Find Academic Supervisors

• Learning to optimize
• Optimization under uncertainty
• Efficient logistics and transport
• +4 more

Prof. Dr. Kevin Tierney is a Full Professor for Decision and Operation Technologies at Bielefeld University's Faculty of Business Administration and Economics. He also serves at the Department of Management Science & Business Analytics and is affiliated with the Bielefeld Center for Data Science (BiCDaS) and Center for Uncertainty Studies (CeUS). Chair of Business Administration, Decision and Operation Technologies Member of BIGSEM Graduate School PhD (2013) - IT University of Copenhagen Sc.M. (2010) & BS (2008) - Brown & RIT Research Interests His work focuses on: Learning to Optimize: Using deep reinforcement learning to automate solution heuristics for complex problems like routing and scheduling. Optimization under Uncertainty: Developing models that incorporate probabilistic elements for decision-making in unpredictable environments. Efficient Maritime Logistics: Specializing in container shipping, terminal operations, and fleet routing with real-world constraints. Recent publications demonstrate expertise in algorithm configuration, constraint programming, and machine learning applications to logistics challenges. Scientific Recognition Distinguished Paper Award - European Conference on Artificial Intelligence (2020) Projects & Grants Principal Investigator in projects: Self-learning methods with Deep Reinforcement Learning (DFG 2026) itsowl-MOVE (Land NRW 2024) AIPlan4EU Meta-planning engine (EU H2020 2023) Academic Leadership Module responsible for: Quantitative Business Administration Data Science Production and Operations Management

Matthias UnterhuberView profile

Lecturer

• Logic
• Philosophy of Language
• Neuroscience
• +4 more

Dr. Matthias Unterhuber is a postdoctoral researcher and lecturer at the Department of Philosophy II, Ruhr University Bochum. He holds a PhD in Philosophy and combines expertise in Philosophy (four-year combined bachelor/master’s) and Psychology (five-year combined bachelor/master’s). Education: PhD in Philosophy, four-year combined bachelor/master’s in Philosophy, five-year combined bachelor/master’s in Psychology His research spans Logic , Philosophy of Language , and Philosophy of Science , with a focus on conditional reasoning, ceteris paribus laws, and Bayesian pragmatics. He actively investigates pragmatic influences on sentence meaning using EEG studies and develops Bayesian models for linguistic interpretation in collaboration with Prof. Dr. Markus Werning. His publications highlight interdisciplinary work bridging Philosophy and Neuroscience , particularly in conditional logic, connexive logic, and the intersection of pragmatic effects with empirical neuroscience methods like EEG. Key themes include probabilistic reasoning, formal semantics, and cognitive modeling. Dr. Unterhuber contributes to the DFG-funded project The Interaction of Bayesian Pragmatics and Lexical Semantics under the XPrag framework, utilizing the university’s EEG lab to explore situated cognition and pragmatic prediction mechanisms.

• Biomedical Engineering
• Neuroscience
• Cognitive Neuroscience
• +7 more

Mingzhou Ding is Distinguished Professor and J. Crayton Pruitt Family Professor in the J. Crayton Pruitt Family Department of Biomedical Engineering at the University of Florida, within the College of Engineering. His research bridges physics, engineering, and neuroscience to investigate brain function through advanced neuroimaging and signal processing techniques. His research interests include cognitive neuroscience , multimodal neuroimaging , signal processing , affective neuroscience , and computational modeling of brain networks . He employs methods such as EEG-fMRI integration, multivariate pattern analysis, and effective connectivity modeling to study how emotional and cognitive processes are represented in the brain. Recent publications highlight a strong trend in decoding emotional content in visual cortex, attentional control mechanisms, and neuromodulation of brain networks. His work frequently involves analyzing neural representations of affective scenes, conflict processing in psychiatric conditions, and the role of reentrant feedback from limbic structures in shaping visual perception. Specialty Chief Editor, Frontiers in Human Neuroscience Guest Associate Editor, Frontiers in Neural Circuits Guest Associate Editor, Cognitive Neuroscience , Frontiers in Human Neuroscience Prof. Ding has contributed to numerous editorial roles and has collaborated widely across neuroscience and biomedical engineering. He advises several researchers and has been involved in studies supported by significant NIH and NSF funding, though specific grants are not detailed here. His lab focuses on brain imaging and stimulation, with a strong emphasis on experimental design and computational analysis of neural data. He leads a research team specializing in brain network dynamics, neuroimaging methodology, and cognitive neuroscience experiments, particularly in the domain of emotion and attention.

