Find Academic Supervisors

• Medical Informatics
• Computational Biology
• Machine Learning in Virology
• +7 more

Nico Pfeifer is a W3 Professor in Computer Science (Methods in Medical Informatics) at the Faculty of Science, University of Tübingen. He leads a research group focused on statistical learning in computational biology, with a strong emphasis on HIV and SARS-CoV-2 antibody dynamics, viral resistance, and machine learning applications in virology. His work bridges computer science and medical informatics, contributing to high-impact therapeutic and diagnostic developments. Educational Background: Dr. rer. nat. in Bioinformatics/Computational Biology, University of Tübingen (2009) M.Sc. in Applied Computer Science, University of Göttingen (2005) B.Sc. in Applied Computer Science (Bioinformatics), University of Göttingen (2004) His research interests include medical informatics, computational biology, machine learning in virology, HIV drug resistance, antibody dynamics, and next-generation sequencing analysis. His work leverages statistical learning and computational modeling to address critical challenges in viral evolution and immune response prediction. The recent publications highlight a strong trend in applying machine learning and bioinformatics to understand HIV-1 and SARS-CoV-2 antibody therapies, viral resistance mechanisms, and immune responses. His research spans computational modeling, clinical data analysis, and translational medical informatics, with a consistent focus on improving viral treatment strategies and diagnostics. Scientific Awards: No specific awards mentioned in the provided text. Nico Pfeifer has been actively involved in advising and collaborative research, particularly in large-scale virology studies involving monoclonal antibodies and viral resistance. His work has been supported through institutional and collaborative grants, especially during his time at the Max Planck Institute and Microsoft Research. He has led significant research projects in computational virology and continues to contribute to high-impact interdisciplinary science. He is associated with the Methods in Medical Informatics research group at the University of Tübingen, where he leads efforts in developing computational tools for viral genomics and antibody response modeling. His team likely includes researchers and students working on machine learning applications in medical informatics and virology.

• Partial Differential Equations
• Nonlocal Problems
• Regularity Theory
• +3 more

Dr. Ho-Sik Lee is a researcher affiliated with the Faculty of Mathematics at Bielefeld University, specializing in partial differential equations, nonlocal problems, and regularity theory. He contributes to projects within the International Research Training Group 2235 and the Bielefeld Graduate School in Theoretical Sciences. Faculty of Mathematics, Bielefeld University Bielefeld Graduate School in Theoretical Sciences International Research Training Group 2235 His research focuses on advanced mathematical analysis of nonlinear and nonlocal equations, including degenerate weights, mixed local/nonlocal operators, and regularity estimates for problems arising in calculus of variations and applied contexts. Recent work explores the Zaremba problem, fractional p-Laplacian, kinetic Fokker-Planck equations, and double phase functionals. Analysis of his 15 most recent publications (2023–2025) reveals expertise in parabolic equations, nonlocal diffusion, Hölder continuity, gradient estimates, and weighted Sobolev spaces. These works intersect with mathematical physics, functional analysis, and applications in nonlinear dynamics.

Denis BelomestnyView profile

Professor

• Statistics of Stochastic Processes
• Financial Mathematics
• Econometrics
• +5 more

Denis Belomestny is a Professor of Applied Stochastics at the Department of Mathematics, University of Duisburg-Essen. His academic journey includes a PhD from Lomonosov Moscow State University (2002), postdoctoral work at the University of Bonn, and research positions at WIAS Berlin and Humboldt University Berlin. He currently leads research at the intersection of stochastic processes and financial mathematics. PhD in Mathematics, Lomonosov Moscow State University (2002) W3 Professorship in Applied Stochastics, University of Duisburg-Essen (2011–present) His research focuses on statistics of stochastic processes , optimal stopping/control , and Monte Carlo methods , with applications in financial mathematics and machine learning. Key collaborations include work with John Schoenmakers on multilevel approximation algorithms and Alexey Naumov on variance reduction techniques. Recent publications (2025–2023) explore deep neural networks for SDEs , generative adversarial networks , and nonparametric estimation in complex stochastic models. His work spans stochastic differential equations , financial derivatives pricing , and machine learning-driven statistical inference . He supervises doctoral students, including Sascha Nolte (research: robust optimal stopping without reference models). Current projects involve McKean-Vlasov SDEs , gamma-driven processes , and reinforced optimal control .

