Battista Biggio is a Full Professor at the University of Cagliari, Italy, affiliated with the Department of Electrical and Electronic Engineering under the Faculty of Engineering and Architecture. His research focuses on machine learning security, adversarial attacks, and cybersecurity. He co-founded the cybersecurity firm Pluribus One and has pioneered foundational work in poisoning attacks and adversarial robustness. Education: MSc (2006), PhD (2010). He holds editorial roles as Associate Editor-in-Chief for Elsevier's Pattern Recognition Journal and serves on IEEE TNNLS and IEEE CIM editorial boards. His awards include the 2022 ICML Test of Time Award and the 2021 Pattern Recognition Medal. He chairs IAPR TC1 and organizes conferences like S+SSPR and AISec. Research interests span adversarial machine learning, malware detection, and secure AI systems. He leads initiatives such as the sAIfer Lab and co-develops the SecML-Torch library. Teaching includes courses on Machine Learning Security and Industrial Software Development. Notable contributions include seminal papers like 'Poisoning Attacks against Support Vector Machines' and 'Wild Patterns.' He manages over 10 research projects and advises on AI security for industrial applications. His work bridges academic research with practical cybersecurity solutions.
Thompson S.H. Teo is a Professor in the Department of Analytics and Operations (DAO) at the National University of Singapore (NUS) Business School. He holds editorial roles in top journals including European Journal of Information Systems, International Journal of Information Management, and Communications of the AIS. His research spans information systems strategy, business-IT alignment, e-commerce, sustainability, and AI's societal impacts. With over 200 publications, he ranked #143 globally in 2023 among business scientists (research.com) and was recognized in Stanford's top 2% global scientists (2021-2023). Awards include the Best Associate Editor Award (2017) and AIS Distinguished Membership. Thompson's research interests include IT adoption, cyberloafing, supply chain digitalization, and green innovation. He teaches courses on innovation, Industry 4.0, and strategic IT at undergraduate, masters, and executive levels. Notable contributions include frameworks for commute experience analysis and AI adoption market signals. His work bridges theory and practice, addressing challenges in sustainability, organizational behavior, and digital governance. Affiliations: NUS Business School, Distinguished Editorial Advisory Board (IJoIM), Senior Member (INFORMS) Grants & Leadership: Extensive editorial leadership, supervised China Scholarship Council PhD students, and executive programs on innovation design thinking. Key Contributions: Over 270 publications, four co-edited books on IT/e-commerce, and policy-relevant studies on environmental regulations and green tech adoption. His research often employs mixed methods (e.g., QCA for corruption analysis, PLS-SEM for cyberloafing models) and addresses global issues like fake news mitigation via ChatGPT and pandemic-era customer engagement in short videos.
Jeffrey P. Bigham is an Associate Professor at the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University . His research spans human-computer interaction , human-AI interaction , accessibility , dialog systems , NLP , and crowdsourcing . Current PhD Students: Hamza El Alaoui, Jessica Yin Huynh, Sara Kingsley, Peya Mowar, Yi-Hao Peng, Atieh Taheri PhD Graduates: Erin Brady, Yu Zhong, Ting-Hao Huang, Anhong Guo, Cole Gleason, Prakhar Gupta, Stephanie Valencia, Kundan Krishna, Jason Wu His work is funded by Apple , Bosch , DARPA , Google , Microsoft , the National Institute of Disability Rehabilitation Research , the National Science Foundation , and Yahoo! He also holds a CMU HCII Career Development Fellowship . Selected Awards: NSF CAREER Award 2019 Best Paper at ASSETS 2021 Best Paper Nomination at CHI 2024 Best Paper Nomination at CHI 2021 Best Paper Nomination at DIS 2021
Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.
