جست‌وجوی استاد و پژوهشگر

Alex Kaleمشاهده پروفایل

استادیار

• Data Visualization
• Uncertainty Communication
• Decision Making
• +۳ مورد دیگر

Alex Kale is an Assistant Professor in the Department of Computer Science at the University of Chicago, where he leads the Data Cognition Lab. His research develops visualization tools for data-driven decision making, uncertainty communication, and human-AI collaboration. Kale earned his PhD from the University of Washington and received multiple best paper awards from IEEE VIS. Research Focus Kale's work addresses cognitive pitfalls in data interpretation through participatory design of analytical tools. His lab investigates how visualization supports statistical reasoning, causal inference, and community-centered data practices. Awards & Recognition IEEE VIS Best Paper Award (2020) IEEE VIS Honorable Mention Award (2021) Teaching & Service Kale teaches courses on data visualization, human-computer interaction, and data science methodology. He has secured funding from the Department of Education for redesigning evidence-based decision tools in education.

Ana Cukarskaمشاهده پروفایل

پژوهشگر

• Machine learning for dynamical systems
• Numerical aspects of machine learning algorithms
• Koopman operator theory
• +۳ مورد دیگر

Ana Cukarska is a PhD candidate at the Technical University of Munich (TUM) within the School of Computational Engineering and Science, Department of Computer Science. She joined TUM in November 2023 under the supervision of Prof. Felix Dietrich. Her academic background includes an M.Sc. in Computational Science and Engineering (2021-2023) and a B.Sc. in Mechanical Engineering (2017-2023). Research Interests: Ana focuses on machine learning applications in dynamical systems, with particular interest in numerical aspects of ML algorithms, Koopman operator theory, and its applications in crowd dynamics modeling. Her work bridges computational science and machine learning methodologies. Teaching Involvement: She has contributed to various courses at TUM, including Scientific Computing and Machine Learning , Machine Learning in Crowd Modeling & Simulation , and multiple iterations of Physics-Informed Machine Learning exercises.

• Financial Econometrics
• Statistical Analysis
• Risk Management
• +۳ مورد دیگر

Wolfgang Karl Härdle is a Professor of Statistics at Humboldt University of Berlin, holding the Ladislaus von Bortkiewicz Chair of Statistics. He is affiliated with the School of Business and Economics and leads the Institute of Statistics and Econometrics. Additionally, he serves as Director of the Data研究中心 (Data Research Center) and the Digital Asset研究所 (Digital Asset Research Institute), and is a Foreign Expert Professor at Xiamen University. His career spans over three decades, including roles as an Ordinary Professor at CORE (Catholic University of Louvain) and visiting positions at institutions like Tilburg University and Hosei University. Education: He earned a Dr. rer. Nat. (Doctorate) in Mathematics from Heidelberg University in 1982 and completed his Habilitation in Statistics and Econometrics at Bonn University in 1988. His research focuses on financial econometrics, statistical methodology, and risk management, with contributions to cryptocurrency markets, machine learning applications, and high-dimensional data analysis. Research Interests: Härdle's work bridges statistical theory and practical applications, particularly in finance and economics. He develops methods for analyzing financial markets, risk modeling, and big data. Notable areas include the CRIX cryptocurrency index, regulatory risk indices, and network-based financial risk analysis. His research often integrates computational tools like R and Python to enable reproducible results. Publications: Over 350 publications, including seminal textbooks such as Applied Multivariate Statistical Analysis and Statistics of Financial Markets . His work frequently appears in top journals like Journal of Econometrics and Computational Statistics . Awards: Recognized as a "Highly Cited Scientist" by ISI (2003–2014), Fellow of the International Statistical Institute (1997), and recipient of the Multa Scripsit Award (2012). He is a member of prestigious societies like the Society for Financial Econometrics (SoFiE) and the International Society for NonParametric Statistics (ISNPS). Advising & Grants: Supervised numerous PhD students and led research projects funded by the DFG (IRTG 1792) and international collaborations. He advises institutions like the Guanghua School of Management (Peking University) and the Ferrell Asset Management (Singapore). Labs/Teams: Active in the IRTG 1792 program on High-Dimensional Nonstationary Time Series and the Collective Cognition & Cooperation Network (CoCCoN) with Princeton University. His team develops open-source tools like Quantlet and Quantinar for data analysis.

