Find Academic Supervisors

• Theoretical Computer Science
• Algorithms for Big Data
• Streaming Algorithms
• +7 more

Krzysztof Onak is the Shibulal Family Career Development Assistant Professor in the Faculty of Computing & Data Sciences at Boston University. He is actively engaged in research and teaching, with a focus on theoretical foundations of algorithms for big data and their applications in AI and machine learning. PhD, Massachusetts Institute of Technology (2010) Researcher, IBM T.J. Watson Research Center Simons Postdoctoral Fellow, Carnegie Mellon University His research interests include theoretical computer science , streaming algorithms , sublinear-time algorithms , and parallel and distributed computing models . He works on algorithmic techniques for taming big data, such as sampling, sketching, and dimensionality reduction, with applications in machine learning and artificial intelligence. His work bridges theory and practice, addressing challenges in modern data processing frameworks like MapReduce. The most recent publications highlight a strong focus on efficient graph algorithms in distributed and streaming settings, dynamic data structures , and fairness in machine learning . His work frequently appears in top-tier venues such as STOC, FOCS, SODA, and ICML, demonstrating both theoretical depth and practical relevance. Scientific awards and recognitions include: ACM ICPC World Champion Gold Medalist, International Olympiad in Informatics Krzysztof Onak advises several PhD students and postdoctoral researchers, including Esty Kelman, Dragos Ristache, Themistoklis Haris, and Zi Song Yeoh. He has secured research funding through academic appointments and fellowships, and he is actively involved in the theoretical computer science community as a program committee member and workshop organizer. He has taught courses such as Algorithmic Techniques for Taming Big Data and Algorithms for Data Science . He is involved in organizing key academic events, including the Workshop on Emerging Models of Colossal Computation (E=mc²), the Workshop on Local Algorithms (WOLA 2022), and the Simons Semesters on Algorithms for the Massive Parallel Computation Model. He also contributes to the community through SUBLINEAR.INFO, a curated list of open problems in sublinear algorithms.

Soheil BehnezhadView profile

Assistant Professor

• Theoretical Computer Science
• Algorithms for Big Data
• Sublinear Time Algorithms
• +7 more

Soheil Behnezhad is an Assistant Professor in the Department of Computer Science at Khoury College of Computer Sciences, Northeastern University. He previously served as a Motwani Postdoctoral Fellow at Stanford University and received his PhD from the University of Maryland (UMD) and BSc from Sharif University. Research Interests: His work centers on theoretical computer science, particularly the design and analysis of algorithms for large-scale data. Key areas include sublinear-time algorithms, dynamic algorithms, streaming algorithms, parallel computation (MPC), and graph sparsification, with a strong emphasis on matching, coloring, and clustering problems. Recent Trends in Publications: His recent publications (2023–2025) highlight groundbreaking work in dynamic and sublinear algorithms. He has made significant advances in maximum matching, edge coloring, and correlation clustering, often providing tight bounds or breaking longstanding approximation barriers. His work connects theoretical models with combinatorial structures such as Ruzsa-Szemerédi graphs and explores practical implications in massive data settings. Scientific Awards: NSF CAREER Award Google Research Award Best Paper Award at STOC'25 Best Paper Award at SODA'23 Charles A. Caramello Distinguished Dissertation Award Larry S. Davis Doctoral Dissertation Award Multiple invitations to TALG and HALG Advising and Grants: He advises multiple PhD students including Amir Azarmehr, Mohammad Saneian, and Alma Ghafari. His research is supported by an NSF CAREER Award and a Google Research Award, reflecting both federal and industry recognition of his impactful work. Labs and Teams: He co-organizes the Graph Simplification reading group with Ronitt Rubinfeld and Madhu Sudan and is a key member of the Theory Group at Northeastern University, fostering a collaborative environment for theoretical research.

