Find Academic Supervisors

• Statistical Learning
• Game Theory
• Decision-Making Algorithms
• +6 more

David Leslie is a Professor of Statistics and Director of Engagement in the Department of Mathematics and Statistics at Lancaster University. He specializes in statistical learning, decision-making algorithms, and game theory, with applications in real-time website optimization through bandit algorithms. Previously, he served as a Senior Lecturer at the University of Bristol and co-directed a cross-disciplinary decision-making research group. He has led significant projects such as the EPSRC/NERC-funded DSNE initiative and contributed to strategic partnerships like ALADDIN (with BAE Systems and EPSRC) and NG-CDI (with BT). His work emphasizes bridging theoretical research with practical industry solutions. Education details are not explicitly provided, but his career trajectory includes prior roles at prestigious institutions. Research interests focus on statistical methodologies, AI-driven decision systems, and environmental data science. He advises multiple PhD students in areas like Statistical AI and Extreme Value Theory. Notable projects include AI Hub, NABS+, and ProbAI, reflecting his engagement with cutting-edge technologies and interdisciplinary collaboration. David actively participates in research groups such as STOR-i Centre for Doctoral Training and the Statistical Artificial Intelligence group. His contact information includes an office at B73 in the PSC building and a direct email address. No specific scientific awards are listed, but his extensive project leadership and contributions to foundational AI research highlight his academic impact.

Alistair BarrosView profile

Professor

• Service Computing
• Enterprise Systems
• Business Process Management
• +5 more

Professor Alistair Barros is Head of School and Academic Program Leader of Service Science at QUT’s School of Information Systems (Faculty of Science). He holds a PhD from the University of Queensland, with extensive ICT experience in academia and industry (e.g., Global Research Leader at SAP AG). His research focuses on enterprise systems design, optimization, and interoperability in cyber-physical environments leveraging cloud, IoT, and blockchain technologies. Key areas include service computing methods (microservices, business process management), model-based systems re-engineering, and distributed service coordination. Education: PhD in Information Systems from the University of Queensland. Research Interests: Enterprise systems modernization, service-oriented architectures, Industry 4.0 applications, and digital transformation in sectors like construction, manufacturing, and supply chains. He has published 180+ peer-reviewed articles (including 6 edited books) and holds 17 US patents. Notable contributions include the BPMN 2.0 standard co-authorship and the 'Workflow Patterns' paper (most cited in BPM). Awards: Test of Time Award (BPM 2015), Wharton Education Award (TeachConnect platform), and impactful industry collaborations (e.g., SAP NetWeaver, Federal Government WPIT project). Grants & Projects: Led ARC, EU FP6, and industry-funded projects. Current focus includes re-engineering legacy systems for microservices, EV charging network design, and adversarial ML in cybersecurity. Labs/Teams: Active in QUT’s Centre for Data Science, focusing on service science and cyber-physical systems integration.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +5 more

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Garth V. CrosbyView profile

Associate Professor

• Internet of Things (IoT)
• Industrial Internet of Things (IIoT)
• Cyber-physical Systems Security
• +5 more

Garth V. Crosby is an Associate Professor at Texas A&M University's Department of Engineering Technology and Industrial Distribution within the College of Engineering. He is also affiliated with the Multidisciplinary Engineering program. His research focuses on IoT and IIoT security, cyber-physical systems, and STEM education innovation. Crosby holds a Ph.D. in Electrical Engineering from Florida International University (2007), an M.S. in Computer Engineering, and a B.S. in Electronics (Applied Physics) from the University of the West Indies. His work spans cybersecurity frameworks for emerging technologies, including blockchain-based federated learning, post-quantum cryptography, and IoT threat mitigation. He has contributed to educational advancements through online lab design and faculty efficacy studies in hybrid learning environments. Crosby's recent publications emphasize securing robotic IoT systems, supply chain blockchain applications, and ransomware evasion techniques using generative AI. His research portfolio includes over 40 peer-reviewed articles in journals like IEEE Transactions and conferences such as ASEE and FiCloud. Key themes include volunteer cloud reliability models (ProTrust), edge computing security, and educational technology evaluation. Crosby's interdisciplinary approach bridges engineering systems with pedagogical innovation, addressing both technical and human factors in modern technological challenges.

