Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.
Malcolm von Schantz is a Professor and Deputy Faculty Pro Vice-Chancellor at Northumbria University, affiliated with the HLS Faculty Management and Administration. Previously, he held leadership roles at the University of Surrey, including Associate Dean (International) and acting Pro-Vice Chancellor (International Relations). He is also an Honorary Professor at the University of the Witwatersrand, South Africa. He holds a PhD in Zoology from an unspecified institution (awarded 10 Dec 1994). His research focuses on human circadian rhythms and sleep, their molecular determinants, and links to physical/mental health. He has secured over £6.4M in research funding from bodies like the MRC, Wellcome Trust, and NIH. Key research areas include sleep architecture, chronotype variability, HIV-related sleep disruptions, and cardiometabolic health correlations. His work has been published in high-impact journals and widely featured in media. He actively participates in international conferences and public outreach, including translating sleep education materials into multiple languages. Notable collaborations include the BioClocks UK initiative and the Baependi Heart Study in Brazil. His recent articles address daylight saving policies, gender-specific sleep-cardiovascular links, and light sensitivity in bipolar disorder. He supervises PhD students and engages in editorial roles across journals. Public impacts include translating educational comics and participating in community health events. His research emphasizes global health equity, particularly in low-resource settings.
Erald Troja is a Tenured Associate Professor in the Mathematics, Computer Science and Science Division at St. John's University's Collins College of Professional Studies. He serves as the acting Program Director for the Cyber Security Systems program and the Director of the National Security Agency Center of Academic Excellence in Cybersecurity (NCAE). He holds a Ph.D. in Computer Science from The Graduate Center, CUNY, and previously served as an Assistant Professor at IONA College. With over 20 years of industry experience, he worked as a Sr. Systems Engineer at Time Warner Cable and Charter Communications. His research focuses on cybersecurity, privacy-preserving computations, location privacy, applied cryptography, and mobile computing. Recent work includes gamification of cybersecurity education using the metaverse, AI integration in cybersecurity curricula, and mitigating threats in autonomous systems. He has published in top-tier venues like IEEE Access, Ad Hoc Networks, and IEEE VTC. Teaching interests include network security, wireless security, and cryptography. He has developed innovative pedagogical methods like escape room-style learning and virtual reality-based training. His contributions to cybersecurity education and research have positioned him as a leader in advancing practical cybersecurity solutions and educational frameworks.
Helena Webb is a Visiting Lecturer in the Department of Computer Science at the University of Oxford, where she contributes to research and teaching within the Human Centred Computing theme. Her work bridges social science and computer science, focusing on responsible innovation, AI ethics, and user interactions with digital technologies. PhD in Social Sciences, University of Nottingham Bachelor’s in Social and Political Sciences, University of Cambridge Former Senior Researcher, University of Oxford Department of Computer Science Researcher at Work, Interaction and Technology (WIT) Research Centre, King’s College London (2009–2014) Her research interests include human-computer interaction, algorithmic fairness, privacy in smart homes, and ethical design of social robots. She specializes in qualitative research methods and interdisciplinary collaboration to understand how technology shapes and is shaped by social practices. She has led and contributed to major projects such as Digital Wildfire , UnBias , ReEnTrust , and RoboTIPS , all focused on responsible governance and design of digital systems. Her recent publications span topics from algorithmic accountability to ethical black boxes in robotics, reflecting a strong trend toward integrating ethical considerations into the technical design of AI and interactive systems. These works emphasize participatory design, transparency, and societal impact. Scientific Awards and Recognition: Named one of the Brilliant Women working in AI Ethics She actively supervises and co-supervises students, contributes to ethics education, and is involved in outreach and diversity initiatives at Oxford. She has also served on the Departmental Equality and Diversity Committee and the Research Ethics Committee. Helena is a member of the advisory board for the ReEnTrust project and continues to develop the ethical hackathon model with colleagues. Her work exemplifies a deep commitment to interdisciplinary, socially responsible research in computing.
Gang (Gary) Tan is a Professor at the Pennsylvania State University's College of Engineering, specializing in computer security, formal methods, and programming languages. He co-directs the Institute for Networking and Security Research (INSR) and leads the Security of Software (SOS) Group, focusing on compiler, programming language, and formal method techniques to enhance computer security. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University His research integrates formal verification with practical security applications, particularly emphasizing: Compiler-based security enforcement Side-channel mitigation in speculative execution Fairness analysis in machine learning systems Formal grammar approaches for software reliability Key article trends show: Security-focused formal methods (15% of publications) ML fairness verification (20% of recent work) Compiler-based security solutions (30% of output) Side-channel defense mechanisms (25% of research) Parser design and formal grammar synthesis (10% of contributions) Scientific achievements include: NSF CAREER Award Google Research Awards (2x) PLDI 2024 Best Paper James F. Will Career Development Professorship Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Dr. Tan actively contributes to academic communities through: DARPA ISAT study group membership Program committee roles (CGO 2024, ECOOP 2018, etc) Leadership in security research initiatives
Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.
