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Daniel HoView profile

Professor

• AI Governance
• Environmental Policy
• Legal Technology
• +3 more

Daniel Ho is the William Benjamin Scott & Luna M. Scott Professor of Law at Stanford University, holding concurrent appointments in Political Science and Computer Science (courtesy). He serves as Director of the Regulation, Evaluation, and Governance Lab (RegLab), and as Senior Fellow at multiple institutes including Stanford HAI and the Institute for Economic Policy Research. His roles include advising the White House on AI policy, serving on the National AI Advisory Commission, and advising the U.S. Department of Labor on responsible AI implementation. Ho holds a J.D. from Yale Law School and a Ph.D. from Harvard University, with prior judicial clerkship experience at the U.S. Court of Appeals. His research focuses on AI governance, legal technology, environmental policy, and algorithmic fairness, integrating computational methods with public administration challenges. Key research interests include AI's societal impacts, legal reasoning automation, regulatory innovation, and equity in algorithmic systems. His work bridges technical and legal domains, addressing challenges in cybersecurity, environmental protection, tax auditing, and racial disparity analysis. Recent projects involve developing benchmarks for AI legal reasoning and frameworks for auditing generative models. Ho leads interdisciplinary teams advancing AI policy implementation, including tools for mapping historical racial covenants and assessing environmental enforcement through satellite imagery. His work emphasizes practical solutions for government agencies, combining technical rigor with legal expertise to shape equitable outcomes.

Meghdad KurmanjiView profile

Research Fellow

• Machine Learning
• Privacy
• Trustworthy AI
• +4 more

Meghdad Kurmanji is a Research Fellow at the University of Cambridge, working in the CaMLSys lab under Prof. Nic Lane. Their research focuses on machine unlearning, privacy, and trustworthy AI, with applications in computer vision, language models, and federated learning. They hold a PhD from the University of Warwick, where they developed novel solutions for updating machine learning models in learned database systems, addressing both continual learning and unlearning challenges. Education: PhD in Computer Science at the University of Warwick (supervised by Prof. Peter Triantafillou). Prior work includes contributions to learned database systems and approximate query processing. Research interests emphasize bridging gaps between theoretical machine unlearning and practical regulatory requirements, particularly in large language models (LLMs). Key themes include optimizing communication in foundation model training, designing scalable unlearning pipelines, and ensuring ethical AI compliance. Recent publications highlight advancements in unlearning techniques for LLMs (e.g., LUNAR), communication-efficient distributed training (DES-LOC), and foundational analyses of unlearning challenges. Their work also explores dynamic data systems and out-of-distribution detection in learned databases. Labs/Teams: Active member of the CaMLSys lab, collaborating on machine learning systems research.

• Epidemiology of Ageing
• Public Health Modelling
• Dementia Research
• +5 more

Dr. Sara Ahmadi Abhari is a Lecturer in the Epidemiology of Ageing at the School of Public Health, Faculty of Medicine, Imperial College London. She holds an MD from Tehran University (2008) and a PhD in Epidemiology from the University of Cambridge (2014), supported by the Gates Cambridge Scholarship and Krishnan-Ang Scholarship. Her research focuses on dementia biomarkers, public health modeling for disability/dementia burden forecasting, and evaluating interventions' impacts on aging populations. She has prior experience in the Iranian National Malaria Control Program (UNDP) and UCL epidemiology research (2014–2018). Key research areas include Alzheimer’s biomarkers, cardiovascular-dementia linkages, and pandemic effects on older adults. She has published extensively on epidemiological modeling, including studies on hypertension trends, diabetes-dementia comorbidities, and social restrictions' impact on physical activity. Her work integrates clinical, demographic, and AI-driven methods to address public health challenges. Awards: Gates Cambridge Scholarship, Trinity College Krishnan-Ang Scholarship Education: PhD Epidemiology (Cambridge), MD (Tehran University) Her grants and collaborations involve forecasting dementia/disability burdens, analyzing large cohort data (e.g., Whitehall II Study), and evaluating health policy implications. She contributes to global initiatives like the May Measurement Month blood pressure screening campaigns.

