Find Academic Supervisors

• Behavioral Economics
• Consumer Behavior
• Market Design
• +3 more

Prof. Botond Kőszegi is a Professor of Economics at the University of Bonn, affiliated with the Institute for Applied Microeconomics. His research focuses on behavioral economics, exploring topics such as consumer decision-making, regulatory policy, and market dynamics. He has held roles in research consortia like ECONtribute CRC and EPoS, and his work integrates theoretical models with empirical insights to address issues like self-control problems, consumer naivete, and the welfare implications of market structures. His key research interests include behavioral economics, consumer behavior, and the design of policies to mitigate market failures arising from cognitive biases. He has contributed to understanding phenomena like the role of limited attention in shaping competition, the effects of deceptive pricing in financial markets, and the psychological underpinnings of self-esteem and decision-making. Prof. Kőszegi's recent work examines secondary markets' impact on social responsibility, the welfare costs of consumer naivete, and the dynamics of steering in digital ecosystems. His publications span leading journals such as the Quarterly Journal of Economics, Review of Economic Studies, and American Economic Review. His affiliations include academic roles at the University of Bonn's economics department and participation in collaborative research initiatives. Despite no listed awards, his prolific and influential research has shaped behavioral economics and industrial organization literature.

Klaus DiepoldView profile

Professor

• Machine Learning
• Signal Processing
• Ethical AI
• +2 more

Prof. Dr.-Ing. Klaus Diepold is a Full Professor and Chair of Data Processing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He earned his PhD in Electrical Engineering from TUM in 1992. His research focuses on machine learning, multimedia signal processing, moral machines, and affect modeling for cognitive systems. He actively promotes entrepreneurship through the Center for Digital Technology and Management (CDTM). Academic Career: Diepold held visiting professorships at the University of Alberta, Canada, and NICTA, Australia. He served as Dean of Studies at TUM (2005–2010) and Senior Vice President for Diversity and Talent Management (2013–2015). He has contributed to MPEG standards and led projects in telepresence (SFB-453) and cognitive systems (CoTeSys). Research Interests: His work spans ethical AI, reinforcement learning, biomedical applications of machine learning, and interdisciplinary education. Notable projects include CellFace, Sirrel, and the development of AI-driven diagnostic tools using digital holographic microscopy. Awards: He has received the TUM Teaching Prize (2004), Fellowship for Innovation in University Teaching (2012), and the 2024 IEEE German Education Conference Best Paper Award. His work on 'Komputer & Creativität' highlights innovative teaching at the intersection of engineering, AI, and theater. Labs & Teams: His team includes researchers like Sven Gronauer and Alice Hein, focusing on projects like GenAI chatbot agents, clinical applications of computational medicine, and cybersecurity. The Chair of Data Processing collaborates with industry partners like fortiss and Rohde & Schwarz.

Martin DillerView profile

Researcher

• Formal Argumentation
• AI Systems
• Probabilistic Argumentation
• +3 more

Martin Diller is a Researcher at the International Center for Computational Logic (ICCL) at TU Dresden, where he has been since May 2019. He is part of the 'Logic Programming and Argumentation' group led by Sarah Gaggl. His current research focuses on formal models of argumentation and their application in AI systems, particularly within the SEMECO interdisciplinary cluster on AI-assisted regulatory workflows for medical systems and cybersecurity. Education: Holds a joint MSc in Computational Logic from TU Wien, TU Dresden, and University of Bolzano (via the EMCL program with an Erasmus Mundus scholarship). Previously completed a MA in Philosophy (Logic & Epistemology) and BSc in Computer Science at Universidad Nacional de Córdoba, Argentina, with postgraduate funding from CONICET. Also conducted research internships at University of Aberdeen, UCL, NICTA (Canberra), and others. Key Projects: Active in the Transregional Collaborative Research Center ‘Foundations of Perspicuous Software Systems’ (2019–2022), and the Center for Scalable Data Analytics and Artificial Intelligence (2023). Currently leads research in probabilistic argumentation and flexible dispute derivations for assumption-based arguments. Key Contributions: Developed the 'flexABle' system for argumentation framework analysis, contributed to admissibility criteria in probabilistic argumentation, and advanced methods for integrating natural language processing with formal argumentation systems. Grants & Funding: Involved in EU-funded interdisciplinary projects and German collaborative research initiatives. Labs/Teams: Core member of the Logic Programming and Argumentation group at ICCL, collaborating with institutions worldwide.

