Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +3 more

Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.

Colin JahelView profile

Research Fellow

• Model Theory
• Dynamics
• Probability Theory

Colin Jahel is a Research Fellow at Technische Universität Dresden, specializing in mathematical research at the intersection of Model Theory, Dynamics, and Probability Theory. He completed his PhD in 2021 at the University of Lyon, France, under the supervision of Lionel Nguyen Van Thé and Todor Tsankov. Prior to his current position, he was a postdoctoral researcher at Carnegie Mellon University. His research explores connections between model-theoretic methods and dynamical systems, with emphasis on invariant measures, automorphism groups, and structural properties of mathematical objects. Key themes include ergodic theory applications to countable structures, topological dynamics of Polish groups, and probabilistic aspects of mathematical logic. Jahel's publications demonstrate consistent focus on interactions between model theory and dynamics, with recurrent themes of invariant measures, group actions, and classification of infinite structures. His collaborative work frequently addresses problems in geometric group theory, combinatorial limit theory, and measurable dynamics. He maintains active research collaborations with mathematicians across institutions, including co-authors from Carnegie Mellon University, Charles University, and other European universities.

Gregory S. ChirikjianView profile

Professor

• Robotics
• Controls
• Computer Vision
• +6 more

Gregory S. Chirikjian is the Willis F. Harrington Professor and Department Chair at the University of Delaware's College of Engineering, Department of Mechanical Engineering. His expertise spans robotics, controls, computer vision, and applied mathematics. He is a Fellow of both IEEE and ASME. Education: PhD in Applied Mechanics, California Institute of Technology MSE in Mechanical Engineering, Johns Hopkins University BS in Engineering Mechanics, Johns Hopkins University BA in Mathematics, Johns Hopkins University Research Focus: Chirikjian's work integrates group theory with robotics, kinematics, and motion planning. Current projects include affordance-based reasoning for robotic systems, medical image registration, and the mechanics of macromolecules. His research explores both theoretical foundations and practical applications in robotics and engineering systems. Research Trends: Recent work emphasizes learning-free grasping techniques, uncertainty propagation in robotics, and deployable structures. His articles reflect interdisciplinary approaches combining mathematical rigor with practical robotic systems design. Awards: Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the American Society of Mechanical Engineers (ASME) Advising & Labs: Chirikjian leads the Spencer Lab, focusing on robotics and mathematical modeling. Though student names aren't listed here, his work involves graduate research in robotics systems and applied mathematics. Major grants support projects in soft robotics, sensorless manipulation, and robotic affordance learning. Future Directions: Ongoing work includes self-replicating robotic systems, probabilistic motion planning, and applications of non-Abelian Fourier analysis in engineering problems.

Hans-Georg MüllerView profile

Professor

• Functional Data Analysis
• Fréchet Analysis
• Longitudinal Studies
• +3 more

Hans-Georg Müller is a Distinguished Professor of Statistics at the University of California, Davis. His work focuses on functional data analysis, Fréchet analysis of random objects, and applications in longitudinal studies, brain imaging, and aging research. He has contributed to foundational methodologies such as Fréchet regression and Wasserstein-based statistical models. Key research interests include the statistical analysis of non-Euclidean data, covariance structures in high-dimensional spaces, and temporal dynamics of complex systems. Müller has published extensively in top-tier journals like the Annals of Statistics and IEEE Transactions on Information Theory. Awards: 2022 Rietz Lecture & Award (IMS), 2017 Humboldt Research Award, and multiple fellowships from prestigious societies. Advising: Recognized with the 2020 Graduate Program Advising & Mentoring Award at UC Davis. Publications: Over 200 papers, including influential works on Fréchet regression, Cox process regression, and longitudinal brain development studies. His research bridges theoretical statistics with applied domains, particularly in understanding neurodevelopmental processes and the statistical mechanics of aging biomarkers.

