David Noyce is a Professor and Executive Director of the Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin-Madison's Department of Civil & Environmental Engineering. He also serves as Associate Director of the Safety Research Using Simulation (SaferSIM) Center, a University Transportation Center. His work focuses on transportation safety, traffic operations, and automated/connected vehicle systems. Key roles include leading over $35M in research since 2003 through the TOPS Lab and advancing technologies like V2V/V2I communication, winter maintenance strategies, and work zone safety. Education: PhD (Texas A&M, 1999), MS (Civil Engineering, UW-Madison, 1995), MBA (UW-Whitewater, 1994), BS (UW-Madison, 1984). Research interests span traffic control devices, driver behavior analysis, crash reconstruction, and multi-modal transportation systems. He pioneered the national implementation of flashing yellow arrow signals and developed the Wisconsin Connected and Automated Transportation Consortium. Current projects include C-V2X localization frameworks, automated vehicle safety testing, and winter maintenance efficiency. Recognized with 20+ awards, including ITE and ASCE Fellowships and the Dr. Arthur F. Hawnn Professorship. His labs offer full-scale driving simulators and field-testing capabilities for real-world validation of innovations.
Furong Huang is an Associate Professor at the University of Maryland's Department of Computer Science, with affiliations at the Institute for Advanced Computer Studies, Center for Machine Learning, Maryland Robotics Center, and Applied Mathematics, Statistics, and Scientific Computation Program. Her research bridges trustworthy machine learning, sequential decision-making, and foundation models for robotics, emphasizing reliability, interpretability, and ethical standards. Research Interests: Trustworthy AI Generative AI Reinforcement Learning AI Security Algorithmic Fairness Foundation Models for Robotics Recent Publications span leading conferences (NeurIPS, ICML, ICLR, CVPR) and journals, focusing on: Robustness in Vision-Language Systems Trustworthy Generative AI Foundation Models for Sequential Decision-Making AI Security and Watermarking Scientific Awards MIT TR35 Innovator Under 35 (Asia Pacific 2022) Best Paper Award, AdvML Frontier Workshop, NeurIPS 2024 NSF NAIRR Pilot Awardee Microsoft Accelerate Foundation Models Research Award (2023) JP Morgan Faculty Research Awards (2019–2022) Advising and Grants : Her lab has graduated students to roles at OpenAI, Google, Meta, and Netflix. Research funded by DARPA, NSF, ONR, AFOSR, and industry partners like Microsoft, Adobe, and Capital One. Labs & Teams : Leads research groups focused on Trustworthy AI and Robotics at the University of Maryland, collaborating with the Maryland Robotics Center and Applied Mathematics Program.
WANG Ye is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD in Information Technology from Tampere University of Technology, Finland, and has been a tenured faculty member at NUS since 2002, following his industry research role at Nokia Research Center. He is the director of the Sound and Music Computing Lab at NUS, leading cutting-edge research in AI-driven music and health technologies. PhD, Information Technology, Tampere University of Technology, Finland (2002) MSc, Telecommunications, Braunschweig University of Technology, Germany (1993) BSc, Telecommunications, South China University of Technology, China (1983) His research is centered on Sound and Music Computing for Human Health and Potential (SMC4HHP) , with a focus on eHealth, eLearning, mobile/wearable computing, and music information retrieval. His work spans AI for stroke rehabilitation, language learning through singing, singing voice synthesis, and automatic music transcription. He has pioneered systems like SLIONS (language learning via karaoke), CocoLyricist (AI co-creation for stroke recovery), and SinTechSVS (expressive singing voice synthesis). The latest articles highlight a strong trend in AI-driven music and health technologies , particularly in controllable lyric generation, singing voice synthesis, automatic pronunciation assessment, and multimodal music transcription. The research increasingly integrates large language models, explainable AI, fairness, and real-world deployment, reflecting a shift from theoretical exploration to practical, human-centered applications in healthcare and education. Dr. Wang has received numerous scientific honors, including: Best Paper Awards at ACM MM, ISMIR, IEEE ISM, and CHI First Prize, Asia Pacific Assistive, Rehabilitative, and Therapeutic Technologies Challenge (2015) Faculty Teaching Excellence Award, NUS School of Computing (2024) Top Paper Award, ACM Multimedia 2022 AI in Medicine Collaborative Grant for CocoLyricist project He has supervised over 11 PhD and 20 MComp students and is currently guiding six PhD candidates. His grants come from MOE, NRF, A*STAR, Nokia, and Smule. He has served as General Chair of ISMIR2017 and TPC Co-Chair of ICOT2017, and is on the editorial boards of IEEE Transactions on Multimedia and Journal of New Music Research. He has also developed and taught the first course on Sound and Music Computing in Singapore. Dr. Wang leads the Sound and Music Computing Lab (SMC Lab) , a multidisciplinary team exploring the synergy of music computing, AI, mobile technology, and cloud systems for health and education. The lab actively collaborates with medical institutions such as NUS Yong Loo Lin School of Medicine, Singapore General Hospital, and Harvard Medical School, and is currently working on projects in AI-supported language learning, stroke rehabilitation, and intelligent music interfaces.
Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.
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.
Lin Tan is a Professor of Computer Science at Purdue University , holding the Mary J. Elmore New Frontiers Professorship . She joined Purdue in 2019 after serving as a Canada Research Chair and associate professor at the University of Waterloo. She is an ACM Distinguished Member and IEEE Senior Member . Education: PhD in Computer Science, University of Illinois Urbana-Champaign BS in Computer Science and Technology, Zhejiang University Research Interests: Professor Tan’s research lies at the intersection of software engineering , artificial intelligence , and security . Her work focuses on software-AI synergy , software dependability , defect detection & repair , and software text analytics . She leverages machine learning and natural language processing to enhance software reliability, and conversely uses software techniques to improve the dependability of AI systems. Her recent projects include building binary foundation models (Nova), evaluating large language models for code generation and repair, and developing interactive debugging tools that reduce debugging time by one-third. She also explores robot task planning with LLMs and automated front-end development . Awards & Honors: Best Paper Award Finalist, ICRA 2025 ELATES Fellow, 2024-2025 ACM SIGSAC Distinguished Paper Award, CCS 2024 J.P.Morgan AI Faculty Research Awards (2020, 2021, 2022) ACM SIGSOFT Distinguished Paper Awards (ASE 2020, MSR 2018, FSE 2016) Canada Research Chair (2017) Ontario Early Researcher Award (2015) NSERC Discovery Accelerator Supplements Award (2015) Google Faculty Research Awards (2010, 2014) IEEE Micro Top Picks (2006) Advising & Funding: Professor Tan currently advises eight PhD students and has graduated 20+ PhD and Master’s students now thriving in academia (York University, Concordia University, University of Alberta) and industry (Microsoft, Meta, Amazon, Google). Her group is generously supported by NSF , Meta/Facebook Research Awards , J.P.Morgan AI Faculty Awards , and NSF REU programs. Labs & Teams: She leads the Software Reliability & AI Lab at Purdue, recruiting postdocs, PhD, MS, and undergraduate researchers year-round. Lab interests span binary recovery , LLM-based program repair , testing deep-learning libraries , and data-free model extraction .
Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).
Chaowei Xiao is an Assistant Professor at the University of Wisconsin-Madison (starting 2023), affiliated with the School of Computer, Data & Information Sciences. His research focuses on securing AI systems, particularly exploring robustness in trustworthy machine learning, autonomous systems, and large language models (LLMs). He holds a Ph.D. from the University of Michigan, Ann Arbor, and a B.S. from Tsinghua University. Before joining UW-Madison, he worked as a research scientist at NVIDIA (2020–2022) and at Arizona State University (2022–2023). His work bridges model and system perspectives to ensure practical robustness and provable guarantees in AI applications like autonomous driving, healthcare, and IoT. Key research areas include adversarial robustness, AI security, and ethical AI. Recent contributions include frameworks for detecting LLM hallucinations, mitigating jailbreak attacks, and securing multi-modal systems. His work on diffusion models for adversarial purification and physical-world attacks on autonomous driving systems has been widely recognized. Notable awards include the 2024 USENIX Security Distinguished Paper Award, ACM Gordon Bell Finalist (2024), and Schmidt Sciences AI2050 Fellowship. He has advised students like Xiaogeng Liu (NVIDIA Fellow) and secured grants from Amazon, Apple, and UW-Madison. His lab actively publishes at top venues like NeurIPS, ICML, and CVPR.
Liangming Pan is an Assistant Professor at the University of Arizona's College of Information Science. His research focuses on building trustworthy large language models (LLMs) with an emphasis on logical reasoning, truthfulness, and safety. He holds a PhD in Computer Science from the National University of Singapore (2022), a Master's from Tsinghua University, and a Bachelor's from Beihang University. Education : PhD in Computer Science, National University of Singapore (2022) Master of Engineering in Computer Science, Tsinghua University (2017) Bachelor of Engineering in Computer Science, Beihang University (2014) Research Interests : Dr. Pan's work centers on enhancing LLMs' reliability through: Logical reasoning mechanisms to ensure faithful deductions Truthfulness verification to combat misinformation Safety protocols to mitigate societal harm Key Contributions : Developed TART, an open-source framework for explainable table-based reasoning Created benchmarks like SCITAB and FactCheck-Bench for evaluating LLMs Advanced techniques for knowledge editing and causal reasoning Awards : Best Paper Runner-Up at NeurIPS Table Representation Workshop (2024) Area Chair Award for Question Answering (IJCNLP-AACL 2023) Service & Outreach : He serves as an Area Chair for EMNLP (2024), COLING (2025), and ACL (2024). He has delivered invited talks at Tsinghua University, Peking University, and other institutions.
