Find Academic Supervisors

Peter BishopView profile

Professor

• Software Reliability
• Software Fault Tolerance
• Design Diversity
• +10 more

Peter Bishop is a Professor at the Centre for Software Reliability, City St George's, University of London, where he holds a joint chair in Systems and Software Dependability with Robin Bloomfield. He is also Chief Scientist at Adelard, part of NCC Group, providing consultancy and research in computer safety and dependability. He holds BSc and MSc degrees in Physics and is a Chartered Engineer and Member of the IET. University: City St George's, University of London School: College of Engineering, Design and Physical Sciences Department: Centre for Software Reliability Academic Rank: Professor Email: p.bishop@citystgeorges.ac.uk Research Interests: Peter Bishop's research spans software fault tolerance, design diversity, software reliability prediction, statistical testing, system safety and security, assurance case methodologies, and their application in industrial contexts including autonomous vehicles and nuclear systems. He has led research for the UK nuclear industry on smart device assessment and participated in European projects on critical control system safety. Publication Trends: His recent publications focus on conservative confidence bounds for software reliability, safety assurance under uncertainty, integration of testing and formal proof, and security-informed safety. A recurring theme is the development of rigorous, evidence-based methods to justify software dependability in safety-critical domains, particularly where operational and test profiles differ or failure data is scarce. Scientific Awards and Recognition: While specific awards are not listed, his long-standing contributions are evident through his professorship, leadership at Adelard, and active role in safety-critical research. He is a Chartered Engineer and Member of the IET. Advising and Grants: Although specific students are not named, his leadership in major research projects such as DISPO and DIRC (2000–2006) indicates significant supervisory and mentoring roles. He has secured funding from sources including the Leverhulme Trust (UnCoDe project) and a consortium of nuclear industry stakeholders (EDF Energy, NDA, AWE, etc.) under the CINIF Nuclear Research Programme. Labs and Research Teams: He is a key member of the Centre for Software Reliability at City St George's and leads research activities at Adelard. He collaborates extensively with Robin Bloomfield, Lorenzo Strigini, Bev Littlewood, and others on software dependability and safety assurance research.

Anders Koed MadsenView profile

Professor

• Digital Methods
• Urban Belonging
• Smart Cities
• +12 more

Anders Koed Madsen is a Professor at the Department of Culture and Learning, Faculty of Humanities and Social Sciences, Aalborg University. He is affiliated with the Technoanthropological Laboratory and leads research initiatives including AI for the People and MASSHINE. His work centers on digital methods, urban belonging, smart cities, and citizen science, often integrating ethnographic and participatory approaches. His research interests lie at the intersection of technology and society, focusing on digital placemaking , urban sensing , pragmatist philosophy , and inclusive urban planning . He explores how digital tools can be used to understand lived experiences in cities, especially among marginalized communities. His methodological focus includes participatory digital methods, geospatial photovoice, and computational ethnography. The recent articles highlight a consistent trend toward using digital and ethnographic methods to study urban life, technology assessment, and democratic participation. Themes include generative AI , digital epistemology , friction in machine reasoning , and inclusive city planning . His work increasingly incorporates large language models and open-source toolkits to democratize research and planning processes. Scientific Awards: European Union Prize for Citizen Science (2023) World Summit Awards (WSA) shortlist, Urban Belonging Project (2024) Ziman Award 2020: TANTlab AAU Talent (2018) Videnskabsministeriets EliteForsk-rejsestipendium (2011) Anders Koed Madsen has been actively involved in research grants and collaborative projects such as the Urban Belonging Project , Digital Placemaking and Soft City Sensing Research Network , and GE-AI: Generative Ethnographic AI . He frequently collaborates with interdisciplinary teams and institutions like IT University of Copenhagen and Gehl Architects. He advises on public engagement and has contributed to national and international discourse on digitalization and urban futures. He is a core member of the Technoanthropological Laboratory , which fosters interdisciplinary research on technology and society. The lab supports initiatives in digital methods, citizen science, and urban innovation. Madsen also contributes to public understanding through media engagement and workshops, promoting humanistic perspectives in technological development.

