Xupeng Miao is a Visiting Assistant Professor in the Department of Computer Science at Purdue University, holding the Kevin C. and Suzanne L. Kahn New Frontiers Assistant Professorship. Previously, he was a Postdoctoral Fellow at Carnegie Mellon University's Catalyst Group under Professors Zhihao Jia and Tianqi Chen. He earned his Ph.D. in Computer Science from Peking University (2022) and a Bachelor’s degree from Northeastern University. His research focuses on machine learning systems, distributed computing, and data management, with notable contributions to systems like Hetu (a distributed deep learning framework) and innovations in large language model (LLM) serving (e.g., SpotServe, SpecInfer). He has received awards including the 2024 WAIC Yunfan Award and an Outstanding Paper Award at ACL 2024. Current projects emphasize efficient systems for generative AI, including optimizing LLM training and inference on heterogeneous hardware, fault-tolerant distributed training, and scalable graph-based machine learning. He teaches CS 59200-MLS: Machine Learning Systems at Purdue and actively mentors students in PhD/MS programs and internships. Key achievements include NVIDIA Academic Awards, IEEE Micro Top Picks recognition, and leadership in international conference roles (e.g., Artifact Evaluation Co-Chair for KDD 2025 and MLSys 2025). His work bridges system design, algorithmic innovation, and hardware-aware optimizations to advance scalable AI infrastructure.
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.
Dr. K. Max Zhang is a Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. He is the director of the Energy and the Environment Research Laboratory (EERL) and a fellow at the Atkinson Center for a Sustainable Future. His research is deeply interdisciplinary, focusing on sustainable energy systems, air quality, and environmental justice, with significant impacts on policy and community development in New York and beyond. Ph.D., Mechanical Engineering, University of California-Davis, 2004 B.S., Thermal Engineering, Tianjin University, 1998 B.A., English Language, Tianjin University, 1998 Dr. Zhang’s research centers on the integration of energy and environmental systems. He investigates air pollution dynamics using advanced numerical models like CTAG, with applications in near-source pollution, indoor air quality, and environmental justice. His work on renewable energy systems includes designing sustainable solar farms and managing distributed energy resources such as heat pumps to enhance grid flexibility. He also leads a pioneering initiative to create the first statewide public IoT network in the U.S., enabling hyperlocal weather forecasting and microclimate monitoring. His recent publications reflect a strong trend toward agrivoltaics, peer-to-peer energy markets, and IoT-based environmental monitoring. These works demonstrate a consistent focus on data-driven modeling, community-scale energy solutions, and the integration of social considerations into technical systems. The keywords across his articles highlight expertise in sustainability, machine learning, air quality, and energy transition. Cornell Town-Gown Achievement Award (2022) Engaged Scholar Prize, Cornell University (2017) People's Choice Sign of Sustainability Award, Sustainable Tompkins (2016) Scientific and Technological Achievement Award, Environmental Protection Agency (2015) Fellow of the American Society of Mechanical Engineers Dr. Zhang is actively involved in mentoring students and securing research grants from agencies such as the National Science Foundation (NSF) and the New York State Energy Research and Development Authority (NYSERDA). His projects often involve interdisciplinary collaboration across eight Cornell colleges and 16 academic departments. He has led initiatives such as the Cornell Atkinson Academic Venture Fund projects and the development of a county-level energy roadmap for Tompkins County. He also teaches courses in engineering thermodynamics, future energy systems, and air quality, emphasizing experiential and community-based learning. Dr. Zhang leads the Energy and the Environment Research Laboratory (EERL) and collaborates with the Atkinson Center for a Sustainable Future. His lab functions as a hub for innovation in sustainable communities, combining advanced modeling with real-world applications. Through partnerships with community organizations, government agencies, and industry, his team develops science-driven solutions to urban and rural sustainability challenges.