• Artificial Intelligence and Society
• Emerging Technologies and Business Impact
• Algorithmic Fairness and Ethics
• +3 more

Associate Professor Sandra Peter is Director of Sydney Executive Plus and an Associate Professor at the University of Sydney Business School . She is also a member of The Net Zero Institute . Her work centers on the intersection of emerging technologies, AI, and their strategic implications for business and society, with a focus on ethical governance and future-readiness. Education: PhD in Social Sciences MA/MPhil in Organisation and Strategy BSc (Hons) in Economics and International Relations Research Interests: Sandra’s research explores how organizations and leaders can engage productively with the future, particularly in the context of AI and digital transformation. Her work spans algorithmic fairness, AI ethics, the future of work, open education, and the societal impact of emerging technologies. She actively contributes to public discourse through media commentary and thought leadership. Publications & Contributions: Her recent work emphasizes the human-like qualities of AI, the ethical risks of generative systems, and the evolving role of AI in creative and professional domains. She has co-authored influential reports such as The 2025 Skills Horizon and has been widely cited in major media outlets for her insights on AI, leadership, and the future of work. Advising & Grants: She currently supervises research students including Stefanie Knippschild (working on algorithmic fairness in employment) and Belinda Wang (on responsible AI use in organizations). She has also led educational innovation grants, including a 2013 project on formative feedback in flipped classrooms. Leadership & Outreach: As Director of Sydney Executive Plus , Sandra leads executive education initiatives designed to equip leaders with the skills needed to navigate technological disruption. She co-hosts The Future, This Week and The Unlearn Project podcasts, and is a frequent contributor to The Conversation , AFR , and ABC .

Antonino NoceraView profile

Associate Professor

• Data Science
• Social Network Analysis
• Privacy
• +5 more

Antonino Nocera is an Associate Professor at the University of Pavia's Department of Electrical, Computer and Biomedical Engineering, within the Faculty of Engineering. He specializes in Data Science, Social Network Analysis, Privacy, Security, and Artificial Intelligence. He holds a PhD in Information Engineering from the University Mediterranea of Reggio Calabria (2013). His research focuses on cybersecurity, machine learning applications, and privacy-preserving technologies, with over 85 publications. He is an Associate Editor for *Information Sciences* (Elsevier) and *IEEE Transactions on Information Forensics and Security*. He leads the Digital Content Analysis Lab (DCALab) and collaborates with Microsoft on cloud computing initiatives. Recent projects include a 2020 Hackathon on analyzing the spread of the SARS-CoV-2 virus and a Microsoft Learn mini-course on Azure. His work spans federated learning security, malware detection, and IoT trust models. He actively participates in conference TPCs and promotes ethical AI practices. Key contributions include frameworks like SECTIS for CTI sharing and DROIDTTP for Android application analysis. His teaching emphasizes data science and big data analytics, integrating industry tools like Azure into curricula.

Emanuele RodolàView profile

Professor

• Representation Learning
• Geometry Processing
• Graph and Geometric Deep Learning
• +3 more

Emanuele Rodolà is a Full Professor of Computer Science at Sapienza University of Rome, leading the GLADIA group focused on Geometry, Learning, and Applied AI. He holds an ERC Grant and a Google Research Award. Previously, he was an Assistant and Associate Professor at Sapienza (2017-2020), a postdoc at USI Lugano, a Humboldt Fellow at TU Munich, and a JSPS Research Fellow at The University of Tokyo. He is an ELLIS fellow and member of the Young Academy of Europe, with multiple research awards. His work bridges representation learning, geometry processing, graph/geometric deep learning, and audio/visual applications. Recent research includes unlearning mechanisms, neural model dynamics, and 3D shape reconstruction. He actively serves on top-tier conference committees and organizes workshops in AI and computer vision. Education History: JSPS Research Fellowship, The University of Tokyo (2013) Humboldt Fellowship, TU Munich (2013-2016) Postdoc, USI Lugano (2016-2017) Research focuses on foundational AI models, scalable latent space techniques, and interdisciplinary applications like music generation from EEG signals. His 150+ papers span geometric deep learning, diffusion models, and multimodal systems. Awards include ELLIS fellowship and Young Academy membership.