Muchao Ye is an Assistant Professor in the Department of Computer Science at the University of Iowa. He earned his Ph.D. from Pennsylvania State University's College of Information Sciences and Technology in 2024 and a Bachelor of Engineering in Information Engineering from South China University of Technology. Ph.D., Information Sciences and Technology, Pennsylvania State University (2024) B.Eng., Information Engineering, South China University of Technology His research focuses on the intersection of Artificial Intelligence, Machine Learning, and AI Safety, particularly adversarial robustness in language models and vision-language models. He designs methods to enhance the security and reliability of deep learning systems for safety-critical applications like video surveillance and healthcare. Recent publications highlight adversarial robustness frameworks (e.g., UniT , PAT ), vision-language models for explainable video anomaly detection ( VERA ), and healthcare risk prediction techniques ( MedPath , MedRetriever ). His work appears in top venues such as NeurIPS, KDD, AAAI, ACL, and CVPR. Professional experience includes Applied Scientist internships at Amazon (2022–2023) and teaching roles at the University of Iowa and Pennsylvania State University. He serves as a reviewer for conferences like NeurIPS, ICML, and journals including IEEE TPAMI.
WonSook Lee is a tenured Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa’s Faculty of Engineering. Her expertise spans medical imaging, machine/deep learning, computer graphics, and computer vision. She earned her Ph.D. in Computer Science from the University of Geneva (Switzerland) and holds degrees from POSTECH (Korea) and NUS (Singapore). Before academia, she worked at Korea Telecom, Samsung Advanced Institute of Technology, and Eyematic Interfaces Inc. (USA). Her research focuses on applications such as virtual/augmented reality, MRI/CT/Ultrasound analysis, and 3D mesh modeling. She has authored over 130 publications, including 30+ journal papers, and serves on conference committees and editorial boards. Lee has secured major grants (NSERC, CFI, ORF) as Principal Investigator and contributed to global initiatives like South Korea’s National Research Foundation. Her lab explores cutting-edge techniques in medical imaging, AI-driven object detection, and multimodal systems. Notable projects include adversarial perturbation analysis for model robustness, cross-domain GANs for semantic segmentation, and real-time ultrasound-enhanced pronunciation training. She actively promotes interdisciplinary research in healthcare technology and autonomous systems.
Ghassan AlRegib is the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. He directs the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), the Center for Energy and Geo Processing (CeGP), and previously led Georgia Tech's MENA initiatives (2015-2018). His research spans machine learning, image processing, and seismic interpretation with real-world applications in autonomous vehicles, medical imaging, and subsurface analysis. His research focuses on trustworthy AI systems through three pillars: enhancing interpretability, improving robustness/generalizability, and tackling domain-specific challenges. Key interests include human-in-the-loop frameworks, uncertainty quantification, explainable AI, and physics-driven learning. The OLIVES lab pioneered modern machine learning applications in seismic interpretation and developed open-source datasets for geological fault analysis. Dr. AlRegib's scientific contributions include over 270 publications, multiple U.S. patents, and leadership roles as Technical Program co-Chair for ICIP 2020/2024. His work demonstrates significant impact through awards like the IEEE Fellow designation (2022) and multiple best paper awards at premier conferences. IEEE Fellow (2022) 2023 EURASIP Best Paper Award 2019 ICIP Best Paper Award 2017 Denning Faculty Award for Global Engagement CSIP Research & Service Awards (2003) He has advised numerous PhD students including Dr. Ashraf Alattar (now Auburn professor) and Dr. Zhiling Long (Kennesaw State faculty). His lab structure emphasizes collaborative teams comprising postdocs, senior/junior PhD students, and undergraduates working on high-impact problems from autonomous systems to medical diagnostics. Current research thrusts include trustworthy neural networks, human-in-the-loop frameworks, and deployment of machine learning in seismic interpretation and ophthalmology.
Hua Cai is the Thomas and Jane Schmidt Rising Star Associate Professor at Purdue University's Edwardson School of Industrial Engineering with a joint appointment in Environmental & Ecological Engineering. She holds a PhD in Environmental Engineering & Natural Resources from the University of Michigan, an MS in Environmental Engineering from Penn State, and a BS from Tsinghua University. Her research integrates operations research and production systems to address sustainability challenges, with focus areas including: Environmental implications of emerging technologies Urban sustainability modeling and infrastructure resilience Industrial ecology and complex adaptive systems Sustainable transportation and mobility systems Recent publications demonstrate strong focus on sustainable transportation systems, with extensive analysis of shared mobility patterns (bike/e-scooter systems), autonomous vehicle impacts, and microgrid resilience. Her work consistently applies advanced computational methods including reinforcement learning, agent-based modeling, and spatiotemporal analysis to urban sustainability challenges. Dr. Cai has received recognition including the Thomas and Jane Schmidt Rising Star Professorship for her contributions to sustainable systems engineering. She leads research on renewable energy integration in transportation infrastructure and advises projects on climate-resilient urban systems.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
