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Alexandros StamatakisView profile

Professor

• Algorithms
• Parallel Computing
• Parallel Architectures
• +3 more

Prof. Alexandros Stamatakis is a Professor of High Performance Computing at Karlsruhe Institute of Technology (KIT) and Group Leader of the 'Scientific Computing' group at the Heidelberg Institute for Theoretical Studies (HITS). He holds an adjunct professorship at the University of Arizona. His research focuses on algorithms, parallel computing architectures, and evolutionary bioinformatics. Stamatakis earned his PhD in Computer Science from TU Munich (2004) and has held roles including DFG Emmy-Noether Junior Research Group Leader (2008-2010). He is a member of multiple scientific advisory boards, including Elixir Greece and the Computational Biology Institute in Montpellier. His teaching awards (2015, 2016) highlight his contributions to bioinformatics education. His work spans computational methods for phylogenetic analysis, energy-efficient computing, and genomic data processing, with notable tools like RAxML-NG and EPA-ng. Research interests include parallel computing optimizations, phylogenetic inference algorithms, and applications in evolutionary biology. His recent work addresses challenges in large-scale genomic data analysis, including error detection in DNA barcoding systems and accelerating phylogenetic computations through algorithmic innovations. He leads collaborative projects on avian genome complexity and ancient DNA analysis, contributing to both computational methods and biological discoveries. Stamatakis collaborates internationally, with affiliations spanning institutions in Europe and the U.S. His lab at HITS emphasizes interdisciplinary approaches, merging computer science with biological problems. Key contributions include developing carbon-aware energy management frameworks and predictive models for phylogenetic inference stability.

Tilmann GneitingView profile

Professor

• Computational Statistics
• Probability Theory
• Spatial and Spatio-Temporal Modeling
• +5 more

Prof. Tilmann Gneiting is a renowned academic holding dual roles as Group Leader of Computational Statistics at the Heidelberg Institute for Theoretical Studies (HITS) and Professor of Computational Statistics at the Karlsruhe Institute of Technology (KIT). He specializes in statistical methodology, probability theory, and forecasting applications across environmental, atmospheric, and earth sciences. His research bridges theoretical foundations with practical challenges in weather prediction, climate modeling, and uncertainty quantification. Education: PhD in Mathematics (1997, University of Bayreuth) MA in Mathematics (1992, Boston University) Diplom in Mathematics (1993, University of Bayreuth) Key Roles: Scientific Director of HITS (2023–2024) Affiliate Professor at University of Washington (since 2010) His research interests focus on spatial/spatio-temporal modeling, probabilistic forecasting, and statistical post-processing. He has contributed to advancements in ensemble forecasting, uncertainty quantification, and the evaluation of probabilistic predictions in meteorology and epidemiology. Prof. Gneiting has received prestigious awards, including the 2024 Ulf Grenander Prize in Stochastic Theory and Modeling, recognizing his seminal work in probabilistic forecasting and environmental modeling. He has also held editorial roles in top journals like the Annals of Applied Statistics and the Journal of the Royal Statistical Society. His recent work emphasizes methodological developments for forecasting evaluation, climate science applications, and interdisciplinary collaborations with ECMWF and other institutions.

• Statistical Modeling
• Hidden Markov Models
• Time Series Analysis
• +4 more

Timo Adam serves as a Junior Professor for Statistical Modeling at Bielefeld University within the Faculty of Business Administration and Economics. He is affiliated with the Department of Empirical Methods, Center for Statistics, and BIGSEM - Bielefeld Graduate School of Economics and Management. His research bridges statistical methodology with applications in ecology, animal movement tracking, and financial time series analysis. Adam's research focuses on advanced statistical modeling techniques, particularly Hidden Markov Models, state-switching models, and stochastic differential equations. His work spans multiple disciplines, developing methodological innovations while addressing concrete problems in animal behavior ecology and financial markets. He has made significant contributions to understanding animal movement patterns through electronic tagging data and to financial time series analysis through novel modeling approaches. His publication record shows a consistent trend toward increasingly sophisticated modeling techniques applied to complex real-world problems. Recent work emphasizes 3D movement modeling, state-switching decision trees, and gradient boosting within Markov-switching frameworks. His research demonstrates strong interdisciplinary connections between statistics, ecology, and finance, reflecting Bielefeld University's strategic research area of the Mathematical World. As a Junior Professor, Adam contributes to teaching within the Department of Empirical Methods and participates in the Bielefeld Graduate School in Theoretical Sciences. His methodological expertise supports interdisciplinary research across the university, particularly in projects related to animal movement ecology and financial modeling.

