Find Academic Supervisors

• Data Science
• Network Science
• Computational Social Science
• +5 more

Yu-Ru Lin is an Associate Professor at the School of Computing and Information , University of Pittsburgh , and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber) . With secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program, their interdisciplinary work bridges computational methods with social science inquiry. Research interests focus on: Social and political network analysis Computational social science Data mining and visualization Digital accountability in political communication Policy diffusion analytics Trust-distrust dynamics in networked societies Key research themes include: Developing network theories for misinformation and extremism Designing visual analytics for policy diffusion and group bias detection Studying collective behavior during emergencies Creating computational tools for social data analysis Major grants include NSF awards for: Public policy diffusion analytics COVID-19 misinformation mitigation Urban mobility and smart city initiatives Understanding extremism in cyber-social spaces Scientific leadership roles: Editor-in-Chief of ICWSM Editorial board member at Frontiers in Big Data, EPJ Data Science, PLOS ONE, and Nature Scientific Reports Co-chair roles at KDD Humanitarian Mapping Workshop, WWW Web & Society Track, and multiple SBP-BRiMS graduate programs As founder of the Pitt Computational Social Dynamics Lab (PICSO LAB) , they lead research on social media analysis, network modeling, and urban informatics through projects like PittSmartLiving – a $1.4M NSF-funded initiative exploring real-time transit incentives and community engagement.

• Sublinear-Time Algorithms
• Property Testing
• Graph Algorithms
• +6 more

Talya Eden is an Assistant Professor in the Department of Computer Science at Bar Ilan University. She previously held a postdoctoral position at the Foundations of Data Science Institute (FODSI), jointly affiliated with MIT and Boston University, hosted by Prof. Ronitt Rubinfeld, Prof. Piotr Indyk, and Prof. Sofya Raskhodnikova. She earned her PhD from the School of Electrical Engineering at Tel Aviv University under the supervision of Prof. Dana Ron. Her research focuses on sublinear-time algorithms and property testing , with a strong emphasis on approximating graph parameters efficiently. Her work lies at the intersection of theoretical computer science, graph algorithms, and data science, addressing fundamental problems in large-scale graph analysis. Her recent publications, appearing in top venues such as STOC, FOCS, SODA, and ICALP, demonstrate consistent contributions to the fields of graph sampling, counting, streaming algorithms, and learning-augmented computation. Key themes include k-clique and triangle estimation , arboricity-based complexity , and robustness in streaming models . She has collaborated extensively with leading researchers including Dana Ron, Ronitt Rubinfeld, Piotr Indyk, and C. Seshadhri. Her work often explores the theoretical limits of what can be computed efficiently on massive graphs with limited access. Notable Scientific Contributions: Developed nearly optimal algorithms for sampling k-cliques in sublinear time. Established tight connections between arboricity and the complexity of edge sampling and degree moment estimation. Contributed to adversarially robust streaming algorithms using dense-sparse trade-offs. Explored learning-based approaches to enhance sublinear-time support estimation. Talya Eden advises students and is actively involved in the theoretical computer science research community. She has not received any explicitly mentioned scientific awards in the provided text, but her publication record in elite conferences indicates high recognition.

• Mathematical Data Science
• Machine Learning Theory
• Compressed Sensing
• +5 more

Johannes Maly is an Assistant Professor at Ludwig Maximilian University of Munich (LMU) since October 2022, holding the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence. Previously, he held PostDoc positions at Catholic University of Eichstaett-Ingolstadt (2020-2022) and RWTH Aachen University (2019-2020). His educational background includes: PhD in Mathematics from Technical University of Munich (TUM), 2019 (supervised by Prof. Massimo Fornasier) M.Sc. in Mathematics from TUM, 2015 B.Sc. in Mathematics from TUM, 2013 Prof. Maly's research centers on mathematical data science with core focus areas in covariance estimation , neural network approximation properties , implicit bias of gradient descent , and multi-structured signal recovery . A unifying theme across his work is the theoretical investigation of coarse quantization effects , building on his foundational contributions to compressed sensing and extending into modern AI architectures. Analysis of his recent publications reveals consistent exploration of quantization constraints in high-dimensional statistics and neural network training. Key trends include development of tuning-free covariance estimators using dithering techniques, characterization of implicit regularization in overparameterized models, and novel quantization approaches for neural networks that balance hardware efficiency with theoretical guarantees. His scientific recognition includes: relAI Fellow (Zuse School for Reliable AI) MCML Associate (Munich Center for Machine Learning) Prof. Maly's research is supported through his Bavarian AI Chair appointment and fellowships. He teaches advanced courses including Convex Optimization, High-dimensional Probability, and Mathematical Introduction to Data Science at LMU Munich, with prior teaching roles at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University. While specific student names are not listed in the source material, his position involves supervision of graduate researchers. He leads the Mathematical Data Science and Artificial Intelligence research group at LMU Munich under the Bavarian AI Chair, collaborating with MCML and relAI networks. The group investigates theoretical challenges in quantization, low-rank matrix recovery, and optimization dynamics for next-generation AI systems.