Kailai LiView profile

Assistant Professor

• Robotic Perception
• Sensor Fusion
• State Estimation
• +2 more

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

Yaqing ChenView profile

Assistant Professor

• Metric Space Valued Data
• Non-Euclidean Data Analysis
• Functional/Longitudinal Data
• +5 more

Yaqing Chen is an Assistant Professor in the Department of Statistics at Rutgers, The State University of New Jersey. Her primary affiliation is with the School of Arts and Sciences (SAS). She holds a Ph.D. and M.S. from the University of California, Davis, and a B.S. from Peking University. Her research focuses on statistical methodologies for complex data structures, including metric space valued data, non-Euclidean objects, functional/longitudinal data, and distributional data. Applications span longitudinal studies, biological and medical sciences, and social sciences. Key areas of innovation include distance profile-based inference, gradient synchronization in brain connectivity analysis, and Wasserstein regression techniques. Recent work emphasizes neurodevelopmental applications, such as analyzing brain volume trajectories in children and their associations with maternal education. Methodological contributions include R packages like 'fdaconcur' and 'frechet' for functional data analysis and non-Euclidean statistics. Her research bridges theoretical advancements with practical tools for high-dimensional and non-standard data types. Dr. Chen has secured grant funding supporting her work on temporal dynamics of disease spread and neurodevelopmental modeling. She collaborates with interdisciplinary teams in neuroscience and public health. Her lab focuses on developing scalable statistical methods for high-dimensional biomedical data.

• Distributed Systems
• Blockchain Technology
• Cybersecurity
• +5 more

Romaric LUDINARD is an Associate Professor at IMT Atlantique since 2017, affiliated with the Department of Systems, Networks, Cybersecurity, and Digital Law (SRCD). He holds leadership roles, including co-head of the post-master professional certificate in Cybersecurity (with CentraleSupélec) and Deputy Head of the SRCD department. Previously, he was Associate Professor at ENSAI (2015–2017), where he led the Data Scientist specialization and computer science team. His career includes research and teaching roles at the University of Rennes 1 and international collaborations, such as a postdoc at Sapienza University of Rome (2015). Research Interests: Focus on distributed systems, blockchain protocols (safety, scalability), fault tolerance, and cybersecurity. Key areas include consensus mechanisms, intrusion detection, and Byzantine fault tolerance in large-scale networks. Current projects involve blockchain compression (BC4SSI), federated learning security (RADAR), and 5G radio access networks. Publications & Projects: Over 30 publications in top venues (IEEE/ACM conferences, journals). Notable works include geometric FTM localization techniques, Bitcoin network analysis, and federated learning reputation systems. Active in grants like Beyond5G (BPI France) and ANR JCJC (BC4SSI). Holds patents for anomaly detection in distributed systems. Teaching: Courses on blockchain, distributed systems, cybersecurity, and programming (Python, object-oriented design). Supervised 10+ PhD/Master students, including defended theses on blockchain improvement proposals and federated learning trust frameworks. Service & Talks: Organizer of AlgoTel workshops, TPC member for SSTIC, NCA, and ICDCN. Delivered keynote speeches on blockchain scalability and fork avoidance (e.g., BART Initiative, DSN'16). Active in conference organization and review committees.

• Robotics
• Computational Geometry
• Topology in Robotics
• +3 more

Anastasiia Varava is an affiliated faculty member and PhD student at KTH Royal Institute of Technology, working within the Division of Robotics, Perception and Learning. Her research focuses on computational geometry and topology applied to robotic manipulation, particularly caging theory and its practical implementations for handling rigid and deformable objects. She collaborates with advisors Florian Pokorny and Danica Kragic on theoretical and applied robotics challenges. Her work integrates robotics with interdisciplinary fields such as topology, machine learning, and computer vision. Key topics include path planning, deformable object manipulation, and geometric algorithms for motion verification. Recent contributions emphasize data-driven approaches for latent space modeling and visual action planning in robotics systems. Varava has authored or co-authored over 30 peer-reviewed articles and conference papers, covering topics from caging-based motion planning to molecular screening algorithms inspired by robotics. Her publications span journals like IEEE Transactions on Robotics and conferences such as IROS and ICRA, reflecting a strong focus on practical robotic systems with theoretical rigor. Her doctoral thesis, Path-Connectivity of the Free Space: Caging and Path Existence (2019), established foundational work on caging and path non-existence in robotics. Current projects involve advancing geometric evaluation techniques for data representations and exploring diffusion models for robotic skill learning.

