Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.
Panruo Wu is an Associate Professor in the Department of Computer Science at the University of Houston (UH). He joined UH in 2018 as an Assistant Professor, transitioning to his current rank. His research focuses on high-performance computing, numerical algorithms, parallel and distributed systems, and fault tolerance. He holds a Ph.D. in Computer Science from the University of California, Riverside (2016), advised by Zizhong Chen, and a B.S. in Mathematics from the University of Science and Technology of China (USTC). Research Interests: His work spans high-performance computing, numerical linear algebra, GPU acceleration, fault-tolerant systems, and scalable machine learning. Key projects include LATER (Linear Algebra on Tensor Cores), LibKernel (a scalable kernel machine framework), and Wukong (a serverless parallel computing framework). He emphasizes energy-efficient and hardware-aware algorithms. Publications: Dr. Wu's recent work includes advancements in QR factorization using tensor cores, symmetric eigenvalue decomposition optimizations, and fault-tolerant algorithms for heterogeneous systems. His research often addresses computational challenges in big data and exascale computing. Awards & Grants: Received NSF Grant No. 2146509. His work on high-accuracy matrix computations was a Best Paper Nominee at HPDC'20. He has authored over 30 peer-reviewed publications in top venues like SC, ICS, and IEEE TPDS. Students & Advising: Advises PhD students including Shaoshuai Zhang, Ruchi Shah, Benjamin Carver, and Ao Wang. His students have contributed to projects like LibKernel and fault-tolerant linear algebra libraries. Labs & Collaborations: Leads research in UH's high-performance computing group, collaborating with institutions like Jack Dongarra's Innovative Computing Lab (University of Tennessee) and industry partners on exascale computing initiatives.
Prof. Torsten Wolfgang Kuhlen serves as a Universitätsprofessor at RWTH Aachen University, leading the Teaching and Research Area for Virtual Reality and Immersive Visualization within the Department of Computer Science. He is affiliated with Chair of Computer Science 12 (High Performance Computing), the Visual Computing Institute, and remains an integral part of the RWTH IT Center where his research group operates one of the world's largest Virtual Reality laboratories including the 30 sqm aixCAVE visualization chamber. The group maintains strong connections with Computational Science & Engineering Division, National High Performance Computing Center for Computational Engineering Science (NHR4CES), and VR in Science and Industry Network NRW e.V. Prof. Kuhlen's research spans virtual reality, immersive visualization, and multimodal 3D user interfaces with applications across simulation science, production technology, neuroscience, and medicine. His work combines basic research on advanced methods and algorithms with interdisciplinary collaborations involving RWTH Aachen institutes, Forschungszentrum Jülich, and industry partners. Recent publications demonstrate strong focus on audiovisual perception, immersive analytics, collaborative virtual environments, and practical VR applications in education and manufacturing. His research group has produced significant work on listening effort in virtual environments, immersive authoring techniques, and VR applications for scientific visualization. Notable projects include VRScenarioBuilder for automated vehicle testing and applications in monitoring additive manufacturing processes. The group actively participates in major conferences including IEEE VIS and EuroVis, with several award-winning contributions. Prof. Kuhlen has advised PhD students including Martin Bellgardt who recently completed his doctoral degree on "Increasing Immersion in Machine Learning Pipelines for Mechanical Engineering". The research group maintains state-of-the-art VR infrastructure including the aixCAVE facility which is open to all RWTH research groups.
Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.
Prof. Michael Beigl is a faculty member at Karlsruhe Institute of Technology (KIT), serving as Professor of Pervasive Computing Systems (PCS) and head of the Telecooperation Office (TECO). He is a spokesperson for the KIT Center for Health Technologies (KITHealthTech) and coordinator of the Smart Data Innovation Lab (SDIL), a federally funded big data center. His work focuses on developing wearable sensor systems and AI-driven diagnostics for healthcare and industrial applications, collaborating across disciplines with medical experts and technology partners. Research interests include digital health technologies (e.g., gas sensors in headbands for respiratory monitoring), ubiquitous computing for remote patient tracking, and Smart Data solutions in medicine, energy, and Industry 4.0. His team integrates machine learning for optimized diagnostics and real-time data analysis.