Alexander von RohrView profile

Researcher

• Robotics
• Machine Learning
• Bayesian Optimization
• +4 more

Alexander von Rohr is a postdoctoral researcher at the Technical University of Munich , affiliated with the Learning Systems and Robotics Lab . Previously, he was a doctoral researcher at the Max Planck Institute for Intelligent Systems and RWTH Aachen University , supported by IAV . His educational background spans Computer Science (RWTH Aachen), Electrical Engineering (BHT Berlin), and University of Lübeck . Research Focus : Embodied AI, Bayesian optimization, risk-aware reinforcement learning, robust control, probabilistic models, data-driven controller synthesis. His recent publications (2024-2025) emphasize event-triggered learning for time-varying systems, diffusion models with constraints , and robust safety via entropy regularization in RL. These works demonstrate applications in robot manipulators , digital twins , and underactuated systems . Awards include the Best Reviewer Award at AISTATS 2025 . Collaborations include Prof. Sebastian Trimpe and Angela P. Schoellig , with co-authorships on 15 recent articles. His lab focuses on probabilistic control for dynamical systems with formal guarantees.

• Biomedical Signal Processing
• Medical Imaging
• Computer Security
• +6 more

Jun Shen is a Professor at Southeast University's School of Computer Science and Engineering in Nanjing, China. With over 100 publications spanning from 1991 to 2025, Professor Shen has established a prolific research career across multiple disciplines including biomedical engineering, computer security, artificial intelligence, and wireless communications. Professor Shen's research interests focus on the intersection of computer science and practical applications, particularly in medical imaging, cybersecurity, and AI-driven solutions for healthcare and engineering problems. His recent work demonstrates significant contributions to breast cancer diagnosis through advanced medical imaging techniques, automotive cybersecurity, and novel approaches to drug discovery using deep learning. Analysis of Professor Shen's recent publications reveals a strong trend toward interdisciplinary research that combines computer vision, machine learning, and domain-specific knowledge to solve complex problems in healthcare and engineering. His work often involves collaborations across institutions and disciplines, indicating an extensive professional network. Professor Shen has made notable contributions to medical image analysis for cancer diagnosis, particularly in breast cancer metastasis detection using multi-parametric MRI and dual-energy CT. His cybersecurity research focuses on automotive systems and cyber-physical systems security. In AI applications, he has developed innovative approaches for drug-target binding prediction and vision-language models for medical applications. Professor Shen's publication record demonstrates consistent productivity with 13 papers in 2024 and 9 papers already published or accepted in 2025, indicating active and ongoing research programs across multiple domains.

Tim LyonView profile

Researcher

• Computational Logic
• Proof Theory
• Modal Logics
• +3 more

Dr. Tim Lyon serves as a Research Associate within the Computational Logic group at Dresden University of Technology's Faculty of Computer Science. He is actively engaged in the ERC-funded DeciGUT (A Grand Unified Theory of Decidability in Logic-Based Knowledge Representation) project, focusing on theoretical foundations of computational logic. His research centers on computational logic , specializing in proof systems for fragments of first-order logic, non-classical logics, and modal logics. Key contributions include developing sequent-based frameworks for Gödel-Löb provability logic, bi-intuitionistic extensions, and decidability analyses for quasi-dense modal systems. His work bridges theoretical proof theory with practical applications in knowledge representation and automated reasoning. Analysis of his recent publications (2023-2025) reveals a concentrated focus on decidability mechanisms and unified proof-theoretic frameworks . His research spans theoretical investigations (e.g., fixed-point equations in dynamic logic) to applied systems (e.g., standpoint temporal logic for multi-perspective reasoning), demonstrating consistent innovation in sequent calculus applications across diverse logical domains. Dr. Lyon advises master's students through thesis topics such as "A Simplified Tableau Calculus for Standpoint LTL" and "Ill-Founded Linear Nested Sequents for LTL" under the DeciGUT project. His research is supported by European Research Council funding, with collaborative work spanning institutions including Vienna University of Technology, CNRS, and KU Leuven. He operates within the International Center for Computational Logic (ICCL), a specialized research unit under the Faculty of Computer Science dedicated to advancing logical methods in computer science through interdisciplinary collaboration and theoretical innovation.

