Andi Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, joining in August 2024. Previously, he held an Assistant Professor position at Arizona State University's School of Manufacturing Systems and Networks. His research focuses on applying data science and machine learning to address challenges in engineering systems, including predictive analytics, monitoring, and design optimization. He holds a PhD (2021) from Georgia Institute of Technology, a PhD (2016) from Hong Kong University of Science and Technology, and a BS (2012) from Peking University. Research interests include multi-sensor data fusion for manufacturing process prediction, surrogate modeling for multi-stage engineering systems, and distributed/federated learning applications. His work spans domains like nuclear engineering, additive manufacturing, and semiconductor production. Awards: Multiple IISE and INFORMS awards, including the 2023 IISE Transactions Best Application Paper Award and the 2020 QCRE Division Best Student Paper Award finalist. Advising & Grants: Actively involved in research grants and advising students, though no formal advisee list is currently available. Labs/Teams: Engaged in collaborative projects with industry partners, focusing on industrial systems improvement through data-driven solutions.
Kevin Verbeek is an Assistant Professor in the Applied Geometric Algorithms group at Eindhoven University of Technology (TU/e), affiliated with the EAISI Foundational research institute. His research focuses on computational geometry, information visualization, and algorithms for cartographic challenges. He holds a PhD from TU/e (2012) and conducted postdoctoral research at UC Santa Barbara (2012–2014). He coordinates the TU/e Honors Academy's 'Competitive Programming and Problem Solving' track and is a member of the Eindhoven Young Academy of Engineering. Education: PhD in Applied Geometric Algorithms, TU Eindhoven (2012) Postdoctoral Researcher, UC Santa Barbara (2012–2014) Research Interests: Algorithmic stability for dynamic data visualization Geometric algorithms for social networks and cartography Topological data analysis and computational topology Braided river network modeling Key Projects: NWO Veni Award-funded project on stable geometric algorithms (2015–present) GLAMMap: Interactive geo-visualization for cultural heritage metadata Teaching & Outreach: Coordinator of Honors tracks in Competitive Programming Instructor for courses like 'Heuristic Algorithms' and 'Computer Science Research Project' Awards: NWO Veni Award for 'Stable Geometric Algorithms' (2015) Labs/Teams: Active in EAISI Foundational, focusing on algorithmic spatial data analysis and foundational AI research.
Prof. Manfred Hauswirth is the Managing Director of Fraunhofer Institute for Open Communication Systems (FOKUS) and holds the Chair for Open Distributed Systems at Technical University of Berlin. His research focuses on distributed systems, IoT, stream processing, quantum computing, and blockchain. He has held roles including Vice Director at Digital Enterprise Research Institute (DERI) and professor at National University of Ireland, Galway. He leads multiple strategic initiatives, including the Fraunhofer Quantum Technologies Research Field and the Weizenbaum Institute. His work bridges academia and industry, emphasizing digitalization, quantum computing, and IoT. Education: Dipl.-Ing. (1993), Dr. techn. (1999) in Computer Science from Vienna University of Technology. Postdoctoral work at École Polytechnique Fédérale de Lausanne (EPFL). Research Interests: Prof. Hauswirth’s work spans distributed systems, semantic web technologies, quantum algorithms, and IoT edge computing. He emphasizes real-world applications like smart cities, autonomous driving, and secure data management. Recent trends in his publications include quantum programming frameworks (e.g., Qrisp), scalable graph distillation, and edge-based AI systems. Awards: Not explicitly listed, but his work has been recognized through leadership roles in IEEE, ACM, and Fraunhofer committees. Advising & Grants: Active in funding initiatives like the Berlin Institute for Learning and Data (BIFOLD) and Einstein Center Digital Future (ECDF). Leads projects on quantum benchmarking, energy flexibility markets, and semantic stream processing. Labs/Teams: Directs the Fraunhofer High Performance Center for Digital Networking and chairs the Quantum Computing Competence Network, integrating interdisciplinary teams across quantum computing, IoT, and AI domains.