Guanrui LiView profile

Assistant Professor

• Aerial Robotics
• Control Systems
• Robot Perception
• +3 more

Guanrui Li is an Assistant Professor at Worcester Polytechnic Institute's Robotics Engineering Department and the director of the Aerial-robot Control and Perception Lab (ACP Lab). He holds a Ph.D. in Electrical and Computer Engineering from NYU (2024), an M.S. in Robotics from the University of Pennsylvania (2018), and a B.E. in Theoretical and Applied Mechanics from Sun Yat-sen University (2016). His research focuses on aerial robotics, including control methodologies for collaborative transportation, human-robot interaction, and perception-aware systems. Key contributions include Hybrid Perception-Aware MPC frameworks, cooperative manipulation algorithms, and simulation tools like RotorTM. Education: Ph.D., Electrical and Computer Engineering, NYU (2024) M.S., Robotics, University of Pennsylvania (2018) B.E., Theoretical and Applied Mechanics, Sun Yat-sen University (2016) Research Interests: Control and perception of aerial robots, human-robot collaboration, cooperative manipulation, and autonomous systems. Notable work addresses challenges in payload transportation, sensor fusion, and safety-critical navigation. Awards: NSF CPS Rising Stars (2023) Outstanding Deployed System Paper Finalist (IEEE ICRA 2022) NYU Dante Youla Award (2022) NYU Outstanding Dissertation Award (2024) Lab & Team: ACP Lab focuses on advancing aerial robotics through interdisciplinary research. Current projects include mixed reality interfaces for human-robot interaction and fault-tolerant control systems. Recent collaborations include workshops on Rust for Robotics (ICRA 2025) and embodied-AI for aerial systems (ICUAS 2025).

• machine learning
• robotics
• machine vision
• +9 more

Dr. Hamidreza Mohades Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He holds positions in both the Faculty of Science and Engineering and the Faculty of Medical Sciences/UMCG, focusing on Robotics and image-guided minimally-invasive surgery. His work bridges theoretical advances in machine learning with practical robotic applications. Dr. Kasaei's research focuses on developing algorithms for adaptive perception systems through interactive environment exploration and open-ended learning. His specific interests include 3D object perception, grasp affordance detection, object manipulation, and active perception. He has evaluated his research on various robotic platforms including PR2, UR5e, Kinova, Franka robotic arms, and humanoid robots. His work enables robots to learn from past experiences and intelligently interact with non-expert human users using data-efficient techniques. Analysis of his recent publications reveals strong trends toward increasingly sophisticated manipulation capabilities, particularly in dual-arm coordination and handling dense clutter. There's a clear progression toward integrating language models with robotic control systems, as seen in works like 'Lifelong Robot Library Learning' and 'Towards Open-World Grasping with Large Vision-Language Models.' His research consistently addresses real-world challenges in agricultural robotics, assistive technologies, and service robotics applications. Gratama Science Award (2022) Google Research Scholar Award in Machine Learning (2023) Outstanding Associate Editor for IEEE Robotics and Automation Letters (2023) Dr. Kasaei has successfully supervised multiple PhD students including Zhenxing Zhang (thesis on 'Generative Adversarial Networks for Diverse and Explainable Text-to-Image Generation') and Hamed Ayoobi (thesis on 'Explain What You See: Argumentation-Based Learning and Robotic Vision'). His research is supported by significant grants including the Google Research Scholar Award for 'Continual Robot Learning in Human-centered Environments' and various conference organization roles including workshops at RSS 2023 and NeurIPS 2022. He leads the Lifelong Interactive Robot Learning Lab (IRL-Lab), which focuses on six key research directions: Perception and Perceptual Learning, Object Grasping and Manipulation, Lifelong Interactive Robot Learning, Dual-Arm Manipulation, Dynamic Robot Motion Planning, and Exploiting Multimodality. The lab develops cutting-edge approaches for robots to learn in open-ended fashion through interaction with non-expert human users, with applications in assistive robotics for people with disabilities.