Moritz EgertView profile

Professor

• Harmonic Analysis
• Functional Calculus
• Partial Differential Equations
• +7 more

Prof. Dr. Moritz Egert is a faculty member in the Department of Mathematics at Technische Universität Darmstadt, where he conducts research and teaches in the areas of harmonic analysis, functional calculus, and partial differential equations. He is actively involved in academic leadership, including organizing the RMU Seminar SoCo PDEs starting Winter 2024/25, and supervising PhD students such as Tim Böhnlein, Miriam Buck, and Benjamin Kosmala. His research focuses on deep analytical problems arising in PDEs, particularly those involving elliptic and parabolic divergence form operators, boundary value problems, and Hardy spaces. He frequently collaborates with leading mathematicians such as Pascal Auscher, with whom he co-authored a prize-winning monograph. The recent publications reflect a consistent trend in advancing the theory of functional calculus, regularity of solutions, and boundary behavior in both elliptic and parabolic systems. His work bridges abstract harmonic analysis with concrete PDE applications, often yielding improved estimates and structural insights. Ferran Sunyer i Balaguer Prize (2022) Athene Special Prize for Digital Teaching 2024 (€5,000) Prof. Egert has supervised multiple PhD students and is open to hosting Bachelor’s and Master’s thesis projects. He has been involved in significant digital teaching initiatives, notably the International Internet Seminar on Evolution Equations, which earned him a teaching prize. He also contributes to academic community-building through seminars and outreach, including podcast appearances. He is a core organizer of the new RMU Seminar SoCo PDEs and maintains active research collaborations across institutions. His team includes several PhD candidates, and he continues to publish high-impact research in top journals.

Cosmina CroitoruView profile

Researcher

• Argumentation Frameworks
• Artificial Intelligence
• Algorithmic Graph Theory
• +2 more

Cosmina Croitoru is a researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, specializing in Algorithms and Complexity. She works within Department 1 of the institute, focusing on theoretical aspects of computer science with applications in artificial intelligence and argumentation frameworks. Her research has significant implications for knowledge representation, multi-agent systems, and computational social choice. PhD in Computer Science from Universität des Saarlandes (2017) Master's in Computer Science from Universität des Saarlandes (2012) Bachelor's in Computer Science from University AL. I. Cuza Iasi, Romania (2010) Cosmina's research interests span multiple domains within theoretical computer science and artificial intelligence. Her primary focus is on argumentation frameworks, where she has made significant contributions to the theoretical foundations. She also works extensively on algorithmic graph theory, applying these techniques to problems in artificial intelligence and data science. Her work on planning algorithms demonstrates her ability to bridge theoretical computer science with practical applications. The interdisciplinary nature of her research connects computer science with social choice theory and rational decision making. Analysis of Cosmina's publication record reveals a strong trajectory in theoretical computer science with a focus on argumentation frameworks. Starting with foundational work on abstract argumentation systems, her research evolved to address more complex problems involving combinatorial analysis, polyhedral geometry, and social interaction models. Her publications span prestigious venues in artificial intelligence, theoretical computer science, and computational logic. The consistent theme across her work is the application of rigorous mathematical methods to problems in knowledge representation and reasoning. Cosmina has been actively involved in academic service through conference organization and editorial work: Co-Organizer of ADFOCS 2014-2021 Editorial Assistant of CGTA (Computational Geometry Journal) 2014-2018 Student Organizer of ADFOCS 2012, Algo 2011, CICLing 2010 Organizer of Eurolan Junior 2009 As an educator, Cosmina has taught numerous courses including Artificial Intelligence, Data Science, Optimization, and various specialized topics in computer science. Her teaching spans multiple institutions including MPII, HTW, and Universität des Saarlandes. She has advised students on topics related to argumentation frameworks and graph algorithms, though specific student names aren't documented in the available information. Her primary research affiliation is with the Algorithms and Complexity department at the Max Planck Institute for Informatics, where she collaborates with leading researchers in theoretical computer science. Her work contributes to the institute's broader mission of advancing fundamental research in computer science.

• Operations Research
• Optimization
• Fuzzy Systems
• +4 more

Shu-Cherng Fang is a prominent academic in the fields of Operations Research , Optimization , and Machine Learning . His work spans theoretical advancements and practical applications in Mathematical programming Supply chain network design Fuzzy systems Support vector machines Algorithm development . While specific institutional affiliations and academic rank are not explicitly stated in the provided text, his extensive publication record in high-impact journals indicates a faculty-level role. Research interests include optimization under uncertainty , supply chain logistics , and kernel-free machine learning models . Key trends in recent articles focus on fourth-party logistics (4PL) network design distributionally robust optimization for machine learning mathematical modeling of customer behavior stochastic programming . Co-authors frequently include Min Huang, Zhibin Deng, Jian Luo, and Wenxun Xing, reflecting sustained collaborations. Articles emphasize interdisciplinary approaches combining fuzzy logic , game theory , and computational geometry to solve complex decision-making problems.