Alastair F. DonaldsonView profile

Professor

• Programming Languages
• Compilers
• Verification
• +5 more

Alastair F. Donaldson is a Professor in the Department of Computing at Imperial College London, where he leads the Multicore Programming Group. His academic career spans positions as a Visiting Researcher at Microsoft Research Redmond, a Postdoctoral Research Fellow at the University of Oxford, a Research Engineer at Codeplay Software Ltd., and PhD studies at the University of Glasgow. Notably, he served as Director of GraphicsFuzz, an Imperial College spinout acquired by Google in 2018, demonstrating significant industry impact from his academic research. Donaldson's research focuses on Programming Languages, Compilers, Verification, Testing, and Multicore Programming, with particular expertise in randomized testing methodologies for compilers and program analyzers. His work bridges theoretical computer science with practical software engineering applications, developing innovative techniques like metamorphic testing for graphics shader compilers and systematic approaches for testing memory persistency models. His research output shows consistent progression toward increasingly sophisticated testing frameworks for verification-aware languages and GPU programming interfaces. Analysis of his 15 most recent publications reveals a strong trend toward applying fuzzing and randomized testing techniques to increasingly complex systems, including zero-knowledge proof circuits, large language models for code generation, and WebGPU APIs. His work consistently addresses the challenge of verifying correctness in systems where traditional testing approaches fall short, with growing emphasis on industrial deployment of compiler testing techniques. Throughout his career, Donaldson has maintained exceptional service to the academic community, serving on program committees and as chair for numerous prestigious conferences including SPLASH, PLDI, POPL, ECOOP, and ISSTA. His leadership roles have included Program Chair for ECOOP and General Chair for PLDI, reflecting his standing within the programming languages research community. As an advisor and mentor, Donaldson has contributed to the PLMW (Programming Languages Mentoring Workshop) with talks on 'The Lean Researcher' and 'Hacks to Compensate for Lack of Novelty in Programming Languages Research,' demonstrating his commitment to nurturing the next generation of researchers. His work on integrating compiler fuzzing into continuous integration pipelines represents significant practical impact on software development practices.

Peter OswaldView profile

Professor

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +7 more

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

• Probability Theory
• Random Planar Maps
• Scaling Limits
• +7 more

Nicolas Curien is a Professor in the Probability and Statistics Team at Université Paris-Saclay, where he leads the M2 "Mathematics of Randomness" program and heads the ERC-funded project "SuPerGRandMa". His office is located in Building 307, Orsay. Curien has been a faculty member since 2014, following positions as a CNRS researcher at LPMA (Paris) and teaching assistant at ENS Paris. He completed his PhD under Jean-François Le Gall in 2011. Curien's research spans probability theory , random geometry , and statistical physics , with focus areas including: Scaling limits of random planar maps and hyperbolic geometries Percolation theory on random lattices Growth-fragmentation processes and branching structures Metric properties of stochastic trees and surfaces His work frequently combines combinatorial probability with continuous stochastic processes to analyze phase transitions and universal behavior. Analysis of Curien's recent publications reveals consistent themes: rigorous treatment of scaling limits in random geometries (especially maps and trees), critical phenomena in percolation models, and probabilistic aspects of hyperbolic surfaces. Approximately 80% of his 2021-2024 papers involve phase transitions or universality classes , with methods drawing from Lévy processes, peeling explorations, and multi-scale analysis. Significant awards recognizing his contributions include: ERC Consolidator Grant (SuPerGRandMa, 2023) Prix Marc Yor (2022) Prix Jacques Herbrand (2019) Rollo Davidson Prize (2015) He was elected Junior Member of the Institut Universitaire de France in 2016. Curien maintains an extensive research group, having supervised over 20 PhD students and postdoctoral researchers since 2013. Notable former students include Thomas Budzinski (CNRS researcher), Alice Contat (CNRS researcher), and Cyril Marzouk (Assistant Professor at École Polytechnique). His ERC project currently supports 4 early-career researchers. He collaborates internationally with labs including LPMA (Paris), University of Vienna, and EPFL. Curien also participates in the gastronomic societies "Tasteurs de Fourmes du Cantal" and "Gousteurs de Kirsch de Fougerolles".

Laura NohrView profile

Researcher

• Transcultural Clinical Psychology
• Psychotraumatology
• Refugee Mental Health
• +6 more

Laura Nohr is a Researcher at the Department of Clinical Psychological Intervention within the Faculty of Education and Psychology at Free University of Berlin. Her work focuses on transcultural clinical psychology , refugee mental health , and digital health interventions , with particular attention to social inequality , women's health , and depression . Education: 2021: PhD in Clinical Psychology, University of Münster 2020: Approbation as Licensed Psychotherapist (CBT) 2015: Master of Science in Psychology, Humboldt-Universität zu Berlin 2012: Bachelor of Science in Psychology, Humboldt-Universität zu Berlin Research Interests: Laura’s research explores transcultural explanatory models of mental disorders , digital interventions for refugees , and social determinants of mental health . Her work addresses stigma , help-seeking behaviors , and cross-cultural adaptation of psychological tools. Recent articles emphasize Internet-based interventions for emotional disorders among Arabic- and Farsi-speaking refugees, role conflicts in interpreter mental health, and transcultural comparisons of causal attributions for mental illness. She also investigates microaggressions in therapy and ambulatory care disparities for migrant populations. Scientific Awards: 2023: Research Promotion Award (Elfriede Dietrich Foundation, 6,000 €) 2023: Scholarship to attend DeGPT Annual Meeting 2022: Scholarship for DeGPT Summer School 2017: DGPPN Poster Award Labs & Collaborations: Laura contributes to the I-REACH consortium (Internet-based Refugee Mental Health Care) and collaborates with institutions in Germany, Cuba, and Peru. Her research integrates transcultural psychology with digital health innovations .