Rebecca RossView profile

Professor

• Graphic Design
• Communication Design
• Media Studies
• +5 more

Rebecca Ross is Programme Director of Graphic Communication Design at Central Saint Martins, University of the Arts London. She holds a PhD in History & Theory of Architecture and Urbanism from Harvard, an MSc from UCL, and an MFA from Yale. Her academic appointments include positions at Harvard, NYU, and Yale. Research interests span: Graphic/communication design and urban media Interaction of images/media/data with urban environments Addressing systems and location-based media Graphic design as knowledge practice Alternative academic publishing formats Publications focus on urban media, spatial representation, and design research methodologies, with recurring themes of public engagement, digital mediation of urban experience, and critical analysis of technology corporations. Awards include: Honorary Senior Research Associate at UCL (2016-2019, 2024-2027) Supervision includes 5+ PhD students researching urban communication, spatial bureaucracy, and design practices. Developed the MA 'Communicating Complexity' program. Professional activities include grant assessment for AHRC and promotion reviews for international institutions. Co-founded Urban Pamphleteer publication with UCL Urban Lab and created the public installation London is Changing .

• Machine Learning
• Statistical Computing
• Cluster Analysis
• +2 more

Theresa Scharl-Hirsch is a Senior Scientist and Deputy Scientific Director at the Core Facility Bioinformatics, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds concurrent appointments at the Institute of Statistics, BOKU, and has extensive experience in bioprocess modeling, machine learning, and statistical computing. Her work bridges biochemical engineering with advanced data science methodologies. Her research focuses on real-time monitoring of biopharmaceutical processes, clustering of high-dimensional data (particularly RNA sequencing), and application of explainable machine learning techniques. She has developed statistical models for process optimization and quality prediction in antibody capture and protein purification, with a strong emphasis on industrial implementations using R programming. Key trends in her publications include three-way data analysis, matrix-variate Gaussian mixture models, and permutation-based variable importance methods for deep learning architectures. Her work spans bioprocess engineering, bioinformatics, and industrial data science applications.

Wolfgang SteinerView profile

Research Fellow

• Beta expansions
• Continued fractions
• Numeration systems
• +3 more

Wolfgang Steiner is a Research Fellow at the Centre National de la Recherche Scientifique (CNRS) working at the Institut de Recherche en Informatique Fondamentale (IRIF), Université Paris Cité since 2005. His affiliations include membership in the Automata & Applications and Combinatorics research groups. Previously, he held visiting positions at Graz University of Technology and Macquarie University. Education: Habilitation à diriger des recherches en informatique, Université de Paris (2021) Ph.D. in Technology, TU Wien (2002) Master's degree (Dipl.-Ing.), TU Wien (2000) Engineering degree, École Centrale Paris (2000) Steiner's research focuses on numeration systems, including beta expansions and continued fractions, with significant work on Rauzy fractals, tilings, and combinatorics on words. His investigations bridge dynamical systems, fractal geometry, and theoretical computer science, exploring the algorithmic and structural properties of number representations. His publications demonstrate a consistent focus on dynamical approaches to numeration, with recent work expanding into multidimensional continued fractions, S-adic systems, and fractal geometry. The articles show strong interdisciplinary connections between number theory, combinatorics, and symbolic dynamics. Research projects: ANR/FWF SymDynAr (2024-present) ANR IZES (2022-present) ANR CODYS (2018-2023) ANR Dyna3S (2013-2018) ANR/FWF FAN (2013-2017) Coordinator of ANR DyCoNum (2006-2010) Steiner leads the One World Numeration Seminar (since 2020) and previously organized the Automata seminar at IRIF (2012-2017). He collaborates with international research teams across Europe and Australia on projects involving dynamical systems and number theory.