• Demography
• Population Health
• Mortality Forecasting
• +3 more

Ricarda Duerst is an Affiliate Researcher at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, affiliated with the Laboratory of Population Health within the Population Health Research Group. Her work focuses on statistical demography, particularly advancing methodological rigor in demographic forecasting through probabilistic modeling and validation frameworks. Her research spans Demography, Population Health, and Statistical Demography with specialized expertise in Mortality Forecasting and Fertility Forecasting. She develops innovative statistical approaches for short-term demographic projections, including empirical prediction intervals for mortality trends, calibration techniques for fertility rate forecasts, and systematic validation workflows. Her work addresses critical challenges in quantifying uncertainty and improving reliability of demographic predictions used in public health and policy planning. Recent publications reveal a concentrated research trajectory on forecast validation methodologies, with emphasis on mortality and fertility projections. Her 2023-2024 work establishes practical frameworks for assessing forecast accuracy, calibrating probabilistic paths using historical errors, and quantifying excess deaths through empirical prediction intervals. These contributions advance computational demography by bridging theoretical statistics with real-world demographic applications, particularly in European contexts like Finland. No scientific awards were mentioned in the provided information. Information regarding academic advising responsibilities and research grants was not provided in the source text. Duerst operates within the Laboratory of Population Health at MPIDR, a specialized unit under the Population Health Research Group focusing on labor demography and demographic determinants of health. This laboratory contributes to MPIDR's broader mission through interdisciplinary research connecting demographic methods with population health outcomes, leveraging the institute's expertise in computational demography and sustainable well-being metrics.

• Astrophysics
• Cosmology
• Galactic Astronomy
• +5 more

Gregory Green is an independent research group leader at the Max Planck Institute for Astronomy (MPIA) in Heidelberg, Germany, specializing in galactic evolution and interstellar dust mapping. His work combines observational data from the Gaia mission, photometric surveys, and computational models to reconstruct three-dimensional structures of the Milky Way. Key research areas include dust extinction curves, stellar dynamics, and data-driven astrophysical inference. Institution: Max Planck Institute for Astronomy Department: Galaxies and Cosmology Position: Sofia Kovalevskaja Group Leader Contact: +49 6221 528-460 | Königstuhl 17, 69117 Heidelberg Green's research focuses on computational astrophysics and galactic structure analysis. He develops novel methods for mapping dust extinction in 3D, studies the dynamics of the Milky Way's gravitational potential, and applies machine learning to stellar population analysis. His work bridges observational astronomy with theoretical modeling, particularly in understanding the interstellar medium's role in galaxy evolution. The 15 most recent publications highlight his emphasis on Gaia data exploitation, dust mapping algorithms (including normalizing flows), and galactic dynamics. Articles span topics from binary star census to radiative properties of the interstellar medium, with methodological innovations in astrometric inference and spectral analysis. Scientific Awards: Sofia Kovalevskaja Award recipient for establishing independent research group Green leads the Galaxy Evolution group at MPIA, contributing to cosmological applications of galactic dust mapping. His work informs models of galaxy formation and provides foundational data for studies of stellar populations and dark matter constraints via satellite galaxies.

• Collective Behaviour
• Cultural Evolution
• Cognitive-Computational Modeling
• +7 more

Dominik Deffner is a computational behavioral scientist and Professor at the Department of Psychology, Philipps University Marburg . His work bridges psychology, evolutionary biology, and statistics to study how individuals, groups, and populations adapt to changing environments through computational modeling and empirical research. Education: PhD in Biology (2021), MSc in Evolutionary and Comparative Psychology (2017), BSc in Psychology (2016), BA in Social and Cultural Anthropology & Philosophy (2016) Research interests include: Collective Behavior - Using immersive games and field research to model real-world group dynamics. Cultural Evolution - Simulations to study how culture emerges from individual decisions. Causal Inference - Developing transparent statistical frameworks to justify causal claims in psychology. Cross-cultural Methods - Creating workflows for comparing data across populations. Animal Adaptation - Applying computational models to non-human species' cognitive processes. Publications (2025–2019) span cultural evolution, collective decision-making, risk-sensitive learning, and causal modeling , with a focus on interdisciplinary approaches. Recent work includes applying Bayesian methods to cross-cultural studies and analyzing social information use in human and animal groups. Students : No formal advisees listed in the provided texts.

