Zhan Ma is a Professor and PhD Advisor at the School of Electronic Science and Engineering, Nanjing University. He leads research in Neural Video Communication, Smart Cameras, and Computational Vision Models. His work focuses on end-to-end learning for compression, networking, and hardware-software co-design. Dr. Ma holds a PhD from New York University's Tandon School of Engineering (2010), and prior to his current role, he served as Senior Staff Researcher at Huawei (2013-2015) and Senior Researcher at Samsung (2011-2013). Research highlights include pioneering work in point cloud compression (adopted into IEEE standards) and dual-camera systems for high-resolution video acquisition. His algorithms are deployed in WeChat/WeChat Video for rate-quality optimization and in ISO standards for video complexity indicators. Recent work emphasizes machine learning-driven approaches for image/video compression and adaptive streaming frameworks. Honors include the 2023 IEEE CAS Society Outstanding Young Author Award and multiple best paper awards at IEEE WACV, BMSB, and other venues. He leads the Vision Lab at Nanjing University and collaborates with industry partners on practical implementations of his research.
Ole Winther is a Professor at the Department of Biology, University of Copenhagen, specializing in Computational and RNA Biology. He also holds a joint appointment as Professor at DTU Compute, Technical University of Denmark. His research bridges machine learning, bioinformatics, and natural language processing with applications in biological sequence analysis, transcriptomics, and health informatics. Education: 1998: PhD in Physics, University of Copenhagen 1994: Master of Science in Physics, University of Copenhagen Winther's research focuses on developing advanced machine learning methodologies for biological applications. He has pioneered protein language models for sequence analysis (DeepLoc, SignalP, DeepTMHMM), interpretable deep learning for RNA subcellular localization, and benchmarking frameworks for DNA language models. His work spans latent variable models, variational inference, diffusion models, and novel architectures for deep generative modeling, with increasing emphasis on practical healthcare applications including rare disease diagnosis through findzebra.com and medical question answering with large language models. Scientific Recognition: ELLIS Fellow (2021) Head of ELLIS Copenhagen Unit H-index of 61 (Google Scholar, May 2023) 19,700+ citations (Google Scholar, May 2023) Winther has supervised 25+ PhD students to completion with 7 currently in progress, along with over 100 master's projects. He frequently serves as PhD opponent and committee chairman across European institutions. His research is supported by substantial funding including multiple Novo Nordisk Foundation grants totaling over 60 million DKK for the Center for Basic Machine Learning Research in Life Science and CAZAI projects, plus significant funding from the Danish Independent Research Fund. He leads an active research group developing cutting-edge machine learning approaches for bioinformatics and NLP challenges. Winther co-founded two spin-out companies: findzebra.com (2014, 2018), a search engine for rare diseases, and raffle.ai, an NLP startup for enterprise search. He initiated DTU's popular BSc in AI and Data program and teaches the highly enrolled MSc course in Deep Learning (450+ students) and PhD course in Bayesian Data Analysis.
Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .
Gabriele Liga is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems (SPS) Group. He holds a Marie Curie Eurotech Fellowship focusing on signal shaping techniques for nonlinear optical fiber channels. His academic journey includes a Ph.D. in optical communications from University College London, followed by postdoctoral research in digital signal processing and nonlinearity compensation. Education: B.Sc. in Telecommunications Engineering from Università degli Studi di Palermo (2005), M.Sc. in Telecommunications Engineering from Politecnico di Milano (2011), and a Ph.D. in Optical Communications from University College London (2017). Research Interests: Digital communications, information theory, fiber-optic systems, nonlinearity compensation, channel coding, and multi-user optical communication theory. His work emphasizes achieving transmission limits through signal shaping and advanced signal processing techniques. Projects: Active roles in NESTOR (Next-gen optical networks), QuNEST (quantum communication security), Fun-NOTCH (nonlinear optical channel fundamentals), and SSTOC (signal shaping tailored to optical channels). Collaborations span institutions globally, focusing on optical fiber communication challenges. Awards: 2023 ACP/POEM Best Student Paper Award and 2019 OECC Best Paper Award. Serves as a reviewer for IEEE journals and OSA publications. Labs/Teams: Core member of the SPS Group and involved in interdisciplinary projects blending theory and experimental validation.
