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Dawn SongView profile

Professor

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +7 more

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, affiliated with multiple research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Center for Responsible, Decentralized Intelligence (RDI). She holds leadership roles in AI safety, cybersecurity, and blockchain research. Her work bridges machine learning, security, and distributed systems, with notable contributions to formal verification, privacy-preserving technologies, and AI governance. Education: Ph.D. (2002) and M.S. (1999) in Computer Science from UC Berkeley and Carnegie Mellon University, respectively. She previously served as an Assistant Professor at Carnegie Mellon University before joining UC Berkeley in 2007. Research Interests: Dawn's work focuses on AI safety, cybersecurity, and the ethical implications of frontier AI. She explores topics such as large language model vulnerabilities, zero-knowledge proofs, blockchain security, and policy frameworks for AI governance. Her research combines theoretical rigor with practical applications, addressing challenges in secure systems, adversarial machine learning, and privacy-preserving computation. Publications: Her recent work includes groundbreaking studies on LLM memorization, smart contract decompilation, and AI agent cybersecurity evaluation. She also leads initiatives like the Singapore Consensus and California Report on AI safety priorities, emphasizing global collaboration in risk mitigation. Notable Awards: MacArthur Fellowship (2010), ACM Fellow (2019), IEEE Fellow (2019) Advising & Grants: She advises projects on AI policy and cybersecurity, securing grants from NSF, DARPA, and industry collaborations. Her lab develops tools like CyberGym for evaluating AI agents and zkPyTorch for secure machine learning. Labs & Teams: Her research groups at UC Berkeley focus on cutting-edge projects in AI safety, blockchain, and cybersecurity, collaborating with industry leaders and policymakers to advance both technical and ethical standards.

Adrian WellerView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Harris H. WangView profile

Associate Professor

• Systems Biology
• Synthetic Biology
• Genome Engineering
• +17 more

Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.

Bo DaiView profile

Assistant Professor

• Reinforcement Learning
• Machine Learning
• Data-Driven Decision Making
• +3 more

Bo Dai is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology, and a Staff Research Scientist at Google DeepMind. His research focuses on Agent AI, Generative Models, and Representation Learning, aiming to create decision-making agents through world modeling. He holds a Ph.D. from Georgia Tech (2013–2018) and previously worked at Google Brain. Dai has authored numerous influential papers in top conferences like NeurIPS, ICML, and ICLR, and received the AISTATS Best Paper Award (2016). Education: Ph.D., School of Computational Science and Engineering, Georgia Tech (2013–2018) Research Interests: Reinforcement Learning and Representation Learning for decision-making agents Generative Models and their integration with Agent AI Provable and scalable algorithms for real-world applications His work bridges theory and practice, emphasizing spectral representations and provable guarantees in complex systems. Key Article Trends: His recent work emphasizes scalable spectral methods for multi-agent systems, diffusion policies, and representation-based techniques in reinforcement learning. He also explores LLM alignment and sim-to-real transfer learning. Awards: AISTATS Best Paper Award (2016) NeurIPS Workshop Best Paper (2017) Ross Fellowship (2011–2012) Advising & Grants: Supervises 6 current Ph.D. and M.S. students. Active in organizing workshops on reinforcement learning and serves as an Area Chair for top conferences. Labs & Software: Leads development of Representation-based Reinforcement Learning and Repr-Control toolboxes for nonlinear control and stochastic systems.

• Distributed Systems
• Cryptocurrencies
• Computer Security
• +7 more

David Mazières is a Professor at Stanford University , affiliated with the School of Engineering and the Department of Computer Science . He serves as a software engineer at the Stellar Development Foundation . His work bridges academic research and industry applications in distributed systems and security. University: Stanford University Academic Rank: Professor Email: dm@scs.stanford.edu Research Interests: David Mazières' research focuses on distributed systems , cryptocurrencies , and computer security . Key areas include consensus protocols (e.g., Stellar ), low-latency scheduling ( Syrup , Shinjuku ), and cryptographic techniques for privacy and security ( SafetyPin , CCFI ). Key Achievements: His notable works include: TCP-ENO (RFC 8547) for secure transport protocols Stellar Consensus Protocol for decentralized finance SOSP 1999 Best Paper for 'Separating key management from file system security' Teaching Contributions: He has taught core and advanced courses at Stanford since 2005, including CS212 (Operating Systems) , CS240h (Functional Systems in Haskell) , and CS251 (Cryptocurrencies and Blockchain Technologies) . Previously taught courses at NYU (2001-2005).