• Digital Twins
• Edge Computing
• Federated Learning
• +6 more

Paolo Bellavista is a Professor at the University of Bologna, Department of Electrical, Electronic, and Information Engineering, specializing in Computer Science and Engineering. His research focuses on cutting-edge technologies bridging the physical and digital worlds, with particular emphasis on Digital Twins, Edge Computing, Federated Learning, and IoT systems across various application domains. His research interests span Digital Twins implementation in industrial settings, Edge Computing architectures, Federated Learning techniques, Internet of Things applications, 5G/6G networking, Cloud Computing paradigms, and Smart Manufacturing systems. His work demonstrates a strong focus on practical implementations of theoretical concepts, particularly in the context of distributed systems where latency, reliability, and security are critical concerns. Analysis of his recent publications reveals a strong trend toward integrating Digital Twin technology with edge computing infrastructure, developing privacy-preserving federated learning techniques, and creating efficient communication protocols for industrial IoT applications. His work spans both theoretical contributions and practical implementations across smart factories, transportation systems, aviation, and urban infrastructure. While specific awards are not mentioned in the available data, his extensive publication record in top-tier venues demonstrates significant recognition within the academic community. His collaborations span numerous institutions and researchers across Europe and beyond. Professor Bellavista actively supervises research in distributed systems, with numerous PhD students and postdoctoral researchers contributing to his projects. His research has been supported by various grants focused on next-generation networking, edge computing, and industrial digitalization initiatives. His laboratory work centers around the IoTwins platform for implementing distributed digital twins in industrial manufacturing and facility management settings, with applications spanning smart factories, urban environments, and transportation systems. Current research directions include entanglement-aware middleware for digital twins, federated unlearning techniques, and adaptive resource management in cloud-to-edge continuum environments.

Erin GeeView profile

Adjunct Professor

• Digital Instrumentation
• Experimental Sound Art
• Feminist Technology
• +3 more

Erin Gee is an Adjunct Professor at the Faculté de musique , Université de Montréal. She holds a Doctorate in Music from Université de Montréal (2020), alongside degrees from the University of Regina (B.Mus.Ed., BFA) and Concordia University (MFA). Her research explores the intersection of technology, emotion, and the body, using biosensors, AI, and robotics to bridge human and computational sensory systems. Education: Bachelor of Music Education (University of Regina, 2006), BFA (2009), MFA (Concordia, 2014), PhD (UdeM, 2020). Affiliations: Member of the Board (CA) at artist collective perte de signal . Research Interests : Focused on emotion’s biological functions, feminist critiques of tech, and embodied interaction. Projects like Swarming Emotional Pianos and Machine Unlearning explore biofeedback, voice, and AI-generated art. Funded by SSHRC’s cinEXmedia project (2022–2030). Artistic Works : Includes Pinch and Soothe (interactive biofeedback instrument), VR game H.E.A.R.T , and performances addressing ASMR and robotic choirs. Exhibited globally, including Toronto Biennial, Ars Electronica, and Musée d’Art Contemporain de Montréal. Grants : Received a Partnership Grant (SSHRC, PV128152) for cinEXmedia , fostering inclusive tech use in health and education.