Srinivas ShakkottaiView profile

Professor

• Networking
• Wireless Networks
• Reinforcement Learning
• +6 more

Srinivas Shakkottai is a Professor in the Department of Electrical and Computer Engineering (ECE) and affiliated faculty in the Department of Computer Science and Engineering (CSE) at Texas A&M University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2007), followed by a postdoctoral stint at Stanford University. Since 2008, he has been at Texas A&M, advancing through roles from Assistant to Associate Professor before attaining full Professor status. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign (2007) Postdoctoral Associate, Stanford University Research Interests: Focus on algorithms for communication, energy, and transportation networks. Key areas include wireless networks, reinforcement learning, caching/content distribution, multi-agent learning, game theory, networked markets, and systems design. He co-directs the LENS Laboratory and the Texas A&M Initiative on Connected Intelligence (TICI). Key Awards: NSF CAREER Award (2012) Google Faculty Research Award (2010) Defense Threat Reduction Agency Young Investigator Award (2009) Grants & Labs: Leads research projects funded by NSF, including collaborative efforts on EdgeRIC (NextG cellular networks) and Caching systems. Active in labs focused on AI-driven network optimization and intelligent control systems. Lab & Teams: Co-director of LENS Lab and TICI, emphasizing real-time intelligent control, edge computing, and networked intelligence.

Gaurav SharmaView profile

Professor

• Data Analytics
• Cyber Physical Systems
• Signal and Image Processing
• +3 more

Gaurav Sharma is a Professor at the University of Rochester, holding joint appointments in the Department of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology. He is also a Distinguished Researcher at the Goergen Institute for Data Science and Artificial Intelligence. His research focuses on Data Analytics, Cyber Physical Systems, Signal and Image Processing, Computer Vision, Media Security, and Communications. Previously, he served as Director of the Center for Emerging and Innovative Sciences (2008-2010) and held roles at Xerox Research (1996-2003). He earned a PhD in Electrical and Computer Engineering from North Carolina State University (1996), alongside degrees from Indian institutions including the Indian Institute of Science and IIT Roorkee. Education: PhD (NCSU), M.S. Applied Mathematics (NCSU), M.S. Electrical Communication Engineering (IISc), B.Eng (IIT Roorkee) Awards: Fellow of IEEE, SPIE, and IS&T; Elected Sigma Xi member Editorial Roles: Editor-in-Chief of IEEE Transactions on Image Processing (2018-2020) and Journal of Electronic Imaging (2011-2015) Leadership: Chair of IEEE Signal Processing Society committees and SPIE/EI conferences His research spans interdisciplinary areas like medical imaging analytics, cybersecurity in IoT systems, and computational methods for biomedical applications. Recent work includes studies on cardiac mortality patterns, viral protein inhibition, and federated learning for privacy-sensitive healthcare tasks. Dr. Sharma’s contributions to academic governance include roles on IEEE publication committees and strategic planning boards, reflecting his commitment to advancing technical standards and open science practices.

• Cryptography
• Computer Security
• Privacy
• +3 more

Gene Tsudik is the Peter & Lois Griffin Professor at the University of California, Irvine (UCI), affiliated with the School of Information and Computer Sciences. His research focuses on cryptography, computer security, privacy, and applied cryptographic protocols. He has authored over 300 publications in top-tier venues, addressing challenges in secure communication, key management, privacy-preserving technologies, and network security. Key research areas include group key agreement, secure data possession, privacy-preserving protocols, and mobile agent systems. His work has influenced foundational aspects of computational grid security, cloud storage integrity, and post-quantum cryptography. Collaborations include prominent institutions like KAIST, University of Surrey, and NYIT. His publications span topics from theoretical cryptographic proofs to practical system implementations, reflecting a balance between academic rigor and real-world applicability.

