Find Academic Supervisors

Giuseppe BelgioiosoView profile

Assistant Professor

• Control Systems
• Game Theory
• Optimization Algorithms
• +4 more

Giuseppe Belgioioso is an Assistant Professor at the Division of Decision and Control Systems within the Digital Futures Faculty at KTH Royal Institute of Technology. His research focuses on control systems, game theory, and optimization algorithms applied to complex systems such as smart grids, data centers, and multi-agent networks. He collaborates with Stockholm University and RISE Research Institutes of Sweden through the Digital Futures initiative. His work bridges theoretical foundations with practical applications in energy markets, networked systems, and algorithmic fairness. Belgioioso’s research interests include distributed optimization, equilibrium seeking in games, and data-driven control methodologies. He explores carbon-aware computing strategies for sustainable data centers and designs algorithms to mitigate polarization in recommendation systems. His contributions often integrate game-theoretic models with feedback control principles to address real-world challenges in energy systems, traffic routing, and peer-to-peer energy trading. His recent work emphasizes online learning and model-free approaches for dynamic systems, with applications to multi-area power grids and autonomous decision-making. He has published extensively on topics such as hypergradient-based optimization, decentralized equilibrium seeking, and stability analysis of distributed algorithms. His research is characterized by a strong interdisciplinary approach, combining systems theory with practical engineering solutions.

Luis GerhorstView profile

Researcher

• Distributed Systems
• Operating Systems
• Cyber-Physical Systems
• +3 more

Luis Gerhorst is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He focuses on systems software, embedded systems, energy-efficient computing, and security mitigations against transient execution attacks like Spectre. His work spans kernel-level optimizations, carbon-aware cloud systems, and embedded system resilience. Education: Completed Bachelor's and Master's theses in system software at FAU, focusing on system-call aggregation and Linux kernel interrupt handling. Research Interests: His primary areas include operating systems, distributed systems, and energy-aware resource management. Notable contributions include the AnyCall system-call aggregation framework and VeriFence , a Spectre defense mechanism for BPF programs. He also explores carbon footprint modeling in cloud environments through projects like carbond . Publications: Recent work addresses energy-efficient embedded systems (vNV-Heap), power-failure resilient network stacks (PfIP), and reverse-engineering Wi-Fi drivers for energy analysis. His research often bridges hardware-software co-design and environmental sustainability. Grants/Advising: Supervised over 10+ theses on topics like carbon-aware timers, BPF sandboxing, and Rust-based alternatives to BPF. Active in open-source projects like Linux kernel contributions and GitHub repositories for systems research. Labs/Teams: Member of Lehrstuhl für Informatik 4, collaborating on projects related to system software and embedded systems resilience. Maintains active GitLab/ GitHub repositories for research tools and prototypes.

Yi MaView profile

Professor

• Semantic Communication
• Machine Learning for RF Design
• MIMO Technology
• +6 more

Prof. Yi Ma is a Chair Professor at the Institute for Communication Systems (ICS) and the Home of the 6G Innovation Centre (6GIC) at the University of Surrey. He holds a Professorial rank in the School of Computer Science and Electronic Engineering. His research focuses on semantic/effective communication, machine learning for RF and physical layer design, MIMO technology, and opportunistic networking. Current PhD students include Yupeng Zheng, Ashkan Jafari, Zohre M. Bakhsh, and Mehdi Tafazolli. Research interests span scalable quantum-based wireless communications, distributed hybrid beamforming, and satellite communication systems. Recent publications highlight advancements in SA-MIMO, non-uniform quantization, and distributed MIMO-LEO networks. Prof. Ma teaches courses such as EEEM017 and EEE3006, and serves as an EEE examination officer. His work integrates theoretical contributions with practical applications, emphasizing future wireless systems and sensing technologies. Collaborations with industry and academia, including projects on 6G innovation, underscore his commitment to cutting-edge research.