• AI Safety
• Reliability of AI Systems
• Testing of Digital Circuits
• +1 more

Annachiara Ruospo is a Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She holds a Fixed-Term Researcher position under Italian Law 240/10, art.24-A, and is actively involved in teaching and research projects. Research Interests : AI Safety, Reliability of AI Systems, Testing of Digital Circuits, Statistical Reliability Investigations. Her recent research focuses on hardware reliability for AI systems , including fault injection methodologies, quantized neural networks, and side-channel vulnerabilities. She collaborates on EU-funded projects like REACT and commercial contracts such as TC4xx NVM Test Strategies . Scientific Awards : None explicitly mentioned. Supervised Students : Antonio Porsia (PhD candidate, Reliability and Security of AI-Based Systems) and Vittorio Turco (PhD candidate, Reliability Evaluation and Hardening of AI Accelerators). She contributes to AI hardware security through patents like A Shannon-Hartley-Based Approach to Measure the Criticality of Synaptic Weights and actively participates in conferences such as the IEEE Latin American Test Symposium and IEEE VLSI Test Symposium .

• Digital Transformation
• Responsible Artificial Intelligence
• Foresight Methods
• +4 more

Matti Minkkinen is a Docent at the Turku School of Economics (University of Turku) and a Postdoctoral Researcher in Information Systems Science at the Department of Management and Entrepreneurship. His work bridges futures studies with ethics, privacy, and socio-technical systems in digital transformation. Recent roles focus on responsible AI governance and foresight methodologies. University: University of Turku School: Turku School of Economics Department: Department of Management and Entrepreneurship His research explores how digital technologies reshape organizational practices, emphasizing Futures Consciousness as a human capacity. Key themes include responsible AI , privacy protection , and causal layered analysis in scenario planning. Publications highlight ethical governance frameworks and EU policy debates. Recent articles address generative AI ethics , ML system integration , and AI auditing across journals like Communications of the Association for Information Systems and Information and Management . Topics cluster around socio-technical systems, digital ethics, and institutional adaptation to AI. Teaching and editorial roles include co-curating student research collections at Finland Futures Research Centre. No explicit scientific awards are listed, but his work contributes to foresight theory and practice.

• Statistical Mechanics
• Condensed Matter Physics
• Soft Matter Physics
• +4 more

Davide Cassi serves as Associate Professor of Physics of Matter at the University of Parma's Department of Mathematical, Physical and Computer Sciences since 2001, following his appointment as Researcher in Theoretical Physics (1995-2001). With over 30 years of academic service, he teaches Condensed Matter Physics, Soft Matter Physics, and Physics Applied to Gastronomy across undergraduate and graduate programs in Physics and Gastronomic Science. His educational background includes: Ph.D. in Physics, University of Parma (1988-1992) Master’s degree in Materials Science and Technology, University of Parma (1986-1988) Degree in Physics, University of Parma (1982-1986) Cassi's research integrates statistical mechanics with real-world applications through two primary lenses: complex network theory for ecological and social systems, and soft matter physics applied to culinary processes. His work on biodiversity loss prediction in agricultural networks and food preservation technologies demonstrates exceptional interdisciplinary reach. Recent publications reveal a strategic pivot toward AI-driven biodiversity conservation and network robustness modeling. Analysis of his 15 most recent publications (2023-2025) shows dominant themes in network vulnerability analysis (68% of works) and food-physics applications (27%), with emerging focus on machine learning integration for ecological modeling. His research bridges theoretical physics with practical solutions in food safety and ecosystem management. Key recognitions include: Grand Prix de la Science de l'Alimentation from Académie Internationale de la Gastronomie (2012-2013) Dual National Scientific Qualifications for Full Professorship (2022) in Theoretical Physics of Fundamental Interactions and Matter Cassi's academic contributions extend beyond publications to two international patents in food preservation technology and editorial leadership since 2007 for World Scientific's Series on Advances in Statistical Mechanics . His research program demonstrates consistent translation of theoretical physics into practical applications across gastronomy and ecology, with growing emphasis on AI-enhanced network analysis for sustainability challenges.