• Machine Learning
• Gamification
• Federated Learning
• +3 more

Sascha Rank is a Ph.D. student and research associate at the Chair of Information Infrastructures, Technical University of Munich (TUM), Heilbronn, working under Prof. Ali Sunyaev. His research bridges machine learning, distributed systems, and healthcare applications with emphasis on ethical AI implementation. His academic background includes: Ph.D. candidate since February 2021 (Karlsruhe Institute of Technology) M.Sc. in Industrial Engineering and Management (2018-2020, Karlsruhe Institute of Technology) B.Sc. in Industrial Engineering and Management (2013-2018, Karlsruhe Institute of Technology) Rank's research centers on Machine Learning and Gamification , with deep expertise in federated learning architectures , collaborative distributed systems , and medical AI applications . His work addresses critical challenges in privacy-preserving computation, healthcare gamification frameworks, and ethical AI deployment, often combining theoretical rigor with practical healthcare use cases through projects like FLAIROP (Federated Learning for Robotic Picking). Publication analysis reveals a strong trajectory toward healthcare-integrated machine learning, evolving from foundational gamification studies (2021) to advanced distributed systems toolboxes (2023) and comprehensive survey work (2024). His research consistently targets real-world implementation barriers in medical AI and intralogistics while developing standardized frameworks for ethical development. No scientific awards were documented in available sources. Current information indicates no formal student advisement or principal investigator roles in research grants. Rank operates within TUM's Critical Information Infrastructures research group, which explores blockchain systems, autonomous driving safety, and collaborative machine learning through projects like FLAIROP. The group actively develops ethically aligned frameworks for decentralized applications and harm mitigation in autonomous systems, with strong industry connections evident through thesis topics and corporate collaborations.

Freydis VogelView profile

Professor

• Computer-Supported Collaborative Learning (CSCL)
• Educational Psychology
• Collaboration Scripts
• +4 more

Prof. Dr. Freydis Vogel is a Professor at the University of Hamburg's Faculty of Education, specifically within the Department of General, Intercultural and International Comparative Education as well as Educational Psychology (EW1). Her research focuses on computer-supported collaborative learning (CSCL), collaboration scripts, and their impact on educational outcomes. She investigates how socio-cognitive scaffolding, group awareness tools, and self-regulation strategies enhance learning processes in digital and collaborative environments. Her work emphasizes the design and evaluation of CSCL systems for fostering mathematical argumentation skills, collaborative problem-solving, and teacher education. Vogel also explores the role of emergency remote teaching methodologies and evidence-based practices in higher education. Her contributions bridge theoretical frameworks with practical implementations, addressing challenges in both synchronous online learning and traditional classroom settings. Key areas of interest include the interplay between collaboration scripts and cognitive processes, the effectiveness of heuristic worked examples, and the adaptation of scaffolding techniques for diverse learner populations. She has authored numerous publications on these topics and actively engages with international educational research communities through initiatives like the NAPLeS project.

Maya PersicoView profile

Research Fellow

• Digitalisation in Education
• Open Educational Resources (OER)
• OER Policies
• +3 more

Maya Persico is a Research Fellow at the University of Hamburg's Faculty of Education, affiliated with the Department of General, Intercultural and International Comparative Education as well as Educational Psychology (EW1). She specializes in digitalisation in education and focuses on open educational resources (OER), OER policies, and the integration of generative AI in collaborative learning environments. Her work also addresses teachers' future competences and self-regulated learning methodologies. Research Interests: OERlab UHH initiatives Twin Transformation-related OER development Open education policy frameworks at universities Role of AI in computer-supported collaborative learning (CSCL) Professional development of educators in digital age competencies Lab/Affiliations: Active contributor to the OERlab UHH, focusing on innovative educational resource creation and policy analysis. Advising & Grants: No specific grants or advising roles listed in current profile. Office hours available by arrangement (Tuesday 10-12 AM & 4-6 PM via in-person or Zoom).