Mehrtash Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University. He joined Monash in 2018 after five years at Canberra Research Laboratory-NICTA working with Prof. Richard Hartley and Prof. Fatih Porikli, and earlier at Queensland Research Laboratory-NICTA with Prof. Brian Lovell. His research focuses on machine learning, computer vision, and geometric learning with applications in medical imaging and diffusion models. Recent Research Trends (2025): 3D Gaussian splatting compression, diffusion transformers for visual correspondence, hyperbolic geometry in hierarchical structures, and robust learning from noisy labels. Scientific Awards: Outstanding Reviewer, CVPR'21 Advising Highlights: Mentored students contributing to papers at ICCV'24, CVPR'25, ICLR'25, and Nature Machine Intelligence. Labs & Teams: Collaborates with Data61-CSIRO, ARC, and US Air Force Research Laboratory.
Professor Dinh Phung is the Head of the Department of Data Science & AI at Monash University. His research focuses on machine learning, deep learning, generative AI, and robust AI systems. He has authored over 250 publications, with applications in NLP, computer vision, digital health, and cybersecurity. Phung holds a PhD and BSc(Hons) in Computer Science from Curtin University. He leads major projects like 'Can Machines Unlearn?' and 'Trustworthy Generative AI', funded by the Australian Research Council and the Department of Defence. Education: Doctor of Philosophy, Computer Science, Curtin University (2005) Bachelor of Science (Honours), Computer Science, Curtin University (2001) Research Interests: Machine learning, deep learning, and generative models Optimal transport and Bayesian methods Robust and trustworthy AI Applications in digital health, cybersecurity, and autism research Key Projects (2023–2029): Can Machines Unlearn? (2025–2029): Safety in AI Trustworthy Generative AI (2024–2026): Foundation models Robust Machine Learning via Optimal Transport (2023–2025) Awards and Grants: Australian Research Council grants for AI safety and robustness Department of Defence funding for robust learning systems Collaborations: Global partnerships in AI ethics, cybersecurity, and healthcare. Active advisory roles, including with the Victorian Parliamentary Library.
Susan E. Clark is an Assistant Professor of Physics at Stanford University, where she investigates cosmic magnetic fields, magnetohydrodynamic processes, and the interstellar medium (ISM) through observation, simulation, and analytic theory. Prior to Stanford, she was a NASA Hubble Fellow and postdoctoral member at the Institute for Advanced Study in Princeton, New Jersey, after earning her Ph.D. in Astrophysics from Columbia University (2017) and B.S. in Physics from the University of North Carolina at Chapel Hill (2012), supported by a Morehead-Cain scholarship. Education: Ph.D., Astrophysics, Columbia University (2017) — NSF Graduate Fellow B.S., Physics, University of North Carolina at Chapel Hill (2012) — Morehead-Cain scholarship Her research focuses on Galactic and extragalactic magnetism, interstellar turbulence, star formation, and polarized cosmological foregrounds. She leads an interdisciplinary group tackling these problems via observational data, numerical simulations, and machine learning techniques. Current projects include characterizing magnetic field alignment, modeling 3D ISM structure, and analyzing data from experiments like the Atacama Cosmology Telescope, Simons Observatory, CMB-S4, CCAT-Prime, LiteBIRD, and the Galactic Australian SKA Pathfinder (GASKAP). Recent publications highlight her work on dust filament misalignment, HI morphology for gas phase separation, equipartition magnetic field estimation, and tomographic MHD simulations of galactic magnetic fields. These studies integrate astrophysics, computational methods, and observational astronomy to decode the universe's magnetic structure and dynamics. Scientific Awards: Alfred P. Sloan Research Fellowship NSF Graduate Fellowship NASA Hubble Fellowship Clark co-founded the Pan-Experiment Galactic Science Group and co-directs Stanford's Center for Decoding the Universe, fostering interdisciplinary collaborations. Her lab includes postdocs, graduate students, and undergraduates, with alumni transitioning to roles in academia and industry. Funding comes from NSF, NASA, and the Sloan Foundation.