Felix HöflingView profile

Professor

• Statistical Physics
• Biological Physics
• Molecular Dynamics
• +3 more

Prof. Felix Höfling holds a W2 non-tenure professorship for 'High-performance computing in molecular dynamics' at Freie Universität Berlin (since 2015). He leads the Computational Statistical & Biological Physics group within the Department of Mathematics and Computer Science. Prior roles include research associate positions at the Max Planck Institute for Intelligent Systems (2010–2015), postdoctoral research at the University of Oxford (2009), and doctoral work at Ludwig-Maximilians-Universität München (2006, summa cum laude). His research focuses on computational methods in statistical and biological physics, including molecular dynamics simulations, anomalous transport in crowded environments, and high-performance computing. Notable contributions include studies on liquid-vapor interfaces, colloidal dynamics, and the interplay between microscopic and macroscopic transport phenomena. His work integrates theoretical modeling, numerical simulations, and interdisciplinary applications in soft matter and biophysics. Editorial Board Member of Communications Physics (since 2023) Director of the CECAM node 'Mathematics and Computation in Molecular Simulation' (since 2022) Head of the MSc Examination Board for Computational Sciences (since 2019) His research articles explore topics such as surface tension modulation, confined fluids, and active matter systems. Key achievements include a highly cited review on anomalous transport in biological cells (2013) and contributions to the development of the H5MD molecular data format. Awarded 'Distinguished Referee' by the European Physical Journal (2013) Recipient of grants for projects on open systems and parallel computing Prof. Höfling oversees the Computational Statistical & Biological Physics lab, emphasizing collaborative research in computational physics and interdisciplinary applications.

Matthias KrügerView profile

Professor

• Non-equilibrium Statistical Physics
• Quantum Thermal Fluctuations
• Radiative Heat Transfer
• +7 more

Prof. Dr. Matthias Krüger is a Professor at the Institute of Theoretical Physics within the Faculty of Physics at the University of Göttingen. His research focuses on non-equilibrium statistical physics with applications in quantum thermal fluctuations, radiative heat transfer, and Casimir interactions. He leads an active research group investigating fundamental phenomena in complex systems. His primary research interests span Non-equilibrium Statistical Physics , Quantum Thermal Fluctuations , and Nanoscale Phenomena . The group develops theoretical frameworks for understanding thermal transport in media, propulsion forces via Casimir effects, and stochastic motion in nonlinear/non-Markovian fluids. Current projects include studies of demixing dynamics in multi-component fluids and frictional damping on solid surfaces, supported by major grants from the Volkswagen Foundation and DFG (SFB1432, SFB1073). Key scientific achievements include the development of the Mean Back Relaxation (MBR) observable for detecting broken detailed balance and a general nonlinear Langevin approach for driven probes. His work bridges fundamental theory with experimental implications in nanoscale energy conversion and material science. Volkswagen Foundation Grant for quantum electrodynamics research DFG Grant (SFB1432) for nonlinear/non-Markovian Brownian motion DFG Grant (SFB1073) for nanoscale friction studies His research group actively investigates thermal transport in dissipative media, Casimir interactions in nonreciprocal materials, and viscoelastic properties of biological systems. Current experimental collaborations focus on recoil dynamics in viscoelastic fluids and nanoscale friction control in manganite superlattices, with implications for energy materials and nanotechnology.

Stefanos FasoulasView profile

Professor

• Space Systems Engineering
• Aerospace Engineering
• Plasma Wind Tunnel Testing
• +3 more

Stefanos Fasoulas is a Professor of Space Transportation Technology and Managing Director of the Institute of Space Systems at the University of Stuttgart. He has held leadership roles including Dean of Faculty 6 - Aerospace Engineering and Geodesy and has been instrumental in advancing research in aerospace engineering, plasma dynamics, and orbital aerodynamics. Education: Studied Aerospace Engineering at the University of Stuttgart, earning his diploma (1990) and PhD (1995). His research spans space systems engineering, gas-surface interaction modeling, and life support technologies. Recent work focuses on machine learning for scattering kernels, drag-based collision avoidance, and heterogeneous catalytic reactions in spacecraft environments. Key trends in his publications include: Innovations in DSMC and PICLas simulations for reentry analysis Development of atmosphere-breathing propulsion systems Environmental impact assessments of launchers and space debris He has acquired numerous research grants, particularly for projects like DISCOVERER and planetary sunshade concepts. His leadership extends to founding roles in the Competence Center Aerospace Saxony/Thuringia and the University Center for Aerospace at TU Dresden.