• Rationality
• Heuristics
• Decision Making
• +10 more

Jan K. Woike is a Visiting Researcher at the Max Planck Institute for Human Development , specializing in adaptive rationality, heuristics, and decision-making. His research spans behavioral economics, social learning, and evolutionary computation, with a focus on understanding human inference and policy capturing. 2008: PhD in Psychology, Bochum University 2001: Diploma in Psychology, Bochum University 1997-1998: Special Student, Harvard University Woike's research explores heuristics in uncertain environments , destructive competition in groups , and privacy in Big Data . He employs computer simulations , online tools , and evolutionary models to analyze decision-making in economic contexts and social dynamics. His publications examine topics such as relative performance feedback in group competition, crowdsourcing hypothesis testing , and equal weighting in venture capital decisions . These works bridge psychology, behavioral economics, and computational modeling. Woike has held postdoctoral roles at the University of Basel (2011-2012), University of Lausanne (2007-2012), and Erfurt University (2005-2007). He has also taught courses in business statistics, psychological diagnostics, and negotiation at institutions including Arizona State University and Bochum University. His interdisciplinary methods include evolutionary computation and interactive group research tools , contributing to the Adaptive Rationality Research Team's mission to understand how humans navigate uncertainty.

Carlos AmendolaView profile

Assistant Professor

• Algebraic Statistics
• Applied Algebraic Geometry
• Nonlinear Algebra
• +5 more

Carlos Amendola is an Assistant Professor (tenure-track) of Algebraic and Geometric Methods in Data Analysis at the Institute of Mathematics of the Technical University of Berlin. Previously, he was a postdoc at TU Munich in Mathematical Statistics and Optimization and Data Analysis groups, and served as a substitute professor at Ulm University. His academic journey includes a PhD from TU Berlin under Bernd Sturmfels and Christian Haase, an M.Sc. from NYU's Courant Institute, and dual B.Sc. degrees from Mexican institutions. B.Sc. in Applied Mathematics, Instituto Tecnológico Autónomo de México B.Sc. in Mathematics, Universidad Nacional Autónoma de México M.Sc. in Mathematics, Courant Institute at New York University Ph.D. in Mathematics, Technical University of Berlin (2017) Amendola's research centers on Algebraic Statistics, with significant contributions to Applied Algebraic Geometry and Nonlinear Algebra. His work explores the intersection of algebraic methods with statistical modeling, particularly focusing on Gaussian mixtures, likelihood geometry, moment varieties, graphical models, and path signatures. He has developed novel approaches connecting tropical geometry with max-linear graphical models for extreme value theory, and has made important contributions to understanding maximum likelihood estimation through algebraic and geometric perspectives. His recent publications demonstrate a strong focus on applying algebraic techniques to statistical problems, with particular emphasis on path signatures, tropical geometry, and graphical models. The research shows a clear trajectory toward developing computational methods for complex statistical models using algebraic geometry, with applications spanning machine learning, causal inference, and extreme value analysis. Amendola serves as an editor for the Algebraic Statistics (AStat) journal since 2022 and has organized major conferences including Algebraic Statistics 2025. He has successfully secured significant research funding through Berlin Mathematics Research Center MATH+ Project AA3-16, DFG Collaborative Research Center CRC/TRR 388, and DFG Priority Program SPP 2458. As an advisor, Amendola currently supervises four PhD students (Janike Oldekop, Kamillo Ferry, Francesco Nowell, and Gabriel Riffo) and two postdocs (Rosa Preiß and Leonard Schmitz). His teaching portfolio includes courses on Algebraic Statistics, Discrete Geometry, and various mathematics subjects at both undergraduate and graduate levels.

Michael HabeckView profile

Professor

• Bayesian Methods
• Structural Biology
• Biophysics
• +11 more

Michael Habeck is a Professor at the University of Jena and an MPI-NAT Fellow specializing in computational methods for statistical inference and scientific data analysis applied to structural biology and biophysics . His research focuses on the development of the Inferential Structure Determination (ISD) framework, which uses Markov chain Monte Carlo simulations to derive macromolecular structures from NMR , cryo-EM , and other structural data. Broad research areas include Bayesian computation , dynamic biological systems , and image processing for structural biology. Current projects involve advancing tools for modeling protein complexes and chromosomes , as well as developing general algorithms for Bayesian inference.

Stephan GünnemannView profile

Professor

• Machine Learning
• Data Analysis
• Graph Neural Networks
• +4 more

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis. Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018). His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications. Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015) Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.