Taylor Sparks is a Professor of Materials Science and Engineering at the University of Utah, where he also serves as Director of Graduate Affairs for the John and Marcia Price College of Engineering. He holds a PhD in Applied Physics from Harvard University, an MS in Materials from the University of California, Santa Barbara, and a BS in Materials Science & Engineering from the University of Utah. His research focuses on advancing materials discovery using machine learning to streamline and optimize material design, with applications in energy materials, dental materials, and sustainable engineering. His work integrates big data and materials informatics to explore new synthetic techniques, structure-property relationships, and sustainable materials that balance performance with economic factors. The Sparks Research Group has secured funding from agencies including DOE, NSF, DOD, and various industry partners. Sparks' recent research output demonstrates a strong trend toward leveraging artificial intelligence and machine learning to accelerate materials discovery, with particular emphasis on large language models for materials science, Bayesian optimization for experimental design, and novel approaches to crystal structure prediction. His work bridges the gap between theoretical predictions and experimental validation in materials science. NSF CAREER Award Royal Society Wolfson Visiting Fellow Acta Materialia Outstanding Reviewer Award for 2020 Honorary Outstanding Faculty Teaching Award of 2020-2021 Materials Science & Engineering Department Research Award for 2023 John G. Francis Prize for Undergraduate Student Mentoring Sparks has advised numerous graduate students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, DOE, DOD, Army Research Office, and industry partners. His group has developed innovative tools including the Materialism Podcast, a materials science YouTube channel, and the Honegumi interface for Bayesian optimization, demonstrating his commitment to both research excellence and science communication. The Sparks Research Group operates multiple laboratories focused on materials characterization, synthesis, and informatics. They collaborate extensively with other institutions globally, host visiting researchers, and run outreach initiatives including the Materialism Podcast and YouTube channel to make materials science more accessible to broader audiences.
Véronique Perdereau is a Full Professor at Sorbonne Université's Institute of Intelligent Systems and Robotics (ISIR) , specializing in robotics and dexterous manipulation. She leads the ASIMOV research team and serves as a principal investigator for multiple European projects, including SOFTMANBOT, INDEX, and CORSMAL, focusing on tactile feedback, multimodal control, and human-robot interaction. Role: Full Professor (2024) Email: veronique.perdereau@sorbonne-universite.fr Office: H13, Campus Pierre et Marie Curie, Paris Research Focus Perdereau's work centers on tactile-driven robotic control systems, human-inspired manipulation strategies, and multimodal fusion for industrial automation. Her research bridges theoretical modeling (e.g., Cosserat mechanics, dual quaternions) with practical applications in manufacturing sectors. Key Themes: Tactile Feedback Systems Dexterous In-Hand Manipulation Human Motion Data Integration Autonomous Grasp Stability Flexible Object Handling Reproducibility in Robotic Experiments Notable Projects She has spearheaded projects like: SOFTMANBOT (2019–2023): Advanced robotic technology for handling soft materials in manufacturing. INDEX (2019–2022): Developing motor action dictionaries for robotic hands. CORSMAL (2019–2022): Multimodal fusion for collaborative object recognition. Scientific Leadership Perdereau has led work packages in 10+ international collaborations with institutions across Europe, including Decathlon, IIT, and Queen Mary University. Her research emphasizes safety, ethics, and industry 4.0 applications.
Fangzhou LU is an Assistant Professor of Finance at the HKU Business School, The University of Hong Kong, since 2020. His research focuses on fintech, cryptocurrency, behavioral finance, and China's economic development. He received his academic training at prestigious institutions: PhD and MS in Financial Economics from Massachusetts Institute of Technology (MIT), 2020 BS in Business from New York University, Stern School of Business, 2014 Dr. Lu's research interests encompass Fintech, Cryptocurrency, Behavioral Finance, Entrepreneurial Finance, Household Finance, Emerging Markets, consumption, fiscal stimulus, bond markets, and China's economic development. His work often leverages large datasets from China to examine how technological innovations and government policies impact financial markets and firm behavior, particularly during crises like the COVID-19 pandemic. His recent publications demonstrate a strong trend toward applied research in emerging markets, with significant contributions on SPAC IPOs, government subsidy effectiveness for failing firms, and stock market informativeness in China. These works appear in top finance journals including the Journal of Financial Economics and Journal of Financial and Quantitative Analysis, highlighting his expertise at the intersection of finance, technology, and policy analysis.
Mark Andrejevic is a Professor in the School of Media, Film, and Journalism at Monash University. He specializes in the socio-cultural implications of digital surveillance, data mining, and automated decision-making. His research spans Big Data ethics, privacy regulation, and the impact of technology on democracy. He leads major projects like the ARC Centre of Excellence for Automated Decision-Making and Society, focusing on AI’s societal implications. Andrejevic has been awarded Fellow of the Australian Academy of Humanities and the Nancy Baym Book Award. His work intersects with UN Sustainable Development Goals addressing justice and innovation. Research interests include automated surveillance systems, digital privacy policies, and the ethical governance of emerging technologies. He collaborates on projects such as facial recognition technology ethics and pandemic-era surveillance practices. His recent publications analyze platform accountability, algorithmic bias, and the societal effects of workplace monitoring technologies. He actively contributes to public discourse via media engagements and policy-focused talks on topics like facial recognition in education and pandemic-era data collection. Projects: ARC QEII Fellowship ($390k, 2010–2014), Australian Public Attitudes to AI, and facial recognition ethics studies. Grants: Over $390k from ARC for privacy research, plus ongoing funding through collaborative initiatives. Awards: Australian Academy of Humanities Fellowship (2020), Nancy Baym Book Award (2014). He supervises graduate students in Digital Media, Surveillance Studies, and Critical Theory. His interdisciplinary work bridges academia, policy, and industry to address technology’s societal challenges while advocating for democratic values.