• Differential Geometry
• Computational Geometry
• Numerical Analysis
• +2 more

Erchuan Zhang is an Adjunct Research Fellow at the School of Physics, Mathematics and Computing, The University of Western Australia. His research focuses on differential geometry, computational geometry, and numerical analysis, particularly in the areas of Riemannian manifolds, geodesics, and variational curves. Dr. Zhang's work centers on geometric modeling and optimization on non-Euclidean spaces, with significant contributions to cubic curve construction via the de Casteljau algorithm on manifolds and efficient computation of multi-objective variational curves. His research bridges theoretical differential geometry with practical computational methods for solving complex geometric problems. Recent publications (2022-2025) consistently address geometric modeling on manifolds, featuring themes like geodesic convergence analysis, boundary value problem solutions for Lagrangian systems, and cubic curve generalizations. These works appear in high-impact journals including Computer Aided Geometric Design and the SIAM Journal on Numerical Analysis , reflecting strong interdisciplinary connections between pure mathematics and computational science.

Marco PanzaView profile

Professor

• History of Mathematics
• Philosophy of Mathematics
• Philosophy of Logic
• +3 more

Marco Panza is a Full Professor at Chapman University and holds the Kennedy Endowed Chair in Philosophy . He co-founded and directs the Doctor of Science in Mathematics, Philosophy, and Physics (MPP) program within Schmid College of Science and Technology . Additionally, he serves as a Research Director at the Institute for History and Philosophy of Science and Technology (IHPST) , CNRS and University of Paris 1, where he supervises French Ph.D. students. Education : Bachelor of Science, University of Milan Doctor of Philosophy, University of Genoa (1990) Habilitation à Diriger des Recherches (HDR), École des Hautes Études en Sciences Sociales (EHESS), Paris (2000) Panza's research spans the history and philosophy of mathematics and logic , focusing on topics such as Platonism , Euclidean proofs , and Frege's theories . His recent work explores the epistemic economy of mathematical practices, formalizing Euclid's logic, and debates in non-standard analysis. His publications include over 100 articles and books. Notable works analyze Descartes' geometry , Frege's real numbers , and data science methodology , reflecting interdisciplinary engagement with philosophy, mathematics, and historical epistemology. Scientific Awards : Kennedy Endowed Chair in Philosophy Habilitation à Diriger des Recherches (HDR), EHESS Panza has directed multiple Ph.D. students through his role at IHPST and has received grants for collaborative research projects in mathematical theory and philosophy. His work bridges classical geometry, modern logic, and contemporary epistemological challenges.

Scott C. SchmidlerView profile

Associate Professor

• Computational Biology
• Bioinformatics
• Statistical Science
• +12 more

Scott C. Schmidler is an Associate Professor in the Department of Statistical Science and Computer Science at Duke University, affiliated with the Duke Center for Systems Biology. His research lies at the intersection of statistics, computer science, and molecular biology, focusing on the development of probabilistic and computational methods for understanding biological systems. Department of Statistical Science, Duke University Department of Computer Science, Duke University Duke Center for Systems Biology Dr. Schmidler's research interests include Monte Carlo simulation, Markov chain convergence, shape analysis, structural biology, and biophysics. He develops statistical models and algorithms to study protein folding, molecular simulation, and systems biology. His work integrates geometric and stochastic methods to model complex biological data, particularly in the context of structural alignment and evolutionary modeling. His recent publications and talks reflect a strong focus on convergence diagnostics in MCMC, adaptive sampling methods in molecular simulations, Bayesian inference, and the statistical challenges in modeling biomolecular systems. The articles span topics from theoretical foundations of Markov chains to practical applications in vaccine design and protein evolution. He has served as Associate Editor for the Annals of Applied Statistics and as Program Coordinator for the Master's program in Statistical Science at Duke. He has co-organized several major workshops, including events at BIRS and SAMSI, indicating active leadership in the statistical and computational biology communities. Co-organizer, BIRS Workshop on Advances in Scalable Bayesian Computation (2014) Co-organizer, SAMSI Workshop on Molecular Motors, Neuron Models, and Epidemics on Networks (2010) Associate Editor, Annals of Applied Statistics (2009–present) Program Coordinator, Master's in Statistical Science Dr. Schmidler advises students and postdoctoral researchers in computational biology and statistical science. While specific names are not listed, his group is involved in cutting-edge research at the interface of statistics and molecular biology. He has secured participation in major collaborative programs such as SAMSI’s Stochastic Dynamics program, reflecting external recognition and funding support for his work. He is involved in several research teams and centers, including the Duke Center for Systems Biology, and has contributed to interdisciplinary collaborations in enhanced sampling methods, Bayesian computation, and network-based biological modeling. His ongoing work continues to advance the statistical foundations of molecular simulation and systems biology.