Eva SeifriedView profile

Researcher

• Educational Psychology
• Higher Education
• Teacher Education
• +3 more

Eva Seifried is a researcher in Educational Psychology at Heidelberg University, serving as Junior Research Group Leader since 2019. She previously held roles including PostDoc researcher (Akademische Rätin auf Zeit) and Ph.D. student in the same department. Her work focuses on innovative teaching-learning concepts, higher education pedagogy, and teacher training. Education: Earned a Diploma in Psychology (2010) and Ph.D. in Psychology (2016) from Heidelberg University. She has extensive editorial experience as Managing Editor of Psychology Learning & Teaching (2012–2018). Research emphasizes evidence-based teaching practices, educational psychology misconceptions among trainee teachers, and formative assessment strategies. Notable grants include the 'Ask for Evidence' project (2017–2019), funded by Baden-Württemberg Ministry and Heidelberg University's Excellence Initiative. Active in peer review for journals like European Journal of Psychology of Education and Learning and Instruction . Professional memberships include the German Psychological Society (DGPs) and European Association for Research on Learning and Instruction (EARLI). Her conference contributions span topics such as preservice teachers' evidence usage patterns, stereotype threat comparisons between Germany and Finland, and learning activity correlates in university settings. Publications address automated essay assessment, plagiarism detection, and pedagogical software applications.

Thomas KürnerView profile

Professor

• Terahertz Communications
• Radio Wave Propagation
• Wireless Network Planning
• +3 more

Thomas Kürner is a Professor at the Institute of Communications Engineering , Technical University of Braunschweig , specializing in Terahertz Communications , Radio Wave Propagation , and Wireless Network Planning . His work bridges theoretical modeling with practical implementation, focusing on 300 GHz systems for data centers, vehicular communication , and industrial applications . As an IEEE Fellow and Senior Member , he contributes to standardization efforts in IEEE 802.15 and URSI Commission F . Research spans Terahertz channel modeling , ray tracing simulations , and device discovery algorithms , with recent emphasis on reconfigurable intelligent surfaces and automated network planning . His 15 most recent articles (2021-2025) address challenges in high-speed data transmission , blockage analysis , and vehicular network optimization . Scientific awards include the ITG Sponsorship Prize (1994), IEEE Senior Member (2001), and IEEE Fellow (2020). He has advised 15+ students in mobile communication systems and contributed to Horizon 2020 projects like ThoR and iBroW .

Mingshuai ChenView profile

Assistant Professor

• Formal Verification
• Programming Theory
• Logical Aspects of Computer Science
• +5 more

Mingshuai Chen is an Assistant Professor at Zhejiang University, leading the Formal Verification Group. He previously held a postdoctoral position at RWTH Aachen University. Education: Ph.D. in Computer Science from Institute of Software, Chinese Academy of Sciences (2019) B.Sc. in Computer Science from Jilin University (2013) His research focuses on formal verification, synthesis, programming theory, and probabilistic/quantum systems. Notable contributions include Exact Bayesian Inference and Lower Bounds for Probabilistic Programs . Scientific awards include: NSFC Excellent Young Scientists Fund Program (Overseas) Distinguished Paper Award at ATVA 2018 Best Paper Award at FMAC 2019 CAS-President Special Award (2019) 2nd Prize@ChinaSoft'24 He serves on program committees for OOPSLA 2026, TACAS 2026, and multiple other conferences.