Esther Rolf is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder. Her research focuses on blending methodological and applied machine learning techniques to address social and environmental challenges, emphasizing usability, data-efficiency, and fairness. Her work includes developing algorithms for environmental monitoring with satellite imagery and advancing geospatial ML systems. Her research explores the multifaceted role of data representation in machine learning, particularly how spatial distribution and data acquisition impact model fairness and efficacy. Projects include formalizing representivity in training data and tackling evaluation challenges in geospatial ML applications. Recent publications highlight her expertise in satellite-based poverty mapping, multimodal data efficiency, and geospatial foundation models. She integrates specialized architectures and domain-specific benchmarks into her work. Best Paper Award, ICML (2018) Best Paper Award, NeurIPS Workshop on AI for Social Good (2019) NSF Graduate Research Fellowship Google Research Fellowship Esther's lab at CU Boulder recruits PhD students and postdocs interested in statistical/geospatial ML and context-driven research for real-world problems. She teaches graduate courses in machine learning and geospatial ML at CU Boulder.
Hima Lakkaraju is an Assistant Professor at Harvard University with dual appointments in the Harvard Business School and the Department of Computer Science. Her research focuses on trustworthy AI, including machine learning interpretability, fairness, privacy, and safety. She holds a PhD from Stanford University and has received accolades such as the Alfred P. Sloan Fellowship and NSF CAREER Award. Her work bridges algorithmic foundations and societal implications of AI, with applications in healthcare, policy, and business. Education: PhD in Computer Science from Stanford University (2013-2017). Academic background includes roles at IBM Research, Microsoft Research, and Adobe. Research Interests: Algorithmic Foundations of AI Interpretability and Explainable AI Fairness and Bias Mitigation Privacy-Preserving ML Generative Models and LLMs Ethical AI Policy and Regulation Key Achievements: Over 100 publications in top venues like NeurIPS and ICML; co-founder of the Trustworthy ML Initiative; featured in MIT Tech Review, Forbes, and Harvard Business Review. Current projects include the AI4LIFE research group and work on regulatory frameworks for AI. Advising and Grants: Supervises over 30 students across PhD, master's, and postdoc levels. Research supported by NSF, Sloan Foundation, Schmidt Sciences, Google, Amazon, and others. Initiatives include the Regulatable ML workshop and NeurIPS ethics co-chair roles. Labs and Collaborations: Leads Harvard's AI4LIFE group and collaborates with industry partners like Fiddler AI. Active in policy discussions on AI regulation and societal impact.
Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford’s Institute of Biomedical Engineering. He leads the Computational Health Informatics (CHI) Lab, focusing on AI-driven healthcare solutions with a strong emphasis on translational research in low- and middle-income countries (LMICs). His work spans digital health technologies, medical imaging analysis, and AI ethics. Clifton holds multiple fellowships, including from the Alan Turing Institute and Fudan University. Key affiliations include co-directorship of the Oxford-CityU Centre for Cardiovascular Engineering and involvement in the Wellcome Trust’s Flagship Centre in Vietnam. His research has been commercialized through spinouts like OBS Medical and Oxehealth. Notable projects include AI tools for non-invasive vital sign monitoring and pandemic response strategies using audio-based health data. Clifton’s awards include the IEEE Early Career Award (2022) and the Vice-Chancellor’s Innovation Prize. His lab’s Suzhou branch focuses on open-source digital health research using public datasets. Current research themes include multimodal data integration, generative AI in healthcare, and equitable AI deployment across global health systems.
Steven W. Popper is a Professor of Policy Analysis at the RAND School of Public Policy and an Adjunct Senior Economist at the RAND Corporation. He also serves as Distinguished Professor of Decision Sciences at Tecnológico de Monterrey’s School of Government and Public Transformation, reflecting his international academic engagement. His work bridges economics, science and technology policy, and strategic decision-making under uncertainty. Research Interests: Popper's expertise lies in decision-making under deep uncertainty (DMDU), robust decision making (RDM), assumption-based planning, and science, technology, and innovation policy. He has made seminal contributions to long-term policy analysis, particularly through his co-authorship of Shaping the Next One Hundred Years (2003). His research addresses complex systems in economic development, international relations, and environmental planning. Publication Trends: His recent work applies RDM methodologies to diverse domains such as U.S.-China economic competition, Israeli defense and energy policy, transportation planning, and future technologies. His publications often involve scenario analysis, foresight, and adaptive strategies for policy resilience in uncertain futures. Scientific Awards and Leadership: Founding officer and current Finance Chair, Society for Decision Making under Deep Uncertainty Past Chair, Industrial Science and Technology Section, American Association for the Advancement of Science Consultant to the World Bank, OECD, and multiple national governments Advising and Grants: While specific advisees are not listed, Popper has led numerous high-impact research projects funded by U.S. federal agencies and international bodies. His role as Associate Director of the Science and Technology Policy Institute (1996–2001) involved providing analytic support to the White House Office of Science and Technology Policy, indicating extensive grant-funded research leadership. Labs and Teams: Popper is affiliated with RAND’s research teams focused on policy analysis, innovation, and strategic foresight. He collaborates with interdisciplinary teams applying modeling and simulation tools to public policy challenges, particularly through the Robust Decision Making framework.
Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.
Hanbyul Joo is an Assistant Professor in the Department of Computer Science and Engineering at Seoul National University (SNU). Prior to joining SNU, he was a Research Scientist at Facebook AI Research (FAIR) in Menlo Park. He completed his Ph.D. in the Robotics Institute at Carnegie Mellon University, working with Yaser Sheikh, and received his M.S. in Electrical Engineering and B.S. in Computer Science from KAIST, Korea. Dr. Joo's educational journey began at KAIST, where he earned both his Bachelor's and Master's degrees. He then pursued his Ph.D. at Carnegie Mellon University's Robotics Institute, completing his dissertation titled "Sensing, Measuring, and Modeling Social Signals in Nonverbal Communication." His doctoral work focused on developing the Panoptic Studio, a unique sensing system with over 500 synchronized cameras for capturing social interactions. Dr. Joo's research primarily focuses on endowing machines and robots with the ability to perceive and understand human behaviors in 3D . His goal is to build "social Artificial Intelligence" that can interact with humans using social signals (body languages). He pursues this direction using data-driven methods where data is collected by measuring the wide spectrum of social signals transmitted during interpersonal social interaction. His research spans computer vision, machine learning, computer graphics, and robotics , with particular emphasis on 3D human pose estimation, human-object interaction, and social signal processing. His recent publications demonstrate a clear trend toward leveraging diffusion models for 3D reconstruction and generation tasks, with a focus on human-centric applications. His work bridges the gap between 2D image understanding and 3D scene reconstruction, often utilizing pre-trained models to overcome data limitations. The research consistently addresses fundamental challenges in understanding human behavior, interaction with objects, and social dynamics in 3D space. Dr. Joo is a recipient of several prestigious awards including the Samsung Scholarship and the CVPR Best Student Paper Award in 2018 . His paper "Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies" received this honor at CVPR 2018. His research has been widely recognized in top computer vision and AI conferences, with multiple oral presentations at venues like CVPR, ICCV, and ECCV. Dr. Joo actively mentors a large group of students, with approximately 15 current students working toward MS/PhD degrees under his supervision. His lab, the SNU VCLab, focuses on cutting-edge research in computer vision and graphics. He has secured significant research funding through his work, though specific grant details aren't provided on his website. Dr. Joo frequently serves as an area chair for major conferences including CVPR, ICCV, and NeurIPS, demonstrating his standing in the academic community. Dr. Joo leads the SNU VCLab, which has developed several notable datasets and tools including SNU ParaHome, FrankMocap, and the CMU Panoptic Studio Dataset. His lab maintains strong industry connections, with students interning at leading companies like Meta. The lab's research focuses on building the infrastructure and algorithms needed for social AI, with an emphasis on practical applications that can be deployed in real-world settings.
