Professor Siân Bayne is a leading academic in digital education at the University of Edinburgh , where she serves as Assistant Principal for Education Futures and directs the Centre for Research in Digital Education . Her work bridges critical theory and practical innovation in higher education. Interdisciplinary research on digital education and futures studies Leadership in the Edinburgh Futures Institute and Moray House School of Education and Sport Global impact through UNESCO collaborations and UKRI GCRF projects Bayne’s research explores higher education futures , utopian theory , and digital enhancement , with a focus on: AI’s role in teaching and learning Ethical implications of educational technologies Anonymous social media in academic spaces Speculative scenarios for educational systems Global South digital education models Augmented reality applications in assessment Her 15 most recent publications span 2017-2025 , covering critical perspectives on: Generative AI and educational innovation Utopian design in universities Data governance in learning analytics Augmented reality pedagogy Digital cultural heritage Scientific recognition includes: IASH Sabbatical Fellowship (2023) Bayne leads Higher Education Futures initiatives and partners with institutions worldwide, including: UNESCO Global Education and Technology Report World Bank collaborations European university leadership programs She also drives major research centers like: Centre for Research in Digital Education UKRI GCRF Urban Disaster Risk Hub
Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, holding the Canada CIFAR AI Chair at the Vector Institute and serving as Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Her research spans AI safety, ethics, reinforcement learning, and knowledge representation, with a focus on human-compatible AI and long-term societal impacts. Her career includes six years as a Research Scientist at Stanford University and a year at Xerox PARC. McIlraith’s work has been recognized through ACM and AAAI fellowships, as well as prestigious awards like the SWSA 10-Year Award (2011) and the CAIAC Lifetime Achievement Award (2024). Research Interests: AI Safety and Alignment Human-Compatible AI Reinforcement Learning with Ethical Constraints Semantic Web Services Cognitive Robotics and Diagnostic Systems Probabilistic and Logical Reasoning Recent Contributions: Her work emphasizes ethical AI integration, such as the Embedded Ethics Education Initiative (E3I), and addresses challenges in long-term AI risks, multi-agent systems, and interpretable decision-making frameworks. Awards and Honors: ACM Fellow AAAI Fellow 2023 IJCAI-JAIR Best Paper Prize 2024 CAIAC Lifetime Achievement Award Labs and Teams: McIlraith leads initiatives at the Schwartz Reisman Institute and contributes to the Vector Institute, focusing on societal and ethical dimensions of AI technology.
Giuseppe Ateniese is a Professor and Eminent Scholar in Cybersecurity at George Mason University, affiliated with the Department of Computer Science and Cyber Security Engineering. Formerly, he held the Farber Endowed Chair and chaired the Department of Computer Science at Stevens Institute of Technology. His research focuses on cloud security, applied cryptography, blockchain technology, and AI-driven cybersecurity solutions. He has received prestigious awards including the NSF CAREER Award and Google/IBM Faculty Awards. AIFurther, he teaches courses on blockchains, cryptography fundamentals, and cryptofinance. His work includes pioneering contributions to rewritable blockchains, password cracking via AI (e.g., PassGAN), and secure decentralized systems. Education: PhD in Computer Science, University of Genoa (2000) Laurea (M.Sc.), University of Salerno (1995) Master of Engineering (Honoris Causa), Stevens Institute of Technology (2020) Research Interests: Ateniese’s work bridges theoretical and applied cryptography, with emphasis on blockchain scalability, privacy-preserving machine learning, and secure data storage. He has developed foundational techniques like proxy re-cryptography and accountable storage systems. His recent efforts address AI’s dual role as both a cybersecurity tool and threat vector, including AI-based password cracking and defense strategies against LLM-driven attacks. Awards: NSF CAREER Award (for privacy/security research) Google Faculty Research Award (cloud security) IBM Faculty Award IEEE CISTC Technical Recognition Award Teaching & Labs: He teaches advanced courses on blockchains (CS695), cryptography fundamentals (CYSE/ECE476), and introductory cryptography (CS487/587). His research team focuses on cryptographic protocols, decentralized systems, and AI-security intersections. Collaborations include projects with Accenture on editable blockchain prototypes and NSF-funded work on password security.
Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.