Beatriz Tucciمشاهده پروفایل

پژوهشگر

• Cosmology
• Astrophysics
• Theoretical Physics
• +۳ مورد دیگر

Beatriz Tucci is an academic affiliated with the Institute of Theoretical Physics (IFT) at UNESP in São Paulo, Brazil, and the University of São Paulo (USP). Her research focuses on cosmology, astrophysics, and theoretical physics, with a particular emphasis on applying machine learning to cosmological problems and studying the large-scale structure of the universe. She has collaborated with prominent researchers such as Raul Abramo (USP) and has contributed to topics like Baryon Acoustic Oscillations (BAO), galaxy clustering, and halo-galaxy connections. Her academic interests are reflected in her work on improving BAO constraints via field-level inference, exploring the kinetic Sunyaev-Zel’dovich effect, and analyzing galaxy formation through EFTofLSS. She is also engaged with educational initiatives, citing resources from institutions like ICTP-SAIFR and lecturers such as David Tong and Daniel Baumann. Beatriz’s publications span theoretical and applied cosmology, with a recent focus on combining machine learning with astrophysical data analysis. While no specific awards or grants are listed, her active research output indicates ongoing contributions to the field.

• Vegetation dynamics
• Permafrost ecosystems
• Climate change impacts
• +۲ مورد دیگر

Dr. Stefan Kruse is a Research Fellow at the Alfred Wegener Institute's Polar Terrestrial Environmental Systems group. His research focuses on vegetation development in extreme environments, particularly at the Siberian treeline, and investigates ecosystem responses to climate change using field studies, remote sensing, and computational modeling. His primary research interests include: Vegetation dynamics at ecological boundaries Permafrost-ecosystem interactions Population genetics of boreal species Individual-based ecological modeling Dr. Kruse employs advanced techniques including UAV-based photogrammetry, sedimentary DNA analysis, and satellite data interpretation to study above-ground biomass changes and long-term vegetation patterns across Arctic regions.

Nikos Gianniotisمشاهده پروفایل

پژوهشگر

• probabilistic machine learning
• dimensionality reduction
• Bayesian inference
• +۱ مورد دیگر

Nikos Gianniotis serves as a Staff Scientist in the Astroinformatics group at the Heidelberg Institute of Theoretical Studies (HITS), leveraging his computer science expertise to develop advanced machine learning solutions for astronomical data analysis. His role focuses on probabilistic modeling and algorithmic innovation within a dedicated research environment. His academic credentials include: PhD in Computer Science from the University of Birmingham (2007) MSc in Natural Computation (with Distinction) from the University of Birmingham (2003) BSc in Computing Science (First class) from the University of Aberdeen (2002) Dr. Gianniotis specializes in probabilistic machine learning , Bayesian inference , and dimensionality reduction techniques applied to astronomical challenges. His research bridges computational statistics and astrophysics, with emphasis on interpretable models for complex observational data. Recent work demonstrates sophisticated integration of Gaussian processes with time-series analysis for cosmic phenomena. Analysis of his 15 most recent publications (2016-2024) reveals consistent innovation in machine learning for astronomy, particularly in time-delay estimation for active galactic nuclei, spectral analysis, and time-series dimensionality reduction. His methodology frequently combines Gaussian processes, variational inference, and neural architectures like autoencoders to address astronomical data challenges. No scientific awards were documented in the provided materials. While no student advisement or grant details were specified, his publications indicate active collaboration within the astroinformatics community. The Astroinformatics group at HITS provides his primary research environment, focusing on scalable computational frameworks for next-generation astronomical datasets from missions like Kepler.

• Design of Experiments (DoE)
• Statistical Computing
• Regression Analysis
• +۳ مورد دیگر

Ulrike Grömping is a Professor of Applied Statistics and Business Mathematics at Beuth University of Applied Sciences Berlin, Department of Mathematics, Physics, and Chemistry. She specializes in Design of Experiments (DoE), statistical computing with R, and variable importance in regression models. Her academic career includes roles at Dortmund University and Ford Motor Company. She is a Book Review Editor for the Journal of Statistical Software and contributes to R packages such as DoE.base , FrF2 , and relaimpo . She holds a PhD in Statistics from Dortmund University (1996) and has published extensively on factorial designs, algorithmic methods, and statistical methodology. Education: 1991: Diploma in Statistics, Dortmund University 1996: PhD in Statistics, Dortmund University Research Interests: Design of Experiments, statistical software development, regression analysis, biostatistics, and combinatorial optimization. Professional Roles: Book Review Editor, Journal of Statistical Software Editor of Departmental Report Series Freelance Statistical Consultant Software Contributions: Maintainer of R packages for DoE and statistical inference, including DoE.base , FrF2 , and ic.infer . Her work bridges theoretical statistics with practical applications, emphasizing reproducible research and open-source tools. She collaborates across disciplines, including biotechnology and transportation safety, and has advised on EU-funded projects like TRACE.

