Ann Nowe is a Professor at the Department of Electronics and Informatics (ETRO) at Vrije Universiteit Brussel. Her research focuses on reinforcement learning, multi-agent systems, robotics, and energy management. She leads the Federated labs AI and Robotics, emphasizing interdisciplinary collaboration in artificial intelligence and sustainable robotics. With a prolific publication record (555+ outputs) and 152 active projects, her work spans theoretical advancements and applied solutions in AI ethics, explainability, and energy systems. Her research interests include AI security, neuro-symbolic integration, and federated robotics. Notable contributions include frameworks for explainable AI in multi-agent environments and predictive maintenance algorithms for wind turbines. She has received multiple awards, including the ALA Best Paper Award (2018) and Best Demonstration Award (2019). Dr. Nowe actively participates in conferences and workshops on trustworthy AI, co-organizing events like the World Conference on eXplainable AI (2025). Her work integrates ethical considerations and human-centric design principles across robotics, energy systems, and collective decision-making.
Dr. Daniel Raggi is a researcher at the Department of Computer Science and Technology , University of Cambridge, affiliated with research themes including Human-Centred Computing , Machine Learning and Artificial Intelligence , and Programming Languages, Semantics and Verification . His work bridges cognitive science and computer science, focusing on representation systems, knowledge encoding, and human-computer interaction. His research explores the cognitive properties of visualizations , representation selection for problem-solving , and automating transformation of mathematical knowledge . The 15 most recent publications highlight interdisciplinary trends in formal methods, cognitive modeling, and visual reasoning. Scientific awards and student advising details are not explicitly mentioned in the provided texts. Further details about his work can be inferred through his contributions to representation theory and its applications in AI and mathematical reasoning.
Dr. Jiaoyan Chen is a Lecturer (teaching & research) in the Department of Computer Science at The University of Manchester, transitioning to Senior Lecturer (Associate Professor) in July 2025. Previously, she was a Senior Researcher at the University of Oxford (2017-2022) and held visiting roles at Zurich University and Heidelberg University. She holds a PhD and BEng in Computer Science from Zhejiang University. Her research focuses on neural-symbolic systems, integrating knowledge graphs/ontologies with large language models and machine learning. Key areas include knowledge graph construction/curation, ontology embeddings, and data-intensive systems. She leads EPSRC-funded projects like 'OntoEm' (427.9K GBP) and collaborates on initiatives such as the Manchester-Melbourne-Toronto AI for Child Protection project. Teaching responsibilities include units like 'Data Engineering Technologies' and 'Advanced Topics in Knowledge Representation.' She actively supervises 9 PhD/postdoc researchers across topics like knowledge-aware AI and semantic search. Professional services include associate editor roles at Transactions on Graph Data and Knowledge and membership in the EPSRC Peer Review College. Her software contributions include OWL2Vec* (ontology embedding framework) and DeepOnto (ontology engineering toolkit). Recent achievements include ACL 2025 acceptances and an IEEE TKDE survey paper on ontology embeddings.
David Leake is a Professor of Computer Science at Indiana University's Luddy School of Informatics, Computing and Engineering, where he served as Executive Associate Dean from 2012-2021. He holds a Ph.D. in Computer Science from Yale University (1990) and is Editor in Chief Emeritus of AI Magazine after 17 years of service. His research focuses on artificial intelligence and cognitive science, including case-based reasoning, explanation generation, neuro-symbolic AI, intelligent information systems, and multistrategy learning. He has authored over 200 publications with 9,000+ citations. Recent publications demonstrate strong focus on integrating case-based reasoning with deep learning and large language models, particularly for explainable AI. Research spans hybrid AI systems, neural network implementations, and human-centered explanation methods. Scientific Awards: AAAI Distinguished Service Award (2014) Five-time best paper winner at International Conference on Case-Based Reasoning He advises graduate students and leads research funded by NSF, NASA, and ONR. Directs projects including SWALE (creative explanation) and the Stamping Advisor system. Maintains the IUCBRF Java framework and pedagogical resources for case-based reasoning.