Prof. Amel BOUZEGHOUB is a Professor at Telecom SudParis, affiliated with the SAMOVAR research center. Her work focuses on AI, IoT, and data-driven systems with applications in smart environments, robotics, and education. She has contributed to over 50 peer-reviewed publications spanning machine learning, reinforcement learning, and semantic data processing. Research Interests: Her research bridges theoretical advances in machine learning with practical applications in smart homes, autonomous systems, and educational technology. She explores topics like human activity recognition, anomaly detection in social networks, and real-time data stream processing. Recent Trends: Her 2023-2024 work emphasizes explainable AI, reinforcement learning for autonomous systems, and multi-agent frameworks for stream reasoning. Earlier contributions include IoT-based supply chain traceability and distributed human activity recognition models. Grants & Projects: Key contributions include the ANR INCOME project on multi-scale context management for IoT systems and ACMES initiatives in educational technology. Labs/Teams: Active within the SAMOVAR lab at Telecom SudParis, collaborating with international teams in AI and robotics research.
Aaron Sidford is an Associate Professor in the Department of Management Science and Engineering and the Department of Computer Science at Stanford University. He holds a PhD in Electrical Engineering and Computer Science from MIT, advised by Jonathan Kelner. His research focuses on optimization theory, algorithm design, and computational complexity, with significant contributions to convex optimization, graph algorithms, numerical linear algebra, and machine learning theory. He has taught courses such as Introduction to Optimization Theory (MS&E213/CS269O) and Discrete Mathematics and Algorithms (CME305/MS&E316), emphasizing theoretical foundations and large-scale problem-solving. His work bridges continuous and discrete optimization, often leading to efficient algorithms with proven convergence guarantees. Award highlights include the Best Paper Award at FOCS 2022 and COLT 2022, along with notable recognitions for contributions to dynamic graph algorithms and convex optimization. He advises PhD students focusing on optimization theory and its applications, and his research has been supported by grants from NSF, ONR, and industry partnerships. His current research explores cutting-edge techniques in optimization, including faster max-flow algorithms, memory-efficient convex optimization, and adaptive gradient methods. He collaborates widely, contributing to both theoretical advancements and practical algorithmic implementations.
Neil Hurley is an Associate Professor and Head of School in the School of Computer Science at University College Dublin. He holds a BSc and MSc from University College Dublin and a PhD from Trinity College Dublin. Before academia, he worked at the Hitachi Dublin Laboratory from 1989 to 1999, leading research in parallel computing and knowledge-based systems. He joined UCD in 1999 and founded the Information Hiding Laboratory in 2001, focusing on digital content security. His research spans recommender systems, social network analysis, high-performance computing, and data hiding technologies. He has secured over €1 million in research funding from agencies like Enterprise Ireland and the EU. His teaching includes coordinating modules on Artificial Intelligence, Recommender Systems, and Computational Science. He has reviewed for journals such as IEEE Transactions on Image Processing and serves on the EMPS Graduate School Board. His recent work emphasizes scalable recommendation algorithms, privacy-preserving distributed systems, and strategic network analysis.
Yuepeng Wang is an Assistant Professor at the School of Computing Science, Simon Fraser University, Canada. He received his PhD and MSc from the University of Texas at Austin and BEng (honors) from the University of Science and Technology of China. Previously, he was a postdoctoral researcher at the University of Pennsylvania. Academic Honors: Distinguished Paper Award (OOPSLA'24, OOPSLA'17) Research Focus: Programming languages, formal verification, program synthesis, software engineering, and databases His research combines program verification and synthesis techniques across database applications, smart contracts, and software refactoring. Recent work focuses on SQL query equivalence, smart contract verification, and synthesis-driven database transformations. He supervises multiple graduate students and teaches advanced courses in programming languages and formal verification. He has contributed 15+ publications to top venues including PLDI, OOPSLA, ICSE, and POPL. His service includes program committee roles at POPL'26, SAS'25, and artifact evaluation committees for OOPSLA'23 and CAV'20. He also received the Distinguished Reviewer Award from PLDI'24.