Ramesh KarriView profile

Professor

• Hardware Cybersecurity
• Trustworthy Integrated Circuits
• Cyber-Physical Systems Security
• +6 more

Ramesh Karri is a Professor and Chair of the Electrical and Computer Engineering Department at New York University Tandon School of Engineering. He co-founded the NYU Center for Cyber Security (CCS) in 2009 and co-directed it from 2016-2024. He is also a Fellow of the IEEE and has led initiatives such as the Embedded Systems Challenge (ESC), a red-blue team cybersecurity competition. Education: Ph.D. in Computer Science and Engineering, University of California, San Diego (1993) B.E. in Electrical and Computer Engineering, Andhra University (1985) Research Focus: His work centers on hardware cybersecurity, including trustworthy integrated circuits, cyber-physical systems, nano-enabled security, and biochip security. He pioneered security-aware CAD tools and has developed metrics for hardware trojan detection through initiatives like the Trust-Hub. Awards & Leadership: Recipient of the Humboldt Fellowship and NSF CAREER Award Editor-in-Chief of ACM Journal of Emerging Computing Technologies Leadership roles at IEEE conferences (ICCD, HOST, DFTS) Grants & Labs: Directs the Center for Advanced Technology in Telecommunications (CATT) and NYU CCS. His labs focus on additive manufacturing security, digital microfluidic systems, and AI-driven hardware design.

Lisa DicksonView profile

Associate Professor

• Cybercrime
• Privacy
• Data Protection
• +3 more

Lisa Dickson serves as Head of Kent Law School and Reader in Law at the University of Kent, specializing in cybercrime, privacy, and forensic evidence in criminal trials. Her teaching spans Privacy, Data Protection, Cybersecurity Law, and Criminal Law modules. She holds an LLB, LLM, and PGCHE from the University of Southampton, establishing her foundation in legal theory and pedagogy. Dickson's research interrogates privacy through cultural and technological lenses, examining legal objects like the Mashrabiya screen and blockchain systems. Her work bridges law with cybersecurity, emphasizing GDPR compliance and privacy-aware design in digital ecosystems. As a Kent Centre for Cyber Security member (GCHQ-recognised Academic Centre of Excellence), she integrates technical and legal perspectives to address evolving cyber threats. Her publication trajectory reveals sustained focus on privacy-law intersections, particularly in blockchain communications (2020), cultural privacy metaphors (2017), and forensic evidence admissibility (2013). These works collectively critique procedural gaps in regulating emerging technologies while advocating for culturally responsive privacy frameworks. Dickson actively supervises research in privacy, data protection, and cybercrime contexts. She contributes to the EPSRC-funded PriVELT project developing privacy-aware data management for leisure travellers, demonstrating commitment to applied, interdisciplinary solutions. She operates within the Kent Centre for Cyber Security and NeCS CESTN Cyber Security Training Network, collaborating on national cybersecurity initiatives that shape policy and practitioner training through GCHQ-recognised research excellence.

• Network Architecture
• SDN/NFV Performance Evaluation
• QoE-Centric Network Management
• +4 more

Thomas Erich Zinner is a Professor at the Department of Information Security and Communication Technology (IIK) at the Norwegian University of Science and Technology (NTNU). He leads the Networking Group@IIK and has held roles including Head of the FG INET at TU Berlin and Head of the Next Generation Networks group at the University of Würzburg. He earned his diploma (2006) and PhD (2012) from the University of Würzburg. His research focuses on network eco-systems, performance evaluation of SDN/NFV architectures, QoE-centric management, and 5G/6G innovations. Key projects include TeraFlow, aiming to revolutionize SDN traffic management in beyond 5G networks. His work bridges network technologies, applications, and protocols, emphasizing optimization for emerging paradigms like software-based networking and adaptive video streaming. Recent publications address AI integration in 6G, 5G NR performance, and QoE-aware network slicing. He collaborates internationally, publishing in venues like IEEE Transactions and CNSM. His research addresses challenges in network determinism, inter-domain connectivity, and geo-distributed analytics.

Fabio CaraffiniView profile

Associate Professor

• Evolutionary Computing
• Heuristic & Hyperheuristic Optimization
• Algorithm Behaviour
• +5 more

