Gilbert Fridgen is Full Professor in Digital Financial Services and holder of the PayPal-FNR PEARL Chair at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the FINATRAX research group focusing on blockchain, digital identity, and sustainable digital solutions. His research explores decentralized technologies in finance, energy systems, and public administration. Current projects investigate blockchain applications in industrial demand response, NFT markets, EU digital identity wallets, and DeFi institutional adoption. Prof. Fridgen examines the intersection of technology with regulatory frameworks and user behavior in energy and financial systems. He collaborates extensively with industry partners including PayPal and Spuerkeess. Prof. Fridgen actively contributes to EU digital policy discussions, particularly regarding the European Digital Identity Wallet framework and decentralized finance governance.
Laura Ray is the Myron Tribus Professor of Engineering Innovation and Senior Associate Dean for Faculty Development at the Thayer School of Engineering, Dartmouth College. Her research focuses on autonomous robotics, system dynamics, and machine intelligence with applications in polar exploration and agriculture. She leads NSF- and NASA-funded projects, including the development of robots like Yeti and Cool Robot for remote terrain analysis. Education: BSE in Mechanical and Aerospace Engineering from Princeton University (1984), MSE in Mechanical Engineering from Stanford University (1985), and PhD in Mechanical and Aerospace Engineering from Princeton University (1991). Research Interests: Her work spans robotics navigation, cooperative multi-robot systems, and acoustic signal processing. Recent projects include tactile sensing for agricultural robotics and terrain identification algorithms for autonomous vehicles. Awards: Honored with the 2022 Outstanding Service Award, National Academy of Inventors Senior Membership (2020), and multiple fellowships in engineering. Notable contributions include patents for noise-canceling devices and adaptive filtering technologies. Leadership: Served as Interim Dean of Thayer School (2018–2021) and co-leads initiatives like the NSF-funded PhD Innovation Program. Teaches courses in control theory (ENGS 26) and systems engineering (ENGS 22). Key Achievements: Developed robots that reduce risks in polar regions, supported NASA missions, and pioneered precision agriculture technologies. Her lab's innovations have been highlighted in Science, National Geographic, and IEEE Spectrum.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.
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.
Kevin D. Ashley is a Professor of Law at the University of Pittsburgh School of Law. He is also a senior scientist at the Learning Research and Development Center and an adjunct professor of computer science at the University of Pittsburgh. His interdisciplinary work bridges artificial intelligence, legal analytics, and ethical reasoning. JD, Harvard Law School M.A. and PhD, University of Massachusetts BA, Princeton University His research focuses on computational modeling of legal reasoning , AI and ethics , legal text analytics , and case-based reasoning . He has pioneered AI applications for legal decision support, automated argumentation, and bias detection in legal corpora. Recent publications include studies on large language models in legal annotation, argument mining, and empirical legal analysis. His work has been supported by multiple National Science Foundation grants, emphasizing fairness in AI and access to justice. 2022 Codex Prize for Computational Law 2015 University of Massachusetts Outstanding Achievement Award 2002 AAAI Fellow for AI in Law contributions 2000 Chancellor’s Distinguished Research Award A former President of the International Association for Artificial Intelligence and Law, Ashley has held visiting roles at the University of Bologna, Stanford CodeX, and IBM Watson Research Center. He co-edits the journal Artificial Intelligence and Law and teaches courses on applied legal analytics.
Matthias Hein is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Tübingen. His research focuses on Machine Learning , Adversarial Robustness , and Out-of-Distribution Detection , with applications in computer vision and medical imaging. He has received notable recognition including the Best Paper Honorable Mention Prize at ICLR 2021 and Outstanding Paper Award at CVPR 2021. His work includes developing benchmarks like RobustBench and Spurious ImageNet , and frameworks such as Sparse-RS and DIG-IN . His recent publications emphasize adversarial robustness across multiple domains (vision, text), counterfactual explanations for classifiers, and improved OOD detection methods . Collaborators include prominent researchers like Francesco Croce, Julian Bitterwolf, and Alexander Meinke. Scientific Awards : Best Paper Honorable Mention (ICLR 2021) CVPR 2021 Outstanding Paper Award Key Research Areas : Adversarial Robustness Vision-Language Models Medical Imaging AI Neural Network Calibration
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)
Jieyu Zhao is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), part of the Viterbi School of Engineering. She holds a Ph.D. from UCLA and was a NSF Computing Innovation Fellow at the University of Maryland. Her research focuses on fairness in machine learning and NLP, particularly addressing biases in AI systems and promoting ethical AI practices. Education: Ph.D. in Computer Science (UCLA, 2021), Postdoc at University of Maryland (2022-2023), and multiple research internships at Microsoft Research, Google Research, and AI2. She also completed her undergraduate studies at the University of Virginia. Research interests include bias detection and mitigation in NLP models, trustworthy AI, and social responsibility in technology. Her work has been recognized with awards such as the EMNLP Best Long Paper Award (2017), Microsoft PhD Fellowship (2020), and EECS Rising Star (2021). She has delivered talks at conferences like NeurIPS, EMNLP, and USC Law School, and serves on committees including AAAI workflow chair. Her lab explores topics like alignment of LLMs, fairness in sequential decision-making, and ethical model interventions. She actively participates in initiatives promoting diversity and inclusion in AI, such as the UN-WOMEN Beijing panel on gender equality. Awards: 100 Brilliant Women in AI Ethics (2025), Capital One Research Award (2024), Microsoft PhD Fellowship (2020) Grants: NSF Computing Innovation Fellowship, USC Viterbi funding Labs/Teams: Leads the USC Lab focused on trustworthy AI and ethical NLP
Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.
Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.
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.
Jillian Bohlen is an Associate Professor at the University of Georgia within the College of Agricultural & Environmental Sciences , specifically the Department of Animal and Dairy Science . Her work focuses on dairy cattle reproduction, teaching, and youth development in agricultural sciences. Education: B.S. in Dairy Science, University of Georgia (2003) M.S. in Dairy Science, University of Georgia (2005) Ph.D. in Animal Sciences, Clemson University (2012) Dr. Bohlen's research emphasizes applied reproduction in cattle , including synchronization techniques, ultrasound applications in reproductive programs, and embryo competency. She integrates students into all research projects despite lacking a formal research appointment. Her teaching interests span dairy cattle production , reproductive management , and contemporary agricultural issues , with courses like ADSC 3620 (Dairy Cattle Production), ADSC 4010 (Issues in Animal Agriculture), and ADSC 4410 (Applied Reproductive Management). Scientific awards include: Student Career Success Influencer Award (2024) Russell Award for Excellence in Undergraduate Teaching (2023) UGA Creative Teaching Award (2022) CAES Early Career Teaching Award (2022) Hoard's Dairyman Youth Development Award (2021) Larry Benyshek Teaching Award (2017) She actively promotes agricultural education through programs like the Commercial Heifer Project , Dairy Cattle Judging , and Dairy Quiz Bowl . Additionally, she participates in funded projects related to methane emissions , heat stress mitigation , and antibiotic alternatives in dairy farming.
Dr. Oana Cocarascu is a Senior Lecturer in Artificial Intelligence at the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences, King's College London. She holds a PhD and MEng in Computing (Artificial Intelligence) from Imperial College London and conducts applied research focusing on how artificial intelligence can be deployed to support real-world applications, with machine learning and natural language processing as core components of her work. Her research interests span argument mining, explainable AI, machine learning, natural language processing, and symbolic reasoning. She is particularly focused on developing AI systems that can provide transparent, accountable, and ethical decision-making processes. Her work addresses critical challenges in bias mitigation, fairness metrics, fact verification systems, and argumentation-based explanations for complex AI decisions. Analysis of her recent publications (2023-2025) reveals a strong focus on fairness in AI systems, with particular attention to individual fairness metrics and nuanced evaluation frameworks. She has made significant contributions to fact verification systems, especially in multimodal contexts involving charts and tabular data. Her work increasingly integrates argumentation theory with natural language processing to create explainable AI systems that can justify their decisions through structured reasoning. Dr. Cocarascu leads an EPSRC-funded project titled 'A framework for evaluating and explaining the robustness of NLP models' (2024-2027) and is actively involved in the Natural Language Processing Group at KCL. Her research fingerprint shows strong activity in argumentation (100%), decision-making (74%), artificial intelligence (50%), explainable AI (39%), and bias mitigation (35%). She has contributed to numerous high-impact publications in top venues including ACL, EMNLP, AAAI, and the Journal of Artificial Intelligence Research, with a particular focus on making AI systems more transparent, accountable, and aligned with human values. Her work intersects with UN Sustainable Development Goals, particularly those related to reducing inequality and building resilient infrastructure.
Deian Stefan is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). His research focuses on building secure systems through interdisciplinary approaches spanning security , programming languages , and systems . Key areas include WebAssembly security , JavaScript JITs , constant-time programming , memory safety , information flow control , and program analysis tools. He co-founded Intrinsic , a web-security startup later acquired by VMWare, and contributed to standards like W3C WebAppSec and Node.js Security Working Groups. Education: PhD in Computer Science from Stanford University (2015), advised by David Mazières, John C. Mitchell, and Alejandro Russo BE and ME in Electrical Engineering from Cooper Union (2011) Research Trends: His work targets secure systems (Web frameworks, sandboxing, runtime systems), language-based security (constant-time programming, memory safety), and verification for security . Recent publications focus on mitigating speculative execution attacks, hardware-assisted sandboxing, and WebAssembly security. Awards & Recognition: Distinguished Paper Award at POPL 2021 and IEEE MICRO Top Picks 2024 Best Paper Award at CollaborateCom 2010 2010.01 Prize by Daniel J. Bernstein (DJB) Most Influential Paper Award at ICFP 2022 Teaching: He has taught graduate and undergraduate courses including Graduate Computer Security (CSE 227) , Building Secure Systems with Rust , and Programming Languages: Principles and Paradigms at UCSD since 2018. Prior teaching experience includes Stanford and Cooper Union.
