Find Academic Supervisors

Joachim KrugView profile

Professor

• Statistical Physics
• Evolutionary Biology
• Population Genetics
• +4 more

Prof. Joachim Krug is a Professor at the University of Cologne's Institute for Theoretical Physics, affiliated with the Center for Data and Simulation Science. His research focuses on theoretical models of evolutionary biology, particularly population genetics and fitness landscapes. He investigates how populations adapt to new environments, emphasizing the role of epistasis, mutational pathways, and evolutionary predictability, with applications to antibiotic resistance in microbes. Key research areas include the geometry of fitness landscapes, clonal interference in large populations, and the interplay between recombination and evolutionary robustness. His work bridges statistical physics and biology, applying concepts from stochastic processes and nonlinear dynamics to evolutionary systems. Notable contributions include studies on evolutionary accessibility, adaptive walks, and the role of deleterious mutations in long-term evolution. Publications highlight interdisciplinary approaches, such as analyzing β-lactamase resistance mechanisms in E. coli and developing models for RNA translation dynamics. His research underscores the importance of quantitative methods in understanding evolutionary processes and predicting evolutionary trajectories.

• Explainable AI
• Machine Learning
• Software Verification
• +6 more

Alnis Murtovi is an Assistant Professor at TU Dortmund University's Faculty of Computer Science, Department of Software Engineering. They are a prominent researcher in the fields of explainable AI, machine learning, and software verification, collaborating extensively with Professor Bernhard Steffen and other colleagues at the university. Dr. Murtovi's research focuses on making machine learning models more interpretable and verifiable, particularly through work on decision trees, random forests, and neural networks. Their recent publications demonstrate a strong emphasis on developing practical tools and compilation-based approaches to explain complex models while maintaining performance. The research spans theoretical foundations in formal methods to practical implementations like the Forest GUMP tool for verification and explanation. The publication trend shows a consistent output with increasing focus on explainable AI in recent years, with significant contributions in 2023-2025. Their work bridges the gap between theoretical computer science and practical machine learning applications, particularly in verification techniques for AI models. The research has strong connections to software engineering principles and formal methods. Dr. Murtovi has made substantial contributions to the field through numerous publications in top venues including TACAS, STTT, and various conference proceedings. Their work on Forest GUMP represents a significant practical contribution to the community for model verification and explanation. As part of the research group led by Bernhard Steffen at TU Dortmund, Dr. Murtovi contributes to interdisciplinary projects that combine language-driven engineering approaches with practical software development challenges. Their research has implications for both academic and industrial applications where model interpretability and verification are critical requirements.

• Algorithmic Algebra
• Computational Complexity
• Invariant Theory
• +4 more

Prof. Peter Bürgisser is a Professor in the Institute of Mathematics at Technische Universität Berlin, leading the Research Group on Algorithmic Algebra. His work focuses on computational complexity, invariant theory, algebraic geometry, and probabilistic methods in mathematics. He holds a prominent role in advancing theoretical computer science and algorithmic algebra through his research and publications. Research interests include Algorithmic Algebra, Computational Complexity, Invariant Theory, Optimization, and Probability Theory. His contributions span foundational work in geometric complexity theory, probabilistic algebraic geometry, and efficient algorithms for algebraic problems. Publications highlight his expertise in topics like random fewnomials, nonarchimedean geometry, and polynomial time algorithms for invariant theory. His work is published in top journals such as Journal für die reine und angewandte Mathematik and Foundations of Computational Mathematics . Prof. Bürgisser is actively involved in academic activities, including teaching and advising, though specific student names are not listed in the provided texts. His research has been supported by grants and collaborations with institutions worldwide.