• Philosophy of Science
• Formal Epistemology
• Decision Theory
• +۴ مورد دیگر

Ina Jäntgen is a Postdoctoral Fellow at LMU Munich's Faculty of Philosophy, Science Theory, and Religious Studies, affiliated with the Chair of Science Theory. She joined the Munich Center for Mathematical Philosophy (MCMP) in October 2024. Her research bridges philosophy of science, biomedical/social sciences, formal epistemology, and decision theory, focusing on how scientific knowledge can inform rational decision-making for policymakers. PhD in Philosophy, University of Cambridge (2023) Research Master's in Philosophy & Economics, Erasmus University Rotterdam Bachelor's in Philosophy and Economics, University of Freiburg The only article listed in her work, How to measure effect sizes for rational decision-making (2023), explores methodologies for quantifying scientific interventions' impacts on decision-making processes. This aligns with her broader interest in statistical inference and evidence-based policy. Dr. Jäntgen's office is located at Ludwigstr. 31, Room 124, and her email is Ina.Jaentgen@lmu.de.

Tom Sterkenburgمشاهده پروفایل

پژوهشگر ارشد

• Philosophy of Induction
• Epistemology of Machine Learning
• Philosophy of Artificial Intelligence
• +۱ مورد دیگر

Dr. Tom Sterkenburg leads the Emmy Noether research group 'From Bias to Knowledge: The Epistemology of Machine Learning' at Ludwig-Maximilians-Universität München's Munich Center for Mathematical Philosophy. His PhD thesis received the Wolfgang-Stegmüller-Award from the German Society for Analytic Philosophy. His research investigates philosophical questions in machine learning and artificial intelligence, with emphasis on induction, statistical learning theory, and epistemological foundations. He examines universal prediction models and limitations of learning algorithms. Publications analyze truth convergence in Bayesian methods, no-free-lunch theorems in supervised learning, and meta-inductive justification frameworks. His work bridges computational learning theory and philosophical inquiry.

Mihail Bogojeskiمشاهده پروفایل

پژوهشگر

• Geometric Deep Learning
• Quantum Chemistry
• Computational Biology
• +۳ مورد دیگر

Dr. Mihail Bogojeski is a postdoctoral researcher at the Technical University of Berlin , affiliated with the Machine Learning Group and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) . His research focuses on advancing machine learning techniques for applications in the physical sciences and medical sciences , particularly in quantum chemistry , computational biology , brain-computer interfaces , and sequential data analysis . B.Sc. in Software and Information Engineering (2014), Vienna University of Technology M.Sc. in Computer Science (2017), TU Berlin Ph.D. in Computer Science (2023), TU Berlin His research integrates geometric deep learning with quantum chemistry to develop models for predicting electronic structure of molecular systems and electron densities . Recent work includes equivariant neural networks for molecular wavefunction prediction, generative time-series models for industrial aging processes, and explainable AI frameworks for catalyst discovery. Publications highlight his expertise in machine learning , quantum chemical accuracy , and multimodal data augmentation . His 15 most recent articles focus on machine learning applications in chemistry , neuroscience , and industrial process modeling , with subfields such as generative models , equivariant networks , density functional theory , and neural signal processing .

Ali Hashemiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Ali Hashemi is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and a Postdoctoral Researcher at Technical University of Berlin. He holds a PhD (Dr. rer. nat.) with distinction from TU Berlin (2022) and an MSc in Electrical Engineering from Sharif University of Technology (2013). His research focuses on machine learning, Bayesian methods, and neuroimaging applications, particularly in inverse problems and explainable AI. He has contributed to advancements in structured noise modeling, robust source localization, and 3D neural networks for brain imaging. His work bridges signal processing and AI to improve biomedical data analysis. Education: PhD in Machine Learning, Technical University of Berlin (2022) MSc in Electrical Engineering, Sharif University of Technology (2013) Research Interests: Machine Learning & Optimization Bayesian Inference Neuroimaging (M/EEG, fMRI) Explainable AI & Diffusion Models Signal Processing for Biomedical Applications Key Achievements: Developed the 'Champagne Algorithm' for noise estimation in brain imaging Published widely in top venues like NeurIPS and NeuroImage Recipient of PhD distinction award for outstanding dissertation Labs & Affiliations: Active in TU Berlin's Uncertainty & Inverse Modeling group and Charité’s Brain and Data Science lab. Collaborates on open-source tools for M/EEG analysis (e.g., contributions to MNE-Python).