Christian HolmView profile

Professor

• Computer simulations of soft matter
• Theoretical modeling of charged systems
• Polyelectrolyte solutions
• +7 more

Christian Holm is a Professor at the Institute for Computational Physics of the University of Stuttgart. His research focuses on computational modeling of soft matter systems with electrostatic and magnetic interactions, including polyelectrolytes, colloids, and hydrogels. Member of collaborative research centers SFB 716, SFB 1313, SFB 1333, FOR 2811, and MultiXscale Develops machine learning potentials and advanced simulation methods for complex molecular systems Co-developer of the ESPResSo simulation package for soft matter research His recent work explores: Mechanisms of polyelectrolyte complexation and gel swelling Machine learning approaches for quantum-accurate interatomic potentials Dynamics of active colloids and biofilm formation in porous media Multi-agent reinforcement learning for smart active systems He maintains affiliations with CECAM's Soft Matter and Statistical Mechanics node and contributes to open science initiatives through software development.

• Medical Natural Language Processing
• Clinical Text Analysis
• Radiology Informatics
• +5 more

Chaitanya Shivade is a prominent researcher specializing in medical natural language processing with significant contributions to clinical text analysis, radiology informatics, and behavioral health documentation. His work bridges computational linguistics and healthcare applications, focusing on practical solutions for clinical documentation challenges. Shivade's research spans multiple critical areas: developing evaluation frameworks for behavioral therapy notes (TN-Eval), creating shared tasks for medical summarization (MEDIQA), advancing visual dialog systems for radiology, and pioneering synthetic clinical note generation. He has made substantial contributions to textual inference in clinical domains through the MedNLI dataset and has explored fundamental linguistic challenges like negation detection and gradable term analysis in medical text. As a workshop organizer for the NLP for Medical Conversations series, he has helped shape community standards and foster collaboration. His publication record demonstrates consistent leadership in applying NLP to real-world healthcare problems, with particular emphasis on evaluation methodologies, dataset creation, and practical clinical applications. Shivade has collaborated extensively with medical professionals and researchers across institutions to ensure clinical relevance of his technical work. Organized MEDIQA shared tasks (2019, 2021) Co-organized NLP for Medical Conversations workshops (2019, 2020) Developed TN-Eval framework for therapy note quality assessment Created MedNLI dataset for clinical textual inference Pioneered synthetic clinical note generation approaches His work consistently addresses the tension between clinical utility and technical innovation, with growing emphasis on evaluating LLM performance in healthcare contexts. The progression from foundational clinical NLP techniques to complex evaluation frameworks demonstrates his evolving research trajectory toward ensuring reliable AI deployment in medical settings.

• Software Security
• Systems Security
• Smart Contract Security
• +5 more

Lucas Vincenzo Davi is a Professor of Computer Science at the University of Duisburg-Essen and Director of the paluno - the Ruhr Institute for Software Technology . He leads the SYSSEC research group and is a Principal Investigator (PI) in the SFB CROSSING and CASA excellence cluster. His work focuses on practical software and systems security, including memory corruption vulnerabilities, exploit mitigation, and blockchain smart contract security. Davi holds an ERC Starting Grant for Smart Contract Security research. Education: PhD in IT Security, TU Darmstadt (2011–2015), Dissertation: "Code-Reuse Attacks and Defenses" Master of Science in IT Security, Ruhr-Universität Bochum (2007–2009) Diplom (FH) in Business Informatics, FOM Neuss (2003–2007) Research: Davi's work emphasizes real-world security challenges like runtime attacks, trusted execution environments (TEE), and secure embedded systems. His contributions include Wemby (WebAssembly security analysis), HCC (smart contract hardening), and rowhammer mitigations like CATT. His research has influenced industry practices, e.g., Microsoft EMET v5.1 improvements. Awards: Best Teacher Award (2025) Finalist German IT Security Prize (2022, 2020) ACM SIGSAC Dissertation Award (2016) CAST Förderpreis (2010) Grants & Leadership: ERC Starting Grant (Smart Contracts), SFB CROSSING PI, CASA Cluster PI, and leadership in interdisciplinary initiatives like Nano Security (DFG Priority Program). Labs/Teams: Director of paluno, SYSSEC group leader, and collaborator in projects like HERA (hotpatching for embedded systems) and DMA’n’Play (DMA-based attestation).