Marios Polycarpou is a Professor of Electrical and Computer Engineering and Director of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He holds honorary positions at Imperial College London and is a member of Academia Europaea. His expertise spans intelligent systems, adaptive control, machine learning, and critical infrastructure. Education: B.A. Computer Science (Rice University, 1987) B.Sc. Electrical Engineering (Rice University, 1987) M.S. Electrical Engineering (University of Southern California, 1989) Ph.D. Electrical Engineering (University of Southern California, 1992) Research Focus: Polycarpou’s work emphasizes fault diagnosis in cyber-physical systems, water distribution networks, and adaptive control. He pioneers digital twin technologies for infrastructure resilience and develops algorithms for real-time anomaly detection and system optimization. Article Trends: His recent publications address adaptive control strategies, cybersecurity in networked systems, and AI-driven solutions for water management. Key themes include distributed control, event-triggered mechanisms, and transformer-based anomaly localization. Awards: 2023 IEEE Frank Rosenblatt Technical Field Award 2016 IEEE Neural Networks Pioneer Award Fellow of IEEE and IFAC Grants & Leadership: He secured prestigious grants including ERC Advanced and Synergy Grants. He led KIOS CoE’s Horizon 2020 projects and served as IEEE Computational Intelligence Society President (2012–2013). Labs & Teams: Directs the KIOS CoE, a hub for AI in critical infrastructure. Collaborates on projects like ERC Water-Futures, focusing on long-term water system transitions and contamination mitigation.
Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.
Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.
Xia Ben Hu is a Professor in the Department of Computer Science at Rice University's Brown School of Engineering. He leads research in automated and interpretable machine learning algorithms with applications across social informatics, health informatics, and information security. His work has resulted in widely adopted systems including AutoKeras, TODS, and RLCard. Education: PhD from Arizona State University (supervised by Dr. Huan Liu) Master and Bachelor degrees from Beihang University Prof. Hu's research focuses on developing automated and interpretable machine learning algorithms for large-scale, networked, dynamic and sparse data. His work spans automated machine learning (AutoML), deep learning, fairness in AI, time series analysis, and interpretable AI. He has made significant contributions to neural architecture search, collaborative filtering, anomaly detection, and reinforcement learning in imperfect information games. His publication record shows a clear progression from foundational work in network embedding and collaborative filtering (2017) to more recent work on LLM optimization, quantization, and extending context windows (2024). A consistent thread throughout his research is the focus on making complex machine learning systems more accessible, efficient, and interpretable. Selected Awards: ACM SIGKDD Rising Star Award (2021) NSF CAREER Award (2018) Teaching + Research Excellence Award, Rice University (2023) Multiple Best Paper Awards at top venues including ICML, CIKM, and AMIA Prof. Hu has successfully mentored numerous graduate students, with recent graduates securing tenure-track positions at major universities. His research is generously supported by federal agencies including DARPA (XAI, D3M, NGS2), NSF (CAREER, III, SaTC), NIH, and industrial sponsors such as Adobe, Apple, Google, LinkedIn, and JP Morgan. He has served as General Co-Chair for WSDM 2020 and ICHI 2023, and Program Chair for AIHC 2024. He leads the DATA Lab at Rice University, which develops open-source systems for automated machine learning and reinforcement learning. The lab's AutoKeras system has over 8,000 GitHub stars and 1,000 forks, and their work has been integrated into TensorFlow, Apple production systems, and Bing production systems.
Sri Kolla, Ph.D. is a tenured Professor in the Department of Electronics and Computer Engineering Technology at Bowling Green State University (BGSU) , where he has served since August 2002. He also served as a Visiting Professor at the Indian Institute of Science (2017) and as a Fulbright Research Scholar (2008-2009). His academic career spans faculty roles at Penn State University, University of Toledo, and consortium graduate faculty at Indiana State University. Education: Ph.D. in Electrical Engineering and Computer Science (University of Toledo, 1989) M.S. in Electrical and Computer Engineering (University of Saskatchewan, 1986) M.E. in Electrical Engineering (Indian Institute of Science, 1983) B.E. in Electrical Engineering (Andhra University, 1981) Research Interests: Dr. Kolla specializes in Electrical Power and Energy Systems with Smart Grid applications, Control Systems for networked environments, and Machine Learning techniques for power system diagnostics. His work focuses on fault detection in microgrids using LSTM networks, stability robustness of discrete-time systems, and multi-agent protection schemes for power infrastructure. Scientific Contributions: Developed robust control frameworks for microgrid systems under parameter variations (2023-2025) Pioneered AI-based fault identification in induction motors and transformers (1995-2000) Advanced networked control system designs addressing time delays (2002-2012) Published 82+ technical articles in IEEE, ISA Transactions, and conference proceedings Honors and Recognition: Recipient of the Fulbright-Nehru Academic and Professional Excellence Award and Whiteford Scholarship . Senior member of IEEE and ISA , with listings in Marquis Who’s Who and fellowships in The Institute of Engineers (India) .
Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.