Prof. Andrey Ustyuzhanin is an Adjunct Professor of Computer Science at Constructor University's School of Computer Science & Engineering and a Visiting Research Professor at the National University of Singapore (NUS), affiliated with the Institute for Future Intelligent Machines (IFIM). He holds a PhD in Computer Science from the Institute of System Programming (Russian Academy of Sciences) and advanced degrees from Moscow Institute of Physics and Technology (MIPT). His research focuses on developing machine learning methods to address complex scientific challenges in particle physics, materials science, and data-driven discovery. He has contributed to projects like the LHCb experiment at CERN, optimizing online triggers and BDT-based processing, and has pioneered initiatives like the Tracking Machine Learning Challenge and the Code4ML dataset. His work bridges AI and fundamental science, emphasizing interdisciplinary applications. He is also the Director of AI/ML Research at Acronis and a co-organizer of international summer schools in machine learning for particle physics. Education PhD in Computer Science, Institute of System Programming (RAS), 2007 M.Sc. in Applied Mathematics & Physics (Autonomous Control Systems), MIPT, 1994–2000 M.Sc. in Innovative Management, MIPT, 1998–1999 B.Sc. in Applied Mathematics, MIPT, 1994–1998 Mathematics & Physics, Moscow Chemical Lyceum, 1991–1994 Research Interests Prof. Ustyuzhanin specializes in machine learning for scientific discovery, including particle physics (LHCb experiment), materials science (defect analysis in 2D materials), and AI-driven experimental optimization. His work also explores symbolic expression generation, code semantics classification (Code4ML), and cybersecurity frameworks like EAGLEEYE for malicious event detection. He advocates for reproducible science and end-to-end optimization of experimental designs using differentiable programming. Key Projects & Contributions Co-developed the Tracking Machine Learning Challenge to advance high-throughput physics analysis Co-created the Code4ML dataset for annotated machine learning code Designed algorithms for LHCb’s online triggers and scintillator tracking systems Co-founded the annual summer schools on ML in particle physics Labs & Collaborations Director of AI/ML Research at Acronis Head of the LAMBDA Lab at HSE University PI at IFIM, NUS Collaborator on CERN-Yandex research programs
Debbie Yuster is an Associate Professor of Data Science and Mathematics at Ramapo College of New Jersey, joining in 2020. She holds a Ph.D. in Mathematics from Columbia University and a B.A. in Mathematics with a Computer Science concentration from Cornell University. Her research focuses on combinatorics, polyhedral geometry, and machine learning, with contributions to algebraic geometry and STEM education. Education: Ph.D., Mathematics, Columbia University B.A., Mathematics (Computer Science concentration), Cornell University Research Interests: Combinatorics: Cluster methods, enumerative problems Polyhedral Geometry: Hyperdeterminants, triangulations Machine Learning: Interdisciplinary applications STEM Education: K-12 collaboration and curriculum development Publications: Her work bridges pure mathematics and computational biology, with key contributions in tropical geometry and epistatic modeling. Recent focus includes applying combinatorial techniques to high-dimensional geometric problems. Awards/Grants: None listed in current materials. Advising/Teaching: Teaches courses in data science (Python, ethics), mathematics (calculus, linear algebra), and collaborates on interdisciplinary programs. No formal advisees listed in profile. Labs/Teams: Active in the School of Theoretical and Applied Science's research initiatives, including the STEM Center and bioinformatics programs.
Saurabh Sinha is a Professor in the Department of Electrical Engineering at Tshwane University of Technology's College of Science, Engineering and Technology, with an extensive publication record spanning software engineering, millimeter-wave circuit design, and computational biology. His research bridges theoretical computer science with practical hardware implementation, focusing on critical areas of modern technological development. His primary research interests include REST API testing methodologies enhanced by large language models, millimeter-wave and terahertz integrated circuit design, 3D IC implementation, and computational biology applications. Recent work demonstrates a strategic pivot toward leveraging AI in software engineering, particularly in automated testing frameworks where he has developed novel approaches using neuro-symbolic systems and multi-agent reinforcement learning. His research group has produced significant contributions to understanding LLM limitations in code translation and developing robust testing frameworks for modern API ecosystems. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary research that combines traditional electrical engineering with cutting-edge AI techniques. Approximately 60% of his recent work focuses on REST API testing enhanced by large language models, while 30% addresses millimeter-wave circuit design for next-generation telecommunications, and 10% explores computational biology applications. This distribution highlights his strategic focus on the intersection of software engineering and hardware implementation for modern communication systems. Sinha has mentored numerous researchers who have become first authors on significant publications, including Myeongsoo Kim, Rangeet Pan, and Rahul Krishna. His collaborative network spans multiple continents, with strong connections to researchers in South Africa, the United States, and Europe, reflecting the global impact of his work.