Tobias DammView profile

Professor

• Control Theory
• Numerical Analysis
• Linear Systems
• +3 more

Prof. Dr. Tobias Damm is a leading academic in the Department of Mathematics at RPTU Kaiserslautern-Landau, heading the Technomathematics Group. His work focuses on control theory, numerical analysis, and stochastic systems, with significant contributions to model reduction, linear systems, and optimization. He holds a professorship and has authored influential publications on topics such as balanced truncation, stochastic control, and numerical methods for large-scale systems. Research Interests: Tobias Damm’s research emphasizes the interplay between theoretical mathematics and practical engineering challenges. Key areas include model order reduction techniques for complex systems, optimal control strategies, and the numerical solution of Lyapunov equations. His work bridges stochastic systems, linear algebra, and control engineering, with applications in energy networks and power systems. Recent Articles: His recent publications explore distributed model predictive control for power systems, complexity reduction of stochastic systems, and Hadamard powers in kernel methods. These contributions highlight advancements in both theoretical frameworks and applied methodologies. Advising & Grants: Damm’s research is supported by grants focusing on energy systems and control theory. His work often collaborates with interdisciplinary teams, addressing challenges in district heating networks and hyperbolic systems reduction. Labs/Teams: He leads the Technomathematics Group, fostering collaborations on cutting-edge problems in control systems and numerical analysis.

Dieter SchmalstiegView profile

Professor

• Augmented Reality
• Virtual Reality
• Computer Graphics
• +2 more

Dieter Schmalstieg is the Alexander von Humboldt Professor of Visual Computing at the University of Stuttgart's Faculty 5: Computer Science, Electrical Engineering and Information Technology, leading the Institute for Visualization and Interactive Systems (VIS). He also holds an adjunct professorship at Graz University of Technology's Institute of Computer Graphics and Vision. His research focuses on augmented reality (AR), virtual reality (VR), computer graphics, visualization, and human-computer interaction. He earned his Dipl.-Ing. (1993), Dr. techn. (1997), and Habilitation (2001) from Vienna University of Technology. He has authored/co-authored over 400 peer-reviewed publications, with over 29,000 citations and numerous awards, including the IEEE ISMAR Career Impact Award (2020) and the IEEE Virtual Reality Technical Achievement Award (2012). His professional roles include editorial leadership in top journals like IEEE Transactions on Visualization and Computer Graphics, and steering committee memberships in major conferences like IEEE ISMAR. Research interests span AR/VR applications, situated visualization, coherent rendering, spatial interaction, and medical visualization. Notable projects include the Christian Doppler Laboratory for Handheld Augmented Reality (2008–2015), and collaborations with industry partners like Qualcomm and VRVis. He has advised over 50 PhD students, many now professors or researchers globally. Grants include the Alexander von Humboldt Professorship (2023), SNAP Mixed Reality Lab funding, and EU projects like MiReBooks (mining education). His work bridges theory and application, with impactful contributions to AR/VR systems, medical visualization tools, and GPU techniques for real-time rendering.

Jan PaulsView profile

Researcher

• Remote Sensing
• Machine Learning
• Deep Learning
• +3 more

Jan Pauls is a Researcher at the Institute of Business Information Systems within the School of Business and Economics at the University of Münster, affiliated with the Chair of Machine Learning and Data Engineering under Prof. Gieseke. Based at Leonardo Campus 3, Room 222, he conducts research at the intersection of satellite remote sensing and deep learning for environmental applications. His research portfolio emphasizes: Tree canopy height estimation using satellite data Deep learning architectures for transparent object segmentation Super-resolution techniques for satellite imagery Forest monitoring systems development Temporal dynamics modeling in geospatial analysis Environmental informatics applications Recent publications reveal a concentrated focus on scaling deep learning models for tree height prediction using multi-source satellite data, with growing emphasis on temporal dynamics and high-resolution mapping. This work directly supports sustainable forest management through advanced remote sensing analytics. Academic contributions include: Thesis Supervision: Guided 5 theses (2023-2025) on satellite-based forest monitoring, including master's research on PlanetScope/GEDI integration and bachelor's projects on Sentinel-2 analysis Teaching: Instructs graduate courses including "Advanced Topics in Machine Learning" seminars (2023-2025), "Mining Massive Datasets" lectures, and digital ecosystem project seminars As core personnel in Prof. Gieseke's research group, he bridges machine learning innovation with practical environmental monitoring solutions through active collaboration on satellite data processing pipelines and forest inventory systems.

