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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.

Chua Tat SengView profile

Professor

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +6 more

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

Yuri BobbertView profile

Professor

• Cybersecurity
• Information Systems Management
• Digital Security Leadership
• +3 more

Yuri Bobbert is a Professor of Information System Science at Antwerp Management School (AMS), part of the University of Antwerp. He holds dual PhDs from Radboud University and the University of Antwerp, focusing on IT performance measurement. His career includes roles as Global Group CSO of NN Group and UWV (Government), leading cybersecurity integrations during mergers like NN Group and Delta Lloyds. As CEO of B-ABLE (IT consultancy), he developed cybersecurity frameworks. Currently, he advises on NIS2 compliance and Zero Trust strategies. His research spans cybersecurity ROI, regulatory frameworks (e.g., NIS2), and leadership in digital security. Education PhD in Information Systems (Radboud University) PhD in Cybersecurity (University of Antwerp) Research Interests Cybersecurity governance and compliance ROSI (Return on Security Investment) CISO leadership and board communication Zero Trust architecture Risk quantification in digital ecosystems Publications & Books Authored books: Strategic Approaches to Digital Platform Security Assurance , Cybersecurity in 60 Minutes , and Digital Security Leadership Over 100 peer-reviewed publications on cybersecurity performance and governance Grants & Awards Recipient of NIS2 implementation research grants Contributor to EU cybersecurity policy discussions Labs & Teams Leads AMS cybersecurity education programs and collaborates with ON2IT on global cybersecurity strategies.

• Environmental Health Engineering
• Wastewater-Based Epidemiology
• Bioremediation
• +7 more

Rolf Halden is a Professor and Center Director at Arizona State University, holding appointments in the School of Sustainable Engineering and the Built Environment, the Biodesign Center for Environmental Health Engineering, and the Global Futures Scientists and Scholars program. He is a leading expert in environmental health engineering, wastewater-based epidemiology, and bioremediation. Education: Ph.D. in Civil (Environmental) Engineering, University of Minnesota (1997) M.S. in Civil (Environmental) Engineering, University of Minnesota (1994) M.S. in Biology, Technical University of Braunschweig, Germany (1992) Postdoctoral Fellow, Environmental Science, Lawrence Livermore National Laboratory (1998) Research Focus: Halden’s research integrates environmental chemistry, public health, and sustainability to understand how anthropogenic chemicals affect ecological and human health. His lab uses advanced mass spectrometry (LC-MS/MS, MALDI-TOF) to track pollutants such as PPCPs, microplastics, pesticides, and dioxins across air, water, soil, and biological systems. He pioneered the use of wastewater treatment plants as public health observatories, enabling real-time tracking of disease, drug use, and environmental exposures. Scientific Awards & Recognition: Leadership Award, Arizona State University (2018) Rocky Mountain Emmy Award (2018) Leroy E. Burney Lecturer, Johns Hopkins School of Public Health (2011) ACS Expert Program Member (2014–present) NIEHS Superfund R01 Working Group National Leader (2012–2014) Labs & Teams: Halden is the Founding Director of the Biodesign Center for Environmental Health Engineering at ASU, where he leads a multidisciplinary team focused on environmental proteomics, bioremediation, and diagnostic tool development. He also co-leads the Human Health Observatory , a national wastewater monitoring network. Grants & Funding: Halden has secured major funding from NSF, NIH, EPA, DoD, and private industry. Notable projects include the development of in-situ groundwater monitoring devices, national wastewater surveillance of SARS-CoV-2, and studies on microplastics and neurodegenerative disease.

• Operations Research
• Machine Learning
• Optimization under Uncertainty
• +4 more

Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.