Saskia HekkerView profile

Professor

• Asteroseismology
• Stellar Evolution
• Internal Structures of Stars
• +3 more

Saskia Hekker is a Professor of Theoretical Astrophysics at Heidelberg University's Center for Astronomy (ZAH), specifically at the Landessternwarte Königstuhl (LSW) observatory. Since September 2020, she has also served as Group Leader for the Theory and Observations of Stars (TOS) at the Heidelberg Institute for Theoretical Studies (HITS). Her academic journey includes significant positions at the Max Planck Institute for Solar System Research in Göttingen (2014-2020), where she led an independent research group, and earlier postdoctoral positions at the University of Amsterdam, University of Birmingham, and Royal Observatory of Belgium. Dr. Hekker's research focuses on asteroseismology, stellar evolution, and the internal structures of stars, particularly solar-like oscillators and red giants. Her work bridges theoretical modeling with observational data to understand stellar interiors and evolutionary processes. She has made significant contributions to understanding mixed modes in red giants, stellar rotation, and the application of asteroseismology to determine stellar ages and evolutionary status. Her research often involves large-scale missions like Kepler and PLATO, as evidenced by her numerous publications in high-impact journals. Her recent publications (2024-2025) reveal a strong focus on asteroseismic analysis of red giants, stellar rotation, magnetic effects on stellar oscillations, and the development of methods for asteroseismic modeling. Her work frequently appears in Astronomy & Astrophysics, The Astrophysical Journal, and Nature Astronomy, demonstrating her leadership in applying asteroseismology to solve fundamental questions about stellar structure and evolution. ERC Consolidator Grant (2020-2026) for project 'DipolarSound' Merac prize for observational astrophysics (2015) ERC starting Grant (2013) Veni fellowship from Dutch National Funding Agency (2010) Multiple poster prizes at international conferences Dr. Hekker leads the Theory and Observations of Stars group at HITS, which focuses on developing and applying asteroseismic techniques to study stellar structure and evolution. Her team works closely with data from space missions like Kepler and PLATO, contributing to major catalogs like APOKASC-3. Her ERC Consolidator Grant 'DipolarSound' (2021-2026) supports research into the coupling of mixed oscillation modes in red giants, which has implications for understanding angular momentum transport in stellar interiors.

Anna KrauseView profile

Researcher

• Machine Learning
• Deep Learning
• Time Series Analysis
• +7 more

Dr.-Ing. Anna Krause is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. She leads the Deep Learning for Dynamical Systems Group and has been actively involved in teaching at the university since 2019, including courses on Machine Learning for Time Series Analysis and Data Mining. Doctoral degree in Electrical Engineering (2019), University of Hannover Diploma in Electrical Engineering (2009), Technical University Dresden Her research focuses on Environmental Sensing and Time Series Analysis , particularly on enhancing physics-based models using machine learning techniques for meteorological applications and sparse sensor networks. She has made significant contributions to explainable AI, climate modeling, and fraud detection systems. Anna's recent publications demonstrate expertise in climate modeling (ConvMOS, ICLR 2024-2025), physics-informed neural networks (TaylorPDENet, ECMLPKDD 2023), and fraud detection (MIDAS workshops, ECMLPKDD 2020-2023). She actively contributes to conferences as organizer and PC member, including ECMLPKDD and ICLR workshops. Scientific Awards Best ML Innovation Award (2020) for Deep Learning in Climate Modeling Best Student Paper Award (2020) for Multi-Task Land Use Regression Best Paper Award (2020) for Financial Fraud Detection with INALU The DynaBench dataset introduced in 2023 provides benchmark tools for learning dynamical systems from low-resolution data. Her work combines theoretical advancements with practical implementations, including edge computing applications for beekeeping monitoring systems.

• Data Deduplication
• Spatial Data Mining
• GPU Computing
• +3 more

Witold Andrzejewski is an active researcher in computer science, focusing on data deduplication pipelines, co-location pattern mining, and GPU-accelerated algorithms. His work bridges academia and industry, with publications analyzing customer record deduplication in the financial sector, performance optimization of spatial data processing, and comparative studies of statistical modeling versus machine learning approaches. 2025: Co-location pattern mining with Euclidean metrics 2024: Customer data deduplication parameter tuning 2023: Text similarity measures in financial applications