Tim AlthoffView profile

Associate Professor

• Artificial Intelligence
• Human-Centered Computing
• Data Science
• +5 more

Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .

• Machine Learning Security
• AI Safety
• Privacy in Machine Learning
• +4 more

Florian Tramèr is an Assistant Professor in the Department of Computer Science at ETH Zurich, Switzerland, leading research at the intersection of machine learning security, privacy, and AI safety. His work focuses on identifying and mitigating security vulnerabilities in machine learning systems, particularly in large language models and other AI systems. Tramèr's primary research interests include adversarial machine learning, membership inference attacks, privacy-preserving AI, and the security implications of large language models. His work has significantly advanced our understanding of how machine learning models memorize training data, how this memorization creates privacy risks, and how to evaluate the robustness of machine learning systems against various attacks. His recent publications demonstrate a strong focus on practical security challenges in deployed AI systems, including data extraction from language models, adversarial attacks against generative AI, and developing more rigorous evaluation methodologies for machine learning security. Tramèr's research has been published in top venues including ICLR, NeurIPS, ICML, and IEEE Security & Privacy. Tramèr is actively collaborating with leading researchers in the field including Nicholas Carlini, Matthew Jagielski, and Javier Rando, contributing to important initiatives like the International AI Safety Report. His work bridges theoretical security concepts with practical implications for real-world AI deployment.

Xiangyu ZhangView profile

Professor

• AI Security
• Software Engineering
• Programming Languages and Compilers
• +5 more

Xiangyu Zhang is the Samuel D. Conte Professor of Computer Science at Purdue University. He specializes in AI security, software analysis, and cyber forensics, with a focus on detecting vulnerabilities in traditional software and AI systems. His research includes dynamic slicing, program profiling, and securing AI models against backdoors. He has led projects funded by DARPA, IARPA, NSF, and industry, securing over $13 million in grants. He has mentored 30+ PhD students and postdocs, many of whom hold academic positions or industry roles. His work has been recognized with awards like the ACM SIGPLAN Distinguished Dissertation Award and NSF Career Award. Education: PhD, Computer Science, University of Arizona (2006) MS & BS, Computer Science, University of Science and Technology of China (2000 & 1998) Research Interests: AI Security: backdoor detection, adversarial attacks, model integrity Program Analysis: dynamic slicing, static/dynamic debugging Cyber Forensics: log analysis, attack investigation Software Reliability: fault localization, vulnerability detection Key Projects: DARPA V-SPELLS: domain-specific program analysis IARPA TrojAI: AI backdoor detection competitions NSF-funded research on model debugging and co-reasoning of software/NL artifacts Awards & Recognition: Top performer in IARPA TrojAI competitions (13/18 rounds) ACM SIGPLAN Distinguished Dissertation Award (2006) NSF Career Award (2009) Ranked 8th globally in systems research productivity (2024) Career Contributions: Secured over $13M in research funding Guided 25 PhDs and 8 postdocs Published 89+ top-tier papers (2014–2024)

Tapio SchneiderView profile

Professor

• Atmospheric Dynamics
• Climate Modeling
• Geophysical Turbulence
• +4 more

Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).

