Find Academic Supervisors

Andreas LangenohlView profile

Professor

• Economic Sociology
• Financial Markets
• Temporal Sociology
• +7 more

Andreas Langenohl is a full Professor at the Institute of Sociology , Justus Liebig University Giessen , holding the Chair in General Comparative Studies. He has held fellowships at institutions like the Kulturwissenschaftliches Kolleg (Konstanz) and the Collaborative Research Center in Freiburg, and is an Extra-Ordinary Professor at North-West University (South Africa). PhD in Social Sciences (1999, magna cum laude ), Justus Liebig University Giessen M.A. in Sociology and Slavic Philology (1996, with highest honors ) His research spans economic sociology , financial market temporality , security studies , and cultural foundations of integration . He explores intersections of finance and politics, infrastructural security, and socio-temporal dynamics in economic theory. Recent work focuses on geoeconomic hegemony during the Ukraine conflict, pandemic security governance, and decentralized finance algorithms. Scientific Awards : Karl-Polanyi-Award (2010) for economic sociology Wolfgang-Mittermaier-Award (2007) for academic teaching Knowledge Unlatched Open Access selection (2020) Grants : He has secured multiple DFG and foundation grants for projects on financial infrastructures, securitization, and EU-Russia relations, including collaborations with institutions like Cornell University and the Einaudi Center for European Studies. Labs & Teams : Langenohl leads projects within the Collaborative Research Centre/Transregio 138 Dynamics of Security (Giessen & Marburg) and contributed to the Center of Excellence Cultural Foundations of Integration (Konstanz).

• Cryptography
• Security
• Blockchain Technology
• +4 more

Elaine Shi is a Professor at Carnegie Mellon University's Computer Science Department and Electrical and Computer Engineering Department, with an Adjunct Professor appointment at the University of Maryland. Her research spans cryptography, security, blockchain technology, algorithms, and privacy-enhancing techniques. Co-founder of Oblivious Labs, Inc. Co-developer of cryptographic protocols adopted by Signal, Meta, and Google Co-founder of CyLab's crypto seminar series Her work has been recognized with prestigious awards including the Packard Fellowship, Sloan Research Fellowship, ACM Fellow, and IACR Fellow. She has advised numerous PhD students and postdocs, many of whom now hold academic or industry positions. 2023 ACM CCS Test of Time Award 2020 CyLab Distinguished Alumni Award 2016 ONR YIP Award Recent publications focus on advancing cryptographic protocols, privacy-preserving algorithms, and blockchain security, with key contributions in garbled RAM, oblivious computation, and differentially private mechanisms.

Yongkai WuView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +7 more

Dr. Yongkai Wu is an Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University, where he focuses on advancing Responsible AI , Causal Inference , and Machine Learning . His research addresses fairness, trustworthiness, and transparency in AI systems through causal modeling and has been published in top-tier venues like AAAI, NeurIPS, and KDD. Education: Ph.D. in Computer Science (2020) and M.S. in Computer Science (2018) from the University of Arkansas; B.Eng. in Electronic Engineering (2014) from Tsinghua University. Dr. Wu’s research spans Responsible AI and Causal Inference , with applications in healthcare, computer vision, and cybersecurity. He explores Causal Fairness in non-IID settings, Responsible LLMs , and Robust Learning via hyperspectral data. His work integrates ethics into AI/ML systems, ensuring equitable outcomes in dynamic environments. His recent articles highlight trends in Fairness through causal inference, Explainable AI in healthcare, and Efficient LLMs . Collaborations with institutions like the University of Maryland and Prisma Health underscore real-world impact. Scientific Awards: Best Paper Award (SIGKDD'25), travel awards from SBP-BRiMS, IJCAI, KDD, and NeurIPS. Dr. Wu’s grants include NSF , SC EPSCoR , Prisma Health , and United States Army CCDC funding for projects on Responsible AI in Healthcare , Hyperspectral AI , and Robust Learning . He mentors students through summer programs and directed research, emphasizing hands-on experience with Python, PyTorch, and ethical AI frameworks.