Maria Burns is an Assistant Professor in the Department of Construction Management/Political Science at the University of Houston’s College of Technology. Her research focuses on the intersection of data science, security, and logistics, with applications in energy, maritime, and homeland security sectors. She specializes in network analysis, AI-driven risk assessment, and policy analysis for critical infrastructure protection. Her work integrates multidisciplinary approaches including machine learning, time-series forecasting, and data visualization to address complex challenges such as human trafficking, cargo security, and port resilience. Key areas include supply chain vulnerability analysis, maritime security protocols, and the economic/environmental impacts of port activities. Burns has contributed to numerous DoS- and DHS-funded training manuals and courses, including 'Global Supply Chain Security' and 'UN Security Resolutions and WMD.' Her research also explores energy security paradoxes, intermodal transport risks, and crisis management strategies. Recent publications highlight innovative applications of AI in security technology and policy-driven solutions for modern security challenges. Her funded projects emphasize practical outcomes, such as optimizing port operations through 5G connectivity and enhancing border management through strategic convergence of trade and immigration policies. Burns’ work bridges academia and industry, offering actionable insights for public and private sector stakeholders.
Paola Corò is an Associate Professor in Assyriology at the Department of Humanities , Ca' Foscari University of Venice . Her institutional email is coropa@unive.it , and she is based at the Malcanton Marcorà campus. Specializes in Hellenistic Babylonian texts from Uruk Focuses on epigraphy , digital humanities , and machine learning applied to cuneiform studies Research highlights : 2024 publications on Ashurbanipal's library tablets and Seleucid royal narratives 2023 work on Greek identity markers in Babylonian sources 2022 monograph on temple property management in Hellenistic Uruk Scientific recognition : Premio Giovani Ricercatori (2001) Research grants (2003-2005, 2006-2007, 2017-2018) Member of KASKAL journal's Scientific Committee Coordinates the LIBER project (2019-2021) applying machine learning to cuneiform tablets, and participates in the International Association for Assyriology (IAA). She has taught Assyriology at both undergraduate and graduate levels, including blended learning formats and international Erasmus+ programs.
Dr. Alexey Bochkarev is a researcher in the Optimization Department at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), affiliated with the Felix Klein Center. His work focuses on discrete optimization, quantum computing applications, and network security. Current affiliation: RPTU, Optimization Department Contact: Building 31, Room 455, Paul Ehrlich Street, 67663 Kaiserslautern | a.bochkarev@math.rptu.de Research Interests Quantum computing for combinatorial optimization Monte Carlo tree search in adversarial network scenarios BDD-based representations for facility location problems Dynamic optimization under uncertainty Recent Publications His 2024 publications highlight quantum computing advancements and Monte Carlo methods for interdiction problems, while earlier works explore BDD alignment in network optimization. Key trends include hybrid quantum-classical algorithms and stochastic search frameworks for infrastructure protection.
Majid Zamani is an Associate Professor in the Computer Science Department at the University of Colorado Boulder , and a Guest Professor at the Computer Science Department of Ludwig Maximilian University of Munich . He leads the Hybrid Control Systems Lab and previously held an Assistant Professor position (W2 grade) in Electrical Engineering at the Technical University of Munich (2014-2019). His work focuses on verification and control of cyber-physical systems, hybrid systems, and secure-by-construction synthesis. Education: Ph.D. and MA in Mathematics, University of California, Los Angeles (2012) M.Sc. in Electrical Engineering, Sharif University of Technology (2007) B.Sc. in Electrical Engineering, Isfahan University of Technology (2005) Research Interests: Verification and control of cyber-physical systems Secure-by-construction synthesis Information-based control Compositional analysis of interconnected systems Stochastic and nonlinear control systems Recent Articles Trends: Recent works emphasize barrier certificates for safety verification, data-driven control methods, and compositional synthesis for large-scale systems. Key themes include formal guarantees for safety, privacy (opacity), and secure design in stochastic and cyber-physical contexts. Awards: NSF Career Award (2022) ERC Starting Grant (2018) ERC Proof of Concept Grant (2023) George S. Axelby Outstanding Paper Award (2023) Grants and Labs: Active grants include NSF Career and ERC awards. The Hybrid Control Systems Lab investigates topics such as neural barrier certificates, compositional control, and sandboxing AI controllers for safety-critical systems.
Victoria Fernandez Abrevaya is a post-doctoral researcher at the Max Planck Institute for Intelligent Systems (Perceiving Systems department) in Germany. She holds a PhD from Inria Grenoble (France) under Professors Edmond Boyer and Stefanie Wuhrer, and a MSc in Computer Science from the University of Buenos Aires, Argentina. Her work focuses on 3D reconstruction and understanding of humans from 2D data, with emphasis on facial animation, neural rendering, and generative models. Research interests: 3D computer vision and shape modeling Neural rendering techniques Diffusion models for motion and appearance synthesis Biometric fairness in face analysis Real-time face capture systems Geometry-constrained multi-human rendering Recent work explores occluded face expression reconstruction (OFEr 2025), interactive dynamics modeling (InterDyn 2025), and latent realignment for motion diffusion (Lead 2025). Her SPARK system (2025) enables real-time monocular face capture through self-supervised learning. Prior contributions include FLAME (2023), a popular 3D face model framework, and work on multilinear autoencoders for dynamic facial analysis (2018). She co-developed ImAvatar (2022), an implicit morphable head avatar system from videos.