Barbara Caputo is a Full Professor at Politecnico di Torino, leading the VANDAL Laboratory and directing the AI@PoliTo Interdepartmental Lab. She holds a double affiliation with the Italian Institute of Technology (IIT) and has held roles at Idiap-EPFL and Sapienza University. Her research focuses on AI, computer vision, domain adaptation, and federated learning. She contributes to national AI policy, including the Italian Strategy on AI and the National PhD on AI for Industry 4.0. She is an ERC Laureate and ELLIS Fellow, co-founding ELLIS society. Her work spans visual place recognition, action recognition, and cross-domain learning. Education: PhD in Computer Science from KTH Royal Institute of Technology (2005). Major roles include Rector’s Advisor on AI at PoliTo, Board Member of ELLIS, and coordinator of the AI & Industry 4.0 vertical in the National PhD program. Awards include ERC Laureate (2017), ELLIS Fellow (2019), and Inspiring Fifty Italy (2018). Her research emphasizes federated learning, domain adaptation, and AI ethics. Recent articles explore domain generalization, resource-efficient federated models, and AI-environment interactions. She collaborates with institutions like MUR, CNR, and the European Commission on AI policy and tech initiatives.
Ali H. Sayed is the Dean of the School of Engineering (Faculté des sciences et techniques de l'ingénieur - STI) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he also directs the Adaptive Systems Laboratory (Laboratoire de systèmes adaptatifs). Previously, he served as an emeritus professor and chair of the Electrical Engineering Department at UCLA. He is a highly cited researcher and a member of the US National Academy of Engineering and the World Academy of Sciences. Sayed served as president of the IEEE Signal Processing Society in 2018 and 2019. Professor Sayed's research focuses on adaptation and learning theories, data and network sciences, statistical inference, multi-agent systems, adaptive networks, and optimization. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and network science. He has made significant contributions to distributed learning algorithms, social learning over networks, and adaptive signal processing techniques that have influenced both academic research and practical implementations. His recent publications demonstrate a strong focus on multi-agent systems, distributed learning, privacy-preserving techniques, and social learning over networks. The research trends show increasing emphasis on federated learning with privacy guarantees, graph-based learning approaches, and the intersection of social dynamics with information processing. His work consistently addresses fundamental theoretical questions while maintaining relevance to practical applications in communication networks, social media analysis, and distributed artificial intelligence systems. Professor Sayed has received numerous prestigious awards throughout his career, including: IEEE Fourier Award (2022) Norbert Wiener Society Award (2020) IEEE Signal Processing Society Education Award (2015) Papoulis Award from the European Association for Signal Processing (2014) Technical Achievement Award from IEEE Signal Processing Society (2012) Terman Award from the American Society for Engineering Education (2005) IEEE Donald G. Fink Prize (1996) Multiple Best Paper Awards from IEEE and EURASIP Sayed has authored or co-authored over 570 publications and six monographs. He has mentored numerous PhD students and researchers in the fields of signal processing and adaptive systems. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Signal Processing (2003-2005) and EURASIP Journal on Advances in Signal Processing (2006-2007), as well as Founding Editor-in-Chief of the Open Access Book Series on Information and Learning Sciences. At EPFL, Professor Sayed leads the Adaptive Systems Laboratory, which focuses on developing theoretical frameworks and practical algorithms for adaptive systems, networked learning, and distributed signal processing. The lab's research encompasses both fundamental theoretical investigations and applications to real-world problems in communications, social networks, and computational biology.
Najim Dehak is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, part of the Whiting School of Engineering. His research focuses on machine learning applied to speech processing, audio classification, and health applications. He is renowned for developing the I-vector representation for speaker recognition, introduced in 2008 during a workshop at Johns Hopkins’ Center for Language and Speech Processing. Prior to this role, he was a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory. Dehak holds a PhD from the School of Advanced Technology in Montreal (2009). He is a Senior Member of IEEE and contributes to the IEEE Speech and Language Technical Committee. His work bridges AI, healthcare, and signal processing, with notable contributions to neurodegenerative disease detection via speech and handwriting analysis. Research interests include adversarial attacks on speech systems, multimodal biomarker discovery, and robust speech processing across demographics. His lab’s tools, like the Hermespeech Recorder, enable scalable data collection for clinical and research applications. Education: PhD in Advanced Technology (2009), Montreal Affiliations: Johns Hopkins University, IEEE Labs/Teams: Center for Language and Speech Processing (CLSP) His recent work explores AI’s role in aging research, including Alzheimer’s and Parkinson’s disease detection through speech, eye tracking, and handwriting analysis. Ongoing projects address fairness in speaker verification and robustness against adversarial attacks in ASR systems.
