Find Academic Supervisors

• Educational Psychology
• Quantitative Research Methods
• Educational Effectiveness
• +6 more

Benjamin Nagengast is Professor of Educational Psychology and Deputy Director of the Hector Institute for Empirical Educational Research at the University of Tübingen. His work focuses on educational psychology, quantitative research methods, and motivation theory, with a particular emphasis on academic self-concept, intervention programs, and causal analysis in educational settings. He previously served as co-director of the LEAD Graduate School & Research Network and head of the LEADing Research Center until June 2022. His research spans topics including: Educational effectiveness and policy evaluation Motivational dynamics and value-belief interactions Multilevel modeling of educational outcomes Personality-achievement correlations Longitudinal studies of developmental trajectories While not explicitly listing scientific awards, his publications highlight methodological innovations and large-scale empirical studies. He mentors students through collaborative research projects, though individual advisees aren't named. His work frequently appears in top journals like Psychological Science and Journal of Educational Psychology , with recent articles examining detracking effects, twin comparison processes, and the interplay between self-concept and achievement. He maintains active collaborations with institutions like the University of Oxford and University of Jena.

Ghassan KarameView profile

Professor

• Blockchain Security
• Machine Learning Security
• Platform Security
• +3 more

Prof. Dr. Ghassan Karame is a Full Professor of Computer Science at the Ruhr-University Bochum , leading the Chair for Information Security. He is a Principal Investigator in the Cluster of Excellence CASA and Director of the Horst Goertz Institute for IT Security since October 2023. Additionally, he serves as a part-time Chief Scientific Advisor at NEC Laboratories Europe . His research focuses on blockchain security and privacy , platform/storage security , and machine learning security . He has contributed to improving cryptocurrency protocols, hardware-assisted secure systems, and decentralized trust mechanisms. Education : PhD in Computer Science from ETH Zurich (2011) His recent work explores federated learning security, side-channel attacks in TEEs, and practical blockchain scalability solutions. He has served on over 50 program committees and held editorial roles at IEEE TDSC and IEEE TIFS. Notable Awards : 2024 Outstanding Editorial Board Member Award (IEEE Signal Processing Society) 2020 Value Realization Award (NEC) 2019 IEEE TC Best Paper Award Degree among top 2% authors (Stanford-Elsevier) His teaching includes courses on systems security, blockchain security, and practical ML security, with thesis projects in blockchain, ML, and platform security. He leads the Information Security Chair , part of major research initiatives like CASA and HGI.

Giacomo FrancisciView profile

Researcher

• Branching processes
• Depth functions
• Empirical processes
• +3 more

Dr. Giacomo Francisci is an academic staff member at the Institute of Financial Mathematics, part of Ulm University's Faculty of Mathematics and Economics. His research focuses on advanced statistical methodologies including branching processes, depth functions, empirical processes, nonparametric statistics, and stochastic process analysis. He has presented at major international conferences such as the International Conference on Robust Statistics (Stresa, 2025) and German Probability and Statistics Days (Dresden, 2025). His work spans theoretical developments in statistical depth functions and their applications to clustering, as well as practical applications like predicting student performance and analyzing movement patterns in Alzheimer's patients. Key contributions include studies on branching processes in random environments and methodological advancements in statistical depth for abstract metric spaces. Francisci collaborates internationally, with presentations at institutions like George Mason University (USA) and the CMSstatistics conference series. He is affiliated with Scopus and Web of Science, and can be contacted at giacomo.francisci@uni-ulm.de. His office is located in Room 2.27 at the Institute of Financial Mathematics.

• Psychometrics
• Educational Measurement
• Statistical Modeling
• +3 more

Prof. Dr. Steffi Pohl is a Professor of Methods and Evaluation/Quality Assurance at the Department of Methods and Evaluation/Quality Assurance, Faculty of Education and Psychology, Freie Universität Berlin. She holds editorial roles in prominent journals like Psychometrika and Journal of Educational and Behavioral Statistics . Her research focuses on psychometrics, log data analysis, missing data mechanisms, and causal inference in educational assessments. Steffi Pohl's academic journey includes a Diplom in Psychology from Freie Universität Berlin (1998–2004) and a Ph.D. in Psychometrics (2005–2010) from Friedrich-Schiller-Universität Jena. She has held various research positions, including at the National Educational Panel Study (NEPS) and the University of Hertfordshire, UK. Her work emphasizes methodological advancements in testing, missing data, and measurement invariance across populations. Her research interests span psychometric modeling, log data analysis, and addressing challenges in large-scale educational assessments. Key contributions include innovations in response time analysis, disentangling engaged/disengaged test behaviors, and improving cross-cultural comparisons in educational rankings. She has been recognized with the 2020 Early Career Award from the Psychometric Society and the 2011 Gustav A. Lienert Dissertation Prize. In addition to research, Pohl teaches courses on empirical social research methods and multivariate analysis techniques. She actively contributes to university governance as a member of the Academic Senate at Freie Universität Berlin and serves as a trusted advisor for the German National Academic Foundation.