Yu-Fang ChenView profile

Research Professor

• Program verification
• Automata theory
• String constraint solving
• +3 more

Yu-Fang Chen is a research professor at Academia Sinica, Taiwan, active across premier programming-languages venues such as PLDI, POPL, OOPSLA, SAS, APLAS and VMCAI. His work sits at the intersection of program verification , automata theory and constraint solving , with recent emphasis on quantum-circuit verification and string-number constraint solving . Research interests revolve around rigorous methods to ensure software reliability: developing novel automata models (level-synchronized tree automata, position-constrained string automata), building practical solvers that blend length, substring and numeric constraints, and extending automated reasoning to the quantum domain. His papers consistently introduce new decision procedures, learning algorithms and tool-chains that improve the scalability of static analysis and formal verification. Between 2017 and 2025 he (co-)authored more than a dozen peer-reviewed papers and served on over thirty program committees, including steering and organization chair roles for VMCAI 2026 and SAS 2023 . No doctoral students or funded-grant details are disclosed in the supplied sources.

Laetitia ChapelView profile

Professor

• Machine Learning
• Remote Sensing
• Time Series Analysis
• +3 more

Laetitia Chapel is a full professor in computer science at Institut Agro Rennes-Angers , conducting research within the OBELIX team at IRISA , a mixed research unit in computer science, signal/image processing, and robotics. Her work focuses on machine learning for structured data (time series, graphs, hierarchical representations) with applications in remote sensing, and she obtained her HDR in 2022. Research Interests center on advancing optimal transport theory for structured datasets, including time series alignment, hyperbolic embeddings, and Riemannian neural networks. She actively participates in international workshops and conferences, delivering invited talks at venues like NeurIPS and IEEE MLSP. Ambroise Odonnat (2024, Huawei-funded PhD) Léo Buecher (2024, supervised with Nicolas Courty) Sergio Osson (2025, supervised with Charlotte Pelletier) Guillaume Mahey (2024, with Gilles Gasso) Manal Hamzaoui (2023, with Sébastien Lefèvre and Minh-Tan Pham) Titouan Vayer (2020, now at INRIA) Pierre Gloaguen (2017, now assistant professor at UBS) Scientific Awards include the Best paper award at MajecSTIC 2005 and Best paper award at ICPRAI 2022 . Her research projects span from theoretical foundations (e.g., hyperbolic metric learning) to applied remote sensing tasks (e.g., cropland mapping in the Brazilian Amazon).

Marcelo ArenasView profile

Professor

• Data Management
• Semantic Web
• Database Theory
• +3 more

Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering at the Pontifical Catholic University of Chile. He is a Fellow of the Association for Computing Machinery (ACM), former director of the Millennium Institute for Foundational Research on Data, and co-founder of the Center for Semantic Web Research. His Ph.D. in Computer Science was obtained from the University of Toronto in 2005. Research interests include data management , applications of logic in computer science , and Semantic Web technologies. He has published extensively on topics such as SPARQL query complexity , graph database systems , and incomplete database theory , with notable works like MillenniumDB and Foundations of Data Exchange . Scientific accolades: 2016 SWSA Ten-Year Award for "Semantics and Complexity of SPARQL" IBM Ph.D. Fellowship (2004) Nine Best Paper Awards across PODS, ISWC, ICDT, ESWC, WWW, and NeurIPS His work on approximate counting algorithms (e.g., FPRAS for #NFA) and explainable AI frameworks has influenced database theory, while serving on program committees for ICDT 2015, ISWC 2015, and PODS 2018 demonstrates leadership in the field. Current projects focus on probabilistic explanations for decision trees and temporal regular path queries in knowledge graphs.

Ansgar ScherpView profile

Professor

• Data Science
• Big Data Analytics
• Text Analytics
• +7 more

Professor Ansgar Scherp is a leading academic in Data Science and Big Data Analytics at Ulm University , Germany. His career spans multiple institutions including University of Essex, University of Stirling, and Kiel University, where he led the EU Horizon 2020 MOVING project. He holds a habilitation thesis on Semantic Media Management and has pioneered research in combining symbolic and statistical methods for data analysis. Current: Professor for Data Science & Big Data Analytics (Ulm University) Previous: Professor for Knowledge Discovery (Kiel University), Juniorprofessor roles (Mannheim, Koblenz-Landau) Skills: Semantic Web, Graph Mining, Text Analytics, Multimedia Systems His research interests focus on integrating Information Retrieval, Data Mining, Machine Learning, and Semantic Web technologies. He has developed innovative methods like HCF-IDF for semantic profiling and SchemEX for schema-level indexing of Linked Data. His work addresses challenges in media companies' digital transformation and large-scale Knowledge Graph analysis. Scientific awards include: Best Paper Award, Symposium on Document Engineering (2021) Best Student Paper Award, Symposium on Document Engineering (2019) ACM SIGMM Rising Stars Symposium Speaker (2016) Double Winner of Billion Triple Challenge (2008, 2011) klickTel Award (2013) He has supervised 12 PhD theses and over 15 Master's theses , with students now working at Google, Capgemini, and academic institutions. His email addresses are ansgar.scherp@uni-ulm.de and mail@ansgarscherp.net .