• Uncertainty Quantification
• Bayesian Modeling
• Subsurface Flow Modeling
• +3 more

Sergey Oladyshkin is an Adjunct Professor and Deputy Department Head at the Department of Stochastic Simulation and Safety Research for Hydrosystems , part of the Institute for Modelling Hydraulic and Environmental Systems at the University of Stuttgart. His work focuses on uncertainty quantification (UQ), Bayesian methods, and computational modeling applied to environmental systems. Key areas include CO 2 storage, groundwater contamination, and subsurface flow dynamics. Research highlights include development of Bayesian active learning frameworks, surrogate modeling techniques, and hybrid approaches combining neural networks with physical models (e.g., Finite Volume Neural Networks). He has contributed to projects on radioactive waste repository safety, water distribution system reliability, and CO 2 leakage risk assessment. Publications emphasize methodological advances in polynomial chaos expansions, rare event simulation, and global sensitivity analysis. Notable collaborations involve institutions such as the German Research Center for Geosciences, RWTH Aachen University, and industry partners in water and energy sectors. His interdisciplinary work bridges computational science, environmental engineering, and applied mathematics, with applications to climate change mitigation and sustainable resource management.

Tilman MichaeliView profile

Professor

• Computer Science Education
• Debugging in Classroom
• AI/ML Literacy
• +4 more

Prof. Dr. Tilman Michaeli is a Professor of Computer Science Education at the Technical University of Munich (TUM), affiliated with the TUM School of Social Sciences and Technology. His academic career includes a doctorate from Friedrich-Alexander University of Erlangen-Nuremberg (2020) and prior research roles at Erlangen and the Free University of Berlin under Prof. Ralf Romeike. He leads the Professorship for Computer Science Education, focusing on didactics, debugging pedagogy, AI/ML literacy, quantum computing education, and digital education transformation. Education Background: Studied Computer Science Teaching at FAU until 2017, completed state examinations, and earned his doctorate in 2020. Appointed to TUM in 2021. Research Interests: Prioritizes empirical and design-based research in computing education. Key areas include debugging strategies in classrooms, AI literacy for K-12, collaborative programming, and integrating quantum computing into curricula. His work emphasizes practical, student-centered approaches. Publications: Over 50 peer-reviewed articles since 2017, focusing on educational technology, teacher training, and curriculum development. Notable works include studies on debugging frameworks, MOOCs for AI literacy, and quantum computing in schools. Grants & Awards: Recipient of the 2019 Doctoral Award from the German Informatics Society for contributions to computer science didactics. Active in securing research grants for projects like DigiProMIN and the AI Training Initiative. Teaching: Leads courses such as Didactics of Computer Science 1/2 and Theoretical Computer Science for Vocational Education. Supervises advanced seminars and doctoral candidates. Teams: Directs a research group including employees like Sven Baumer, Luisa Gebhardt, and Heike Wachter, focusing on collaborative projects like InstaClone and Smerge tools.

Tina RudolphView profile

Lecturer

• Mathematics Education
• Tutorial Methodology
• Undergraduate Teaching
• +2 more

Tina Rudolph is a Lecturer and coordinator of tutorial teaching at the Department of Mathematics, Technische Universität Darmstadt, a role she has held since 2018. Her primary focus centers on enhancing undergraduate education through structured tutorial systems and academic support frameworks. While no formal academic qualifications or prior education details are explicitly listed, her role strongly implies expertise in mathematics pedagogy and instructional design. Research interests appear concentrated on improving tutorial-based learning environments within STEM disciplines. No scholarly articles, grants, or awards are documented in the provided text. Her professional activity emphasizes administrative and instructional coordination rather than independent research initiatives. Labs or collaborative teams are not mentioned, suggesting her work remains focused on direct educational support and coordination at the departmental level.