Paul SeiferthView profile

Researcher

• Computational Geometry
• Algorithms
• Data Structures
• +3 more

Paul Seiferth is affiliated with the Department of Computer Science at Freie Universität Berlin. His research focuses on computational geometry, algorithms, data structures, and graph theory. He has contributed to topics like Voronoi diagrams, dynamic planar graphs, unit disk routing, and spanner construction. His work emphasizes algorithmic efficiency, time-space trade-offs, and geometric data structures. Education : PhD (2012–2016), Freie Universität Berlin Master's (2010–2012), Freie Universität Berlin Research Interests : His research addresses fundamental problems in computational geometry and graph theory, with applications to wireless networks and geometric algorithms. Key themes include dynamic data structures for proximity problems (e.g., Voronoi diagrams), routing in unit disk graphs, and efficient spanner constructions for directed transmission graphs. Key Contributions : His work on time-space trade-offs for Voronoi diagrams and dynamic planar Voronoi diagrams has advanced algorithmic techniques for geometric problems. He also developed efficient routing schemes for unit disk graphs and reachability oracles for transmission graphs, balancing theoretical guarantees with practical efficiency. Teaching : He teaches ProInformatik I: Logik und Diskrete Mathematik . Labs/Teams : Associated with the AG Theoretische Informatik research group at Freie Universität Berlin.

Jens LangView profile

Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Adaptive Finite Element Methods
• +4 more

Prof. Dr. Jens Lang is a Professor at the Numerical Analysis Group within the Department of Mathematics at Technische Universität Darmstadt . His work focuses on adaptive numerical methods for partial differential equations (PDEs), particularly in computational fluid dynamics, uncertainty quantification, and optimal control.

• Labor Economics
• Policy Evaluation
• Econometrics
• +5 more

Arne Uhlendorff is a Professor of Economics at Ecole Polytechnique and CNRS Research Director at CREST (Centre de Recherche en Economie et Statistiques) within Institut Polytechnique de Paris. He holds concurrent research fellowships at IAB Nuremberg, IZA, DIW Berlin, and Institut des Politiques Publiques (IPP), and directs the Chaire Sécurisation Des Parcours Professionnels. His academic appointments span multiple prestigious European research institutions with a focus on empirical labor market analysis. Uhlendorff's research centers on labor economics and policy evaluation, with particular expertise in gender disparities, unemployment dynamics, vocational training, and structural estimation methods. His work frequently employs advanced econometric techniques including randomized field experiments, regression kink designs, and machine learning applications to evaluate labor market interventions. Key thematic areas include: Gender differences in training access and benefit impacts Early intervention effects for at-risk workers Task-based wage determination mechanisms Strategic behavior in sickness absence Vocational education outcomes His recent publications reveal a strong emphasis on experimental and quasi-experimental methods applied to active labor market policies, with increasing integration of machine learning for employment prediction. The research portfolio demonstrates consistent focus on policy-relevant questions with direct implications for welfare systems and vocational training frameworks across Europe. Scientific recognition includes: Research Fellow status at four major economic research institutes (IAB, IZA, DIW, IPP) Leadership of the Chaire Sécurisation Des Parcours Professionnels Regular publication in top economics journals including Journal of Labor Economics and Economica Uhlendorff actively contributes to policy development through randomized field experiments like the AvenirPro initiative for vocational students and integration agreement trials for welfare recipients. His work bridges academic rigor with practical policy design, particularly in French and German labor market contexts. While specific grant details aren't provided in source materials, his extensive field experimentation suggests significant funding from public policy evaluation programs. As Director of the Chaire Sécurisation Des Parcours Professionnels, he leads a research team focused on professional trajectory security, collaborating with French labor market institutions to design and evaluate interventions for vulnerable workers. The chair serves as a hub for experimental policy evaluation connecting academic research with practical implementation.