Yan Zhang is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences, with a dual affiliation at Simula Research Laboratory in Oslo, Norway. Zhang leads cutting-edge research at the intersection of networking, artificial intelligence, and next-generation communication systems, with particular focus on digital twin networks, 6G technologies, and intelligent edge computing. Zhang's research interests span Internet of Things , Edge Computing , Digital Twin Networks , Wireless Communications , Vehicular Networks , and Federated Learning . Their work bridges theoretical foundations with practical implementations, addressing critical challenges in network architecture, resource optimization, and AI integration for future communication systems. Recent projects have focused on applying diffusion models to network optimization, developing secure federated learning mechanisms resistant to data poisoning, and creating energy-efficient solutions for maritime IoT networks. Analysis of Zhang's recent publication portfolio reveals a strong trend toward integrating artificial intelligence with next-generation networking infrastructure. The research demonstrates significant contributions to digital twin technology for 6G networks, with increasing focus on practical implementation challenges including location uncertainties, imperfect prediction conditions, and energy efficiency constraints. Zhang's work consistently addresses real-world deployment scenarios across multiple domains including intelligent transportation, maritime networks, and UAV swarms. Zhang has served as guest editor for special issues on Digital Twin for 6G Internet of Everything and Empowering Future Mobile Networks With Large Models, reflecting leadership in these emerging research areas. Current research directions include applying generative AI techniques like diffusion models to network optimization problems, developing robust security mechanisms for federated learning in IoT environments, and creating seamless service migration frameworks for mobile edge computing systems. Zhang's work frequently addresses the practical challenges of implementing theoretical concepts in real-world networking scenarios, with growing emphasis on energy efficiency and reliability under uncertain conditions.
Abdelmounaam Rezgui is an Assistant Professor in the Department of Computer Science and Engineering at New Mexico Tech, where he directs the Cloud Computing and Big Data (C2BD) Lab. He holds a Ph.D. from Virginia Tech and specializes in developing efficient cloud computing solutions for big data applications. His research focuses on cloud computing optimization, big data management, and distributed systems design. Key areas include: Efficiency improvements for cloud platforms supporting big data workloads Fault-tolerant architectures for distributed systems Social computing and network analysis techniques Service-oriented computing paradigms Recent publications demonstrate strong emphasis on cloud infrastructure optimization, distributed computing frameworks, and AI-assisted system maintenance. Research consistently addresses practical challenges in large-scale data processing across federated environments. Awards and recognitions include: Best student paper award at IEEE ISCC 2016 Best paper award at ICICS 2016 He has advised multiple graduate students including 9 PhD candidates and 2 MS students, with research often supported through cloud computing infrastructure grants. As director of the C2BD Lab, he leads projects investigating volunteered federated clouds, hardware failure prediction systems, and efficient data processing frameworks. The lab collaborates with industry partners on practical cloud computing implementations.
Dr. Min Chen is an Assistant Professor in Computer Science at Vrije Universiteit Amsterdam's Faculty of Science, with joint affiliation to the Network Institute. Her research focuses on security and privacy challenges in machine learning systems. Her work addresses privacy vulnerabilities in AI through techniques like differential privacy and membership inference attack prevention, with applications to graph neural networks, facial recognition, and text-to-image models. Recent publications explore dataset copyright auditing, poisoning attacks against recommender systems, and privacy-preserving graph data publication. Dr. Chen develops practical frameworks including DPMLBench for privacy algorithm evaluation, PrivGraph for graph anonymization, and FACE-AUDITOR for biometric system compliance. Her research advances both theoretical foundations and practical implementations for trustworthy AI systems.