Corinna JacobiView profile

Professor

• Eating Disorders
• Mental Health Risk Factors
• E-Mental Health
• +4 more

Corinna Jacobi is a Professor of Clinical Psychology and E-Mental Health at Technische Universität Dresden , leading the Department of Clinical Psychology and E-Mental Health. She holds a Diploma in Psychology (1982) and a Dr. rer. biol. hum. (1985) from Hannover Medical School, followed by a venia legendi in Clinical Psychology (1997). Her career includes roles as a clinical psychologist in psychosomatic hospitals, assistant professorships at the University of Hamburg and Stanford University, and endowed professorships at TU Dresden focusing on eating disorders and digital mental health. Her research prioritizes e-mental health , developing and evaluating digital interventions for eating disorders, obesity, and mental health risk factors. She has pioneered cross-national studies on technology-based care models and prevention strategies targeting university populations. Key contributions include randomized controlled trials (RCTs) on interventions like StudentBodies , ICare Prevent , and CORE , emphasizing scalability and accessibility. Dr. Jacobi’s work bridges clinical practice and innovation, addressing gaps in mental health care delivery. She co-chairs the DGPPN/DGPs Task Force on E-Mental Health, advocating for evidence-based digital solutions. Her articles focus on intervention efficacy, adherence, and implementation challenges, with over 135 peer-reviewed publications and a SCOPUS h-index of 24. Education: Diploma Psychology (1982), Dr. rer. biol. hum. (1985), venia legendi (1997) Key Honors: Christina-Barz-Preis (1997), DGPPN/DGPs E-Mental Health Task Force (2016–present) Editorial Roles: International Journal of Eating Disorders (since 2004)

Laxman DhulipalaView profile

Assistant Professor

• Parallel Algorithms
• Graph Processing
• Parallel Clustering
• +7 more

Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research in the Graph Mining team. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoctoral researcher at MIT with Julian Shun. Ph.D., Carnegie Mellon University Postdoctoral Research, MIT His research focuses on efficient parallel algorithms, particularly for graph processing and clustering. He explores theoretical and practical models of parallel computation aligned with modern hardware. His work spans parallel graph algorithms, computational geometry, and scalable systems for massive datasets. The recent publications demonstrate a strong trend in scalable and dynamic graph algorithms, with a focus on hierarchical clustering, connectivity, and benchmarking. Key themes include batch-dynamic updates, memory-efficient data structures, and practical parallel implementations for massive-scale problems. Many works appear in top venues such as SPAA, VLDB, NeurIPS, and SIGMOD. Best Paper Award at VLDB'25 Best Paper Award at SPAA'22 Best Paper Runner Up at VLDB'22 Distinguished Paper Award at PLDI'19 Best Paper Award at SPAA'18 Memorable Paper Award Finalist at NVMW'20 Honorable Mention, CMU SCS Dissertation Award Nominated for ACM Dissertation Award Dhulipala has advised and collaborated with numerous students and researchers, including Quinten De Man, Shangdi Yu, Jessica Shi, and others, contributing to influential projects such as Aspen, ParGeo, GBBS, and ParlayLib. He has received recognition for both theoretical and practical contributions to parallel computing. He teaches courses such as CMSC858N (Scalable Parallel Algorithms and Data Structures) and CMSC451 (Design and Analysis of Computer Algorithms). He is actively involved in building tools and frameworks for parallel algorithm development and evaluation, including benchmark suites and graph processing systems. His dual affiliation with academia and Google Research enables impactful, scalable research bridging theory and practice.

Artem SapozhnikovView profile

Professor

• Probability theory
• percolation
• random walks
• +1 more

Artem Sapozhnikov is a Professor at the Mathematisches Institut of Universität Leipzig, Germany. His research focuses on probability theory, particularly percolation, random walks, and random graphs. He maintains an active research program with numerous publications and supervises PhD students and postdocs. Professor Sapozhnikov teaches a range of mathematics courses for physicists, including Mathematics for Physicists 1-4, Probability Theory I & II, and specialized courses on percolation. His teaching spans both undergraduate and graduate levels, demonstrating his commitment to mathematical education. His research interests center around probability theory , with specific focus on: Percolation theory and phase transitions Random walks and their properties on various structures Random interlacements and Brownian motion Boolean models and geometric probability Models with long-range correlations His work bridges theoretical probability with applications in statistical physics. Analysis of his recent publications shows a strong focus on the properties of random interlacements, Brownian motion, and percolation models. His research examines connectivity properties, phase transitions, and geometric aspects of these stochastic processes. Recent work with Yingxin Mu has explored visibility windows and indistinguishability of components in various models. Earlier collaborations with Caio Alves, Deepan Basu, and Yinshan Chang investigated decoupling inequalities, crossing probabilities, and loop percolation. Professor Sapozhnikov has supervised several PhD students and postdocs, including: Deepan Basu (PhD 2013-2017) Caio Alves (postdoc 2016, 2017-2020) Yinshan Chang (postdoc 2013-2016) Lorenzo Taggi (PhD 2012-2015) Yingxin Mu (postdoc 2021-2023) He currently has open positions for PhD students through the IMPRS program.