• Statistics
• Big Data
• Machine Learning
• +3 more

Guillermo Briseño Sanchez was affiliated with the Department of Statistics at the Technical University of Dortmund, contributing to the Statistical Methods for Big Data research group as a Researcher. His work focuses on advanced statistical methodologies, including copula regression, boosting techniques, and distributional modeling applied to diverse fields such as survival analysis, learning analytics, and policy evaluation. He has published on topics such as bivariate time-to-event data analysis, causal inference, and flexible econometric models. While no specific awards or grants are mentioned, his research emphasizes the development of novel statistical frameworks for complex data structures.

• Statistics
• Time Series Analysis
• Biostatistics
• +5 more

Dr. Jean-Pierre Stockis is affiliated with the Department of Statistics within the College of Mathematics at Rheinland-Pfälzische Technische Universität Kaiserslautern. His work focuses on statistical methodologies applied to interdisciplinary domains including biology, chemistry, medicine, economics, and civil engineering. Research Interests: Applications of statistics, time series analysis, biostatistics, chaos theory, and computational statistical modeling. His publications highlight collaborations in structural engineering, organic synthesis, and nutritional biochemistry. Teaching: He contributes to courses such as GM3 Mathematik/Biostatistik, Financial Statistics, and Statistik II für Wirtschaftswissenschaftler, accessible via university platforms like KIS and OpenOLAT.

• Cognitive Science
• Psychology
• Computational Modeling
• +5 more

Andrew Perfors is a Professor of Psychology at the University of Melbourne, leading the Complex Human Data Hub and the Computational Cognitive Science Lab within the Melbourne School of Psychological Sciences . His research focuses on applying quantitative methods to understand higher-order cognition, including concepts, language, decision-making, and misinformation dynamics. He employs computational models and experimental approaches to investigate how cognitive constraints and social environments shape human behavior at individual and group levels. Education: PhD in Brain & Cognitive Sciences (MIT, 2008), MA in Linguistics (Stanford, 2000), BSc in Symbolic Systems (Stanford, 1999). Research Themes: Cognitive modeling of decision-making, cultural evolution, language acquisition, and misinformation spread. His work bridges computational methods with psychological experimentation, exploring topics like sampling assumptions in reasoning, trust in information sources, and the cognitive basis of gender categorization. Key Projects: Active grants include Understanding Information and Trust (2018–2025), Bridging the Meaning Gap (2023–2027), and Anti-trans Disinformation Campaigns analysis. Recent work addresses misinformation mitigation strategies and the cognitive naturalness of trans-inclusive gender categories. Grants & Collaborations: Funded by ARC and NHMRC. Collaborations include work on contact-tracing technologies during the pandemic and cross-cultural studies on privacy calculus. Labs & Teams: Directs the Complex Human Data Hub, focusing on societal challenges like misinformation and cultural dynamics. Leads interdisciplinary teams in the Computational Cognitive Science Lab, integrating psychology, computer science, and linguistics.

• Cosmology
• Intergalactic Medium Studies
• Astrophysics
• +3 more

Frederick Davies is the MPG Research Group Leader leading the Reionization and Intergalactic Medium (REIGM) Group at the Max Planck Institute for Astronomy in Heidelberg. His research focuses on unraveling the cosmic reionization epoch, the evolution of the intergalactic medium (IGM), and the origins of early supermassive black holes. Key goals include measuring reionization topology via quasar damping wings, constraining black hole accretion timescales, and tracing IGM thermal fluctuations before/during reionization. His work leverages high-redshift quasar spectra from facilities like JWST and Euclid, employing advanced numerical simulations and statistical methods. Research interests span cosmological structure formation, radiative transfer processes, and the interplay between galaxies/quasars and the IGM. Davies' group develops novel techniques to analyze Lyman-α forest data and proximity zones, aiming to quantify ionization histories and cosmic heating. His affiliations include the REIGM Group and the MPIA's Galaxies and Cosmology department. Publications emphasize precision cosmology, quasar evolution, and multi-scale IGM modeling. While no formal advisees are listed, his research team actively contributes to large-scale surveys like ASPIRE and XQR-30. The group's efforts bridge observational data with theoretical frameworks to address fundamental questions about the Universe's early evolution.