• Tensor Decompositions
• Quantum Computing
• Fredholm Integral Equations
• +7 more

Patrick Gelß is a Postdoctoral Researcher at Zuse Institute Berlin, working in the AI in Society, Science, and Technology department. He leads the iol.QUANT research group focused on quantum-inspired and tensor-based methods, with affiliations to Freie Universität Berlin and collaborative projects like MATH+ Cluster of Excellence. Diploma in Mathematics/Physics (2013, Freie Universität Berlin) PhD in Mathematics (2017, summa cum laude, Freie Universität Berlin) Postdoctoral roles at Zuse Institute Berlin (2020-present) and CRC 1114 (2017-2022) His research bridges tensor decompositions, quantum computing, and dynamical systems, with notable contributions to: Graph Isomorphism: Continuous optimization approaches via doubly stochastic matrices and Frank-Wolfe algorithms Quantum Simulation: Tensor network representations for quantum circuits and the open-source WaveTrain package KvN Mechanics: Mathematical analysis of existence/uniqueness solutions for bounded domains Fredholm Networks: Novel training paradigms for neural networks using integral equations Scientific achievements include: Developing Scikit-TT for tensor-train computations Organizing major workshops (QML@SC2024, TMQS 2024) Leading the Thematic Einstein Semester 2024 on Mathematics for Quantum Technologies His work spans chemical kinetics, quantum dynamics, and quantum machine learning, with applications to CO oxidation models, exciton-phonon systems, and mutational hierarchies in medicine.

Andreas IngerlView profile

Lecturer

• New Media Design
• Applied Media Art
• Audiovisual Media
• +2 more

Andreas Ingerl serves as a University Lecturer within the Department of Design and Culture at HTW Berlin - University of Applied Sciences, primarily affiliated with the Communication Design program. He holds key administrative roles including Department Council member, Deputy Chair of the Selection Committee, Chair of the Examination Board, and BAföG Representative, while managing the Physical and Ubiquitous Computing laboratory from his office at Wilhelminenhof Campus WH Building A, 419. His research centers on the intersection of technology and artistic expression, specializing in audiovisual media, new media design, and applied media art. He actively explores creative computing methodologies and ubiquitous computing applications to transform physical environments into interactive experiences, with strong emphasis on virtual reality systems, sensory design, and AI integration for immersive media installations that challenge conventional perception and interaction paradigms. Analysis of his recent publications reveals a cohesive trajectory toward experimental media forms and intelligent systems, exemplified by projects like CINEMIRACLE (public-space VR), Lucid Dream (consciousness exploration), and CFI – Craving Future Intelligence (AI-driven art). These works collectively demonstrate his commitment to bridging technical innovation with critical artistic inquiry, particularly in addressing societal implications of emerging media technologies and redefining educational frameworks through digital experimentation. As Laboratory Manager for Physical and Ubiquitous Computing, Ingerl directs a multidisciplinary research environment focused on developing novel interfaces for immersive storytelling and sensor network integration in urban contexts. The lab functions as a nexus for the Creative Computing research cluster, facilitating collaborations between design, art, and engineering disciplines to pioneer tangible media interactions that dissolve boundaries between digital and physical realms.