Bo LiView profile

Associate Professor

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

Bo Li is an Associate Professor at the University of Illinois at Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science. Her research focuses on trustworthy machine learning, emphasizing robustness, privacy, and security in AI systems. She leads the Secure Learning Lab (SL²), exploring adversarial attacks and defenses across digital and physical domains. Key contributions include foundational work on adversarial examples, robust learning frameworks, and privacy-preserving techniques. Her academic roles include advisory board positions at the Center for Artificial Intelligence Innovation (CAII) and membership in the Information Trust Institute (ITI). She collaborates with institutions like the Advanced Digital Science Center (ADSC) and the Quantum Information Science and Technology Center (IQUIST). Notable recognitions include the IJCAI Computers and Thought Award (2022), MIT Technology Review's 35 Innovators Under 35 (2020), and multiple best paper awards. Recent work addresses AI safety through frameworks like ShieldAgent and AutoRedTeamer, aiming to enhance system resilience against adversarial threats. Her research spans theoretical guarantees, practical defenses, and ethical AI deployment. Students advised include Chulin Xie, Linyi Li, and Boxin Wang, who have received prestigious fellowships such as the IBM PhD Fellowship and Rising Stars in ML Awards. Advising: Guides PhD students in adversarial ML, privacy, and security. Labs/Teams: Secure Learning Lab (SL²), collaboration with ALERT program. Grants/Funding: NSF CAREER Award, Amazon/Google Faculty Awards, and industry partnerships.

Henrik SandbergView profile

Professor

• Cyber-Physical Systems Security
• Power Systems
• Model Reduction
• +6 more

Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm

Fei FangView profile

Associate Professor

• Artificial Intelligence
• Multi-Agent Systems
• Machine Learning
• +5 more

Fei Fang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University (CMU) , where she explores the intersection of artificial intelligence and multi-agent systems . Her work integrates machine learning with game theory to address challenges in security , sustainability , and mobility , aligning with the AI for Social Good mission. Ph.D. in Computer Science, University of Southern California (2016) B.Eng. in Electronic Engineering, Tsinghua University (2011) Recent research focuses on reinforcement learning , large language models (LLMs) , and human-AI collaboration . Her team’s work has been recognized with 15+ awards across prestigious venues like IAAI, AAAI, and IJCAI. Notable accolades include the 2023 Allen Newell Award , 2022 Sloan Fellowship , and NSF CAREER Award (2021) . She actively contributes to educational initiatives , including teaching "Demystifying AI for Everyone" at CMU, and has sought part-time teaching assistants for course development. Her research spans 15+ domains , including AI ethics , cyber defense , traffic optimization , and public health .

Frank LiView profile

Assistant Professor

• Computer Security
• Network Security
• Web Security
• +5 more

Frank Li is an Assistant Professor at Georgia Tech, affiliated with the School of Cybersecurity and Privacy and School of Electrical & Computer Engineering . His research focuses on computer security and privacy , particularly internet, network, and web security, with a data-driven approach. Education : PhD in Computer Science from UC Berkeley, BS in Computer Science from MIT (Course 6-3) Experience : Previously a visiting researcher at Facebook, with internships at Google and Microsoft Research Research interests include empirical computer security , internet measurement, password policies, IoT security, browser extensions, and DeFi vulnerabilities. His work combines large-scale data analysis, network traffic studies, and user behavior experiments. His publications highlight trends in IPv6 adoption , security notifications, password masking, third-party web content, and PLC security evaluation. Teaching awards include the Student Recognition of Excellence in Teaching (Annual CIOS Award and Semester Honor Roll) at Georgia Tech. He leads the BEES Lab (Better Empirically Established Security), which investigates security and privacy issues through internet-wide measurements, network traffic analysis, and data mining. Lab members include PhD and MS students in computer science.

Peter HendersonView profile

Assistant Professor

• AI safety
• Foundation models
• Law and AI
• +7 more

Peter Henderson is an Assistant Professor at Princeton University with joint appointments in the Department of Computer Science and the School of Public and International Affairs. He is affiliated with the Center for Information Technology Policy (CITP), Princeton Language and Intelligence Initiative (PLI), Center for Statistics and Machine Learning (CSML), and Program in Law & Public Policy (PLAW). J.D./Ph.D., Stanford University, 2023 M.Sc., McGill University and Montréal Institute for Learning Algorithms Henderson's research focuses on the critical intersection of artificial intelligence and law, with particular emphasis on AI safety, methods to improve reasoning in foundation models, interdisciplinary approaches to law and AI, and AI governance. His work spans language-grounded reinforcement learning, alignment techniques, strategic decision-making in legal contexts, and public interest artificial intelligence. He investigates how legal frameworks can guide the development of AI systems that benefit society while addressing potential harms. Henderson's recent publications reveal a strong trajectory toward addressing the safety, governance, and legal implications of foundation models. His work combines rigorous technical AI research with deep legal analysis, particularly examining how intellectual property law interacts with AI development, regulatory pathways that balance innovation with harm prevention, and the role of law in shaping responsible AI deployment. A significant thread throughout his research is the development of better evaluation methodologies for AI systems, especially in critical domains like law. SEAS Excellence in Teaching Award (2025) Henderson leads the Princeton Law+Language, AI, & Society (POLARIS) Lab, where he advises students and researchers working at the intersection of AI and legal studies. His research has been supported through collaborations with government agencies, including work with the IRS on audit selection algorithms. His findings have informed practical applications in government efficiency and equity, as well as legal system improvements. Henderson runs the POLARIS Lab at Princeton, which focuses on developing AI systems that work for the public interest, particularly in legal contexts. His team develops sequential decision-making systems for government efficiency, foundation models capable of reasoning about law, and improved safety evaluation frameworks for AI systems. The lab maintains strong connections with legal practitioners and policymakers to ensure research has real-world impact.