Duncan AdamsonView profile

Lecturer

• Combinatorics on Words
• Algorithms for Combinatorial Objects
• Crystal Structure Prediction
• +7 more

Duncan Adamson is a Lecturer in the School of Computer Science at the University of St Andrews, United Kingdom. He has held prior research positions at the Leverhulme Research Centre for Functional Materials Design, the University of Göttingen, Reykjavik University, and the University of Liverpool. His academic foundation includes a PhD from the University of Liverpool supervised by Prof. Igor Potapov and undergraduate studies at the University of Glasgow. His research lies at the intersection of theoretical computer science and discrete mathematics, with a focus on combinatorics on words , algorithm design , crystal structure prediction , and temporal graphs . He explores symmetry in multidimensional words, develops algorithms for combinatorial enumeration, and investigates computational hardness in materials science. His work often bridges abstract theory with real-world applications in chemistry and robotics. The recent publications show a strong trend in combinatorial algorithms , particularly in enumeration, ranking, and optimization over structured words, graphs, and sequences. A significant portion of his work involves the k-centre problem for implicitly defined combinatorial classes and temporal graph colouring , with increasing emphasis on algorithmic complexity and practical implementation. His 2024 paper on harmonious colourings in temporal matchings was awarded best paper at SAND 2024, highlighting the impact of his research. Scientific Awards: Best Paper Award, SAND 2024 – 'Harmonious Colourings of Temporal Matchings' Duncan Adamson has been supported by the Leverhulme Research Centre for Functional Materials Design during his PhD and postdoctoral work. While no formal advisees are listed, his supervision by leading researchers and collaborative projects suggest active mentorship and team integration. He teaches core computer science modules at St Andrews, including CS2001 and CS3302, and maintains a busy research schedule with weekly meetings and supervisory responsibilities. His research is conducted within the Algorithms and Complexity group at St Andrews, continuing collaborations with international teams in Iceland, Germany, and beyond. Projects often involve interdisciplinary efforts, especially in applying computational techniques to materials science and chemistry.

Nicolás Sanhueza-MatamalaView profile

Assistant Professor

• Discrete Mathematics
• Combinatorics
• Extremal Graph Theory
• +7 more

Nicolás Sanhueza-Matamala is an Assistant Professor at the Universidad de Concepción, Faculty of Physical and Mathematical Sciences. He is a member of the Discrete Mathematics Research Group at UDEC, focusing on extremal and probabilistic combinatorics, particularly in graph and hypergraph theory. His research interests include extremal graph theory, probabilistic methods in combinatorics, hypergraph theory, Hamiltonian cycles, Ramsey theory, and structural problems in discrete mathematics. He investigates degree conditions for spanning substructures, cycle decompositions, and robustness of graph properties. The recent publications highlight a strong focus on hypergraph generalizations of classical graph theorems, including Dirac-type conditions, bandwidth theorems, and cycle decompositions. His work frequently involves collaborations with leading researchers such as Richard Lang, Matías Pavez-Signé, and Simón Piga, and is published in top journals like Journal of Combinatorial Theory, Series B , Random Structures & Algorithms , and SIAM Journal on Discrete Mathematics . Best Paper Award at LAGOS '21 Editor's Choice 2022 of Discrete Mathematics He has advised several undergraduate and master’s students in mathematics and computer science and serves as a thesis advisor in doctoral and master’s programs. He has been awarded competitive grants including the Fondecyt Regular (2025–2028) and Fondecyt Iniciación (2022–2024). He is actively involved in the academic community, organizing colloquia and participating in international conferences such as EUROCOMB and RSA. He is a key contributor to the discrete mathematics community in Chile and internationally, with research that bridges theoretical depth and structural insight in combinatorics.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +15 more

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.

Heidemarie KrügerView profile

Professor

• Photonics
• Quantum Detection
• Memristor Technology
• +5 more

Prof. Dr. Heidemarie Krüger serves as the Head of the Research Department Photonics and Quantum Detection at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany. Her work bridges advanced materials science with applied physics and electronics, focusing on photonic and quantum detection systems. Academic Rank: Professor Email: heidemarie.krueger@leibniz-ipht.de Research Interests span photonics, quantum detection, memristor technology, and nanomaterials. She investigates: Electrochemically grown porous platinum for electrocatalysis and optical applications Resistive switching dynamics in multiferroic thin films Magnetooptical properties of ferromagnetic materials Stability enhancements in organic solar cells via amphiphilic additives Memristor-based security solutions like true random number generators Impedance chip development for biochemical monitoring Recent Publications (2024–2025) highlight her interdisciplinary expertise, with trends in: Magnetotransport phenomena in superconductors Optimization of memristive devices for neuromorphic and cryptographic applications Electrochemical and optical engineering of nanostructured materials Stability mechanisms in renewable energy technologies