Wolfgang UtschickView profile

Professor

• Signal Processing
• Wireless Communications
• Radar Technology
• +3 more

Prof. Wolfgang Utschick is a full Professor at the Technische Universität München (TUM), holding the professorship for Methods of Signal Processing since 2002. He is affiliated with the TUM School of Computation, Information and Technology and the Department of Electrical Engineering and Information Technology (EI). Since 2017, he has served as Dean of the EI Department, overseeing academic and research activities. His research integrates applied mathematics into signal processing applications across wireless communications, radar technology, vehicle safety, and machine learning. Prof. Utschick earned his doctorate after industry experience and completed postdoctoral studies at TUM and ETH Zurich. He has led numerous industrial and DFG-funded projects, resulting in patents in signal processing and over 150 peer-reviewed publications. Notable contributions include advancements in channel estimation, MIMO systems, and quantum radar technology. His research interests emphasize the intersection of mathematical theory and practical engineering, with a focus on optimizing communication systems and safety-critical applications. Awards include IEEE Fellow (2021), TUM Sabbatical for Teaching Excellence (2014), and multiple IEEE publication awards. Prof. Utschick’s work bridges academia and industry, addressing challenges in 5G/6G networks, radar innovation, and machine learning-driven signal processing. He remains active in curriculum development and departmental leadership at TUM.

Wolfgang A. WallView profile

Professor

• Computational Mechanics
• Fluid-Structure Interaction
• Multi-Field and Multi-Scale Problems
• +5 more

Prof. Wolfgang A. Wall is a Professor of Numerical Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. He leads the Chair of Computational Mechanics, established in 2003, and serves as Rector of CISM (International Centre for Mechanical Sciences) in Udine, Italy. His research focuses on computational mechanics, including fluid-structure interaction, multi-field and multi-scale problems, and applications in biophysics and biomedicine. He has pioneered numerical methods for complex simulations, integrating modeling, algorithm development, and high-performance computing. Key achievements include ERC Advanced Grants, EUROMECH Fellow status, and membership in the Bavarian and Austrian Academies of Sciences. Educational background: Studied at the University of Innsbruck, earned his doctorate from the University of Stuttgart after a research stay at Princeton University. His work spans engineering disciplines, emphasizing uncertainty quantification and machine learning in computational frameworks. He has contributed to lung mechanics modeling, cardiovascular simulations, and tumor growth analysis, with over 300 publications. Current projects address additive manufacturing, solid-state batteries, and patient-specific medical modeling. Awards: ERC Advanced Grant (2021), Prandtl Medal (2016), Bavarian Academy Membership (2017) Grants: Extensive funding for computational mechanics and biomedical engineering projects Labs: Chair of Computational Mechanics lab at TUM, collaborating with CISM and international partners

Ana DjurdjevacView profile

Assistant Professor

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +3 more

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

Carlos CinelliView profile

Assistant Professor

• Causal Inference
• Statistics
• Machine Learning
• +4 more

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington. He is also a Data Science Fellow at the eScience Institute and Affiliate Faculty at the Center for Statistics and the Social Sciences. He obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences. Key interests include: Causal inference challenges in social and health sciences Intersections of causality with machine learning and AI Sensitivity analysis for omitted variable bias Robust statistical methods for observational studies Instrumental variables and Mendelian randomization Generalizability of experimental findings His publications demonstrate a consistent focus on developing practical sensitivity analysis tools and advancing causal methodology, with recent work emphasizing applications in machine learning and econometrics. Honors include: Best Paper Award at SBE 2024 in Econometrics UCLA Dissertation Year Fellowship (2020) He actively advises PhD students and has received research funding from: NSF/MMS Royalty Research Fund He leads development of several open-source software packages for sensitivity analysis and maintains active collaborations with researchers at UCLA and other institutions.

Adrian DobraView profile

Professor

• Bayesian Statistics
• Hierarchical Modeling
• Statistical Computing
• +4 more

Adrian Dobra is a Professor at the University of Washington with joint affiliations in CSSS (Center for Statistics and the Social Sciences) and BNHS. His office is located in B314 Padelford Hall, and he can be contacted via email at adobra@uw.edu or phone at 206-543-8460. Research Focus: Bayesian statistical methods Hierarchical modeling for social sciences Statistical computing Categorical and count data analysis Clinical trial methodologies Awards & Honors: Elected Member, International Statistical Institute (2024) Fellow, American Statistical Association (2023) Teaching: Offers courses in Bayesian statistics, statistical computing, hierarchical modeling, and clinical trial design across Statistics, Biostatistics, and Social Science departments.