Jeff AlstottView profile

Professor

• Artificial Intelligence
• Cybersecurity
• Complex Networks
• +4 more

Jeff Alstott is a Professor of Policy Analysis at the RAND School of Public Policy, serving as the Director of RAND's Center for Technology and Security Policy (TASP) . He holds a concurrent role as a Senior Information Scientist at RAND and directs a program on technology forecasting and improving R&D investment returns at the National Science Foundation. Previously, he held leadership roles in the U.S. government, including the White House as Director for Technology and National Security at the National Security Council and Assistant Director for Technology Competition and Risks at the Office of Science and Technology Policy. Prior to that, he was a program manager at IARPA, focusing on AI, biosecurity, and science forecasting, and has held academic and research positions at MIT, Singapore University of Technology and Design, the World Bank, and the University of Chicago. Education : Doctorate in complex networks from the University of Cambridge; MBA and bachelor's degrees from Indiana University. Alstott's research spans artificial intelligence , technology forecasting , complex networks , and science and technology policy . His recent publications focus on AI security , existential risk from advanced technologies , and collaborative frameworks for AI safety , reflecting his expertise in balancing technological innovation with national and global security. He has contributed to understanding AI model weight protection , cybersecurity challenges , and policy implications of emerging technologies . Alstott has received recognition for his work, including being part of an award-winning team on technology counterintelligence . His policy recommendations and technical research have informed federal strategies for responding to advanced technologies , emphasizing the need for proactive governance and cross-sector collaboration.

Bimal ViswanathView profile

Associate Professor

• Cybersecurity
• Privacy Engineering
• Machine Learning
• +4 more

Bimal Viswanath is an Associate Professor in the Department of Computer Science at Virginia Tech's College of Engineering. His research focuses on cybersecurity, privacy engineering, and machine learning, particularly addressing adversarial AI and ML system vulnerabilities. He holds a Ph.D. from Saarland University and Max Planck Institute for Software Systems, and has held postdoctoral positions at UCSB and Nokia Bell Labs. Education Ph.D., Computer Science, Saarland University/Max Planck Institute (2016) M.S., Computer Science, IIT Madras (2008) B.Tech, Computer Science, Cochin University (2005) Research interests include: Adversarial attacks using/against ML systems Generative AI security (deepfakes, chatbots) Cybersecurity for online services Grants & Funding Recipient of CCI, NSF, and 4-VA grants supporting projects like robust AI defense mechanisms and adversarial image classification. Recent grants include $1.2M for AI security initiatives (2022-2025). Awards Distinguished Paper Award (SOUPS 2014) Best Paper Award (COSN 2015) AI2000 Most Influential Scholar Honorable Mention (2020) Advising Guided over 15 students including Jiameng Pu (PhD 2022), Connor Weeks (MS 2023), and current advisees Sifat Muhammad Abdullah (PhD 2021-). Notable student achievements include Facebook Fellowship nominations and industry placements at CVENT and University of Michigan. Labs & Teams Leading Virginia Tech's AI Security Lab focusing on generative models, adversarial ML, and applied cybersecurity solutions.

Yannis PaschalidisView profile

Professor

• Optimization
• Control Systems
• Stochastic Systems
• +8 more

Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.

Karthik R. NarasimhanView profile

Professor

• Language Agents
• Reinforcement Learning
• Natural Language Processing
• +3 more

Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.

• Energy Systems Modelling
• Multi-energy Systems Integration
• Renewable Energy Integration
• +4 more

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.