Jan VogesView profile

Researcher

• Genomic Data Compression
• DNA Data Storage
• Bioinformatics
• +3 more

Dr. Jan Voges is a Research Group Leader at the Institute for Information Processing and the L3S Research Center at Leibniz University Hannover, where he leads the BioAI Lab. He has held previous research leadership positions at the same institution (2022-2024), CIMA University of Navarra (2024-2025), and maintains a visiting position at the Peter L. Reichertz Institute for Medical Informatics. Dr. Voges' research spans the intersection of computer science, genomics, and medical informatics, with a focus on: Developing AI methods for multi-omics data analysis DNA sequencing data compression and storage 3D genome reconstruction Single-cell RNA sequencing data modeling International standardization of genomic data His recent work demonstrates a strategic shift toward integrating artificial intelligence with genomic data analysis, particularly for medical applications. The development of Genie, the first open-source ISO/IEC encoder for genomic data, represents a significant contribution to genomic data standards. His research shows strong translational potential, bridging computational methods with clinical applications as evidenced by his P4D Dashboard project for depression cohort monitoring. Dr. Voges has received recognition for his work, most notably earning his Dr.-Ing. degree with distinction in 2022. His active participation in ISO/IEC standardization committees and collaborations with international institutions demonstrates the impact of his work beyond traditional academic boundaries. As a supervisor, Dr. Voges maintains a focused lab structure with 3 PhD students, emphasizing quality mentorship and opportunities for students to contribute to high-impact projects with real-world applications. His lab provides unique opportunities at the cutting edge of genomic data science, with exposure to international standardization processes and interdisciplinary collaborations.

Felix KrahmerView profile

Associate Professor

• Optimization and Data Analysis
• Compressed Sensing
• Unlimited Sampling
• +7 more

Prof. Dr. Felix Krahmer is an Associate Professor for Optimization and Data Analysis at the Department of Mathematics, Technical University of Munich (TUM) . His research focuses on mathematical foundations of data science, with expertise in Compressed Sensing Unlimited Sampling Quantization of Signals and Images Neural Networks Uncertainty Quantification Low-Rank Recovery He leads the Data Science research group within TUM's School of Computation, Information and Technology, collaborating with institutions like TU Berlin, RWTH Aachen, and Technion. His recent publications span topics like Robust blind deconvolution under adversarial noise Kronecker-structured dimensionality reduction Hysteresis in unlimited sampling systems Efficient sparse FFT algorithms Notable scientific awards include two August-Wilhelm-Scheer Visiting Professorships (2016, 2019) for hosting international collaborators. Former and current students include Olga Graf Dominik Stöger Claudio Verdun Felipe Pagginelli Patricio Juana Kostin He has co-organized workshops at SAMPTA, Oberwolfach, and ISIT, serving as associate editor for journals including SIAM Journal on Matrix Analysis and Frontiers in Signal Processing.

Christian ScheidelerView profile

Professor

• Distributed Algorithms
• Data Structures
• Security in Distributed Systems
• +4 more