Professor Madhu Chetty is a distinguished academic in Information Technology at Federation University Australia's Institute of Innovation, Science and Sustainability (IISS). He also serves as the Director of AI and ML Stream within the Health Innovation and Transformation Centre (HITC). With over 35 years of tertiary teaching, research, and leadership experience in Australia and overseas, Professor Chetty has held academic positions at the University of Melbourne, Monash University, and the National Institute of Technology, India. His notable visiting appointments include Indian Institute of Technology Bombay, University of Warwick, Jawaharlal Nehru University, and Delft University of Technology. Amity University, India conferred on him a 'Citation and Lifetime Professorship'. Professor Chetty's research focuses on applying Artificial Intelligence (AI), Machine Learning (ML), Large Language Models, and Blockchain to problems in bioinformatics, health, and energy trading. His interdisciplinary work contributes to FedUni's strategic research centers in Health and IT. Key research areas include modeling genetic networks for cardiovascular and eye disease research, mental health applications using AI techniques for analyzing biopsychosocial data, drug repurposing with IBM collaboration, and blockchain algorithms for energy trading funded by the Qatar government. His publication record shows a consistent focus on computational approaches to biological problems, with recent work emphasizing genetic network modeling, mental health applications of AI, and blockchain technology. The research demonstrates a progression from foundational work in protein structure prediction to current applications of AI in healthcare and energy systems, with a strong emphasis on translating computational methods to real-world problems. 2021 Overall Award for Excellence in Graduate Research Supervision 2024 Dean's award for excellence in PhD thesis (awarded to one of his students) 2021 Vice Chancellor's Certificate of Commendation for Excellence in Community Engagement and Impact Professor Chetty has supervised over 22 PhD students to completion and currently supervises 5 PhD students across diverse topics including dementia prediction, drug repurposing, cancer classification, and mental health. His leadership extends to editorial roles for journals, conference organization, and development of publicly available software tools like GRAMP and GlobalMIT for genetic network analysis. He has secured substantial research funding totaling over $1.1 million as lead investigator, including projects funded by NHMRC, Qatar Research, Development and Innovation, and industry partners. As an academic leader, he has served as Deputy Head of School, member of the School Leadership Team, and HDR Coordinator. His professional service includes roles as General Chair of IEEE International Conference and Vice Chair of the IEEE Victorian/Tasmanian Section, demonstrating significant contribution to the broader academic community.
Prof. Dr. Hasan Demirel is a Professor at the Department of Electrical and Electronic Engineering, Eastern Mediterranean University (EMU). He holds a PhD from Imperial College London (1998) and joined EMU in 2000 as Assistant Professor, advancing to full Professor by 2014. He served as Department Chairman (2014–2020) and Acting Rector/Provost (2020–2023). His research focuses on AI and biomedical image processing, with over 75 SCI publications, 100 conference papers, and 8,700 citations. He has supervised 14 PhD and 29 MSc students. Education: PhD in Electrical and Electronic Engineering, Imperial College London (1998) MSc in Electrical Engineering, King's College London (1993) BSc in Electrical and Electronic Engineering, Eastern Mediterranean University (1992) Research Interests: Prof. Demirel specializes in AI-driven biomedical image processing, including applications in cancer diagnosis, Alzheimer’s disease classification, and facial emotion recognition. His work integrates deep learning, image fusion techniques, and signal processing for healthcare solutions. Professional Contributions: He has served as an associate editor for 15+ journals, reviewed conferences, and chaired sessions. Active in IEEE Signal Processing Society and Cyprus Turkish Chamber of Electrical Engineers. Administrative Roles: Acting Rector/Provost, EMU (2020–2023) Chairman, Department of Electrical and Electronic Engineering (2014–2020) Deputy Director, Advanced Technologies Research and Development Institute Member, EMU Technopark Executive Council
Professor Hubert T.H. Chan is an Associate Professor at the Department of Computer Science, University of Hong Kong, and serves as Programme Director for the BEng(CompSc) programme. He holds a PhD from Carnegie Mellon University (2007) and previously worked as a postdoc at Max-Planck-Institut für Informatik in Germany. His research focuses on algorithms, combinatorial optimization, discrete metric spaces, and security & privacy, with applications in graph theory, network analysis, and privacy-preserving mechanisms. Education: PhD in Computer Science, Carnegie Mellon University, 2007 BEng (Computer Science), details not specified in text Research interests emphasize algorithmic design for dynamic systems, privacy in data aggregation, and efficient optimization techniques. His work bridges theoretical computer science with practical applications in network security and distributed systems. Key grants include Hong Kong RGC-funded projects on oblivious data structures, spectral hypergraph analysis, and dynamic metric problems (2012–2018). His publications span top conferences like SODA, FOCS, WWW, and ICML, addressing challenges in clustering, sorting, privacy, and graph decomposition. He oversees research groups exploring hypergraph learning, secure computation, and algorithmic privacy. His lab's work often intersects with real-world systems, emphasizing both theoretical rigor and practical relevance.