Elisa Ricci is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento and serves as Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. She coordinates the Doctoral Program in Information Engineering and Computer Science at the University of Trento and holds prestigious fellowships from ELLIS and IAPR. Her research focuses on advancing computer vision and deep learning systems capable of operating in open-world environments. Key interests include domain adaptation, continual learning, and self-supervised learning for visual and multi-modal data processing. Her work addresses critical challenges in enabling machines to adapt to new domains without forgetting prior knowledge, with applications spanning robotics perception, medical imaging, and privacy-preserving AI systems. Recent publications reveal a dominant trend toward leveraging vision-language models for open-vocabulary tasks, training-free adaptation methods, and federated learning architectures. Significant research thrusts include machine unlearning for privacy, robustness against bias in visual classifiers, and novel class discovery using foundation models—particularly evident in 2025 publications addressing medical imaging, 3D segmentation, and collaborative generative systems. Her major recognitions include: ELLIS Fellow IAPR Fellow As Doctoral Program Coordinator at the University of Trento, she oversees PhD training while leading the Deep Visual Learning unit at Fondazione Bruno Kessler. Her research group secures competitive grants in AI-driven perception systems, though specific funding sources aren't detailed in the source material. The Deep Visual Learning Research Unit specializes in open-world computer vision challenges, developing frameworks for domain adaptation, continual learning, and multi-modal perception. Current projects integrate generative models with robotics applications while addressing privacy concerns in vision-language systems through unlearning techniques.
Dr. Bisan Alsalibi is a Lecturer at the School of Information Technology, Monash University (Malaysia campus). She holds a PhD (2017) and MSc (2013) in computer science from Universiti Sains Malaysia, specializing in Artificial Intelligence. Previously, she served as Senior Lecturer at Taylor's University (2022–2023) and Teaching Fellow at Universiti Sains Malaysia (2018–2020). Education: PhD in Computer Vision and Optimization (2014–2017), Universiti Sains Malaysia MSc in Artificial Intelligence (2011–2013), Universiti Sains Malaysia Research focuses on AI-driven solutions in feature selection, bio-inspired optimization techniques, and computer vision applications. Her work bridges membrane computing and real-world problem-solving, including privacy-preserving recommender systems for social networks. She currently supervises PhD students exploring bio-inspired algorithms for complex challenges. Advising & Grants: Acknowledged PhD project areas: Bio-inspired optimization techniques and evolutionary machine learning
Jelani Nelson is a Professor and Department Chair in the UC Berkeley EECS Department (College of Engineering). His work focuses on theoretical computer science , particularly algorithms , data streams , dimensionality reduction , and privacy-preserving computation . Advising : Current students include Ishaq Aden-Ali, Xin Lyu, Mihir Singhal, and Hongxun Wu (co-advised with leading researchers). Education : PhD from MIT (George M. Sprowls Award), M.Eng from MIT. Research Highlights : Developed foundational results in Johnson-Lindenstrauss dimensionality reduction (optimality, sparse embeddings). Advancements in differential privacy (lower bounds, private mean estimation, threshold learning). Pioneering work on streaming algorithms for heavy hitters, norm estimation, and graph problems. Innovations in compressed sensing and oblivious subspace embeddings . Scientific Awards : PODS Best Paper Award (2011, 2022) IBM Pat Goldberg Memorial Best Paper Award (2011) George M. Sprowls Award for MIT doctoral thesis (2009) NeurIPS 2020 Spotlight Presentation
Nasir Memon is a Professor of Computer Science and Engineering at the New York University Tandon School of Engineering and concurrently serves as the Dean of Engineering at NYU Shanghai. He has been a faculty member at NYU Tandon since September 1998. Memon is a co-founder of NYU's Center for Cyber Security (CCS) and NYU Abu Dhabi, and the founder of key initiatives such as the OSIRIS Lab, CyberSecurity Awareness Week (CSAW), the NYU Tandon Bridge program, and the Cyber Fellows program. His work focuses on advancing cybersecurity education and addressing systemic biases in AI-driven systems. Education: Ph.D., Computer Science, University of Nebraska Master of Science, Mathematics, Birla Institute of Technology and Science (BITS), Pilani Bachelor of Engineering, Chemical Engineering, BITS, Pilani Research Interests: Media Forensics and Authentication Biometric Security and Privacy Data Compression and Privacy-Preserving Techniques Network Security and Incident Response AI Ethics and Fairness in Machine Learning Cybersecurity Education and Workforce Development Awards and Honors: IEEE Fellow (2010) SPIE Fellow (2014) Jacobs Excellence in Education Award (2002) NSF CAREER Award (1997) Advising and Grants: Advises Ph.D. students like Anubhav Jain and Govind Mittal, and mentors Master’s students such as Rishit Dholakia. Recipient of NSF grants and funding from NYU Abu Dhabi and Indiana University Bloomington for projects like computational tools for fact-checking and AI-driven bias mitigation. Labs and Teams: Directs the OSIRIS Lab, a leading research group in cybersecurity and AI. Leads initiatives at the NYU Center for Cybersecurity (CCS) and collaborates with NYU Abu Dhabi’s Center for Cyber Security.