Dr. David Kelly is a Lecturer in the Department of Sport and Health Science at Dublin City University (DCU), within the Faculty of Science and Health. He teaches across Sport Science, Athletic Therapy, and Physical Activity programs, with a focus on Applied Sport Science. David Kelly obtained his BSc in Sport Science and Health in 2010 and a PhD in Exercise Physiology in 2014, both from DCU. His academic journey reflects a strong foundation in sport and health sciences. His research employs a multidisciplinary approach integrating physiological, nutritional, and biomechanical methodologies. Key interests include Exercise Training Specificity, Energy Demands, Nutritional Knowledge and Dietary Intake of Adolescent Team Sport Players, and Performance Profiling of elite and sub-elite athletes. His work has significant application in Gaelic games and team sports performance. The most recent articles indicate a growing trend toward data-driven athlete monitoring, including machine learning applications for performance prediction, meta-analyses of training methods, physiological comparisons in Gaelic football, athlete recovery perceptions, and nutritional behaviors. These span sports science, physiology, nutrition, and data analytics. David is actively involved in national sports organizations, serving on the Sport Science Working Group for the Gaelic Athletic Association (GAA) and the Player Welfare and Safety Committee for the Gaelic Players Association (GPA). These roles reflect his commitment to athlete welfare and performance optimization. There is no public information on specific research grants or students he has supervised. He is not listed as part-time, retired, or deceased, and there are no mentions of scientific awards in the provided text. He is associated with collaborative research networks and contributes to interdisciplinary projects, particularly in athlete performance and health. His work bridges academic research and practical application in elite sports settings.
Dr. Tim Lynar serves as a Senior Lecturer at the University of New South Wales Canberra within the School of Systems & Computing. With a strong background in both academic research and industry practice, he has established himself as a leading figure in cyber security and computer science. His work bridges theoretical research with practical applications, focusing on innovative solutions for complex computing challenges across multiple domains including IoT security, machine learning applications in cyber defense, and high-performance distributed systems. Dr. Lynar's research interests span a wide spectrum of cyber security applications, with particular emphasis on the application of machine learning techniques to security challenges and the innovative use of epidemiological approaches to understand and combat cyber threats. His work in modeling & simulation, statistical & data analysis, network & systems administration, and high-performance distributed computing demonstrates his commitment to developing comprehensive security frameworks that address evolving threats in digital environments. The interdisciplinary nature of his research connects computer science with biological modeling approaches, creating novel methodologies for understanding security vulnerabilities. Analysis of Dr. Lynar's recent publications reveals a strong trend toward applying advanced machine learning techniques to cyber security challenges, particularly in IoT environments. His work increasingly integrates epidemiological models with security frameworks, creating a unique approach to threat detection and mitigation. The research spans practical applications in network security, drone systems, and AI security, demonstrating both theoretical depth and real-world applicability. A notable pattern is the consistent application of cutting-edge deep learning architectures like Vision Transformers and Variational Autoencoders to solve specific security problems across diverse domains. IBM Master Inventor (2016) Multiple IBM Innovation Awards (2011-2018) Client Value Outstanding Technical Achievement Awards (2015-2016) High Value Patent Awards (2014-2016) Best Article Award – International Journal of Information Systems & Social Change (2010) Multiple research scholarships from 2007-2010 Dr. Lynar's extensive patent portfolio demonstrates significant industry impact, with numerous issued US patents spanning diverse applications from energy efficient supercomputing to vehicle collision avoidance and drone-based microbial analysis. His research has attracted substantial industry collaboration, particularly with IBM, where he received multiple prestigious awards including the IBM Master Inventor designation. The practical applications of his work are evident in the wide range of patented technologies addressing real-world security and optimization challenges across multiple industries. Dr. Lynar's work spans multiple research domains simultaneously, with active projects in cyber security, drone systems, AI safety, and maritime traffic analysis. His research methodology consistently combines theoretical modeling with practical implementation, often leveraging simulation environments to test and validate approaches before real-world deployment. The interdisciplinary nature of his work creates connections between traditionally separate fields, enabling innovative solutions to complex problems.
Luke Rendell is a Professor in Biology at the University of St Andrews, where he holds multiple affiliations including the Scottish Ocean Institute, Sea Mammal Research Unit, Centre for Biological Diversity, Centre for Social Learning and Cognitive Evolution, and the Institute of Behavioural and Neural Sciences. His academic title of Reader (equivalent to Professor in the US system) reflects his senior status within the university. Dr. Rendell's research program centers on the evolution of learning, behavior, and communication in marine mammals. He has conducted extensive work on sperm whale society and ecology, demonstrating how long-lasting social groups use distinctive vocal dialects that appear culturally transmitted. His research spans culture in whales and dolphins, learning in archerfish, human social learning, and evolutionary modeling. Through the East Coast Marine Mammal Acoustic Study (ECOMMAS), he deploys passive listening buoys along the Scottish coastline to monitor windfarm impacts on marine mammals. His recent publications reveal a strong focus on cetacean culture, vocal communication, and conservation. His work increasingly integrates machine learning with traditional bioacoustics to analyze whale communication patterns. There's a clear trend toward examining the conservation implications of cultural transmission in marine mammals, particularly how cultural knowledge affects population resilience to environmental changes. Supervised 9 PhD students to completion since 2009 Principal Investigator on multiple research grants from Scottish Funding Council, Leverhulme Trust, and NERC Co-author of the influential book "The Cultural Lives of Whales and Dolphins" Dr. Rendell maintains active science communication efforts through projects like "Sea Symphonies" that blend marine science with artistic expression. His work contributes to UN Sustainable Development Goals related to life below water and biodiversity conservation.