Hugo PaquetView profile

Researcher

• Probabilistic Programming
• Categorical Semantics
• Game Semantics
• +4 more

Hugo Paquet is a Researcher at INRIA Paris and a member of the ANTIQUE team at École Normale Supérieure , PSL University. He completed a PhD in Computer Science (2015–2019) at the University of Cambridge under Glynn Winskel , focusing on concurrent game semantics for probabilistic programming. His postdoctoral work includes positions at LIPN, Paris (2022–2024, funded by a Marie Skłodowska-Curie Award) and University of Oxford (2020–2022). He has contributed to conferences including LICS , ESOP , FSCD , and POPL . Education : PhD in Computer Science (University of Cambridge, 2019) Research Interests : Probabilistic programming (semantics, inference algorithms, nonparametric models), categorical semantics (game semantics, concurrency models, adjunctions), combinatorial species, and 2-dimensional categories. Teaching : Category Theory (2023–2024), Bayesian Statistical Probabilistic Programming (2021–2022), Lambda-calculus and Types (2020–2021), and small-group teaching at Cambridge (Logic, Discrete Mathematics, Semantics). Awards : Marie Skłodowska-Curie Award under the Paris Region Fellowship Programme Labs : INRIA Paris, ANTIQUE team (2024–present)

Johanna SommerView profile

Researcher

• Robust Machine Learning
• Machine Learning for Graphs and Networks
• Machine Learning for Temporal and Dynamical Data
• +3 more

Johanna Sommer is a researcher at the Technical University of Munich , affiliated with the Department of Informatics under the TUM School of Computation, Information and Technology. She contributes to research and teaching in advanced machine learning domains. Education : M.Sc. Computer Science (TUM, passed with distinction), B.Sc. Applied Computer Science (Baden-Württemberg Cooperative State University). Research Interests : Her work spans Robust Machine Learning , machine learning for graphs and sequential data, Bayesian learning with uncertainty quantification, and efficiency improvements in training algorithms. She applies these to tasks like molecular generation and continuous-time modeling. Teaching : Johanna leads seminars and courses on topics including Advanced Machine Learning: Deep Generative Models , Machine Learning for Graphs and Sequential Data , and Large-Scale Machine Learning , often in collaboration with industry partners. Publications highlight her contributions to robustness analysis of combinatorial solvers, molecule generation from 3D shapes, and efficient alternatives to neural ODEs. These works appear at top venues like ICLR and NeurIPS .

• Transportation planning
• Dynamic traffic assignment
• Traffic estimation and prediction
• +5 more

Xuesong Zhou is a Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment , Arizona State University (ASU). He leads the ASU Transportation+AI Lab and develops open-source tools like DTALite, NEXTA, and OSM2GMNS with over 100,000 downloads. His research focuses on multimodal transportation planning , dynamic traffic assignment , and rail scheduling with methodological contributions to traffic flow theory and operations research . Dr. Zhou's research bridges transportation system operations , computer applications for ITS , and logistics optimization . His work on differentiable programming reformulations and state-space-time network modeling has advanced real-time traffic prediction and multi-echelon facility scheduling . Scientific awards include: 2022 Elsevier Multimodal Transportation Best Article Award 2018 Transportation Research Part C Best Associate Editor Award 2012 INFORMS Railway Applications Section Best Paper Award He has advised 9 PhD students and 6 postdoctoral researchers to completion, with mentees now at institutions like Georgia Institute of Technology and Michigan State University. Current projects include NSF CONNECT and DOE Argonne collaborations on multi-scale traffic simulation and smart campus cyberinfrastructure .

• Applied Geometry
• Discrete Differential Geometry
• Numerical Analysis
• +3 more

Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.