Xiaoyu XiaView profile

Lecturer

• System Privacy and Security
• AI Privacy
• Parallel and Distributed Computing
• +4 more

Dr. Xiaoyu Xia is a Lecturer (equivalent to Assistant Professor in North America) in Cybersecurity & Software Systems at RMIT University's School of Computing Technologies. He received his PhD with the prestigious Alfred Deakin Medal from Deakin University, Australia, and has established himself as a leading researcher in distributed systems and cybersecurity with over 50 peer-reviewed publications in top-tier venues including IEEE S&P, ACM WWW, and IEEE Transactions. Dr. Xia's research spans critical areas at the intersection of computing and security: System Privacy and Security Distributed Systems and Edge Computing AI Privacy and Machine Learning Systems Sustainable Computing Cybersecurity and Privacy-Preserving Technologies His recent work demonstrates a clear trajectory toward developing practical privacy-preserving frameworks for emerging technologies, particularly in edge computing environments and large language models. Dr. Xia has made significant contributions to machine unlearning, secure data management in distributed systems, and energy-efficient edge computing solutions that balance performance with sustainability concerns. Dr. Xia has received notable recognition for his scholarly impact: World's Top 2% Scientists by Stanford University (2022-2024) Alfred Deakin Medal for PhD research excellence (2021) Teaching Excellence Award from Swinburne University of Technology (2021) As an active researcher, Dr. Xia currently leads multiple funded projects including an ARC Discovery Project grant worth over $500,000 for developing privacy-aware intelligent digital twins for secure critical infrastructures. He is open to supervising motivated PhD students with interests in system security and privacy, and distributed ML systems. Dr. Xia serves the academic community through editorial roles as Associate Editor for IEEE Transactions on Dependable and Secure Computing and as a Review Board Member for IEEE Transactions on Parallel and Distributed Systems, and regularly participates in program committees for major conferences including ACM WWW and IEEE ICDCS.

• Resilience in 5G
• Distributed Artificial Intelligence (Edge AI)
• Information assessment
• +3 more

Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)

Paul de VriezeView profile

Academic

• Enterprise Information Systems
• Business Process Management
• Digital Twins
• +12 more

Dr. Paul de Vrieze is a Principal Academic in Web Systems and Technologies at Bournemouth University's Faculty of Science and Technology, Department of Computing and Informatics. He is the coordinator of the EU H2020 FIRST project and leads research in enterprise information systems, digital twins, and Industry 4.0. His work contributes to the UN Sustainable Development Goals, particularly Quality Education, Decent Work and Economic Growth, Industry Innovation and Infrastructure, and Reduced Inequalities. Education: PhD in Information Systems, Radboud University Nijmegen, 2006 MSc in Information Systems Management, Tilburg University, 2002 His research focuses on the design and implementation of complex enterprise information systems, especially in supporting and automating business processes, with a strong emphasis on cross-organizational and collaborative systems. He investigates resilience, interoperability, and service-oriented architectures in virtual factories and digital twins. His recent work applies these concepts in industrial contexts, particularly under Industry 4.0. The 15 most recent publications show a strong trend toward digital twin technologies, predictive maintenance, federated simulation, and compliance in collaborative networks. These works are deeply rooted in computer science and industrial engineering, with applications in smart manufacturing, supply chain resilience, and sustainable production. Scientific Awards and Recognitions: Panel Member, Guardian round table on cloud computing, 2012 Dr. de Vrieze has supervised multiple PhD students to completion, including Olaolu Sofela, John-Paul Kasse, Oluwetoyin Fakorede, Go Sang, and Rushan Arshad. He has secured grants from the European Commission (H2020, Horizon Europe), National Natural Science Foundation of China, British Council, and internal Fusion Funding. His current major grant is the Sustainable Production of High Entropy Alloys from Secondary Metals (Horizon Europe MSCA, starting 2026). Labs and Research Teams: He is central to the EU H2020 FIRST project team, which focuses on virtual factories and interoperation supporting business innovation. He collaborates extensively with Prof. Liang Xu and researchers across Europe and China. He is also involved in the Service Computing Research group at Bournemouth University.

