institute and maintains an affiliation with the Max-Planck-Institut für Astronomie in Heidelberg. His primary research focuses on observational cosmology, particularly approaches that use galaxies to infer physical properties of the Universe. He also conducts significant research on stellar kinematics in the Milky Way and the measurement and discovery of exoplanets. Across all domains, Hogg develops engineering systems and statistical methodologies that enable large-scale astrophysical projects for both his research group and the broader community. Recent work demonstrates expertise in robust statistical methods, particularly dimensionality reduction techniques like Robust-HMF. His research bridges theoretical statistics with practical applications in major astronomical surveys including Gaia, SDSS-V, and SPHEREx. He frequently explores connections between Bayesian and frequentist approaches to astronomical data analysis, with recent work on nuisance parameter integration, anomaly detection, and robust matrix factorization. Research supported by NYU, NASA, NSF, Moore Foundation, Sloan Foundation Additional support from Max Planck Society, Humboldt Foundation, ERC, Simons Foundation Hogg is actively involved in major astronomical projects including Astrometry.net, Gaia, and SDSS, with long-term comprehensive goals of analyzing all galaxies, stars, and astronomical images. His work emphasizes open science principles, reproducible research practices, and the development of publicly accessible tools for the astronomical community.

Christian WaldView profile

Research Fellow

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +3 more

Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

Melanie SchienleView profile

Professor

• Econometrics
• Statistical Methods
• Financial Risk Analysis
• +4 more

Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.

• Computer Architecture
• Hardware Acceleration
• Approximate Computing
• +3 more

Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.

• Algorithms and Complexity
• Logic
• Database Theory
• +2 more

Martin Grohe is a Professor at the School of Logic and Theory of Discrete Systems , part of the Department of Computer Science at RWTH Aachen University . His research spans Algorithms and Complexity , Logic , Database Theory , Graph Theory , and Machine Learning , with a focus on integrating logical frameworks into computational models. His recent work explores graph neural networks , Weisfeiler-Leman algorithms , and parameterized complexity , as seen in publications on isomorphism testing , database repairing , and probabilistic query evaluation . While no specific scientific awards are mentioned, his contributions to graph theory and machine learning are widely recognized through numerous peer-reviewed publications.

Marco CuturiView profile

Professor

• Machine Learning
• Optimal Transport
• Optimization
• +4 more

Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.

• Artificial Intelligence
• Cognitive Science
• Human-Agent Interaction
• +5 more

Prof. Dr.-Ing. Stefan Kopp is a faculty member at Bielefeld University's Faculty of Engineering and serves as Research Group Leader of the Cognitive Systems and Social Interaction Group . He also holds administrative roles as Vice Dean and Deputy CITEC Coordinator . His work focuses on Artificial Intelligence , Cognitive Systems , and Socio-Technical World research areas. Research Group Leader: Cognitive Systems and Social Interaction Group Vice Dean: Faculty of Engineering Deputy Coordinator: Center for Cognitive Interaction Technology (CITEC) Project Manager: TRR 318 "Constructing Explainability" subprojects His research explores human-agent interaction , multimodal conversational agents , and social AI through projects like 39-Inf-11 Human-Machine Interaction and 39-M-Inf-VKI Virtual Humans and Conversational Agents . Publications address topics including adaptive explanation generation , gesture synthesis , and social cognition in dynamic environments. Current research topics span cooperative AI , explainable decision-making , and sensorimotor grounding in artificial systems.

James Urquhart AllinghamView profile

Researcher

• Bayesian Deep Learning
• Deep Generative Models
• Probabilistic Methods in Machine Learning
• +7 more

James Urquhart Allingham is a Research Scientist at Google DeepMind , working on the Gemini project. He completed his PhD in the Machine Learning Group at the University of Cambridge under the supervision of José Miguel Hernández-Lobato, with funding from EPSRC, the Michael E. Fisher Studentship in Machine Learning, and the Qualcomm Innovation Fellowship. He was also part of the ELLIS PhD program, advised by Eric Nalisnick at AMLab UvA. Current affiliation: Google DeepMind (Research Scientist) PhD: University of Cambridge (Machine Learning Group) Academic networks: ELLIS PhD program, Darwin College His research focuses on the intersection of Bayesian deep learning and probabilistic methods in deep learning. Key areas include deep generative models , zero-shot classification , prompt engineering , Monte Carlo gradient estimation , and applications to sustainability and climate change . His work has explored energy-based models , neural architecture search , and equivariance in convolutional networks . Selected scientific awards and grants include the Michael E. Fisher Studentship , Qualcomm Innovation Fellowship , and MPhil in Advanced Computer Science with Distinction . He has collaborated with institutions such as the Amsterdam Machine Learning Group (AMLAB) and University of the Witwatersrand .

Zhen LiuView profile

Assistant Professor

• Generative Models
• 3D Representation Learning
• Computer Vision
• +2 more

Zhen Liu is an Assistant Professor at the School of Data Science, CUHK-Shenzhen. His research focuses on generative models, 3D representations, and the synergy of spatial and semantic understanding in AI systems. With a PhD from Mila and Université de Montréal, he develops foundational methods for physics simulation, 3D assembly, and semantic reasoning in neural networks. His work bridges machine learning with applications in computer vision and graphics, emphasizing: Generative architectures for 3D content creation Diffusion model alignment techniques Efficient parameter finetuning strategies Dr. Liu mentors students in AI research and contributes to advancing 3D generative modeling paradigms.

• Computational Linguistics
• Historical Linguistics
• Phylogenetic Linguistics
• +8 more

Prof. Gerhard Jäger holds the Chair of General Linguistics at the Faculty of Humanities, University of Tübingen . He serves as a Principal Investigator (PI) in the Clusters of Excellence Human Origins and Machine Learning for Science , and leads projects like Phylomilia (funded by Volkswagen Foundation) and CrossLingference (ERC Advanced Grant). His career spans multiple institutions, including Bielefeld University (2004-2009) and Stanford University (visiting scholar, 2004). Habilitation (2002) at Humboldt University Berlin with thesis on Anaphora and Type Logical Grammar PhD (1996) at Humboldt University Berlin on Dynamic Semantics His research bridges computational linguistics , phylogenetic analysis , and game theory , focusing on Bayesian models , language evolution , and cross-linguistic typology . Recent work explores phylogenetic inference from acoustic speech data and geographic influences on language trees . Key contributions include 15+ recent publications on topics spanning phylogenetic typology , cognate detection , and Bayesian language modeling . These works employ machine learning , statistical inference , and evolutionary game theory to analyze language change , typological variation , and linguistic stability . Honors include ERC Advanced Grant , Volkswagen Foundation funding , and DFG-Humanities Centre for Advanced Studies participation. He has taught courses in Computational Historical Linguistics , Phylogenetic Methods , and Bayesian Data Analysis across institutions like Tübingen, Bielefeld, and Stanford. He actively contributes to academic communities through workshop organization (e.g., Quantitative Theoretical Linguistics , Game Theory in Pragmatics ) and serves on the faculty council at Tübingen. His team collaborates with institutions like Max Planck Institute for Evolutionary Anthropology , University of Pennsylvania , and LMU Munich .