Dr. Fabio Caraffini is an Associate Professor in Computer Science at Swansea University's School of Mathematics and Computer Science. He holds dual PhDs in Mathematical Information Technology (University of Jyväskylä, 2016) and Computer Science (De Montfort University, 2014), along with BSc and MSc degrees in Engineering from the University of Perugia. His research focuses on computational intelligence, particularly heuristic optimization methods like evolutionary algorithms and differential evolution. He also holds an honorary position as Senior Research Fellow at De Montfort University (2022–2024). Education History: BSc in Electronics Engineering (University of Perugia, 2008) MSc in Telecommunications Engineering (University of Perugia, 2011) PhD in Mathematical Information Technology (University of Jyväskylä, 2016) PhD in Computer Science (De Montfort University, 2014) Research Interests: Dr. Caraffini's work bridges theoretical optimization and practical applications. Key areas include evolutionary computing, structural bias analysis in algorithms, and interdisciplinary AI applications such as medical imaging, climate risk modeling, and robotics. His SOS Platform and BIAS toolbox are notable contributions to algorithm benchmarking and bias detection. Recent projects include AI-driven solutions for crop mapping, medical record analysis, and rail scheduling optimization. Publications & Trends: His 150+ publications span journals like Information Sciences , IEEE Transactions , and Applied Soft Computing . Themes include algorithmic robustness, constraint handling, and real-world optimization challenges. Notable works address differential evolution improvements, climate transition risk prediction, and medical decision support systems. Awards & Grants: Fellow of the Higher Education Academy (FHEA) Recipient of multiple research grants for projects in optimization and AI applications Advising & Collaboration: Actively supervises PhD students in AI-driven optimization and interdisciplinary applications. Collaborates with institutions globally on topics like microgrid energy management and pandemic prediction through self-organizing maps. Labs & Teams: Engaged in Swansea's Computational Foundry and the Morgan Advanced Studies Institute (MASI), contributing to cross-disciplinary research initiatives in AI and computational science.

Duncan M. WalkerView profile

Professor

• Integrated Circuit Test
• Defect-Based Test
• Delay Test
• +4 more

Duncan M. Walker is an Associate Dean for Graduate Programs and Professor in the Department of Computer Science & Engineering at Texas A&M University. He holds a Ph.D. in Computer Science from Carnegie Mellon University (1986) and has been a faculty member since 2006. His research focuses on integrated circuit testing, defect-based testing, delay faults, IDDQ testing, fault diagnosis, and yield prediction. Walker has held leadership roles including Department Head (2011–2013) and Graduate Advisor. His awards include the ACM Distinguished Scientist (2014), Charles W. Crawford Service Award (2009–2010), and multiple Texas A&M fellowships. He teaches courses on software engineering and professional development for faculty. His research lab (EDA Lab) explores cutting-edge testing methodologies for VLSI circuits, including resistive fault modeling, outlier identification using neighbor current ratios, and parametric yield analysis. Recent work addresses challenges in deep-submicron IC testing and built-in current sensor design. Education: B.S. (Caltech, 1979), M.S. (CMU, 1984), Ph.D. (CMU, 1986) Professional Activities: IEEE Test Conference Program Committee (2020–present), Design Automation & Test in Europe (2018–present) Labs/Teams: EDA Lab, collaborating on defect-based testing and fault modeling Publications emphasize practical testing solutions for emerging semiconductor technologies, with over 40 refereed papers in venues like IEEE VLSI Test Symposium and IEEE Transactions on CAD.

Roberto FurfaroView profile

Professor

• Guidance, Navigation and Control
• Machine Learning in Aerospace
• Space Situational Awareness
• +7 more

Roberto Furfaro is a Professor in the Department of Systems and Industrial Engineering and the Department of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He serves as Deputy Director of the Space, Security, Safety & Sustainability Center (S4C) and is a key figure in space situational awareness and planetary exploration systems. His educational background includes a Laurea Degree (MS equivalent) in Aeronautical Engineering from the University of Rome 'La Sapienza' (1998) and a PhD in Aerospace Engineering from the University of Arizona (2004). His research spans guidance and control of planetary landers, machine learning applications in space systems, hypersonic vehicle G&C, and systems engineering for small body proximity operations. He leverages advanced AI, particularly reinforcement learning and deep learning, to solve complex aerospace challenges. The recent publications reflect a strong trend toward intelligent, autonomous systems in spaceflight, especially using deep reinforcement learning for planetary landing, guidance under uncertainty, and space-based object tracking. His work bridges control theory, astrodynamics, and artificial intelligence. 2021 Da Vinci Fellow, College of Engineering AIAA Associate Fellow (2022) AAS Fellow (2021) Asteroid 133474 Roberto Furfaro named in honor of OSIRIS-REx contributions Prof. Furfaro has served as PI or Co-PI on over $80M in research grants from NASA, AFRL, and DoD. He has advised numerous students and leads research teams in autonomous space systems and machine learning applications. He is the Target Follow-up Working Group lead for the NASA NEO Surveyor Mission and was Systems Engineering Lead for SPOC during OSIRIS-REx Phase B-D. He is actively involved in professional service as Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and technical member of the AIAA Astrodynamics Committee and AAS Space Surveillance Committee.