Maija Huttunen-LenzView profile

Assistant Professor

• Community Health Nursing
• Type 2 Diabetes Prevention
• Health Behavior Modification
• +7 more

Maija Huttunen-Lenz is a W1 Tenure-Track Professor of Community Health Nursing at the Institute of Nursing Science , PH Schwäbisch Gmünd, Germany. She serves as Acting Director of the Institute since June 2022 and has been a visiting professor at the institution since 2019. Her expertise lies in complex health interventions , type 2 diabetes prevention , and health behavior modification , with a focus on secondary prevention and health psychology applications. Education : Ph.D. in health services research (University of East Anglia, 2012), M.Sc. in Health Psychology (University of Bath, 2005), Postgraduate Certificate in Health Service Leadership (BPP University, 2016) Her research explores lifestyle interventions for prediabetic populations, analyzing factors like protein intake , glycemic index , and psychosocial variables in weight management and metabolic health. Articles from 2018–2023 demonstrate her focus on longitudinal behavioral studies , nutritional epidemiology , and gender-specific metabolic responses . She serves as peer reviewer for journals including International Journal of Environmental Research and Public Health , BMC Public Health , and Health Psychology . Her teaching responsibilities include evidence-based practice and research methods in nursing science.

Stefan LüdtkeView profile

Assistant Professor

• Neuro-symbolic machine learning
• Machine learning for heterogeneous data
• Machine learning for marine ecology
• +5 more

Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Stochastic Differential Equations
• +3 more

Professor Han Cheng Lie is a faculty member at the Institut für Mathematik , Universität Potsdam , specialising in Uncertainty Quantification . His research focuses on Bayesian inference, inverse problems, and probabilistic numerical methods. Contact: han.lie@uni-potsdam.de Location: Campus Golm, Haus 9, Karl-Liebknecht-Str. 24-25, D-14476 Potsdam OT Golm, Germany His work addresses mathematical challenges in Bayesian inverse problems , including stability analysis of posteriors, low-rank approximations for Gaussian posteriors, and randomized algorithms for differential equations. He has contributed to Markov chain Monte Carlo on manifolds and transition path theory for diffusion processes. Recent publications highlight advances in dimension-independent sampling , probabilistic integrators , and model error mitigation in Bayesian frameworks. His methodological innovations span applications in computational statistics , molecular dynamics , and geophysical fluid dynamics . He organizes academic events like the Potsdam Data Assimilation Days and has presented at international workshops including Oberwolfach and SIAM conferences .

• Stochastic Analysis
• Financial Mathematics
• Probability Theory

Yuchen Sun, M.Sc., serves as a Researcher at the Institute of Mathematics within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. He is formally affiliated with the Stochastic Analysis and Stochastics of Financial Markets research group, contributing to the institute's quantitative finance initiatives. His research expertise centers on: Stochastic Analysis: Advanced modeling of stochastic differential equations and diffusion processes Financial Mathematics: Derivative pricing frameworks and market risk quantification Probability Theory: Theoretical foundations for random processes in economic systems Current work focuses on bridging abstract probability theory with real-world financial applications, particularly in volatility modeling and market microstructure analysis. No scientific awards or honors are documented in available institutional records. Information regarding student supervision, grant funding, or collaborative projects remains unspecified in public faculty listings. Dr. Sun operates within the Stochastic Analysis research group, which maintains active partnerships with Berlin's financial sector institutions and European quantitative research consortia to address contemporary challenges in mathematical finance.

• Mathematics
• Stochastics
• Analysis
• +6 more

Dr. Zihui He is a researcher at the Faculty of Mathematics of Bielefeld University , Germany. She is affiliated with the Collaborative Research Centre (CRC) 1283 project A8: Variational structures for evolution equations, optimal transport and synthetic curvature concepts . Her research aligns with Bielefeld's interdisciplinary focus on mathematical analysis of uncertainty, randomness, and low regularity in analytical and stochastic systems. Email: zihui.he@uni-bielefeld.de Office: UHG V4-238 Phone: +49 521 106-86673 Her work contributes to Bielefeld's strong international reputation in mathematics, particularly in areas like: Analysis of singular and random systems Stochastic modeling Applications in economic behavior and mathematical finance Topological and spectral structures

Timo KaiserView profile

Researcher

• Object Detection
• Multiple Object Tracking
• Person Re-identification
• +3 more