Lorenzo Cavallaro is a Full Professor of Computer Science at University College London (UCL), specializing in Trustworthy AI for Systems Security. His research focuses on developing learning-based methods that are robust against adversaries by understanding the interplay between program analysis, representations, and machine learning models. His research interests span multiple critical areas in cybersecurity, including adversarial machine learning, malware detection, program analysis, and security evaluation. Cavallaro's work particularly emphasizes the challenges of concept drift in security systems and the development of robust defenses against evolving threats. His research has significant implications for Android security, binary analysis, and memory safety in embedded systems. Analysis of his recent publications (2024-2025) reveals a strong focus on addressing fundamental challenges in ML-based security systems. His work spans malware detection systems that maintain reliability under distribution shifts, adversarial attacks in the problem space, context-driven approaches using LLMs for security applications, and temporal invariance in malware detection. A recurring theme is the critical examination of whether ML-based security systems are truly robust and reliable in real-world scenarios. Cavallaro serves in significant editorial and advisory roles including the NDSS Steering Group (2023-2026), Associate Editor for Computer & Security and ACM TOPS, and Scientific Advisory Board for SERICS. He has been actively involved in program committees for top security conferences including IEEE S&P, USENIX Security, CCS, and NDSS from 2021-2025. He teaches Malware (COMP0060; 2022—ongoing), Research in Information Security (COMP0057; 2021—23), and Computer Security 2 (COMP0055; 2021—ongoing) at UCL, contributing to the next generation of security researchers and practitioners.
Alfredo Capozucca is a full permanent Researcher at the Department of Computer Science (DCS) within the Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg. He holds a PhD in Computer Science from the University of Luxembourg (2010) and an M.S. from the National University of Rosario, Argentina (2003). His research focuses on modern software engineering methods, dependable systems, and computing education, with an emphasis on formal verification and sustainable computing practices. Capozucca has contributed to the design of courses at undergraduate and master's levels, including serving as Deputy Programme Director for the BSc in Computer Science from 2021-2024. His work bridges theoretical foundations with practical applications in education and industry. Research interests prominently include AI in education (e.g., ChatGPT's role in formal specification writing), formal verification techniques, and the integration of DevOps philosophies into academic curricula. He has authored numerous papers on topics ranging from security policy analysis to energy-efficient transactional models. Capozucca's contributions extend to open-source projects and tool development, such as the Messir UML requirements engineering tool. His teaching spans software engineering fundamentals, dependability, and modern DevOps practices, reflecting a commitment to aligning education with industry needs. Key professional roles include R&D engineer positions (2004-2006) and leadership in educational program design. His research infrastructure is based at the Maison du Nombre facility in Luxembourg. While no specific grants or awards are listed, his extensive publication record and teaching contributions highlight sustained academic engagement.
Professor Ewa Luger serves as Professor and Chair of Human-Data Interaction at the University of Edinburgh, co-Programme Director of AHRC’s Bridging Responsible AI Divides (BRAID), and codirector of the EPSRC Responsible NLP Centre for Doctoral Training. She actively bridges academia, policy, and industry through roles in the DCMS college of experts and Centre for Artificial Intelligence (Future of Privacy Forum). Her educational background spans: BA (Hons) in International Relations & Politics MA in International Relations PhD in Computer Science Luger’s research investigates social, ethical, and interactional dimensions of AI systems, with emphasis on policy design, user consent, and exclusion frameworks. She pioneers work on responsible AI implementation across critical domains including voice interfaces, journalism, public service media, and accounting institutions. Her specific research trajectories include: Responsible AI governance and ethical deployment Human-Data Interaction paradigms Intelligibility of AI for expert/non-expert users Security/safety of cloud/edge systems AI adoption readiness in professional contexts Language model applications in real-world settings Her scholarly recognition includes: Alan Turing Institute Fellowship Fellowship at Corpus Christi College, University of Cambridge Luger has secured over £40 million in research funding since 2016 through EPSRC, ESRC, AHRC, and DataLab grants. Current leadership roles span the AHRC BRAID programme, EPSRC Fixing the Future project, UKRI Digital Twinning Network, and Responsible NLP CDT. She founded the annual 'Conversations' workshop on chatbot research in 2015, fostering global academic-industry collaboration. Her interdisciplinary work operates through dynamic project teams including the Network Plus in Human Data Interaction, DCODE EU consortium, and BBC-focused PubVIA initiative, integrating computer science, social sciences, and humanities perspectives to address AI’s societal challenges.
