Find Academic Supervisors

• Risk Analysis
• Structural Reliability
• Civil Engineering
• +5 more

Prof. Michael Beer is the Executive Director of the Institute for Risk and Reliability at Leibniz University Hannover. He holds a professorship in the Faculty of Civil Engineering and Geodetic Science and serves on the Faculty Council. His research focuses on structural reliability, uncertainty quantification, and risk analysis with applications in civil engineering systems. He leads the Collaborative Research Centres (CRC) 871 and 1463, addressing regeneration of complex capital goods and offshore megastructure design, respectively. His work integrates machine learning, Bayesian methods, and stochastic modeling to address challenges in seismic vulnerability, geotechnical systems, and reliability-based design optimization. Beer is also a member of the Leibniz Research Centre Energy 2050, emphasizing interdisciplinary energy systems research. Beer's research interests span probabilistic modeling of dynamic systems, uncertainty propagation in engineering systems, and data-driven methods for reliability assessment. His recent publications emphasize computational methods for reliability, machine learning applications, and seismic risk analysis. He actively contributes to academic leadership roles, including editorial boards and research center management.

• Statistical Machine Learning
• Bayesian Modeling
• Probabilistic Inference
• +3 more

Francisco J. R. Ruiz is a Postdoctoral Research Scientist at Columbia University's Department of Computer Science and affiliated with the University of Cambridge's Engineering Department. He holds a Marie-Sklodowska Curie Fellowship under the EU Horizon 2020 program. His research focuses on statistical machine learning, Bayesian modeling, and inference, with notable contributions to stochastic inference for large categorical distributions. He completed his Ph.D. and M.Sc. at the University Carlos III in Madrid. His work addresses challenges in large-scale machine learning, including computational efficiency for high-dimensional categorical data. Recent contributions include methods like 'Augment and Reduce' for optimizing categorical distribution inference. Key Collaboration: Works with David Blei (Columbia) and Zoubin Ghahramani (Cambridge) Technical Expertise: Probabilistic modeling, neural networks, and scalable algorithms His seminar presentation at Columbia highlighted advancements in reducing computational complexity for large categorical distributions through latent variable augmentation and variational inference techniques.

• Computational Semantics
• Natural Language Processing (NLP)
• Formal Semantics
• +5 more

Aaron Steven White is an Associate Professor of Linguistics at the University of Rochester, with a secondary appointment in Computer Science and affiliation with the Goergen Institute for Data Science. He directs the Center for Language Sciences (CLS) and the Formal and Computational Semantics Lab (FACTS.lab). Previously, he was a postdoctoral fellow at Johns Hopkins University’s Science of Learning Institute (2015-2017) and earned his PhD in Linguistics from the University of Maryland (2015). His research focuses on computational semantics, investigating relationships between linguistic expressions and conceptual categories. Key projects include the MegaAttitude Project, analyzing predicate distributions and inferential properties, and the Decompositional Semantics Initiative (Decomp), which annotates corpora for semantic parsing and NLU systems. Recent work emphasizes integrating behavioral experiment data with corpus evidence to develop unified computational models. He teaches courses on probabilistic dynamic semantics and has presented keynote talks on ontology-driven evaluation of summarization systems. His contributions span theoretical linguistics, NLP, and cognitive science.

• Machine Learning
• Data Mining
• Big Data
• +4 more

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

Ruhul AminView profile

Assistant Professor

• Artificial Intelligence
• Bioinformatics
• Data Science
• +5 more

Ruhul Amin is an Assistant Professor at Fordham University's Department of Computer and Information Science, where he explores the intersection of Artificial Intelligence , Data Science , and Public Health . His work spans multiple domains including Bioinformatics , Natural Language Processing , and Computational Social Science . PhD in Computer Science (Stony Brook University, 2019) MS in Computer Science (Stony Brook University, 2015) BSc in Computer Science (Shahjalal University, 2007) His research focuses on developing deep learning algorithms for genome annotation, anomaly detection in high-cardinality spaces, and assistive technologies for visually impaired users. Notably, he designed the Mongol Dip bilingual screen reading software and the Pipilika Bengali text search engine. Recent publications (2025-2024) demonstrate his expanding interests in large language model alignment , sentiment analysis across languages, and distributed reinforcement learning for cybersecurity applications. His work frequently appears at IEEE conferences and in journals like PLOS One. Scientific Recognition BASIS National ICT Award (Bangladesh) Best Poster Awards at IEEE R10 HTC and CEWIT conferences Bangladesh Prime Minister’s Award (2012) mBillionth South Asia Award (2010) He maintains active collaborations with research teams at University of Toronto , University of British Columbia , and Stony Brook University . Contact: moamin@cs.stonybrook.edu