George Papadopoulos is a Professor of Informatics at the University of Cyprus. He holds a Ph.D. from the University of East Anglia (1989) and has held academic and research roles at institutions including NCSR Demokritos, Aristotle University of Thessaloniki, and the University of East Anglia. His research focuses on Component-Based Systems, Parallel/Distributed Systems, and Cooperative Information Systems. He leads the Software Engineering and Internet Technologies (SEIT) Laboratory and has contributed to EU-funded projects like SciChallenge and AsTeRICS. Key contributions include development of adaptive middleware architectures, context-aware systems, and educational platforms such as the DIGICOMPASS training course. His work spans academic-service integration, assistive technologies for accessibility, and AI applications in healthcare and dementia care. Over 150 peer-reviewed publications reflect his expertise in software engineering, ubiquitous computing, and e-learning systems. Current roles include editorial board memberships for journals like Computing and IEEE Transactions on Education . Active in professional service through EU projects, he emphasizes interdisciplinary collaboration across computing, education, and healthcare domains.
Isabel Valera is a full Professor in the Department of Computer Science at Saarland University in Saarbrücken, Germany, and an Adjunct Faculty member at the Max Planck Institute for Software Systems (MPI-SWS). She is also a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & ML (Sam) Unit. Department of Computer Science, Saarland University Adjunct Faculty, MPI for Software Systems ELLIS Fellow, Robust ML Program Sam Unit, Saarbrücken AI & ML She obtained her PhD and MSc from Universidad Carlos III de Madrid, followed by postdoctoral research at the University of Cambridge and MPI for Software Systems. She previously led an independent research group at MPI for Intelligent Systems in Tübingen and held the Humboldt Post-Doctoral Fellowship and Minerva Fast Track Fellowship. PhD in Machine Learning, Universidad Carlos III de Madrid, 2014 MSc in Multimedia and Communications, Universidad Carlos III de Madrid, 2012 Telecommunications Engineering, Technical University of Cartagena, 2009 Her research centers on developing machine learning methods that are flexible, robust, interpretable, and fair, particularly for heterogeneous, temporal, and high-stakes decision-making systems. She emphasizes applications in medicine, psychiatry, and social domains such as hiring, bail, and lending. Her methodological contributions include Bayesian nonparametric models, latent feature modeling, and temporal point processes. Her recent publications reflect a strong focus on fairness, robustness, and interpretability in machine learning. Key themes include latent feature modeling for mixed data types, clustering temporal event streams, source separation, and fair classification. Her work bridges theoretical innovation with practical applications across healthcare, social networks, and policy-relevant domains. Scientific awards and recognitions include: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow She has been actively involved in teaching and dissemination, delivering tutorials at NIPS and MLSS on temporal point processes and social network analysis. She has also supervised research assistants and mentored junior researchers. Her research has been supported through prestigious fellowships and institutional affiliations. She leads the development of open-source tools such as GLFM, HDHP, and iFDM, promoting reproducibility and accessibility in machine learning research. She is affiliated with the following labs and research groups: Max Planck Institute for Intelligent Systems (former group leader) Max Planck Institute for Software Systems (adjunct, postdoctoral) ELLIS Sam Unit (Saarbrücken AI & ML) Robust Machine Learning Program (ELLIS)