• Control Theory
• Partial Differential Equations
• Drilling Engineering
• +6 more

Ole Morten Aamo is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. His office is located at Elektro D/B2, D344, Gløshaugen, O. S. Bragstads plass 2, and he can be reached at aamo@ntnu.no or by phone at 73594386. Professor Aamo's research focuses on control theory with particular emphasis on partial differential equations (PDEs) and their applications in drilling engineering and the petroleum industry. His work spans multiple areas including boundary control of hyperbolic systems, adaptive control methodologies, vibration control in drilling operations, and leak detection systems for pipe networks. His research combines theoretical control developments with practical applications in the oil and gas sector, particularly addressing challenges related to stick-slip phenomena, torsional vibrations, and pressure oscillations in drilling operations. His publication record reveals a consistent trajectory of high-impact research in control systems, with a notable shift toward integrating machine learning approaches with traditional control theory in recent years. The majority of his work centers around hyperbolic PDE systems, with applications primarily in drilling engineering and fluid dynamics. His research demonstrates a strong connection between theoretical control developments and practical implementations in the petroleum industry. Professor Aamo has actively supervised multiple graduate students, as evidenced by the master's theses listed in his publication record. His work often appears in top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters, as well as petroleum engineering venues like SPE Journal and ASME publications.

Yazan OtoumView profile

Part-Time Lecturer

• Internet of Things (IoT) Security
• Artificial Intelligence for Cybersecurity
• Federated Learning
• +4 more

Yazan Otoum is a Part-Time Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa and concurrently an Assistant Professor in the School of Computer Science and Technology at Algoma University . A licensed Professional Engineer in Ontario, he is internationally recognized for his interdisciplinary work at the intersection of cybersecurity, artificial intelligence, and the Internet of Things . Education Ph.D. in Electrical and Computer Engineering, University of Ottawa (September 2022) M.Sc. in Network Engineering and Management, DePaul University (December 2009) Research Interests Dr. Otoum’s research program is dedicated to securing the rapidly expanding IoT ecosystem. His core themes include: Scalable meta-learning models that adapt to evolving threats in resource-constrained IoT devices. Federated and transfer learning to enable privacy-preserving, collaborative intrusion detection across heterogeneous networks. Healthcare IoT (IoMT) security, ensuring safe and trustworthy medical devices and data streams. Smart-city infrastructures , where AI-driven security safeguards critical urban services. His recent work leverages large language models (LLMs) , blockchain , and differential privacy to push the boundaries of next-generation cyber-defence mechanisms. Publication Trends Across 23 peer-reviewed works (2017-2025), a clear evolution is evident: early studies established foundational deep-learning intrusion detection frameworks (DL-IDS), followed by federated and transfer-learning paradigms tailored for IoT and IoMT. The latest 2024-2025 publications integrate cutting-edge generative AI and blockchain techniques, highlighting a shift toward holistic, scalable, and privacy-preserving security ecosystems for IoT, Internet of Vehicles, and healthcare domains. Professional Recognition & Service Licensed Professional Engineer (P.Eng), Ontario Certifications: CEH, CCNA, CHFI, ISO 27001 Lead Implementer Peer reviewer for IEEE, ACM, and Elsevier journals Invited speaker and mentor in cybersecurity education initiatives Teaching & Mentorship Dr. Otoum currently teaches Data Science and Data Structures and Algorithms at the University of Ottawa. His office hours are held Mondays 11:30 AM–1:30 PM in SITE room 4075. While specific student advisees are not listed, he is actively engaged in mentoring emerging researchers and practitioners in secure AI and IoT systems. Labs & Teams Operating at the intersection of academia and industry, Dr. Otoum collaborates with multidisciplinary teams spanning embedded systems, AI laboratories, and healthcare technology partners, fostering innovation that transitions seamlessly from theory to real-world deployment.

Laurent CaraffaView profile

Researcher

• 3D Data Processing
• Surface Reconstruction
• Point Cloud Processing
• +7 more

Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.