Dadong WangView profile

Adjunct Professor

• Image Analysis
• Computer Vision
• Artificial Intelligence
• +3 more

Dr. Dadong Wang is a Senior Principal Research Scientist and Group Leader of the Imaging and Computer Vision (ICV) Group at CSIRO Data61. He holds academic roles as a Conjoint Professor at UNSW and an Adjunct Professor at UTS. His expertise spans image analysis, computer vision, artificial intelligence, and medical imaging, with a focus on end-to-end solutions for industrial and scientific applications. Education: PhD in Engineering (2002) Doctor of Engineering (1997) Master of Engineering (1993) Bachelor of Engineering (1990) Research Interests: Dr. Wang’s work emphasizes AI-driven solutions for multi-dimensional image data, including applications in agriculture, healthcare, and environmental monitoring. His research bridges theoretical advancements with practical industrial deployment. Awards & Recognition: Winner of APICTA Research & Development Project of the Year (2023) CSIRO Research Achievement Award (2012, 2009) IEEE Senior Member (2010) Keynote speaker at major AI conferences (e.g., Australasia Joint Conference on AI, 2023) Advising & Grants: His team actively seeks PhD candidates in areas like deep learning and augmented reality. Projects include pneumoconiosis diagnosis, fisheries monitoring, and smart agriculture systems. Collaborations span industry and academia, with grants supporting innovation in imaging technologies. Labs & Teams: Leads the ICV Group, which develops cutting-edge computer vision tools and collaborates with industry partners to commercialize solutions. Recent projects include AI for meat quality control and environmental compliance systems.

Mahdi SoltanolkotabiView profile

Professor

• Machine Learning
• Data Science
• Artificial Intelligence
• +3 more

Mahdi Soltanolkotabi is a Professor at the University of Southern California (USC) in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering. He serves as the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Previously, he was a postdoctoral researcher at UC Berkeley, advised by Ben Recht and Martin Wainwright, after completing his Ph.D. in Electrical Engineering at Stanford under Emmanuel Candès. His research bridges theoretical foundations of data science with applied AI, focusing on generative AI, machine learning, signal processing, and computational imaging. Education: Ph.D. in Electrical Engineering (2014, Stanford), Postdoc (2014-2015, UC Berkeley). Research interests include developing mathematical frameworks for generative AI, non-convex optimization, high-dimensional probability, and reliable AI systems for healthcare and scientific discovery. His applied work emphasizes trustworthy AI, medical imaging, and interdisciplinary collaborations with domain scientists. Publications highlight contributions to phase retrieval, subspace clustering, federated learning, and neural network optimization. Recent work emphasizes AI reliability (e.g., MediConfusion, Serpent) and scalable image restoration techniques. Awards: Packard Fellowship, NIH Innovator Award, Sloan Fellowship, NSF Career Award, AFOSR-YIP. Grants/Outreach: NIH-funded projects, collaborations with K-12 STEM education programs (Nava College Preparatory Academy, USC Viterbi Adopt-a-School). Labs/Teams: Leads USC’s AIF4S, focusing on foundational AI for science.

• Decision support systems
• Computational intelligence
• Recommender systems
• +4 more

Distinguished Professor Jie Lu is Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where she also serves as Director of the Australian Artificial Intelligence Institute (AAII). With over two decades of academic service at UTS, she has held progressively senior roles including Professor since 2007, Head of School of Software (2011-2014), and Director of the Centre for Artificial Intelligence (2017-2020) before assuming her current leadership positions. Professor Lu earned her PhD from Curtin University in 2000 and joined UTS the same year. Her academic journey progressed from Lecturer (2000-2001) to Senior Lecturer (2002-2004), Associate Professor (2005-2007), and ultimately Professor (2017-present). Professor Lu's research spans computational intelligence with particular expertise in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. Her work bridges theoretical innovation with practical applications across transportation, telecommunications, healthcare, and education sectors. She has pioneered approaches in data-driven decision making that deliver tangible economic benefits and risk management solutions for industry partners. Her recent publications demonstrate continued leadership in AI research, with a focus on addressing challenges in non-stationary environments, out-of-distribution detection, cross-domain recommendation, and healthcare applications. Her work increasingly integrates large language models with specialized domain knowledge for enhanced decision support. Officer of the Order of Australia (2022) 2023 NSW Premier's Prize for Excellence in Engineering or Information & Communication Technology Australian Laureate Fellow (2019) IEEE Fellow for contributions to fuzzy machine learning and decision making (2018) Fellow of International Fuzzy Systems Association (2017) Australia's Most Innovative Engineer Award (2019) Multiple IEEE Transactions Outstanding Paper awards Professor Lu has supervised over 50 PhD students to completion, with half pursuing academic careers and half working in industry. She has secured over A$10 million in research funding as lead Chief Investigator, including 10 highly competitive ARC Discovery Projects. Her industry collaborations include significant projects with Optus, Sydney Trains, Domain Holdings, and healthcare organizations. As Director of the Australian AI Institute, Professor Lu leads Australia's largest AI research hub with over 250 researchers and PhD students. Under her leadership, AAII has secured 47 ARC grants and over 110 industry projects since 2017. She also serves as Editor-in-Chief of Knowledge-Based Systems, a leading journal in the field.