Wotao Yin is a Professor of Mathematics at the University of California, Los Angeles, with a distinguished research career spanning over two decades in optimization theory and its applications. His work bridges theoretical mathematics with practical applications in machine learning, image processing, and signal analysis. As a leading researcher in optimization algorithms, he has made significant contributions to the development of methods like ADMM (Alternating Direction Method of Multipliers), proximal algorithms, and decentralized optimization techniques. Department: Department of Mathematics School: College of Letters and Science University: University of California, Los Angeles Yin's research focuses on developing efficient algorithms for large-scale optimization problems, with particular expertise in convex and nonconvex optimization, distributed and decentralized optimization, and mathematical foundations of machine learning. His work has profound implications for image reconstruction, signal processing, and modern machine learning systems. He has pioneered methods for handling sparse data, non-smooth objectives, and constrained optimization problems that arise in real-world applications. An analysis of his recent publications reveals a strong trend toward addressing optimization challenges in machine learning, particularly in federated learning, attention mechanisms, and nonconvex problem structures. His work demonstrates a consistent pattern of bridging theoretical optimization with practical machine learning applications, developing algorithms that balance computational efficiency with theoretical guarantees. Recent papers show increasing focus on heterogeneous data settings, large language model optimization, and fundamental limitations of optimization methods in complex learning scenarios. Throughout his career, Professor Yin has mentored numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His collaborative network spans multiple institutions worldwide, with particularly strong connections to researchers in China and across the United States. His work has been supported by various funding agencies recognizing the fundamental importance of optimization theory for advancing computational science. Professor Yin leads a vibrant research group focused on mathematical optimization and its applications, where students and collaborators work on cutting-edge problems at the intersection of mathematics, computer science, and engineering. The group maintains strong connections with both theoretical and applied research communities, participating in major conferences across optimization, machine learning, and computational mathematics.
James Bagrow is an Associate Professor of Mathematics & Statistics at the University of Vermont, affiliated with the Vermont Complex Systems Center. His research focuses on complex systems, network science, and data science, combining mathematical models with large-scale data analysis to understand physical and social systems. He has pioneered methods in network data analysis, including the development of the textbook Working with Network Data (Cambridge University Press, 2024). Bagrow's work spans diverse applications, from human mobility patterns to open-source software dynamics and emergency response modeling. He teaches courses in applied mathematics and data science, including Data Science I/II and Advanced Engineering Mathematics . His interdisciplinary contributions have led to collaborations across computer science, physics, and social sciences, with over 70 peer-reviewed publications. Notable achievements include the FOSS Impact paper award (2021) and a Nature Human Behaviour cover article (2022) on sleep patterns during travel. Bagrow's research emphasizes predictive capabilities in social systems, information flow dynamics, and network visualization. His lab explores computational tools for analyzing complex networks, with applications to urban dynamics, crowd behavior, and disaster response. His recent work bridges symbolic regression and neural networks, aiming to enhance model interpretability and accuracy. Awards: FOSS Impact paper award (2021), Nature Human Behaviour cover article (2022) Grants & Funding: Active in securing NSF and interdisciplinary grants for complex systems research Lab/Team: Vermont Complex Systems Center, fostering collaborations in data-driven science and network analysis
Ramzi Sofiane Dakhmouche is a doctoral student in mathematics at École Polytechnique Fédérale de Lausanne (EPFL) within the School of Basic Sciences (SB), Department of Mathematics (MATH), and the Statistical Field Theory Chair (CSFT). He holds the role of Researcher/Assistant-doctorant at CSFT and serves as President of the SIAM Student Chapter at EPFL. His research focuses on Graph Time Series Forecasting, Graph Reinforcement Learning, and applications in biological/engineering systems. He has contributed to works on robust symbolic regression and topological uncertainty in deep learning, including a provisional patent for a multi-task bandits method. His education includes an MSc in Mathematics, Vision, Learning from École Normale Supérieure Paris-Saclé. Research interests span machine learning theory, uncertainty quantification, and operator learning. Notable works include submissions on robust deep operator learning and papers under preparation on network system forecasting and scalable symbolic regression. He actively writes technical Medium articles on topics like proximal gradient optimization and generalization error analysis.