Kavita BalaView profile

Professor

• Computer Vision
• Artificial Intelligence
• Computer Graphics
• +6 more

Kavita Bala is the 17th Provost of Cornell University and a Professor of Computer Science. She previously served as the inaugural Dean of the Cornell Ann S. Bowers College of Computing and Information Science, leading its transition to a degree-granting college by 2025, and as Chair of Cornell’s Department of Computer Science. Her academic leadership includes expanding faculty, establishing research programs like the Bowers CIS Undergraduate Research Experience (BURE), and securing a new research facility for computing and information science. Education: B.Tech (IIT Bombay), M.S. and Ph.D. (MIT, Computer Science) Bala’s research focuses on computer vision, artificial intelligence, and computer graphics , with groundbreaking work in material and style recognition using deep learning. Her innovations in crowdsourced training data and differentiable rendering have advanced visual search technologies and translucent material modeling, powering her startup GrokStyle. She pioneered AI techniques applied to environmental monitoring through projects like MONITRS and AllClear , addressing Earth observation challenges. Her scientific awards include: American Academy of Arts and Sciences (2025) SIGGRAPH Computer Graphics Achievement Award (2020) IIT Bombay Distinguished Alumnus Award (2021) ACM Fellow (2019) SIGGRAPH Academy Fellow (2020) As Provost, Bala drives strategic initiatives like the Cornell AI Initiative , creating interdisciplinary minors in AI and AI in Society, and establishing the Schmidt AI in Science postdoctoral program. She co-chaired a task force for generative AI guidelines in education.

• Probability Theory
• Stochastic Processes
• Graph Signal Processing
• +4 more

Basile de Loynes is a Lecturer at the French National School of Statistics and Information Analysis (ENSAI), holding a permanent academic position since at least 2016. He maintains a dual affiliation as a CREST (Center for Research in Economics and Statistics) Affiliated Member, contributing to interdisciplinary economic-statistical research. His academic trajectory includes a postdoctoral position at the University of Neuchâtel (2012), followed by temporary lecturer roles at the University of Burgundy (2013-2014) and University of Strasbourg (2014-2016). His research centers on advanced probability theory with specific expertise in stochastic processes on non-Euclidean structures. Key areas include: Random walks on algebraic structures (groups, groupoids, tilings, graphs) Poisson-Martin boundary theory and potential analysis Long memory processes and invariance principles Graph signal processing with Fourier/wavelet methods His publication record shows consistent output in top-tier journals since 2012, with recent work (2021-2023) focusing on graph-based signal denoising and differential privacy applications. Analysis of his 10 most recent publications reveals a strong methodological thread connecting classical probability theory with modern graph-based signal processing. Approximately 60% of his work since 2016 involves graph-structured stochastic models, demonstrating an evolving research trajectory from theoretical random walk properties toward applied graph signal analysis. The recurring subfields across publications include Markov additive processes, spectral graph theory, and wavelet transforms on non-Euclidean domains. His academic service includes developing comprehensive teaching materials for core probability and measure theory courses at ENSAI, with publicly available lecture notes and examinations dating back to 2016.

Stephen TheriaultView profile

Professor

• Homotopy Theory
• Algebraic Topology
• Manifolds
• +4 more

Professor Stephen Theriault is a homotopy theorist at the University of Southampton since 2015. His research spans homotopy theory, Lie groups, gauge groups, and manifolds, with a focus on polyhedral products and toric topology. PhD and MSc from University of Toronto (1991-97) Postdoctoral Fellowships at MIT, University of Illinois at Chicago, and University of Virginia Previous roles at University of Aberdeen (2002-12) Research Interests include: Homotopy theory of spheres and Moore spaces Applications to Lie groups, gauge groups, and manifolds Polyhedral products and toric topology Current projects on loop space decompositions and homotopy types Publications emphasize homotopy classifications, geometric topology, and algebraic structures in distributed computing. Recent work explores Steenrod's problem , quasitoric manifolds , and open book decompositions . Teaching includes MATH1059 Calculus and supervision of BSc/MMath projects. PhD Supervision involves students like Briony Helen Eldridge and Lewis Richard Stanton.

Tobias NeckelView profile

Associate Professor

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

• Machine Learning
• Optimization
• Federated Learning
• +8 more

Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.