Annalisa PillepichView profile

Researcher

• Numerical Galaxy Formation
• Cosmology
• Galaxy Evolution
• +5 more

Dr. Annalisa Pillepich is a Research Group Leader at the Max Planck Institute for Astronomy (MPIA) in Heidelberg, leading the Galaxies and Cosmology Theory group. Her research focuses on modeling cosmic structure formation, galaxy evolution, and cosmological processes within the ΛCDM framework. She employs advanced numerical simulations (e.g., IllustrisTNG) and machine learning techniques to study baryonic feedback, dark matter dynamics, and large-scale structure. Education: Bachelor's and Master's in Physics, University of Pisa PhD in Physics, ETH Zurich Postdoctoral positions at UC Santa Cruz and Harvard University Her group investigates topics such as gas dynamics in galaxies, magnetic fields, and the interplay between galaxies and their environments. They leverage synthetic datasets and statistical analysis to refine cosmological models and interpret observational data from facilities like eROSITA. Current projects include studying stellar halos, galaxy mergers, and cosmological parameter inference. Former members of her group have transitioned to roles at institutions like NASA, University College London, and the University of Groningen. She advises PhD students and mentors interns on topics ranging from galaxy clusters to stellar populations. Dr. Pillepich’s work is supported by collaborations such as the IllustrisTNG project and the STRUCTURES Cluster of Excellence. Her research bridges numerical astrophysics with observational cosmology, advancing our understanding of galaxy formation and the universe’s evolution.

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

Bernhard Schölkopf is a Research Professor and Director of the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen. His work focuses on machine learning, causal inference, and statistical learning theory. He leads a department with 20+ researchers and has contributed to breakthroughs in exoplanet discovery (e.g., K2-18b), gravitational wave research through LIGO, and foundational work in kernel methods. Research interests span causal structures underlying data dependencies, applications in astrophysics, and ethical implications of AI. He co-authored influential books like 'Causality: Methods and Models' (MIT Press, 2017) and pioneered kernel-based learning frameworks. His work bridges theoretical advances with practical applications in astronomy, healthcare, and robotics. Funding sources include Max Planck Society, DFG, EU Horizon 2020, and industry partnerships with Amazon, Bosch, and Google. He advocates for responsible AI development, opposing military uses of autonomous systems. Collaborates with Cyber Valley and ELLIS Institute Tübingen. Active in public discourse through articles in FAZ/SZ and presentations at global AI conferences.

• Machine Learning
• Causal Inference
• Kernel Methods
• +3 more

Bernhard Schölkopf serves as Director of the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading cutting-edge research in machine learning foundations and applications. His core research focuses on: Machine Learning and Causal Inference structures Kernel Methods and Statistical Learning Theory Astrophysical applications including exoplanet discovery (K2-18b water detection) and gravitational wave analysis Professor Schölkopf has mentored numerous students documented in the department alumni list and secures substantial funding from diverse sources including Max Planck Society, DFG, EU, Amazon, Google, and Facebook. His department actively contributes to the LIGO scientific collaboration and publishes foundational work in causality and kernel methods. He maintains strong ethical positions against military AI applications and has published influential societal commentary in major German newspapers regarding the cybernetic revolution and AI implications.

Eleni StraitouriView profile

Professor

• Machine Learning
• Causal Inference
• Large Language Models
• +4 more

Eleni Straitouri is a Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany, where she leads research at the intersection of artificial intelligence safety and human-centered decision systems. Her work bridges theoretical machine learning with practical applications in high-stakes environments requiring rigorous uncertainty quantification and causal reasoning. Her primary research focuses on causal inference in large language models , counterfactual prediction sets , and human-AI complementarity . She develops methods to control algorithmic harm through counterfactual reasoning, enhances model evaluation via conformal prediction, and designs decision support tools that leverage human expertise. Current projects address critical challenges in LLM safety, including bias detection through coupled token generation and harm mitigation in medical/operational decision systems. Analysis of her 13 publications (2021-2025) reveals an evolving trajectory from reinforcement learning foundations toward cutting-edge LLM safety frameworks. Her work consistently integrates causal methods with human factors, producing practical tools for reliable AI deployment across healthcare and operational domains. Recent NeurIPS/ICML publications demonstrate increasing influence in trustworthy AI research. Best Paper Award, Workshop on AI and HCI (ICML), 2023 Spotlight Paper (top 3.5%), ICML 2024 Dr. Straitouri maintains active collaborations with Manuel Gomez-Rodriguez's research group, evidenced by co-authorship on 100% of her recent publications. While specific grant funding isn't detailed in available materials, her work aligns with MPI-SWS's focus on software systems for societal benefit. No information about academic advising or laboratory infrastructure was found in the provided text.