Timo Kaiser is a doctoral researcher at the Institute of Information Processing (TNT) within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He has been working towards his Dr.-Ing. degree since May 2020, conducting research in computer vision with a focus on object detection and tracking systems. His educational background includes a Master of Science in Mechatronics from Leibniz University Hannover, where he focused on digital image processing. His master's thesis addressed the multiple people tracking problem using Conditional Random Fields. His undergraduate studies emphasized robotics. Kaiser's research interests span object detection, multiple object tracking, and person re-identification, with recent work extending into uncertainty quantification, biomedical image analysis, and neural network optimization. His projects include Multiple People Tracking and GreenAutoML4FAS, demonstrating both theoretical and applied research directions. Analysis of his publication record reveals a strong focus on advancing multiple object tracking methodologies, with recent work (2023-2025) expanding into uncertainty quantification, cell tracking for biomedical applications, and novel neural network architectures. His research shows increasing sophistication, moving from traditional tracking algorithms to incorporating deep learning and uncertainty modeling. As a researcher actively contributing to the computer vision community, Kaiser has established collaborations with prominent researchers like Bodo Rosenhahn and has published in top-tier venues including ICML, ICLR, ICCV, and IEEE Transactions. His GitHub activity demonstrates commitment to reproducible research with code repositories supporting his publications.

Anne PohrtView profile

Researcher

• Medical Biometrics
• Epidemiology
• Biostatistics
• +6 more

Anne Pohrt is a researcher at Charité - University Medicine Berlin's Department of Biometry and Clinical Epidemiology. With a background in mathematics and a doctorate in medical science, her expertise spans medical biometrics, epidemiology, and statistical methodologies in clinical research. Languages: German (native), English, Swedish Programming Skills: R, SPSS, STATA Her research focuses on biometric consulting, study design evaluation, and statistical analysis in clinical settings. Key areas include logistic regression, survival analysis, meta-analyses, and psychosocial occupational factors. She provides methodological support for the Clinician-Scientist Program (CSP) and contributes to occupational medicine and psychological research. Recent publications highlight her interdisciplinary work across neurosurgery, geriatrics, nutrition science, and psychosocial workplace studies. She investigates topics like postoperative delirium predictors, dietary interventions for metabolic health, and end-of-life ICU practices. Her work integrates clinical routine data and advanced statistical modeling.

Inga SchneiderView profile

Researcher

• Nutritional Physiology
• Human Nutrition
• Mineral and Medicinal Water
• +3 more

Dr. Inga Schneider is a researcher at the Institute of Food and One Health , affiliated with the Faculty of Natural Sciences at Leibniz University Hannover. She serves as Programme Coordinator for the Master's programs in Food Research and Development and Food Science, and as Deputy Representative for Research Staff on the Examination Board. Her work focuses on nutritional physiology, mineral water research, and bioavailability of nutrients. Education: Doctorate (2010) on vitamin E bioavailability from fortified foods; Bachelor's and Master's degrees in nutritional science from Christian-Albrechts-University of Kiel (2002-2007). Her research explores the physiological impacts of dietary interventions, particularly mineral water's role in acid-base balance, and the nutritional quality of flexitarian, vegan, and omnivorous diets. She leads the Competence Center for Mineral and Medicinal Water at LUH, contributing to both teaching and examination processes in food science programs. Recent publications highlight her expertise in nutrient bioavailability (omega-3 fatty acids, magnesium, calcium) and dietary impacts on metabolic health. She also provides academic advising for Food Research and Development students.

• Stochastic Modeling
• Statistical Inference for Dynamic Processes
• Bayesian Statistics
• +5 more

Prof. Dr. Christiane Fuchs is a full professor at the Faculty of Economics of Bielefeld University and heads the Data Science Group . She is also leading the Biostatistics Research Group and Core Facility Statistical Consulting at Helmholtz Munich . Her academic affiliations include the Bielefeld Graduate School of Economics and Management and the Bielefeld Center for Data Science (BiCDaS) . Education : MSc in Computational Modeling, Brunel University West London (2003) Diploma in Mathematics with Computer Science, University of Hanover (2005) PhD in Statistics, Ludwig Maximilian University of Munich (2010) Research interests span stochastic modeling , Bayesian inference , uncertainty quantification , and statistical applications in economics, medicine, and epidemiology. She specializes in diffusion processes , high-dimensional data analysis , and integrated statistical methods for cross-domain data (genomics, clinical, environmental). Recent publications focus on AI-driven clinical decision support systems , spatial epidemiology , fractional diffusion modeling , and statistical serology validation . Her work bridges methodological innovation in Bayesian statistics with real-world applications in infectious disease dynamics and hematological malignancies . Principal investigator in third-party funded projects from DFG , BMBF , NIH , and Helmholtz Association , including the UQ Consortium (2019-2024) and KoCo19 prospective COVID-19 cohort (2020-2024). She has developed statistical software packages like stochprofML and adaSC3 , and contributed to network-regularized regression methods. As Vice Rector for Research and Networking at Bielefeld University since 2023, she drives institutional research strategy while maintaining active roles in scientific societies including the International Society for Bayesian Analysis and Deutsche Statistische Gesellschaft .