W. Hong Yeo is a Professor in the Woodruff School of Mechanical Engineering and Program Faculty in Bioengineering at the Georgia Institute of Technology, where he also directs the WISH Center. He holds adjunct appointments in the Wallace H. Coulter Department of Biomedical Engineering. Previously, he was an Assistant Professor at Virginia Commonwealth University (2014-2016) and a postdoctoral fellow at the University of Illinois Urbana-Champaign's Beckman Institute. Dr. Yeo's research integrates nanomechanics, soft materials, and nano-microfabrication to develop bio-interfaced systems. Key areas include: Flexible Bioelectronics : Wearable/implantable sensors for health monitoring Human-Machine Interfaces : Neural prosthetics and soft robotics Translational Nanoengineering : Nanoparticle biosensing and diagnostics His publications (2023-2025) demonstrate strong focus on wireless health technologies, including multi-modal wearable systems, implantable sensors for cardiovascular/neurological monitoring, and AI-integrated diagnostics. Trends show increasing emphasis on closed-loop therapeutic systems and scalable manufacturing. Awards & Recognition : BMES Innovation and Career Development Award Virginia Commercialization Award Blavatnik Award Nominee NSF Summer Institute Fellowship Research funding sources include MEDARVA Foundation, NIH, DARPA, and industry partners like CooperVision. He leads the Center for Human-Centric Interfaces & Engineering , developing next-generation bio-interfaced systems.
Professor Barney Tan serves as Professor of Information Systems and Senior Deputy Dean (Impact and Partnerships) at UNSW Business School. Previously, he held the position of Head of School (Information Systems and Technology Management) and was formerly Professor of Strategic Information Systems at The University of Sydney. Professor Tan completed his PhD in Information Systems at the National University of Singapore in 2012, following a Bachelor of Computing (Honors) in Information Systems from the same institution in 2004. His research focuses on strategic information systems, AI management and implementation, digital platforms and ecosystems, fintech, and information systems for sustainable development with particular emphasis on IT trends in the Asia-Pacific region including China and ASEAN countries. As an engaged researcher, Professor Tan is deeply passionate about translational research that contributes toward sustainable development and frequently provides advisory support to organizations he has studied, including national governments through his work with the United Nations International Think Tank for Landlocked Developing Countries. His research has been published in top-tier journals including MIS Quarterly, Information Systems Research, Journal of Association of Information Systems, Information Systems Journal, and The Journal of Strategic Information Systems. Senior Editor, Information Systems Journal (2022-Present) Senior Editor, Information Technology and People (2019-Present) Associate Editor, Internet Research (2020-Present) Associate Editor, Information and Management (2016-Present) Editorial Board Member, Information Systems Research (2022-Present) Editorial Board Member, IEEE Transactions on Engineering Management (2017-Present) Professor Tan has received numerous prestigious teaching and research awards throughout his distinguished career: 22 awards in 11 years at The University of Sydney SUPRA Teacher of the Year award for 2019 and 2020 SUPRA Supervisor of the Year award for 2019, 2020 and 2021 The University of Sydney Business School Dean's Award for Teaching in 2019 Multiple Dean's Citations for Teaching across various courses and semesters Professor Tan has successfully secured multiple competitive research grants: Hong Kong University Grants Committee General Research Fund (2020-2022): 'The Impact of Social Media on Digital Guanxi Formation in the Chinese Workplace', Co-Investigator, HKD$475,617 (A$88,286) The University of Sydney Business School Industry Partnership Grant (2017): 'International Think Tank for Landlocked Developing Countries Research Development Project', Chief Investigator, A$27,238 Zhejiang Gongshang University Strategic Research Grant (2016-2019): 'Emerging Digital Commerce Platforms: Development Patterns and Usage Behavior', Principal Investigator, CNY$300,000 (A$60,535) Multiple University of Sydney Bridging Support Grants (2015-2016) focused on digital commerce and Australian-China trade Professor Tan is frequently featured in mainstream media outlets including ABC News, Business Insider, Sydney Morning Herald, The Australian, News.com.au, The Australian Financial Review, CIO, and Bloomberg Businessweek, where he provides expert commentary on technology-related topics such as cryptocurrency, digital platforms, and China's tech landscape.