• Machine Learning
• Computer Science
• Artificial Intelligence
• +3 more

Amauri Holanda De Souza Junior is a Postdoctoral Researcher affiliated with the Department of Computer Science , focusing on Probabilistic Machine Learning . His work bridges theoretical advancements with practical applications in graph-based models. Active research areas include Graph Neural Networks , Persistent Homology , and Simulation-based Inference . His recent publications highlight innovations in: Topological data analysis for graph representations Robust statistical methods under model misspecification Scalable Bayesian inference frameworks Equivariant architectures for graph learning

• Decision Making
• Cognitive Psychology
• Judgment and Decision Making
• +5 more

Christin Schulze serves as Senior Research Scientist at the Max Planck Institute for Human Development and Adjunct Associate Professor at The Arctic University of Norway since 2019. Her work bridges cognitive psychology and decision science, focusing on how humans develop and execute choices under uncertainty across individual and social contexts. Education Ph.D. Psychology, University of New South Wales (2015) M.Sc. Psychology (Dipl. Psych.), Friedrich-Schiller University Jena (2011) Research Focus : Schulze leads the “Development of Decision Making” research area within the Adaptive Rationality group. Her investigations span experience-based decisions under uncertainty , childhood development of decision strategies , cognitive modeling of judgment , and social/group decision dynamics . She employs experimental paradigms and computational models to dissect how environmental structure, cognitive constraints, and social interactions shape choices from infancy through adulthood. Publication Trends : Her 2015-2021 corpus reveals consistent exploration of probability matching phenomena, social sampling mechanisms, and description-experience gaps. Key contributions demonstrate how group contexts eliminate individual decision biases (Schulze & Newell, 2016) and how bounded rationality governs social information acquisition (Schulze et al., 2021), bridging cognitive, developmental, and social psychology. Scientific Awards Advising & Grants : While specific students and funding sources aren’t detailed in available materials, her leadership of a Max Planck research area implies significant mentorship responsibilities and grant management for decision-science projects. Research Infrastructure : Schulze heads the “Development of Decision Making” unit within the Adaptive Rationality department at Max Planck, directing a team investigating cognitive architectures of choice through behavioral experiments and mathematical modeling.

• Relational Machine Learning
• Deep Learning
• Complex Network Science
• +4 more

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.

Ken SatohView profile

Professor

• Artificial Intelligence (AI) and Law
• Logical Foundations of AI
• Juris-Informatics
• +6 more

Ken Satoh is a full Professor at the National Institute of Informatics (NII) and Sokendai (The Graduate University of Advanced Studies), Japan. He leads the Center for Juris-Informatics within the Joint Support-Center for Data Science Research (ROIS-DS). Previously, he worked at Fujitsu (1981-1995) and was an Associate Professor at Hokkaido University until 2001. He holds a law degree from the University of Tokyo (2006-2009) and passed the Japanese bar exam in 2017. Roles: Director of Center for Juris-Informatics, Principal Investigator in multiple AI/Law projects Research Focus: Juris-informatics (merging informatics and law), logical foundations of AI, legal debugging, and compliance mechanisms for AI systems His work bridges AI and legal systems, including developing the PROLEG framework for legal reasoning and organizing international workshops like JURISIN. Key contributions include applying logical inference to detect legal conflicts in algorithmic governance systems and advancing AI ethics through compliance checks with regulations like GDPR. He has authored over 130 publications, including works on legal norm reasoning, multi-agent systems, and formal methods in law. His research group collaborates globally, hosting competitions like COLIEE to advance legal AI technologies.

• Developmental Psychology
• Cognitive Psychology
• Educational Psychology
• +4 more

Prof. Azzurra Ruggeri is a Professor in the Professorship for Cognitive and Developmental Psychology at Technical University of Munich (TUM). Her research focuses on understanding how children and adults strategically gather information, make decisions, and learn through embodied and active processes. She leads the iSearch Lab (https://isearchlab.org) and explores topics such as active learning, embodied cognition, and social-cognitive development. Her academic background includes a Dr. rer. nat. (PhD) in Psychology. She is based in Munich, coordinating research on developmental trajectories of learning, exploration strategies, and the interplay between motor skills and cognitive planning. Key areas of investigation include children's decision-making in uncertain environments, the impact of active learning on memory, and the role of embodiment in cognitive development. Recent work emphasizes adaptive information search behaviors, the effectiveness of question-asking strategies, and the application of embodied cognition principles to training interventions. Her studies often employ experimental paradigms involving climbing and spatial navigation to explore motor-cognitive interactions.