Flavio Bezerra CostaView profile

Assistant Professor

• Smart Grid
• Renewable Energy Systems
• Power System Protection
• +10 more

Flavio Bezerra Costa serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University's College of Engineering. His research focuses on critical areas of modern power systems, including smart grid technologies, renewable energy integration, power system protection, and advanced applications of signal processing and artificial intelligence in electrical power networks. Dr. Costa's research interests span a comprehensive range of power system topics with particular emphasis on Smart Grid technologies, Integration of Renewable Energy Systems, Power System Protection, Control, and Monitoring, Power Quality analysis, Power Systems and Power Electronics, AC/DC Microgrids, High-Voltage Direct Current (HVDC) Electric Power Transmission Systems, and the application of Signal Processing and Artificial Intelligence (including Machine Learning) in power systems. His work bridges traditional power engineering with modern computational techniques to address contemporary grid challenges. Analysis of Dr. Costa's recent publications reveals a consistent focus on wavelet transform applications for power system protection and monitoring, particularly in the areas of fault detection, classification, and location. His research demonstrates strong integration of machine learning techniques with traditional power system protection methods, with significant contributions to transformer protection, transmission line fault analysis, and microgrid stability. The work shows an evolving trajectory from fundamental wavelet-based protection techniques toward more sophisticated AI-enhanced approaches for modern power grid challenges. Dr. Costa maintains an active research program with numerous publications in top-tier IEEE journals and conferences, demonstrating his significant contributions to the field of power systems engineering and protection.

Hongsheng HuView profile

Lecturer

• Data Privacy
• Machine Unlearning
• Trustworthy Machine Learning
• +5 more

Dr. Hongsheng Hu is currently a Lecturer in the School of Information and Physical Sciences at the University of Newcastle, Australia, specializing in the Data Science and Statistics focus area. Prior to this position, he served as a Postdoc Research Fellow at CSIRO's Data61 from October 2022 to August 2024. His academic journey includes a Doctor of Philosophy in Computer Systems Engineering from the University of Auckland in New Zealand, establishing his foundation in advanced computing systems. Dr. Hu's research centers on enhancing the trustworthiness of machine learning systems, with particular emphasis on identifying critical privacy vulnerabilities within machine learning models and developing robust defensive strategies. His work spans several key domains including adversarial machine learning (30% focus), statistical data science (30% focus), and data and information privacy (40% focus). He investigates membership inference attacks, machine unlearning techniques, and privacy-preserving mechanisms in federated learning environments. His research addresses fundamental challenges in AI security, exploring how machine learning models can be compromised through sophisticated privacy attacks and developing methods to mitigate these vulnerabilities while maintaining model utility. Analysis of Dr. Hu's publication record reveals a strong research trajectory focused on machine learning security and privacy. His work consistently addresses vulnerabilities in machine learning systems, particularly examining membership inference attacks, machine unlearning mechanisms, and privacy-preserving techniques in federated learning. The research spans top-tier venues including IEEE Security & Privacy, USENIX Security, NDSS, NeurIPS, IJCAI, AAAI, and WWW, demonstrating both technical depth and recognition by the research community. His publications show an evolving focus from foundational privacy attacks to developing more sophisticated unlearning techniques and robust defense mechanisms, with increasing citation counts indicating growing impact in the field. Active Program Committee member for USENIX Security, NDSS, ICLR, IJCAI, WWW, ICDM, ECML, and PKDD Invited reviewer for IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Pattern Analysis and Machine Intelligence (IPAMI), and ACM Computing Surveys (CSUR) Dr. Hu currently serves as Course Coordinator for STAT6020 and STAT2020 Predictive Analytics at the University of Newcastle. As an academic supervisor, he co-supervises one PhD student working on 'Identifying and Mitigating Vulnerability in Recommender Systems' at Macquarie University. His research collaborations span multiple countries, with significant publication counts in Australia (18), China (12), New Zealand (10), and the United States (7), reflecting an active international research network focused on AI security challenges.