• Artificial Intelligence
• Constraint Programming
• Computational Social Choice
• +6 more

Toby Walsh is a Scientia Professor of Artificial Intelligence at the University of New South Wales (UNSW), holding multiple prestigious positions including Laureate Fellow, research group leader at Data61, adjunct professor at QUT, external Professor at Uppsala University, honorary fellow at Edinburgh University, and Associate Member of the Australian Human Rights Institute at UNSW. His research spans the breadth of artificial intelligence with particular focus on constraint programming, computational social choice, and fair division. Professor Walsh has made significant contributions to the theoretical foundations of AI, especially in areas related to preference representation, voting systems, and mechanism design. His work bridges theoretical computer science with practical applications in social choice and resource allocation problems. In recent years, he has expanded his research to address pressing ethical issues in AI, including privacy concerns, facial recognition technology, and AI's impact on intellectual property law. Walsh's publication record demonstrates consistent high-impact contributions across decades, with recent work showing increasing attention to societal implications of AI technologies. His research portfolio includes both theoretical advances in constraint satisfaction and practical applications in fair division algorithms, reflecting his ability to connect foundational research with real-world problems. NSW Premier's Prize for Excellence in Engineering and ICT Humboldt Award Research Excellence Award of the Association for Constraint Programming IJCAI Donald E. Walker Distinguished Service Award Professor Walsh has served the academic community extensively through editorial roles as Editor-in-Chief of the Journal of Artificial Intelligence Research and AI Communications, and on the editorial boards of the Journal of the ACM, Journal of Automated Reasoning, and Constraints journal. He has been actively involved in major AI conferences as Program and Conference Chair for Constraint Programming, Automated Reasoning, and Artificial Intelligence venues. His leadership extends to professional organizations where he has served as Secretary of the Association for Constraint Programming and Editor of CP News. As research group leader at Data61, Walsh oversees a team focused on advancing AI research with practical applications. His group has contributed significantly to constraint programming and computational social choice, developing algorithms and theoretical frameworks that have influenced both academic research and practical implementations in resource allocation systems.

• Neuroscience
• Physics of Complex Systems
• Computational Neuroscience
• +5 more

Prof. Viola Priesemann is a physicist and neuroscientist at the Max Planck Institute for Dynamics and Self-organization and University of Göttingen (W3 Professorship, 20% position). Her research spans self-organization in adaptive networks across neural and societal scales, with applications to neural criticality , information processing , and COVID-19 pandemic dynamics . She leads an independent Max Planck Research Group since 2017 and has held affiliations with Caltech , MPI Brain Research , and Bernstein Center Göttingen . Her research combines statistical physics , information theory , and non-equilibrium systems to study: Self-organized criticality in brain networks Subsampling theory for large-scale systems Neural basis of curiosity and learning Test-trace-isolation strategies for epidemics Information spread in social networks Design of energy-efficient AI architectures Key scientific awards include: Communitas Award (Max Planck Society, 2021) Science award of Lower-Saxony (2021) Medaille für naturwissenschaftliche Publizistik (DPG, 2021) Young Scientist Award (DPG, 2024) Her work on cross-scale information flow has influenced both neuroscience and public health policy , including advisory roles in the German government's COVID-19 expert panel .

• Distributed Computing
• Federated Learning
• Cloud Computing
• +12 more

Salman Zubair Toor is a researcher at Uppsala University, Sweden, specializing in distributed computing, federated learning, and cloud/edge infrastructure optimization. His work spans resource scheduling, data streaming, and secure anomaly detection.

Rafael Pereira PiresView profile

Lecturer

• Distributed Systems
• Machine Learning
• Privacy-Preserving Technologies
• +3 more

Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .

• Logistics Operations
• Smart Manufacturing
• Industrial Engineering
• +6 more

Maharshi Dhada is a Research Fellow at Darwin College, University of Cambridge, focusing on logistics operations and smart manufacturing. His work bridges industrial engineering with advanced statistical modeling. University: University of Cambridge College: Darwin College Rank: Research Fellow His research integrates hierarchical statistical models and machine learning for predictive maintenance in engineering fleets, smart logistics , and public health applications like SARS-CoV-2 policy analysis. Recent publications highlight collaborations in radio access networks , road condition prediction , and low-cost monitoring solutions . Key article trends show a focus on Weibull models , multi-agent systems , and federated learning for cross-system knowledge transfer. His work spans applications from truck fleet survival analysis to genomic epidemiology .