Anoosheh Heidarzadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at Santa Clara University's School of Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Carleton University (2012) and previously served as a Visiting Assistant Professor at Texas A&M University (2018-2022) and Associate Research Scientist at the same institution (2015-2017). His postdoctoral research was conducted at the California Institute of Technology (2013-2014). His research focuses on: Information and coding theory : Fundamental limits of data transmission and storage systems Private and secure computing : Protocols for confidential data processing in networked environments Fault-tolerant distributed systems : Resilient computation frameworks for large-scale applications Distributed machine learning : Scalable algorithms for collaborative learning architectures Recent publications (2021-2022) demonstrate strong emphasis on privacy-preserving computation (covering 73% of articles) and distributed coding techniques (67% of articles), with innovations in private information retrieval, matrix operations, and group testing methodologies. Theoretical contributions dominate (87%), while 13% address applied challenges like COVID-19 screening.
Amartya Sanyal is a Tenure Track Assistant Professor at the Department of Computer Science (DIKU), University of Copenhagen, specializing in Machine Learning. He also serves as an Adjunct Professor at the Indian Institute of Technology Kanpur (2023–2025). His research focuses on critical areas of AI safety, data privacy, and robust learning. University: University of Copenhagen Department: Department of Computer Science Academic Rank: Assistant Professor Adjunct Role: IIT Kanpur (2023–2025) His work addresses challenges like differential privacy , data poisoning attacks , machine unlearning , and robust mixture learning . Recent publications analyze privacy-preserving techniques for large language models, fairness in collective action algorithms, and certified data release mechanisms. Amartya has received the Villum Young Investigator Award (2025). His research outputs emphasize online learning , adversarial robustness , and privacy-utility tradeoffs through rigorous theoretical frameworks and practical implementations. Scientific Award: Villum Young Investigator Award His collaborations span institutions like IIT Kanpur and involve interdisciplinary projects with industry partners. Current activities include talks on privacy with correlated data and machine unlearning advancements.
Dr. Arno Solin is a tenured Associate Professor in Machine Learning at Aalto University's Department of Computer Science and an Academy of Finland Research Fellow . He leads the Aalto machine learning research group and serves as Director of the Finnish Doctoral Program Network in AI (AI-DOC) . His work bridges probabilistic modeling with practical applications in sensor fusion and real-time inference. ELLIS Scholar (European Laboratory for Learning and Intelligent Systems) Adjunct Professor at Tampere University Member of Young Academy Finland (2021–2025) Research Interests focus on data-efficient machine learning with probabilistic methods for real-time inference and sensor fusion. Key areas include Gaussian processes, diffusion models, stochastic differential equations, and uncertainty quantification in deep learning. His group develops methods that combine structural constraints with adaptive learning for deployment on resource-limited hardware. Publication Trends show consistent output in top venues (NeurIPS, ICML, ICLR, AISTATS) with emphasis on diffusion models , 3D scene reconstruction , and real-time probabilistic modeling . Recent works explore physics-informed learning, heterophily-aware graph models, and compressed representations for world models in reinforcement learning. Scientific Recognition : Awarded AI Researcher of the Year 2024 by AI Finland Teacher of the Year 2023 at Aalto CS ISIF Jean-Pierre Le Cadre Best Paper Award (2018) MLSP Schizophrenia Classification Challenge Winner (2014) NeurIPS/ICML Reviewer Awards Research Leadership includes coordinating Finland's AI Center of Excellence program and directing the Finnish Doctoral Program Network in AI (AI-DOC). He supervises 15+ doctoral/postdoctoral researchers and has spun off Spectacular AI , a company commercializing sensor fusion technology.
Goran Strbac is a Professor of Energy Systems at Imperial College London's Faculty of Engineering, holding the Chair in Electrical Energy Systems. He leads the Department of Electrical and Electronic Engineering and directs the joint Imperial-Tsinghua Research Centre on Intelligent Power and Energy Systems. His roles include IPCC WG 3 Leading Author, OFGEM RIIO-2 Challenging Group Member, and member of multiple EU energy platforms. Strbac's research focuses on multi-energy systems integration, energy market design, renewable integration, distributed energy resources, and grid resilience. Notable contributions include whole-energy system modeling across operation and investment scales, market mechanisms for flexibility services, and security assessments for low-carbon infrastructure. His work emphasizes resilience and decarbonization, with over 500 publications and 4 co-authored books. He advises governments and regulatory bodies on energy policy, including contributions to UK Smart System Forum and European Technology and Innovation Platforms. His recent articles explore AI-driven grid optimization, hydrogen integration, and transactive energy systems. Strbac leads major initiatives like the UK Centre for Grid Scale Energy Storage and chairs resilience-focused research clusters. His research bridges academia and industry, informing policy through rigorous systems analysis and innovative modeling frameworks.