• Cybersecurity
• Privacy-Preserving Machine Learning
• Cryptography
• +5 more

Dr. Maggie Liu is a Lecturer in Cybersecurity and Software Systems at RMIT University's School of Computing Technologies. Her research focuses on secure multi-party computation and its applications in privacy-preserving machine learning (PPML), with specific emphasis on practical deployments for medical diagnostics and mobile image classification. Key research areas include: Designing lightweight cryptographic protocols Secure computation techniques Machine learning systems security Interdisciplinary privacy-preserving solutions Her recent work involves PPML for medical services, mobile-edge computing, and collaborative learning systems. Notable projects include MediSC, CryptMed, OblivGNN, Sonic, EncSIM, and Leia. Maggie Liu earned her Ph.D. in Computer Science from RMIT University.

Ozan ÖzdenizciView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Robustness and Safety in ML
• +4 more

Assoc. Prof. Ozan Özdenizci is a faculty member at the Institute of Machine Learning and Neural Computation, TU Graz, Austria. His research focuses on robustness, safety, and efficiency in machine learning, particularly in adversarial robustness, spiking neural networks, and privacy-aware learning. He holds a PhD from Northeastern University (2016), and MSc/BSc degrees from Sabancı University (2010/2008). Previously, he served as a postdoc at TU Graz (2016–2020) and a research group leader at Montanuniversität Leoben (2020–2023). Affiliations: TU Graz (2023–present), Montanuniversität Leoben (2020–2023), TU Graz Postdoc (2016–2020) Education: PhD (Northeastern University, 2016), MSc/BSc (Sabancı University, 2010/2008) Research Interests: Developing ML systems with robustness guarantees, efficient deep learning (e.g., spiking networks), and privacy-aware mechanisms. Key areas include adversarial defense mechanisms, sparse network design, and neuromorphic computing applications. His work bridges theoretical foundations with practical implementations in computer vision and autonomous systems. Recent Contributions: Advances in privacy-aware lifelong learning (ICLR 2025), robust spiking networks (TMLR 2024), and weather-resistant vision models (TPAMI 2023). His research emphasizes trade-offs between model efficiency, robustness, and scalability. Awards: Top Reviewer at NeurIPS 2023, Outstanding Reviewer at ICML 2022/ICLR 2022 Labs/Teams: Member of Graz Center for Machine Learning (GraML) and ELLIS Unit Graz

Amit K. Roy-ChowdhuryView profile

Professor

• Computer Vision
• Machine Learning
• Robot Autonomy
• +3 more

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +3 more

Dr. Thomas Tan is an Associate Professor at the School of Computing Engineering and the Built Environment , Edinburgh Napier University . Specializing in Cybersecurity , Internet of Things , and Machine Learning , he leads cutting-edge research in secure distributed systems and edge computing. Focus on federated learning security, malware classification, and vehicular network trust models Recipient of National Research Award 2017 (Oman) and SICSA Supervisor of the Year Honourable Mention Supervises PhD/MSc students in topics ranging from cyberbullying detection to blockchain authentication His 117+ research outputs include IEEE journal articles on UAV task scheduling, graph neural network security, and privacy-preserving IoT frameworks. Active grants cover federated unlearning evaluation (Carnegie Trust £73,564) and AI-driven cybersecurity collaboration (SICSA £2,000).