Stephan SeifertView profile

Professor

• Chemometrics
• Bioinformatics
• Food Profiling
• +3 more

Professor Stephan Seifert leads research at the University of Hamburg's Department of Chemistry, focusing on chemometrics and bioinformatic methods for food profiling and manuscript analysis. His group develops analytical approaches using vibrational spectroscopy, machine learning, and data fusion techniques. Research spans: Chemometric method development (random forests, variable selection) Spectroscopic analysis (FTIR, Raman, NMR) Food authentication and quality control Manuscript material characterization Recent publications demonstrate increasing use of machine learning for metabolomics data interpretation and multi-technique data fusion. Research integrates analytical chemistry with computational approaches to solve problems in food science and cultural heritage. Current projects include the Cluster of Excellence 'Understanding Written Artefacts' and Palm-Leaf Manuscript Profiling Initiative. Leads the Seifert AG research group at the Institute of Food Chemistry.

Francesc DilméView profile

Associate Professor

• Microeconomic Theory
• Dynamic Games
• Bargaining
• +3 more

Francesc Dilmé is an Associate Professor at the University of Bonn's Institute for Microeconomics, affiliated with the Bonn Graduate School of Economics and Hausdorff Center for Mathematics (HCM). Since 2019, he has been a cluster member and received the ERC Starting Grant (2020) for "Information Transmission in Markets" (2021-2026), while serving on the American Economic Review editorial board. His educational background includes: Mathematics and Physics studies in Barcelona Economics MSc in London PhD from University of Pennsylvania (2013) Dilmé specializes in Microeconomic Theory, focusing on dynamic games, asymmetric information, and bargaining. His research analyzes strategic communication, relational contracts, and market design under imperfect information, with emphasis on equilibrium stability and robust information transmission mechanisms. Recent work explores binary private information, random price discounts, and repeated trade dynamics. His 2019-2025 publications reveal a concentrated focus on dynamic microeconomic models, particularly information transmission in bargaining and market contexts. Key trends include equilibrium refinements in signaling games, relational contracting under asymmetric information, and strategic communication with minimal conflict of interest, often bridging theoretical rigor with market applications. Scientific recognition includes: ERC Starting Grant (2020) for "Information Transmission in Markets" He directs ERC-funded research (2021-2026) and co-organizes the European Economic Theory Conference (EETC) and Christmas Microeconomic Theory Conference in Bonn (CriMicTheConBon). As Associate Professor, he likely supervises graduate students through the Bonn Graduate School of Economics, though specific advisees aren't listed. Dilmé actively contributes to collaborative research through the HCM and Bonn's microeconomic theory cluster, focusing on foundational aspects of dynamic strategic interaction and information economics.

• Machine Learning
• Natural Language Processing
• Large Language Models
• +5 more

Christian Pölitz is a researcher specializing in artificial intelligence and machine learning with current affiliation at Microsoft Research. Previously associated with Dortmund University of Technology where he completed his PhD in 2016, his research bridges theoretical machine learning with practical applications in programming assistance and data manipulation. His publication record demonstrates sustained contributions to the field spanning over 15 years. Dr. Pölitz's research focuses on the intersection of AI systems and human workflows, particularly examining how large language models can effectively assist with data-centric tasks and programming activities. His recent work (2022-2025) investigates critical aspects of LLM evaluation, human-AI collaboration in programming contexts, and the development of benchmarks for natural language interfaces to data tools like Excel. This represents an evolution from his earlier work (2010-2017) which centered on domain adaptation, topic modeling, and semantic relatedness. Analysis of his publication trends reveals a strategic shift toward contemporary AI challenges, with significant contributions to understanding how generative AI transforms knowledge work. His collaborations with Microsoft researchers including Advait Sarkar, Ben Zorn, and Andrew Gordon indicate his integration into teams exploring the practical implications of AI 'copilots' in professional environments. His work often features in top-tier venues including AAAI, NAACL, EMNLP, and IEEE/ACM conferences. While specific awards aren't documented in the provided information, his consistent publication record in prestigious venues demonstrates recognition within the AI research community. His collaborative approach is evident through numerous co-authorships across academic and industrial research settings, suggesting an active role in interdisciplinary research teams focused on advancing practical AI applications.