Simon Buckingham ShumView profile

Professor

• Human-Computer Interaction
• Educational Technology
• Learning Analytics
• +5 more

Professor Simon Buckingham Shum is a leading academic in Learning Informatics at the University of Technology Sydney (UTS), serving as inaugural Director of the Connected Intelligence Centre since 2014. His work bridges Human-Computer Interaction, Educational Technology, and Learning Analytics to develop AI-driven tools like OnTask that enhance student belonging through personalized feedback. Key research areas: Human-Computer Interaction, Learning Analytics, AI in Education, Collective Intelligence Recent work focuses: LLM-augmented qualitative analysis, Belonging Analytics frameworks, and ethical AI implementation in education CIC received the 2024 Vice-Chancellor’s Student Experience Award for scaling OnTask across multiple faculties. His contributions span over 30 years, including founding roles at The Open University’s Knowledge Media Institute and innovations in Open Educational Resources (OER), SocialLearn, and FutureLearn platforms. Notable awards: 2024 Student Experience Award (OnTask), Fellow of the Royal Society of Arts (RSA), DEFI STRI Researcher at University of Cambridge Education: B.Sc. in Psychology (York), M.Sc. in Ergonomics (UCL), PhD in Human-Computer Interaction (York) He leads multidisciplinary collaborations with teams across Business, Law, and Education faculties, pioneering tools like Theme Explorer and Qreframer bot. His recent focus on the "metacrisis" reframes educational technology’s role in addressing global systemic challenges through transdisciplinary approaches.

Adriana TapusView profile

Professor

• Robotics
• Human-Robot Interaction
• Machine Learning
• +7 more

Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.

Subhasish MitraView profile

Professor

• Robust Computing
• NanoSystems
• Electronic Design Automation (EDA)
• +3 more

Subhasish Mitra is the William E. Ayer Professor of Electrical Engineering and Computer Science at Stanford University, holding dual appointments in both departments. He leads the Stanford Robust Systems Group and serves on the leadership team of the Microelectronics Commons AI Hardware Hub under the US CHIPS and Science Act. His research spans Robust Computing, NanoSystems, Electronic Design Automation (EDA), and Neurosciences, with breakthroughs in X-Compact test compression, carbon nanotube computing, and 3D integration. He has held international roles like the Carnot Chair at CEA-LETI and Visiting Professorships globally. Education & Honors: Recipient of over 40 awards including the IEEE Computer Society’s Harry H. Goode Memorial Award, ACM/IEEE’s A. Richard Newton Technical Impact Award, and the Intel Achievement Award. He earned top academic accolades from IIT Kharagpur and Jadavpur University, and is a Fellow of ACM and IEEE. Research Impact: Pioneered first-of-their-kind systems like the carbon nanotube computer and monolithic 3D integration. His work on robust computing techniques like QED validation and X-Compact compression has industry-wide adoption, saving billions in manufacturing costs. Collaborates with industry leaders like Intel, Google, and Samsung. Publications & Grants: Over 400 publications, including award-winning papers in DAC, ISSCC, and IEEE journals. Leads grants from NSF, DoE, and industry partnerships. His lab explores cutting-edge topics like neuromorphic computing, 3D thermal scaffolding, and AI hardware acceleration. Administration & Outreach: Serves as Associate Chair (Faculty Affairs) for Stanford’s Computer Science Department. Recognized by students for mentorship, and frequently invited to global forums like the World Economic Forum and National Academy of Engineering.

Steven I-Jy ChienView profile

Professor

• Transportation Engineering
• Urban Rail Transit
• Sustainable Transportation Systems
• +4 more