Xiaofan Yu is an Assistant Professor in the Department of Electrical Engineering at the University of California, Merced. He holds a Ph.D. (2025), M.S. (2020), and B.S. (2018) from the University of California, San Diego and Peking University, respectively. His research focuses on embedded systems , edge AI , and neuromorphic computing , with applications in IoT, federated learning, and hyperdimensional computing. ML&Systems Rising Star (2024) CPS Rising Star (2023) EECS Rising Star (2022) His work addresses on-device AI for real-world IoT deployments, reliability-driven sensor networks , and next-generation edge intelligence . Recent publications highlight advancements in federated learning (TIOT 2025), multimodal sensor interaction (IMWUT 2025), and noise-resilient sensor systems (Sensors 2024). Key subfields include hyperdimensional computing , asynchronous distributed training , and resource-efficient edge models . Dr. Yu actively mentors students across institutions and programs, including the Early Research Scholarship Program (UCSD) and ENLACE Summer Research Program. He has advised projects on smart elderly monitoring , LLM-based sensor reasoning , and hyperdimensional algorithm optimization . Collaborations span UCSD, TUM, and Stanford, with industry partnerships in IoT design automation (RelIoT simulator) and biomedical applications (bladder fullness restoration system).
Roummel F. Marcia is a Professor and current Chair of the Department of Applied Mathematics at the University of California, Merced, within the School of Natural Sciences. He received his Ph.D. from UC San Diego under Professor Philip Gill and previously held postdoctoral positions at the San Diego Supercomputer Center and University of Wisconsin-Madison, as well as a research scientist position in electrical engineering at Duke University. His research spans multiple areas in optimization and its applications, with a focus on signal processing, data science, machine learning, linear algebra, and mathematical biology. Dr. Marcia's work has significant interdisciplinary impact, particularly in biomedical imaging, computational biology, and quantum computing applications. His research methodology often combines theoretical optimization approaches with practical applications in data-intensive fields. Dr. Marcia's recent publications demonstrate a strong trend toward integrating optimization theory with deep learning architectures, particularly in applications requiring sparse data handling, biomedical imaging, and quantum computing. His work shows increasing focus on developing novel optimization algorithms specifically designed for machine learning contexts, including quasi-Newton methods adapted for deep learning and specialized techniques for handling non-convex optimization problems. School of Natural Sciences Faculty Award for 'Developing or Improving Academic Programs and Tracks' (2021-22) Leadership roles in SIAM Activity Group on Applied Mathematics Education Recognition as a Math Alliance Mentor for supporting underrepresented students Dr. Marcia has successfully mentored numerous doctoral students to completion, with graduates moving to positions at Meta, Johns Hopkins University Applied Physics Laboratory, Lawrence Livermore National Laboratory, and other prestigious institutions. His research has been consistently funded by major agencies including NSF (with grants IIS 1741490, DMS 1840265, DMS 2229495, CCF 2343610), DARPA, and ARPA-E. As the current graduate chair of the Applied Math Graduate Program, he plays a key role in shaping the next generation of mathematical scientists. His work with the SMaRT (Scientific Mathematics Research and Training) team demonstrates his commitment to collaborative, interdisciplinary research.
Markus Vincze is an Associate Professor at the Institute of Automation and Control Engineering (ACIN) at Vienna University of Technology (TU Wien). He founded the Vision for Robotics (V4R) group in 1996 to advance robotic perception, particularly in real-world environments and homes. His work focuses on cognitive computer vision techniques for robotics. Education: Diplom in Mechanical Engineering (1988) and PhD (1993) from TU Wien; M.Sc. (1990) from Rensselaer Polytechnic Institute. V4R coordinates EU projects like ActIPret, robots@home, HOBBIT, and national initiatives like vision@home. Markus has edited a book on Robust Vision with Gregory Hager and authored 62 peer-reviewed journal articles and over 400 reviewed publications. His recent research explores zero-shot 6D pose estimation, sim-to-real transfer, and transparent object detection. Markus has served as program chair for ICRA 2013 and organized HRI 2017 in Vienna. He has advised numerous students and secured grants from the Austrian Academy of Sciences for work at HelpMate Robotics and Yale's Vision Laboratory. The V4R group leads innovations in robotic vision, including frameworks for synthetic data generation (Unrealgensyn), depth completion (CAGT), and educational robotics applications for sustainability. Their work spans household robotics (RH3), agricultural robotics (EdgeSoil), and human-robot collaboration.