Shin Yoo is a tenured Full Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), where he leads the Computational Intelligence for Software Engineering (COINSE) research group. He received his PhD from King's College London in 2009 under the supervision of Prof. Mark Harman. Currently, he serves as the General Chair for ASE 2025, which will be held in Seoul, Korea. Professor Yoo earned his PhD in Computer Science from King's College London (2009), following an MSc in Software Engineering with Distinction from the same institution (2006). His academic journey includes positions as Tenured Associate Professor (2021-2025), Associate Professor (2018-2021), and Assistant Professor (2015-2018) at KAIST, as well as Lecturer and Research Associate positions at University College London and King's College London. His research focuses on the intersection of software engineering and artificial intelligence, particularly in search-based software engineering, software testing, automated debugging, SE4AI (Software Engineering for AI), and AI4SE (AI for Software Engineering). Professor Yoo's work bridges theoretical foundations with practical applications, developing innovative techniques for fault localization, test case generation, and debugging using machine learning and genetic programming approaches. His research has significant implications for improving software reliability and development efficiency in both traditional software systems and AI-powered applications. Professor Yoo's recent publications demonstrate a clear trend toward leveraging large language models and deep learning techniques for software engineering tasks. His work spans fault localization, automated debugging, GUI testing, and program analysis, with increasing focus on the challenges and opportunities presented by AI systems. His research shows a consistent evolution from traditional search-based software engineering to AI/ML-enhanced approaches, reflecting the broader trends in the field. ACM SIGEVO HUMIES Silver Medal (2017) for human competitive application of genetic programming to fault localization research IEEE TCSE Most Influential Paper Award (ICST 2024) for work on mutation-based fault localization Professor Yoo has supervised five PhD students to completion, with his former students now holding positions as assistant professors, post-doctoral researchers, and software engineers at institutions including Kyoungpook National University, Max-Planck Institute Security & Privacy, Università della Svizzera Italiana, Roku Korea, and NUS. He currently serves as an associate editor for the Journal of Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology, and has held significant leadership roles in major software engineering conferences including Program Co-chair for SSBSE (2014), ICST (2018), and ICSE NIER track (2020), General Chair for SSBSE (2022), and Testing & Analysis Area Chair for ICSE (2024). As leader of the Computational Intelligence for Software Engineering (COINSE) group at KAIST, Professor Yoo directs research that combines computational intelligence techniques with software engineering challenges. The group focuses on developing novel approaches to software testing, debugging, and analysis using search-based and AI-driven methods. Their work spans both theoretical foundations and practical implementations, with strong connections to industry challenges and applications.
Shashi Kumar is a doctoral student in the Doctoral Program in Electrical Engineering (EDDEE) at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the IDIAP Research Institute (LIDIAP) . He holds the role of Doctoral Assistant at LIDIAP, contributing to research in speech technology and machine learning. His work focuses on advancing automatic speech recognition (ASR), optimal transport frameworks, and variational autoencoders for speech enhancement and signal processing. Research interests include speech recognition systems , multimodal task unification , far-field speech processing , and machine learning applications in signal processing and computer vision. His publications highlight contributions to SLAM-ASR performance analysis, joint speaker change detection, and PCB defect classification using image segmentation techniques. Shashi's research is anchored at the IDIAP Research Institute , where he collaborates on projects involving deep learning, audio signal processing, and speech technology. While no awards or grants are explicitly listed, his work reflects active engagement in international challenges like the Interspeech DiCOVA competition.