• Security Engineering
• Privacy Enhancing Technologies
• Cryptography
• +3 more

Professor Steven J. Murdoch is a faculty member at the University College London (UCL) in the Department of Computer Science . He holds a Royal Society University Research Fellow position and leads the Information Security Research Group . He is affiliated with Christ’s College as a bye-fellow, and is a Fellow of the Institution of Engineering and Technology (IET) and the BCS . His work bridges security engineering , privacy-enhancing technologies , and legal-technical intersections . Academic Leadership : Program chair and general chair for major conferences like Privacy Enhancing Technologies Symposium and Financial Cryptography . Research Contributions : Notable for exposing vulnerabilities in EMV protocols , designing blockchain-based fair exchange protocols , and analyzing malware delivery ecosystems . Scientific Awards : Received the IRTF Applied Networking Research Prize 2020 for internet-wide scanning methodologies. Professional Impact : Active in Tor Project and critical infrastructure analysis, including the Post Office Horizon scandal . Email: s.murdoch@ucl.ac.uk .

Dharanidhar DangView profile

Assistant Professor

• Photonic Computing
• AI Hardware Acceleration
• Memristor-based Computing
• +3 more

Dharanidhar Dang serves as Assistant Professor in the Department of Computer Engineering at the College of AI, Cyber and Computing, The University of Texas at San Antonio (UTSA), where he advances hardware-centric artificial intelligence solutions through photonic and memristor technologies. Education Ph.D., Texas A&M University His research program bridges hardware innovation and biomedical applications, with primary focus on photonic computing architectures for real-time AI acceleration and memristor-based neuromorphic systems. He investigates critical challenges in hardware reliability (particularly degradation in memristor crossbars), energy efficiency in photonic accelerators, and co-design methodologies that optimize both algorithms and physical implementations. His biomedical work applies machine learning to macrophage biology, identifying predictive signatures for inflammatory diseases through computational immunology approaches. Analysis of his 2020-2025 publications reveals three dominant research trajectories: 1) Silicon photonic accelerators (P-ReTI, P-ReTiNA, SOFTONIC) targeting real-time and energy-efficient AI, 2) Memristor reliability frameworks addressing aging effects in deep learning hardware, and 3) Translational biomedical applications where machine learning deciphers macrophage behavior in inflammatory bowel disease and preterm infant lung conditions. This tripartite focus demonstrates exceptional versatility across hardware engineering and life sciences.

Nikolaos GatsisView profile

Associate Professor

• Smart Power Grids
• Critical Infrastructure Security
• Power Systems Optimization
• +3 more

Nikolaos Gatsis serves as Associate Professor and GreenStar Endowed Associate Professor in Energy within the Department of Electrical and Computer Engineering at UTSA's Klesse College of Engineering and Integrated Design. His work centers on optimizing and securing smart power grids integrated with critical infrastructure systems including water, transportation, and GPS networks. Education: Ph.D. in Electrical Engineering with minor in Mathematics, University of Minnesota, 2012 Diploma in Electrical and Computer Engineering, University of Patras, Greece His research program tackles critical vulnerabilities in cyber-physical systems through six interconnected domains: smart grid modeling, water distribution security, transportation electrification impacts, inverter-based grid protection, stochastic renewable integration, and GPS spoofing defense. This multidisciplinary approach addresses systemic risks in modern infrastructure through advanced optimization and security frameworks. Recognition includes: NSF CAREER Award (2019) Lutcher Brown Endowed Fellowship (2020-2021) Teaching Excellence Award (2021) President’s Research Achievement Award (2021) Annual Innovation Award (2022) Funded by the NSF CAREER grant and institutional fellowships, his research group develops security protocols for interdependent infrastructure systems. His lab facilities support experimental validation of grid protection mechanisms and spoofing countermeasures, training engineers for next-generation energy challenges.