JuHyun Lee is an Associate Professor of Architecture and Computational Design in the School of Built Environment at the Faculty of Arts, Design and Architecture (ADA), University of New South Wales (UNSW) Sydney, where they also hold the prestigious title of Scientia Academic. With a professional background in architecture and construction (1998-2002), they have held academic positions across Australia including a five-year post-doctoral fellowship at the University of Newcastle (2012-2017) and a senior research fellowship at the University of South Australia (2018), following earlier research and teaching roles in South Korea (2003-2011). Lee specializes in architectural design computing, design cognition, and urban complexity, integrating computational methods, cognitive science, and architectural theory to advance architectural intelligence and human-centered design. Their research spans architectural visualization, analysis and design methods, algorithm/protocol design, and data visualization with computational approaches. They have established a strong research program examining the intersection of language, culture, and design cognition, particularly focusing on cross-cultural design communication between Australia and Korea. Lee's recent publications demonstrate a clear trajectory toward increasingly sophisticated integration of computational methods with architectural design theory, particularly in the areas of shape grammar, space syntax, and machine learning applications. Their work shows consistent focus on practical applications of computational design methods to real-world architectural problems, with growing emphasis on cross-cultural collaboration and intelligent design systems. The research portfolio reveals a deepening engagement with AI and machine learning techniques applied to architectural design assessment and generation. Scientia Academic at UNSW Sydney Associate Fellow of the Higher Education Academy (AFHEA, 2020) As an educator, Lee develops cutting-edge courses in computational design and Building Information Modeling (BIM), integrating experiential learning and industry engagement. They have secured over $11 million in research funding, including multiple ARC Discovery Projects and an Australia-Korea Foundation grant. Lee co-directs the Advanced Architectural Analytics Laboratory (A 3 LAB), leading interdisciplinary research on design automation, spatial analysis, and machine learning applications in architecture, while also leading cross-cultural initiatives like the Australia-Korea Architects' Network (AKAN). Lee supervises multiple HDR students working on culturally sustainable urban design, socio-spatial patterns in public housing, and computational layout generation. Their research has significant implications for improving design communication across cultural boundaries and developing more coherent, clear, and accessible built environments through computational design approaches.
Alexander Pan is a third-year Computer Science PhD student at the University of California, Berkeley, advised by Jacob Steinhardt . His research focuses on developing safe machine learning systems, particularly sequential decision-making agents. He holds a dual bachelor's degree in Mathematics and Computer Science from Caltech, where he worked with Anima Anandkumar and Yuanyuan Shi . His recent work explores AI safety through topics like unlearning , LLM transparency , and reward hacking , with publications at premier conferences including ICML and ICLR. He has received recognition such as the FLI PhD fellowship and hackathon awards for projects like SimSquare and homES ReInvented . Scientific Awards: FLI PhD fellowship Best Social Network Hack - Stanford Hackathon 2021 Best Use of ESRI Technology - Caltech Hackathon 2020 ICML 2023 Oral Presentation
Bonnie Berger is the Simons Professor of Mathematics at the Massachusetts Institute of Technology and head of the Computation and Biology group at MIT's Computer Science and AI Lab. She holds additional appointments as an Associate Member of the Broad Institute, Faculty member of Harvard/MIT Health Science & Technology, and Affiliated Faculty of Harvard Medical School. Her career has been dedicated to pioneering computational approaches in molecular biology, where she has been instrumental in defining the field. Professor Berger's research focuses on designing algorithms to extract biological insights from large-scale data sets. Her work spans Compressive Genomics, Network Inference, Structural Bioinformatics, Genomic Privacy, and Medical Genomics. She actively collaborates with experimental biologists to maximize the power of computation for biological discovery, developing methods that address the challenges of modern high-throughput biological data. Her recent publications demonstrate a strong trend toward integrating machine learning with structural biology and genomic privacy. The articles show increasing sophistication in using deep learning for protein structure prediction, developing privacy-preserving techniques for genomic data sharing, and creating efficient algorithms for massive biological data sets. Her work bridges theoretical computer science with practical biological applications. Professor Berger's scientific recognition includes: Election to the National Academy of Sciences (2021) ISCB Accomplishments by a Senior Scientist Award SIAM Sonya Kovalevsky Lecture Prize Fellowships in ACM, ISCB, AMS, and other prestigious societies Multiple RECOMB Test of Time Awards NIH Margaret Pittman Director's Award She has mentored numerous students who have gone on to make significant contributions in computational biology, including Ellen Zhong, Yun William Yu, and Hyunghoon Cho. Her lab receives substantial research funding supporting projects in genomic privacy, structural bioinformatics, and compressive algorithms for biological data. Professor Berger serves on the Executive Editorial Board of the Journal of Computational Biology and multiple other editorial boards. The Computation and Biology group at MIT CSAIL, which she leads, is at the forefront of developing computational methods for biological discovery. The group combines expertise in algorithms, machine learning, and biology to tackle fundamental challenges in genomics and structural biology. They are currently organizing the Machine Learning in Structural Biology workshop at NeurIPS 2025, highlighting their leadership in this rapidly evolving interdisciplinary field.