Mai BuiView profile

Lecturer

• Computer Vision
• Medical Image Analysis
• Surgical Robotics
• +5 more

Dr. Mai Bui is a Lecturer at the Deutsches Herzzentrum München (German Heart Center Munich), affiliated with the Chair of Computer Science Applications in Medicine under Prof. Nassir Navab. Her research focuses on integrating advanced computer vision and deep learning techniques into medical applications, particularly in surgical robotics, image-guided interventions, and medical augmented reality. She has contributed to critical areas like catheter tracking, pose estimation, and real-time medical imaging analysis. Dr. Bui teaches multiple courses including Computer Aided Medical Procedures , Medical Augmented Reality , and Introduction to Surgical Robotics . Her work emphasizes low-dose imaging solutions and robust navigation systems for minimally invasive procedures. She collaborates with interdisciplinary teams at labs such as DHM, IFL Lab, and NARVIS Lab to advance translational research in medical AI. Her recent publications highlight innovations in adversarial networks for pose refinement, lightweight camera localization systems, and multimodal inference in ambiguous medical scenes. These contributions address core challenges in surgical navigation and medical device tracking.

• Quantum Information Theory
• Network Information Theory
• Machine Learning Theory
• +3 more

Padakandla Arun is a Professor in the Communication Systems department at EURECOM. His research focuses on quantum information theory, network communication, and coding theory, with recent work on multi-terminal quantum channels and machine learning applications in quantum measurement systems. Affiliation: EURECOM - Communication Systems Academic Rank: Professor Research Interests Prof. Padakandla investigates quantum communication protocols, classical-quantum hybrid systems, and structured code design for multi-user channels. His work bridges information theory with quantum mechanics, emphasizing achievable rate regions and measurement simulation techniques. Scientific Contributions Key trends in his publications include: Quantum MAC and interference channel analysis PAC learning frameworks for POVM hypothesis classes Algebraic structures in network information theory Contact Email: Arun.Padakandla@eurecom.fr

• Uncertainty Quantification
• Bayesian Inverse Problems
• Data Science
• +7 more

Dr. Aretha Teckentrup is a Lecturer in the Mathematics of Data Science at the University of Edinburgh's School of Mathematics. Her research focuses on integrating mathematical models with observational data, particularly in areas like uncertainty quantification, Bayesian inverse problems, and computational methods for partial differential equations (PDEs). She holds a PhD in Mathematics from the University of Bath and has held postdoctoral positions internationally, including in Florida. Her work emphasizes interdisciplinary approaches, blending statistics, numerical analysis, and applied mathematics. Notably, she has contributed to advancing Gaussian processes, multilevel Monte Carlo techniques, and sparse grid methods for high-dimensional problems. Her academic journey reflects a strong commitment to bridging theoretical foundations with practical applications. She has published extensively on topics such as probabilistic numerical methods, error estimation in Bayesian inference, and adaptive sampling strategies. Dr. Teckentrup is an active member of the SIAM community, having received the prestigious SIAG/UQ Early Career Prize in recognition of her contributions to uncertainty quantification. Her research continues to explore innovative solutions for data-driven modeling challenges in science and engineering. Dr. Teckentrup’s work often addresses the growing importance of combining data with physical models, exemplified by her development of numerical methods for weather prediction and stochastic dispersion modeling. She advocates for collaboration across disciplines and emphasizes the transformative potential of integrating computational tools with real-world data. Her career trajectory underscores the dynamic and collaborative nature of modern academic research in applied mathematics and data science.

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

• Automata Theory
• Formal Methods
• Tree Automata
• +4 more

Christof Löding is an Adjunct Professor at the Department of Logic and Theory of Discrete Systems (Computer Science 7) at RWTH Aachen University. His research focuses on automata theory, formal verification, and logical foundations of computer science, with significant contributions to Büchi automata, tree automata, and stochastic games. Academic affiliation: RWTH Aachen University, Germany Research areas: Automata theory, Formal methods, Game theory, Logic in computer science Contact: loeding@informatik.rwth-aachen.de His recent work spans deterministic parity automata construction, finite-valued transducers, and algorithmic solutions for infinite games. Publications emphasize theoretical foundations and practical applications in program verification and XML processing. Articles analyze automata learning, uniformization problems, and lookahead degrees in infinite games, showing interconnections between automata theory and formal verification.