Virginia Smith is the Leonardo Associate Professor of Machine Learning at Carnegie Mellon University (CMU), with a courtesy appointment in the Department of Electrical and Computer Engineering. She holds a Ph.D. from UC Berkeley and undergraduate degrees from the University of Virginia. Her research focuses on developing efficient, privacy-preserving machine learning systems, particularly in federated learning, distributed optimization, and resource-constrained settings. Key areas include federated learning frameworks like CoCoA and Ditto, unlearning algorithms, and privacy amplification techniques. Smith's work bridges theory and practice, addressing challenges such as data heterogeneity, fairness, and robustness. She has led initiatives like the LEAF benchmark for federated learning and has contributed to open-source tools for scalable ML systems. Her awards include the Sloan Research Fellowship, Samsung AI Researcher of the Year (2023), and an NSF CAREER Award. She is actively involved in curriculum development, teaching courses on federated learning and large-scale ML at CMU. Her research group explores cutting-edge topics like federated optimization with sparse communication, unlearning in LLMs, and secure ML pipelines. Collaborations span academia and industry, with applications in healthcare, IoT systems, and AI safety. Smith serves as Program Chair for ICML 2025 and frequently contributes to workshops on federated learning and trustworthy AI.
Amy Bruckman is a Regents’ Professor and Senior Associate Chair in the School of Interactive Computing at Georgia Institute of Technology. As a Fellow of The ACM and member of the SIGCHI Academy , she contributes to social computing research focusing on online collaboration, cross-cultural understanding, and content moderation. Her 2022 book "Should You Believe Wikipedia? Online Communities and the Construction of Knowledge" explores digital knowledge creation. Education: Ph.D. from MIT Media Lab (1997), B.A. in Physics from Harvard (1987) Research Affiliations: GVU Center, IRI People and Technology Her work addresses social computing challenges including: AI ethics in human-AI interaction Algorithmic bias in content moderation Epistemic justice in online communities Comparative platform analysis (Reddit, Discord, TikTok) Grassroots technology movements Recent publications examine: AI support tools for online counseling Community-driven research frameworks Racial bias mitigation in toxic language detection Designing teachable social media experiences Human trafficking intervention ethics Scientific recognition includes: ACM Fellowship SIGCHI Academy membership She has contributed to: Georgia Tech's Computing Education initiatives Research ethics frameworks for Human-Computer Interaction
Timothy M. Hospedales is a Professor of Artificial Intelligence at the Institute of Perception, Action and Behaviour within the School of Informatics at the University of Edinburgh . He also serves as VP AI and Head of Samsung AI Research Centre Europe . His research focuses on efficient and robust AI , emphasizing meta-learning , lifelong transfer-learning , and domain adaptation in both probabilistic and deep learning frameworks. Applications span computer vision , vision and language , reinforcement learning for robotics , and finance . Professor at University of Edinburgh (2020–present) ELLIS Fellow (2021) Head of Samsung AI Research Europe (2020–present) Founding Director of Applied Machine Learning Lab at QMUL (2012–2016) His work includes pioneering contributions to meta-learning , few-shot learning , and self-supervised methods , with notable awards such as the Best Paper Prize at ICML AutoML 2018 and Best Student Paper at ICPR 2018 . He has co-authored 15+ recent papers on topics like Vision-Language Models , Medical AI Fairness , and Diffusion Model Optimization . He served as Program Co-Chair for BMVC 2018 and AAAI 2022 , and authored a book on Visual Adaptation in the Deep Learning Era (2022). Co-Chair, BMVC 2018 Guest Editor, IET CV Special Issue (2016) Keynote Speaker at TASK-CV Workshop (ECCV 2016) Special Issue on Fewer Labels (IEEE PAMI 2020) His leadership extends to organizing workshops like the Learning-to-Learn Workshop at ICLR 2021 , Meta-Learning Workshop at NeurIPS 2020 , and Domain Generalisation Workshop at ICLR 2023 . Current projects include Meta-Omnium (CVPR 2023) for general-purpose meta-learning and MetaAudio (ICANN 2022) for few-shot audio classification benchmarks.
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.
Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.
Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, holding the Canada CIFAR AI Chair at the Vector Institute and serving as Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Her research spans AI safety, ethics, reinforcement learning, and knowledge representation, with a focus on human-compatible AI and long-term societal impacts. Her career includes six years as a Research Scientist at Stanford University and a year at Xerox PARC. McIlraith’s work has been recognized through ACM and AAAI fellowships, as well as prestigious awards like the SWSA 10-Year Award (2011) and the CAIAC Lifetime Achievement Award (2024). Research Interests: AI Safety and Alignment Human-Compatible AI Reinforcement Learning with Ethical Constraints Semantic Web Services Cognitive Robotics and Diagnostic Systems Probabilistic and Logical Reasoning Recent Contributions: Her work emphasizes ethical AI integration, such as the Embedded Ethics Education Initiative (E3I), and addresses challenges in long-term AI risks, multi-agent systems, and interpretable decision-making frameworks. Awards and Honors: ACM Fellow AAAI Fellow 2023 IJCAI-JAIR Best Paper Prize 2024 CAIAC Lifetime Achievement Award Labs and Teams: McIlraith leads initiatives at the Schwartz Reisman Institute and contributes to the Vector Institute, focusing on societal and ethical dimensions of AI technology.
Giuseppe Ateniese is a Professor and Eminent Scholar in Cybersecurity at George Mason University, affiliated with the Department of Computer Science and Cyber Security Engineering. Formerly, he held the Farber Endowed Chair and chaired the Department of Computer Science at Stevens Institute of Technology. His research focuses on cloud security, applied cryptography, blockchain technology, and AI-driven cybersecurity solutions. He has received prestigious awards including the NSF CAREER Award and Google/IBM Faculty Awards. AIFurther, he teaches courses on blockchains, cryptography fundamentals, and cryptofinance. His work includes pioneering contributions to rewritable blockchains, password cracking via AI (e.g., PassGAN), and secure decentralized systems. Education: PhD in Computer Science, University of Genoa (2000) Laurea (M.Sc.), University of Salerno (1995) Master of Engineering (Honoris Causa), Stevens Institute of Technology (2020) Research Interests: Ateniese’s work bridges theoretical and applied cryptography, with emphasis on blockchain scalability, privacy-preserving machine learning, and secure data storage. He has developed foundational techniques like proxy re-cryptography and accountable storage systems. His recent efforts address AI’s dual role as both a cybersecurity tool and threat vector, including AI-based password cracking and defense strategies against LLM-driven attacks. Awards: NSF CAREER Award (for privacy/security research) Google Faculty Research Award (cloud security) IBM Faculty Award IEEE CISTC Technical Recognition Award Teaching & Labs: He teaches advanced courses on blockchains (CS695), cryptography fundamentals (CYSE/ECE476), and introductory cryptography (CS487/587). His research team focuses on cryptographic protocols, decentralized systems, and AI-security intersections. Collaborations include projects with Accenture on editable blockchain prototypes and NSF-funded work on password security.
Rajeev Alur is the Zisman Family Professor in the Department of Computer and Information Science at the University of Pennsylvania, leading the School of Engineering and Applied Science. He is the Founding Director of the ASSET Center for Trustworthy AI and a member of the PRECISE Center. His research focuses on formal methods for system design, integrating AI, cyber-physical systems, and machine learning with logical reasoning to ensure safety in autonomous systems. Alur has held leadership roles in major NSF projects like ExCAPE and has directed the Embedded and Multi-Scale Systems (EMBS) program. His research interests span formal verification, temporal logics, programming abstractions, and synthesis techniques. Notable contributions include the development of Nested Words (visibly pushdown languages), streaming string transducers, and tools like AutomataTutor for education. He has advised over 60 PhD students and postdocs, many of whom now hold academic and industry leadership positions. Alur’s awards include the 2024 Knuth Prize and the 2016 Alonzo Church Award. His work on Verisig and compositional verification of neural networks has advanced safety-critical AI applications. He teaches foundational courses like CIS 2620 and develops educational tools, emphasizing both theoretical rigor and practical impact. Key projects include the ASSET Center’s focus on trustworthy AI, integration of logical specifications in reinforcement learning, and formal verification of closed-loop systems with neural components. His publications span over 350 papers, with recent work addressing neurosymbolic learning, security in large language models, and efficient neural network verification.
Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.