Debopam BhattacherjeeView profile

Researcher

• AI Systems
• Networked Systems
• Low-Earth Orbit Satellite Networks
• +1 more

Debopam Bhattacherjee is a Senior Researcher at Microsoft Research, Bangalore, focusing on AI Systems, Networked Systems, and Low-Earth Orbit (LEO) Satellite Networks. He holds a PhD in Computer Science from ETH Zürich (2021) and a dual Erasmus Mundus Master's in Security and Mobile Computing. His work has been recognized with the IRTF Applied Networking Research Prize (2020) and multiple Best Paper/Dataset Awards. Research interests include LEO satellite networks, sustainable AI systems, network topology design at hypersonic speeds (27,000 km/hour), and green computing via modular data centers. His publications span ACM SIGCOMM, USENIX ATC, IEEE COMSNETS, and arXiv, with recent patents on AI-driven LLM training and inference systems. Key awards: IRTF Applied Networking Research Prize (2020) Best Paper Award at ACM IMC 2020 Best Dataset Award at ACM IMC 2020 He has supervised numerous PhD/Master's students at ETH Zürich and Microsoft Research, including Tella Rajashekhar Reddy, Shubham Tiwari, and Saksham Bhushan. His educational background includes a B.E. in Computer Science & Engineering from Jadavpur University (2009) and a research fellowship at the Max Planck Institute (2019).

• Control Systems
• Optimization
• Machine Learning
• +5 more

Prof. Maryam Kamgarpour is a faculty member at ETH Zurich's Automatic Control Laboratory within the Department of Information Technology and Electrical Engineering. Her research focuses on control systems, optimization, and machine learning, with applications in robotics, power systems, and multi-agent systems. She has contributed significantly to safe reinforcement learning, stochastic control, and distributed optimization frameworks. Her work bridges theoretical foundations and practical implementations in areas like safe trajectory planning, market mechanisms for energy systems, and algorithmic guarantees for greedy methods. Key technical areas include model-based multi-agent reinforcement learning, robust control policies under uncertainty, and parameterization techniques in control theory. Collaborations with institutions like ETH Zurich's Automatic Control Lab and researchers such as Andreas Krause highlight her interdisciplinary approach. Her research has been supported by grants from the Swiss National Science Foundation (NCCR Automation) and the European Union (Reliable Data-Driven Decision Making in Cyber-Physical Systems). Publications span top journals and conferences in control systems, machine learning, and robotics, emphasizing rigorous analysis and practical scalability. Recent work addresses challenges in safe black-box optimization, stochastic hazard management in robotics, and market-based resource allocation in power grids.

• VLSI Design
• Digital Circuit Testing
• Test Data Compression
• +4 more

Χρ. Καβουσιανός is a Professor at the Department of Computer Engineering & Informatics, University of Patras (Polytechnic School). His research focuses on VLSI design, digital circuit testing, and test data compression. He holds a Diploma (1996) and PhD (2000) from the same department, with notable scholarships including the Maritsas Doctoral Scholarship (1999). Key roles include leading the Heraclitus II and Pythagoras II research programs as principal investigator. Collaborations include Duke University (USA) with Prof. Krishnendu Chakrabarty. Research highlights: Pioneered techniques in scan-based testing, test set embedding, and low-power testing. Over 45 publications in top journals/conferences (e.g., IEEE TCAD, DAC). Notable work includes defect-oriented testing methods and Huffman-based compression schemes. Has supervised 9 advisees across doctoral/postgraduate levels. Holds a 2010 US patent application on power switch design.

Zhiguo DingView profile

Professor

• Wireless Communications
• Signal Processing
• Non-Orthogonal Multiple Access (NOMA)
• +7 more