Christian Scheideler is a Professor at the Institute for Computer Science (Theory of Distributed Systems) within the Faculty of Electrical Engineering, Computer Science, and Mathematics at the University of Paderborn. He serves as institute director, advisory committee member of SPAA, and associate editor for Journal of the ACM and Journal of Computer and System Sciences . His editorial and leadership roles extend to managing Journal of Interconnection Networks and steering committees for conferences like DISC, SIROCCO, and ALGOSENSORS. Director, Institute for Computer Science Advisory Committee, SPAA Associate Editor, Journal of the ACM and Journal of Computer and System Sciences Managing Editor, Journal of Interconnection Networks Steering Committee member for DISC, SIROCCO, ALGOSENSORS His research focuses on distributed algorithms, data structures, security in distributed systems, randomized algorithms, stochastic processes, network theory, and discrete mathematics . Notably, he investigates hybrid programmable matter systems, self-stabilizing overlay networks, and robust distributed protocols under adversarial conditions. Recent publications (2021-2025) highlight work on 3D programmable matter shape formation , low-diameter graph decompositions , reconfigurable circuits in amoebot models , and blockchain lightweight state replication . Topics span distributed computing, computational geometry, and network security. Scientific awards include the 2022 Edsger W. Dijkstra Prize in Distributed Computing for foundational work in self-stabilization and overlay networks. He has served as PC Chair (DISC 2022) and organized numerous conferences (SPAA, SSS, SIROCCO).

• Numerical Analysis
• Sparse Grids
• Machine Learning
• +3 more

Prof. Dr. Michael Griebel is a leading academic at the Institute for Numerical Simulation (INS) at the University of Bonn , Germany. His work focuses on numerical methods for partial differential equations (PDEs), sparse grid techniques, and applications in computational science, machine learning, and materials modeling. Research Interests : Numerical analysis, sparse grids, high-dimensional approximation, tensor product methods, stochastic processes, and multiscale simulations. Collaborations : Co-author of over 50 publications with researchers in computational mathematics, fluid dynamics, and quantum chemistry. Prominent research trends include advancements in sparse grid methods for uncertainty quantification, kernel-based regression, and efficient solvers for ill-posed problems. His work on space-filling curves and domain decomposition enables scalable parallel algorithms for complex simulations. Scientific Contributions : Editor of 10+ volumes in Lecture Notes in Computational Science and Engineering . Co-developer of the EXAHD exascale sparse grid framework for plasma physics simulations. Foundational work on tensor product spaces and their applications to electronic structure theory. Innovations in adaptive wavelet solvers and parallel multigrid methods. Software and Projects : Key developer of sparse grid solvers and adaptive algorithms. His INS Preprint Series archives over 200 technical reports, reflecting his group’s impact on computational methods.

Lorenzo GrassiView profile

Researcher

• Symmetric Cryptography
• Cryptanalysis
• Hash Functions
• +5 more

Lorenzo Grassi is a postdoctoral researcher in symmetric cryptography at Ruhr University Bochum's Cluster of Excellence CASA and a consultant at Ponos Technology. He holds a PhD in Mathematics from Graz University of Technology (2019) and a degree in Mathematics from the University of Milano. Funded by the CASA Jump.Start Program for Postdocs since 2023, he focuses on cryptography for future applications, including quantum-resistant protocols and MPC-/FHE-/ZK-friendly schemes. Education: University of Milano (Mathematics), Graz University of Technology (PhD in Mathematics) Positions: Ruhr University Bochum (CASA Cluster), Ponos Technology (Consultant), Radboud University Nijmegen (2020-2023) His research spans symmetric encryption, hash function design, algebraic attacks, and cryptanalysis of modern cryptographic primitives. Recent work includes Gröbner basis attacks, side-channel secure implementations, and quantum cryptanalysis. Key trends in his publications include the development of zero-knowledge proof-friendly hash functions (e.g., Poseidon2), structural analysis of symmetric primitives (e.g., Subspace Trails, Feistel networks), and optimization of cryptographic schemes for MPC and FHE applications. Quantum cryptanalysis and truncated differential attacks also feature prominently. Scientific Awards: ERC Starting Grant 2024 Grassi contributes to advancing cryptographic security through innovative designs like Monolith and Reinforced Concrete, while also analyzing vulnerabilities in schemes like AES and Grendel. His work bridges theoretical analysis with practical implementations, emphasizing efficiency and resistance to emerging threats.