Lars HarbaumView profile

Clinical Professor

• Pulmonary Medicine
• Internal Medicine
• Critical Care Medicine
• +3 more

Dr. Lars Harbaum is a Clinical Professor and senior physician specialized in Internal Medicine and Pneumology at the Faculty of Medicine, University of Hamburg. He is affiliated with the Center for Oncology and II. Medical Clinic and Polyclinic at the University Medical Center Hamburg-Eppendorf (UKE). His expertise focuses on pulmonary hypertension, right ventricular function, and translational research in cardiovascular medicine. Specializes in internistic intensive care medicine and pulmonary hypertension treatment Active in clinical and translational research involving proteomics, genetics, and omics approaches Conducts studies on biomarker discovery, risk stratification, and therapeutic targeting in pulmonary arterial hypertension (PAH) His research spans over 25 peer-reviewed articles since 2013, with recent work emphasizing plasma proteome analysis and genetic determinants of cardiovascular outcomes. He collaborates with international teams in Europe and North America on PAH subtyping and patient stratification.

Elmar BittnerView profile

Researcher

• Theoretical Physics
• Quantum Gravity
• Statistical Mechanics
• +3 more

Elmar Bittner is a theoretical physicist and Researcher at the Institute for Theoretical Physics (ITP) at the University of Heidelberg. He also serves as the administrator of the institute's computer network. His academic background includes a PhD in Physics from Vienna University of Technology (2001) and a Dipl.-Ing. (equivalent to MSc) from the same institution (1998). He has held postdoctoral positions at the Institute of Atomic and Subatomic Physics in Vienna and the University of Leipzig, where he also served as Akademischer Rat (Senior Lecturer) and Head of IT. His research focuses on quantum gravity, lattice models of two- and four-dimensional systems, phase transitions in statistical mechanics, and Monte Carlo simulation techniques. Notable contributions include studies on vortex dynamics in lattice Higgs models, eigenvalue spectra in QCD, and the development of parallel tempering algorithms. Bittner has also explored applications of statistical physics to social phenomena, such as football goal distributions. His publications span topics like Ising model dynamics, critical phenomena, and random matrix theory. He has collaborated with institutions such as the University of Leipzig, Brunel University, and INFN (Italy), contributing to international conferences and workshops. Despite no listed awards, his work reflects significant contributions to theoretical physics and computational methods. Bittner’s roles include managing the ITP’s technical infrastructure, reflecting his dual expertise in theoretical physics and computational administration.

Yuanhua FengView profile

Professor

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +5 more

Prof. Dr. Yuanhua Feng leads the Chair of Econometrics and Quantitative Methods at the University of Paderborn 's Faculty of Business Administration and Economics , focusing on Financial and Economic Data Science . His research includes semiparametric modeling of seasonal time series, long memory processes, spatial time series, and machine learning hybrids. Doctoral supervision for Sebastian Letmathe (2023), Bastian Schäfer (2022), Xuehai Zhang (2018), and others Teaching courses: Econometrics, Financial Econometrics & Quantitative Risk Management Research areas emphasize: Semiparametric GARCH/ACD models for risk management Non-negative financial process forecasting Spatial time series with long memory Hybrid time series/machine learning models Recent publications focus on: Risk measurement with long-memory GARCH Spatial volatility modeling Box-Cox transformed models DeSeaTS seasonal decomposition Notable collaborations include DFG projects and joint PhD supervision with Chinese institutions. Teaching materials integrate R packages like smoots , rugarch , and custom tools for VaR/ES applications.

Dominik LieblView profile

Professor

• Functional Data Analysis
• Nonparametric Statistics
• Econometrics
• +3 more

Dominik Liebl is a Professor of Statistics at the University of Bonn's Department of Economics and a member of the Hausdorff Center for Mathematics (HCM), a Cluster of Excellence funded by the German Science Foundation (DFG). He also holds a visiting associate position at Colorado State University's Department of Statistics. His research spans Functional Data Analysis , Nonparametric Statistics , and Longitudinal Data Analysis , with applications in energy economics, finance, e-commerce, emotion psychology, and biomechanics. Recent methodological work focuses on simultaneous inference and statistical fairness . The articles in his profile demonstrate a strong emphasis on functional data methodologies applied to diverse domains like COVID-19 seroprevalence , electricity markets , and human movement science . Key subfields include confidence band design , biomechanical hypothesis testing , and high-dimensional econometric modeling . He actively contributes to open science through R-packages at CRAN/GitHub and serves as Associate Editor for the Journal of the Royal Statistical Society: Series C.

Jörn OstermannView profile

Professor

• Video Coding
• Computer Vision
• Machine Learning
• +9 more

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.