Cathy Wu is an Associate Professor at MIT, with affiliations in the Laboratory for Information and Decision Systems (LIDS), Department of Civil and Environmental Engineering (CEE), and Institute for Data, Systems, and Society (IDSS). Her research group focuses on integrating machine learning with model-based optimization to solve complex problems in transportation systems and cyber-physical systems. Academic Leadership: Class of 1954 Career Development Associate Professor (MIT) Research Grants: NSF CAREER Award, Amazon Robotics, Mathworks, MIT Mobility Initiative, US DOT, Microsoft Research, Cintra, Symbotic Research Interests : Wu's work bridges AI and engineering challenges in transportation. Key areas include: Hybrid ML/Model-based Optimization (large neighborhood search, branch-and-cut) Sustainable Mobility (Project Greenwave, eco-driving) Multi-Agent Coordination (warehouse automation, cooperative driving) Cyber-Professional Systems (generalization in RL, transfer learning) Recent work demonstrates significant advances in eco-driving (11-22% emissions reduction), large-scale multi-agent path finding (1000+ agents), and foundational RL methods for traffic control. Her group has produced 15+ major publications since 2015, with notable media coverage in Science, Wired, and NewScientist. Selected Scientific Awards NSF CAREER Award (2023) Ole Madsen Mentoring Award (2025) IEEE ITSS WiE/YP Fellowship (2024) Harold L. Hazen Teaching Award (2022) Her lab has advised 12+ graduate students and postdocs, including: Vindula Jayawardana (PhD '24, now at Anthropic) Sirui Li (PhD '25, now at Microsoft Research) Yining Ma (Postdoc, active researcher) Zhongxia Yan (PhD '24, now at Anthropic)
Yang Song is an ARC Future Fellow and Scientia Associate Professor at the School of Computer Science and Engineering , University of New South Wales (UNSW) . She serves as Associate Head of School (Research) and Co-Director of iCinema , focusing on AI and Computer Vision applications for social good. Education: BEng in Computer Engineering (Nanyang Technological University, Singapore), PhD in Computer Science (UNSW, 2013) Research Areas: Biomedical image analysis, human-centred AI, graph data modeling, neuro-symbolic learning, and AI trustworthiness. Her work develops domain-specific deep learning models for radiological segmentation, histopathology cancer analysis, and 3D reconstruction. Recent projects address explainability in LLMs, fairness in AI, and human-robot interaction frameworks. With over 200 peer-reviewed publications in top venues like CVPR , MICCAI , and NeurIPS , her research spans biomedical imaging, robotics, and general multimodal AI. Scientific Awards include: 2024: ARC Industrial Transformation Research Hub for Human-Robot Teaming 2023: Google Inclusion Research Award 2022: NHMRC Ideas Grant for computational brain imaging 2021: UNSW Engineering Research Excellence Award 2020: Scientia Fellowship (UNSW) 2019: ARC Future Fellowship She supervises 24 current PhD/MPhil students and has graduated 15 advisees, including placements at Harvard University and Siemens Healthineers. Her grants include collaborations with Surf Life Saving Australia and industry partnerships for AI-driven solutions.
Professor Dinusha Mendis is a leading academic in Intellectual Property and Innovation Law at Bournemouth University, where she serves as Director of the Centre for Intellectual Property Policy and Management (CIPPM). Her expertise bridges copyright law with emerging technologies like 3D printing, AI, blockchain, and the Metaverse, informed by extensive research and consultation with entities such as the European Parliament, UKIPO, and corporations like HP and Chanel. She holds a PhD from Edinburgh University and has authored seminal works on the intersection of technology and IP. BSc in Law from Aberdeen University LLM and PhD from Edinburgh University Called to the Bar of England and Wales Her research focuses on copyright challenges in immersive environments , AI-generated content regulation , and blockchain/NFTs . She led major studies for the European Commission and UKIPO , including the first peer-reviewed work on 3D printing and IP. Her 2019 co-edited book with Stanford and QUT scholars established foundational frameworks for additive manufacturing legal analysis. Recent publications highlight AI copyright disputes (2024 The Conversation ), NFT legal ambiguities (2022), and pandemic-era 3D printing (2020). Trends show increasing emphasis on decentralized IP systems and machine learning's impact on creative industries . Scientific Awards & Grants JSPS Visiting Professorship (2023) Daiwa Anglo-Japanese Foundation Grant (2024) EU Commission Research Funding (2020) AHRC Grant for 3D Jewellery Study (2017) As a PhD supervisor, she mentors researchers on AI copyright (Benjamin White), crypto-art (Bahar Dagli), and music creation law (Elizabeth Bailey). She contributes to global IP policy through the World Economic Forum's Metaverse Governance Team and EU IPR Enforcement Projects .