Zhiguo Ding is a Professor in Communications at the University of Manchester, UK, and holds a Distinguished Adjunct Professor position at Khalifa University. He is also an Academic Visitor at Princeton University, collaborating with Prof. H. Vincent Poor. Prior roles include positions at Queen's University Belfast, Imperial College London, Newcastle University, and Lancaster University. Education : Ph.D. in Electrical Engineering, Imperial College London (2005) B.Eng. in Electrical Engineering, Beijing University of Posts and Telecommunications (2000) Research Interests span 6G Networks , NOMA , Physical Layer Security , Machine Learning for Communications , Beamforming , Cooperative Relaying , and Wireless Caching . His work explores hybrid NOMA architectures, hardware impairments in antenna systems, and cross-layer optimization for security and throughput. Recent Article Trends focus on Pinching Antenna Systems (2020-2024), Hybrid NOMA for OFDMA , THz-NOMA , and Grant-Free Transmission in IoT. Collaborations with Prof. Poor at Princeton emphasize analytical optimization, CRLB (Cramér-Rao Lower Bound) perspectives, and hardware-aware designs. Scientific Awards : IEEE Fellow (2020) Web of Science Highly Cited Researcher (2019-2024) IEEE Heinrich Hertz Award (2018) IEEE Jack Neubauer Memorial Award (2018) EU Marie Curie Fellowship (2012-2014) IEEE SPCC Technical Recognition (2021) IEEE GLOBCOM 2024 Best Paper Award Advising includes Ph.D. students like Yujie Ma (Flexible Antenna Systems), Wenqi Huang (NOMA-MEC), and Yushen Lin (NOMA Machine Learning), alongside 12+ past Ph.D. advisees. Grants include EPSRC, EU H2020-RISE, Royal Society, and FP7 Marie Curie funding for projects like Scalable Hybrid Architecture for Wireless Federated Learning (2023-2026) and MANGO (2017-2020). Labs & Teams : Leads research in NOMA, MIMO, and secure wireless systems at Manchester. Collaborates with IEEE and serves as Area Editor for IEEE TWC , TCOM , and OJ-SP , contributing to standards in 5G/6G and IMT frameworks.

Joanne KuaiView profile

Research Fellow

• Digital Journalism
• AI and Media
• Algorithmic Governance
• +4 more

Dr. Joanne Kuai is a Research Fellow in the School of Media and Communication at RMIT University, Australia, with expertise in digital journalism, AI governance, and the social implications of algorithmic systems. She holds a PhD from Karlstad University, Sweden, and serves as an Affiliate of the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S). Her educational background includes doctoral studies at Karlstad University where she focused on AI's impact on journalism in China's media landscape. Prior to academia, she gained practical experience as a reporter, editor, and news anchor in China. Dr. Kuai's research centers on the intersection of journalism, artificial intelligence, and media governance, with particular attention to Chinese media contexts. Her work explores how algorithmic systems are reshaping news production, distribution, and consumption while examining the policy frameworks needed for responsible AI implementation in media environments. Her publications reveal a strong focus on cross-cultural comparisons of AI applications in journalism, particularly examining political information retrieval across different language contexts. She investigates how search engine chatbots powered by large language models perform in political information retrieval, highlighting issues of bias, representation, and governance in algorithmic media systems. Co-guest editor of Information, Communication & Society special issue on Generative AI Governance Regular contributor to New Books Network podcast Faculty at JournalismAI Academy, Polis, London School of Economics Dr. Kuai actively participates in academic mentoring and knowledge sharing, contributing to the JournalismAI Academy while maintaining connections with emerging scholars in the field. Her work bridges academic research with practical applications in journalism and media organizations navigating the AI transformation.

Ricardo Miguel Ferreira MartinsView profile

Assistant Professor

• Analog Integrated Circuit Design Automation
• Machine Learning in EDA
• Ultra-Low Power Circuits
• +3 more

Ricardo Miguel Ferreira Martins is an Assistant Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), University of Lisbon. His research focuses on analog/mixed-signal integrated circuit design automation, leveraging machine learning for layout optimization, sizing, and post-layout performance prediction. He leads multiple projects funded by institutions like FCT and Sony, emphasizing AI-driven EDA solutions for 5G/6G and biomedical applications. Education: Doctoral Degree in Electrical and Computer Engineering (IST/UL) Research Interests: Deep learning for analog IC placement and routing Ultra-low power biomedical circuits RF/mmWave IC design automation Yield-aware synthesis methodologies Article Trends: Recent work emphasizes integrating machine learning into EDA workflows, particularly for layout-aware optimization , human-AI collaboration , and autonomous design tools . Key contributions include PACOSYT (passive component synthesis) and DeepPlacer (AI-driven placement). Grants & Projects: ACTON Project (2025-2026): 5G/6G mmWave IC design via deep learning GENERALISE Project (2023-2026): Generative AI for analog chip design Student Supervision: Mentors 21+ graduate students in AI-driven circuit design and EDA automation. Ongoing works explore generative AI for layout generation, reinforcement learning in design space exploration, and cloud-based EDA tools. Conference Roles: Holds leadership positions in major EDA conferences like SMACD and DATE, serving as Technical Program Chair and Awards Chair.