• Privacy-preserving machine learning
• Adversarial robustness
• Differential privacy
• +5 more

Saeed Mahloujifar is a researcher in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. He has established himself as a prominent researcher in machine learning security, privacy, and robustness, with extensive collaborations with leading researchers including Prateek Mittal, Mohammad Mahmoody, and Somesh Jha. His research primarily focuses on security and privacy challenges in machine learning systems. Mahloujifar has made significant contributions to understanding and mitigating membership inference attacks, developing defenses against poisoning and backdoor attacks, advancing differential privacy techniques, and exploring the fundamental limits of robust machine learning. His work bridges theoretical foundations with practical applications, often developing novel algorithms and frameworks that have been widely adopted in the security and privacy communities. The analysis of his recent publications reveals a strong research trajectory centered on privacy-preserving machine learning, with particular emphasis on large language models, differential privacy guarantees, and security vulnerabilities. His work spans theoretical foundations of privacy through practical security mechanisms like watermarking and robustness benchmarking. A notable trend is his increasing focus on large language models and their security implications, alongside continued contributions to foundational privacy theory. Mahloujifar has co-authored over 90 publications since 2017, with a significant acceleration in output since 2020. His work appears consistently in top-tier venues including NeurIPS, ICML, IEEE S&P, and USENIX Security, demonstrating both breadth and depth in his research contributions.

• Phenomenology
• Intersubjectivity
• Online Sociality
• +7 more

Lucy Osler is a Lecturer in the Department of Philosophy at Cardiff University's School of English, Communication and Philosophy. She completed her PhD at the University of Exeter in 2021 and has established herself as a significant contributor to contemporary philosophical discourse, particularly at the intersection of phenomenology, online sociality, and embodiment. Her research focuses on phenomenological approaches to intersubjectivity, online sociality, shared space, embodiment, perception, emotions, and psychopathology, as well as analytic philosophy of mind and 4E (embodied, embedded, extended, enactive) approaches to cognition and affectivity. Dr. Osler has made notable contributions to understanding how digital technologies shape social experiences, including empathy, belonging, and narrative formation in online contexts. Her recent publications reveal a strong focus on how digital environments transform social interactions, with particular attention to loneliness, empathy, and the phenomenology of social anxiety disorder. She has developed innovative frameworks for understanding how objects can embody loneliness through 'obsolete affordances' and how digital technologies function as 'narrative devices' that shape our self-understanding. Her work demonstrates how seemingly disembodied online interactions can still facilitate meaningful social connection and how environments—both physical and digital—never remain neutral in supporting certain bodies, behaviors, and emotions while suppressing others. Dr. Osler has co-edited the forthcoming volume For, Against, Together: Antagonistic Political Emotions with Thomas Szanto (Cambridge University Press) and is working on Phenomenology in Action for Researching Networked Learning Experiences . Her research has appeared in leading journals including Topoi , Philosophy, Psychiatry, and Psychology , Inquiry: An Interdisciplinary Journal of Philosophy , and Philosophy and Technology . Her work demonstrates significant interdisciplinary reach, connecting philosophy with psychology, cognitive science, and digital studies.

• Graph Theory
• Combinatorics
• Theoretical Computer Science
• +3 more

Christian Elbracht is a Researcher in the Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences at the University of Hamburg. He is based in Geomatikum (Bundesstraße 55), Room 232, Hamburg, with office hours conducted online by appointment. His primary academic activities involve teaching exercise sessions for mathematics courses targeting computer science students and physicists. His research focuses intensely on advanced graph theory, particularly tangle theory, separation systems, and structural decompositions in both finite and infinite graphs. Key themes include tree structures, connectivity hierarchies, submodular systems, ubiquity conjectures, and combinatorial optimization. His work bridges theoretical mathematics with applications in clustering algorithms and data science, as evidenced by collaborations extending into machine learning domains. Christian's publication trends reveal a concentrated specialization in tangle-tree duality and structural graph theory since 2018, with significant contributions to abstract separation systems and infinite graph theory. His collaborative work frequently involves researchers like J. Kneip and M. Teegen, with publications appearing in journals such as the Journal of Combinatorial Theory and Advances in Combinatorics . While no scientific awards are publicly documented, his research output demonstrates consistent contributions to combinatorial theory. His teaching responsibilities include leading exercise sessions for foundational mathematics courses for computer science students (Mathematics I/II) and specialized topics like Graph Theory I and Optimization. Christian maintains active research collaborations across multiple institutions, particularly evident in his work on graph ubiquity theorems and clustering applications. His technical work centers on structural analysis of graphs, with implications for theoretical computer science and discrete mathematics.