Steven I-Jy Chien serves as Professor and Director of the Transportation Program within the Department of Civil and Environmental Engineering at the New Jersey Institute of Technology (NJIT). His career spans academia, government, and industry with significant contributions to transportation engineering research and practice. His educational credentials include: Ph.D. in Civil and Environmental Engineering from University of Maryland-College Park (1995) M.S. in Civil and Environmental Engineering from University of Maryland-College Park (1991) B.S. in Civil Engineering from Tamkang University (1983) Professor Chien's research focuses on transportation system resilience, sustainable transit operations, and advanced traffic modeling. His work addresses critical challenges in urban rail networks, demand-responsive transit services, and energy-efficient transportation systems. He employs sophisticated computational methods including genetic algorithms, machine learning, and Bayesian approaches to solve complex transportation problems involving network vulnerability, passenger behavior, and infrastructure optimization under uncertainty. Analysis of his recent publications reveals strong emphasis on transportation resilience against cascading failures, sustainable operations for electric transit systems, and pandemic-responsive aviation scheduling. His research spans diverse geographic contexts including major Chinese metropolitan areas and U.S. transportation networks, demonstrating global applicability of his methodologies. His professional recognition includes: 2024 Keynote Speaker at Union-Tech Lecture, National Taiwan Ocean University 2022 Keynote Speaker at International Conference of Chinese Institute of Transportation 2022 Best Poster Award from New Jersey Department of Transportation Professor Chien has secured research funding from federal agencies including the Federal Highway Administration (FHWA) where he contributed to CORSIM traffic simulation software development. His industry experience with China Engineering Consultants Inc. and Information Dynamic Inc. informs his practical approach to transportation challenges. As Transportation Program Director, he oversees curriculum development and industry partnerships that prepare students for transportation sector careers. He maintains active collaboration with transportation agencies globally, particularly through his leadership in the Transportation Program at NJIT which serves as a hub for innovation in urban mobility solutions.

Brent MittelstadtView profile

Professor

• Data Ethics
• AI Ethics
• Technology Governance
• +4 more

Professor Brent Mittelstadt is a leading academic in data ethics and AI governance at the University of Oxford's Oxford Internet Institute (OII), serving as Professor of Data Ethics and Policy and Director of Research. He coordinates the Governance of Emerging Technologies (GET) programme, focusing on ethics, law, and technology interplay. His work bridges academic research with policy impact, contributing frameworks like 'counterfactual explanations' and fairness metrics ('Conditional Demographic Disparity') used globally. He leads high-profile projects such as Trustworthiness Auditing for AI and A Right to Reasonable Inferences in Advertising and Financial Services. Research interests span AI ethics, medical ethics, technology governance, and non-discrimination law. He has pioneered ethical frameworks addressing algorithmic bias, GDPR compliance, and AI accountability. His work has influenced policy bodies like the UK Information Commissioner’s Office and the European Commission, and is implemented by tech giants such as Google and Microsoft. Awarded O2RB Excellence in Impact Awards (2018, 2021) and PLSC Best Paper (2019) Funded by Wellcome Trust, Sloan Foundation, and others Advisory roles for NHSx, GSK Consumer Healthcare, and regulatory bodies Recent work focuses on AI regulation (EU AI Act), generative AI ethics, and healthcare AI governance. He co-leads the OxonFair toolkit for algorithmic fairness and investigates societal impacts of deepfake proliferation and large language models.

• Biomedical Data Privacy
• Genomics
• Machine Learning
• +3 more

Hyunghoon Cho is an Assistant Professor at Yale School of Medicine in the Department of Biomedical Informatics & Data Science, with a secondary appointment in the Department of Computer Science. He received his PhD in Electrical Engineering and Computer Science from MIT (2019) and MS/BS in Computer Science from Stanford University (2013). His research focuses on computational challenges in biomedical data privacy, single-cell genomics, and network biology. Assistant Professor (Primary): Biomedical Informatics & Data Science Assistant Professor (Secondary): Computer Science Appointments: Yale School of Medicine | Broad Institute (Schmidt Fellow) Research Themes: Privacy-Enhancing Technologies for genomic and health data Scalable AI/ML tools for omics data analysis Structured biological modeling for system-level discovery His work includes secure GWAS, transcriptomic privacy assessment, and sfkit - a federated genomic analysis toolkit. He received the NIH Director's Early Independence Award and leads NSF-funded projects on confidential genome analytics. Awards: NIH Director's Early Independence Award Lab Members: Haris Smajlović (Postdoc), Vincent Angelo (CBB MS), Denis Loginov (Senior Software Engineer), Lucy Zheng (CBB PhD)