Robert Vandermeulenمشاهده پروفایل

پژوهشگر

• Anomaly Detection
• Nonparametric Statistics
• Deep Learning
• +۳ مورد دیگر

Dr. Robert Vandermeulen is a Postdoctoral Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), Technical University of Berlin. His research focuses on anomaly detection, nonparametric statistics, and aligning neural networks with human cognitive processes. He holds a PhD and two Master's degrees from the University of Michigan in Electrical Engineering and Mathematics. Education: PhD in Electrical Engineering, University of Michigan (2016) MS in Mathematics, University of Michigan (2015) MS in Electrical Engineering, University of Michigan (2012) Research Interests: Deep anomaly detection in high-dimensional data Nonparametric density estimation and tensor methods Human vs. neural network alignment for interpretable AI Applications in medical imaging and explainable AI Key Research Trends: His recent work addresses challenges in anomaly detection across domains like healthcare imaging and text analysis. He explores nonparametric methods to overcome the curse of dimensionality and integrates human cognitive insights to improve neural network robustness. Contributions include novel frameworks like input Hessian regularization and VICE (Variational Inference for Concept Embeddings). Affiliations & Prior Work: Previously a postdoctoral researcher at Technische Universität Kaiserslautern, he collaborates with BIFOLD on foundational machine learning research. His work bridges theoretical statistics and applied deep learning, with a focus on interpretable and reliable AI systems. Labs & Teams: Affiliated with BIFOLD, a cross-disciplinary institute advancing learning theory and data science fundamentals at TU Berlin.

Pengzhou (Abel) Wuمشاهده پروفایل

پژوهشگر ارشد

• Causality
• Machine Learning
• Generative Models
• +۳ مورد دیگر

Pengzhou (Abel) Wu serves as a Postdoctoral Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Technical University of Berlin since July 2023, focusing on the intersection of causality and machine learning methodologies. His academic foundation includes: Ph.D. in Statistical Science (2022) from The Graduate University of Advanced Studies M.E. in Information and Communication Engineering (2013) from Zhejiang University B.E. in Electronic and Informational Engineering (2010) from Xiamen University Dr. Wu's research spans causal inference and discovery, generative deep learning models, and nonlinear independent component analysis (ICA) with emphasis on identifiability. His work bridges machine learning with physical sciences and cognitive science, developing science-inspired ML frameworks and advancing ML applications for scientific discovery. Key methodologies include probabilistic modeling and causal structure learning. He operates within BIFOLD's research ecosystem, a premier Berlin-based institute dedicated to data science foundations and machine learning innovation.

Paul von Bünauمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Healthcare Technology
• Machine Learning
• +۴ مورد دیگر

Dr. Paul von Bünau serves as Managing Director (CEO) of IDALab, a strategic advisory firm focused on artificial intelligence in healthcare and biotechnology. He concurrently holds an adjunct academic role at Europa-Universität Viadrina Frankfurt (Oder), where he teaches courses on AI strategy and regulatory compliance in healthcare. His educational background includes a PhD in Machine Learning (TU Berlin), M.Sc. in Pure Mathematics (University of St Andrews), and B.Sc. in Computer Science (University of Potsdam). **Professional Background**: Paul provides strategic AI guidance to multinational healthcare companies, biotech startups, and medical device manufacturers. Key engagements include building AI innovation units for a medtech company, accelerating drug discovery through AI-driven R&D, and developing AI tools for healthcare market access under EU regulations. His approach emphasizes aligning technical solutions with organizational needs and regulatory landscapes. **Research & Teaching**: At Viadrina, he delivers seminars on AI strategy (2024) and regulatory compliance in healthcare AI (2025). His research focuses on non-stationary data analysis, clinical data harmonization, and AI applications in neuroscience. Recent work includes a validated clinical data pipeline for scalable AI deployment (JMIR Medical Informatics, 2023) and algorithms for stationary subspace analysis (Physical Review Letters, 2009). **Key Takeaways from Projects**: Companies with strong engineering cultures often underestimate the cultural and structural changes required for AI integration. Rapid prototyping, talent alignment, and cross-disciplinary teams (e.g., AI + healthcare experts) are critical for success. His work highlights the transformative potential of AI in healthcare while emphasizing ethical and regulatory considerations.

Roland Pfisterمشاهده پروفایل

استاد

• Cognitive Psychology
• Action Control
• Rule Violations
• +۴ مورد دیگر

Prof. Roland Pfister is a Professor of Psychology at the University of Trier, specializing in cognitive psychology and action control. His research focuses on rule violations, perception-action integration, and decision-making processes. He leads the General Psychology team and has contributed extensively to understanding mechanisms underlying dishonesty, social interaction dynamics, and methodological advancements in psychological research. His work bridges theoretical frameworks with empirical studies, emphasizing experimental rigor and statistical innovation. Key research interests include deliberate rule-breaking, action-effect anticipation, and the cognitive burdens of dishonest behavior. Pfister has published widely in journals like Cognition , Psychological Review , and Attention, Perception, & Psychophysics , often exploring topics such as temporal binding, sensory attenuation, and the neural correlates of action control. He collaborates internationally and has developed influential methodologies, including tools for mouse-tracking analyses and Bayesian statistical approaches. His research also extends to historical perspectives in psychology, challenging traditional timelines of the discipline. Pfister’s contributions to teaching and mentoring are reflected in his involvement with student-led projects and his role in shaping experimental psychology curricula. His work has implications for fields ranging from legal decision-making to virtual reality applications in embodied cognition.