Georgios Arvanitidis is an Associate Professor at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Section for Cognitive Systems (CogSys). He has established himself as a leading researcher in geometric machine learning, focusing on the application of differential geometry principles to enhance machine learning models. His work bridges theoretical mathematics with practical applications in artificial intelligence, with particular emphasis on understanding the geometric structure of data manifolds and latent spaces. Dr. Arvanitidis completed his educational journey with a Bachelor's degree from the Department of Informatics at the Aristotle University of Thessaloniki, followed by a Master's degree in Computer Science from Saarland University supported by the Max Planck Institute for Informatics. He earned his PhD at DTU's Cognitive Systems section under the supervision of Søren Hauberg, with additional research experience at Philipp Hennig's Probabilistic Numerics group. Prior to his current position as associate professor, he was a PostDoc at the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. Dr. Arvanitidis's research primarily focuses on differential geometry in machine learning , where he explores how geometric structures can enhance representation learning and statistical modeling. His work in generative models investigates how learning the geometry of data manifolds can improve deep learning architectures. In the domain of deep learning theory , he examines why deep learning models generalize effectively on unseen data, with particular attention to the curvature properties of loss landscapes. His research in approximate Bayesian inference applies geometric principles to improve uncertainty quantification in neural networks. Through his innovative approaches, Dr. Arvanitidis has established himself as a leading researcher in geometric machine learning, contributing to both theoretical foundations and practical applications across various domains including robotics and life sciences. The publication trends of Dr. Arvanitidis reveal a consistent and evolving focus on geometric approaches to machine learning problems. His recent work (2023-2025) demonstrates increasing sophistication in applying Riemannian geometry to deep learning architectures, with particular emphasis on latent space geometry, optimization on manifolds, and geometric interpretations of neural network behavior. A notable pattern is the progression from foundational work on geometric representations to more applied research in areas like robotics and causal inference. His publications span top-tier conferences including NeurIPS, ICML, ICLR, and AISTATS, reflecting the high impact of his research. The interdisciplinary nature of his work is evident in collaborations across mathematics, computer science, and robotics domains, with recent papers addressing challenges in multimodal sampling, safety guarantees for dynamical systems, and counterfactual explanations. Dr. Arvanitidis has received several notable scientific awards and recognitions: Sapere Aude starting grant from the Independent Research Fund Denmark (DFF) GADL funding i-Rase, Pathfinder, and EIC (European Innovation Council) funding Best reviewer award for NeurIPS 2019 Best reviewer award for NeurIPS 2018 Best student paper award at Robotics: Science and Systems (R:SS) 2021 Dr. Arvanitidis actively mentors PhD students and researchers, currently supervising Alejandro Valverde, Johanna Gegenfurtner, and Albert Kjøller Jacobsen. He has previously co-supervised Alison Pouplin's PhD and worked with research assistant Georgios Pantis. His group receives substantial funding through multiple prestigious grants including the Sapere Aude starting grant from the Independent Research Fund Denmark, as well as European Innovation Council funding. He has been instrumental in creating opportunities for students interested in geometric machine learning, offering BSc and MSc thesis projects focused on generative models, deep learning theory, and optimization techniques. Dr. Arvanitidis also contributes significantly to the academic community as a reviewer for top conferences including ICLR and TMLR, and as an area chair for NeurIPS, ICML, AISTATS, and UAI. He co-organized the Machine Learning Summer School 2020 in Tübingen, further demonstrating his commitment to education and community building. Dr. Arvanitidis leads a vibrant research group focused on geometric machine learning within the Cognitive Systems section at DTU. His team includes multiple PhD students working on cutting-edge research at the intersection of differential geometry and artificial intelligence. The group has developed notable software tools, including the "geometric_ml" GitHub repository with over 70 stars, which contains implementations for applying Riemannian geometry in machine learning. His research has practical applications in robotics, where geometric approaches enable more robust motion planning, as evidenced by his work on "Reactive Motion Generation on Learned Riemannian Manifolds" which received a best student paper award. Additionally, his methodologies have found applications in life sciences, as mentioned in his 2022 AISTATS paper. The collaborative nature of his work is evident through extensive partnerships with researchers at institutions including the Max Planck Institute for Intelligent Systems, University of Cambridge, and various European universities. His recent news items indicate active engagement with the academic community through talks, conference presentations, and ongoing supervision of new PhD students joining his group.
Yannis Stylianou is Professor of Speech Processing at University of Crete and Senior Research Scientist at Apple. Former positions include AT&T Labs Research, Bell-Labs, and Toshiba Cambridge Research Lab. IEEE Fellow with PhD from ENST-Paris and over 200 publications. Research spans: Adaptive speech/audio modeling Neural speech synthesis/enhancement Biomedical signal processing Awards include: IEEE Fellowship French Ministry Research Fellowship ENST Graduate Scholarship Recent work focuses on neural TTS architectures, intelligibility enhancement, and multimodal synthesis. Organizes annual International Summer School on Speech Processing.
Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.