• Econometrics
• Time Series Analysis
• Discrete Choice Models
• +3 more

Prof. Dr. Dietmar Bauer holds the Chair of Econometrics at the Faculty of Business Administration and Economics at Bielefeld University. He maintains multiple affiliations including the Department of Empirical Methods, Center for Statistics, Institute for Technological Innovation, Market Development and Entrepreneurship, BIGSEM (Bielefeld Graduate School of Economics and Management), and the Bielefeld Graduate School in Theoretical Sciences. His academic credentials include a Habilitation in Econometrics (2007), Doctorate in Technical Mathematics (1998), and Diploma in Technical Mathematics (1995), all from TU Wien, Austria. Prior to his position at Bielefeld, he served as Senior Scientist at the Austrian Institute of Technology (2005-2014) and Assistant at TU Wien (1995-2005), with additional postdoctoral positions at Yale University, University of Linköping, and University of Newcastle. Prof. Bauer's research focuses on two primary areas: time series analysis and discrete choice models. His work in time series investigates subspace methods for state space models, particularly for integrated and seasonally integrated processes. In discrete choice modeling, he examines computational efficiency in Probit models and transportation mode choice, with special attention to the MACML approach. His publications demonstrate consistent contributions to both theoretical econometrics and applied transportation research. Multa Scripsit Award from Econometric Theory (2013) Best paper awards at UrbComp 2012 (with Jameson Toole, Marta Gonzalez, and Michael Ulm) Oskar Morgenstern Award of IHS, Vienna (with Martin Wagner) Prof. Bauer teaches numerous courses across four institutes including Software Applications for Economists, Data Analysis, Statistical Methods, and Econometrics. He supervises diploma theses, master's theses, and doctoral dissertations. His research has been supported by funding from DFG, FWF, FFG, and EU-level projects, reflecting his strong record in securing competitive research funding. He is actively involved with the Center for Statistics and the Bielefeld Center for Data Science, contributing to interdisciplinary research initiatives.

Tim GollischView profile

Professor

• Neurobiology
• Retinal Processing
• Neural Coding
• +5 more

Prof. Tim Gollisch is a Professor of Sensory Processing in the Retina at the School of Medicine, University of Göttingen. His research focuses on understanding how retinal neural networks process visual information, emphasizing neural coding and computation. Key areas include retinal dysfunction analysis, optogenetic restoration of vision, and adaptive mechanisms in retinal circuits. Education: Diploma in Physics, University of Heidelberg (2000) PhD in Biophysics, Humboldt University Berlin (2004) Research Interests: Neural coding mechanisms in retinal neurons Synaptic connectivity and functional diversity Optogenetic therapies for degenerative retinal diseases Integration of experimental data with computational models Research Trends in Publications: Advances in nonlinear signal processing and receptive field modeling Applications of machine learning in retinal circuit analysis Investigations into primate retinal responses to natural stimuli Labs/Teams: Head of the Sensory Processing in the Retina research group at University Medical Center Göttingen Collaborations with Harvard University and the Max Planck Institute of Neurobiology

• Theoretical Physics
• Quantum Mechanics
• Condensed Matter Physics
• +1 more

Prof. Müller-Kirsten leads the Department of Physics Workgroup Müller-Kirsten. Their academic affiliation is indicated through the workgroup designation, though specific university and college affiliations are not explicitly stated in the provided text. Research interests are inferred from the department's focus areas, likely encompassing theoretical physics and related disciplines. No publications, awards, or educational background details are available in the current dataset.

• Natural Language Processing
• Machine Learning
• Biomedical Informatics
• +7 more

Ning An is an active academic researcher primarily affiliated with Hefei University of Technology in China, with additional connections to Shandong University and Xi'an Jiaotong University. With over two decades of publication history spanning from 1998 to 2025, Dr. An maintains a highly productive research trajectory, particularly evident in the substantial publication output in recent years (2023-2025). Dr. An's research spans multiple interdisciplinary domains at the intersection of computer science and health informatics. Key research interests include natural language processing (particularly few-shot learning and named entity recognition), multimodal sentiment analysis, time series forecasting, causal inference, and health monitoring technologies. A significant portion of recent work focuses on applications in elder care technology and medical diagnostics, demonstrating a commitment to translating technical innovations into practical healthcare solutions. The publication trends reveal a strong focus on few-shot learning approaches for natural language processing tasks, with numerous 2024-2025 papers addressing nested named entity recognition. There's also a notable thread of research in multimodal sentiment analysis and time series forecasting, particularly using frequency domain approaches. The consistent collaboration pattern with researchers like Jiaoyun Yang, Lian Li, and Lili Jiang suggests a well-established research group or laboratory environment. Dr. An's work demonstrates significant impact across multiple venues including top conferences like ACL, NeurIPS, and IJCAI, as well as prestigious journals such as Engineering Applications of Artificial Intelligence and Expert Systems with Applications. The research has practical applications in healthcare, particularly in elder care monitoring, depression assessment, and non-invasive health monitoring systems. As a mentor, Dr. An has guided numerous researchers including Jiaoyun Yang, Lian Li, and Lili Jiang, who frequently appear as co-authors on publications. The collaborative nature of the work extends to international partnerships, with publications involving researchers from institutions across China and potentially beyond.