Nathaniel Daw holds the Huo Professorship in Computational and Theoretical Neuroscience at the Princeton Neuroscience Institute , Princeton University. His research integrates computational, neural, and behavioral approaches to study decision-making through trial-and-error learning and reward/punishment processing. Research Interests: Computational Neuroscience Decision-Making Under Uncertainty Model-Based and Model-Free Learning Neural Mechanisms of Self-Control Publications (2025-2019) explore intersections of machine learning and neuroscience, focusing on reward-guided behavior, memory systems, and psychiatric implications like anorexia nervosa and obsessive-compulsive disorder. Key themes include neural replay, cognitive effort allocation, and predictive modeling of human and animal learning. Scientific Awards: Princeton-Rutgers $16M Research Grant for mental illness studies Grants and Collaborations: Highlighted by a major interdisciplinary grant with Rutgers University to advance understanding of mental illness through computational frameworks. Labs and Teams: Leads the Daw Lab at Princeton Neuroscience Institute, focusing on neurocomputational models of decision-making and self-control.
Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).
Douglas E. Comer is a Distinguished Professor of Computer Science at Purdue University, where he has made significant contributions to the fields of computer networking, operating systems, and distributed computing. He is renowned for his foundational work on TCP/IP protocols and for authoring influential textbooks that have shaped academic and professional understanding of computer science concepts. Comer's research interests focus on networking fundamentals, operating system design (particularly the XINU OS), and cloud computing architectures. His professional activities include consulting for industries, delivering seminars on TCP/IP, DNSSEC, and network processor design, and advising on large-scale web systems. He has authored over 20 books translated into multiple languages, including Computer Networks and Internets , Internetworking with TCP/IP , and Operating System Design . His work emphasizes practical education through experimental projects and lab guides, such as Hands-on Networking with Internet Applications . He is also known for his guidance on Ph.D. programs and academic careers in computer science, advocating for rigorous research and mastery of domain knowledge. Comer's contributions extend to open-source software implementations, including protocol stacks and educational tools. His consulting practice focuses on network security, distributed systems design, and emerging technologies like edge computing and software-defined networking.
Ben Reis is an Associate Professor of Pediatrics at Boston Children's Hospital and an Affiliate Member of the Harvard Medical School Department of Biomedical Informatics. He leads the Predictive Medicine Group, focusing on AI-driven disease prediction and pandemic tracking. His work includes large-scale studies on vaccine effectiveness, suicide prediction systems, and biodefense advisory roles for governments. Key honors include White House recognition and designation as a top global health innovator. His research spans predictive modeling in healthcare, pharmacovigilance, and leveraging digital data for public health insights. Notable projects include developing systems to forecast clinical conditions years in advance and advising on pandemic infrastructure for events like the Olympics. Grants: NIH-funded projects including R01MH116042 (psychosis prediction) and R01GM089731 (pharmacovigilance). Labs/Teams: Director of the Predictive Medicine Group. Scientific achievements include over 78 publications, with recent work emphasizing vaccine efficacy, AI in clinical documentation, and spatial analysis of healthcare access. His interdisciplinary approach bridges machine learning, epidemiology, and clinical practice.
Dr. Oiwi Parker Jones is a Hugh Price Fellow in Computer Science at Jesus College, University of Oxford, and Principal Investigator leading the Parker Jones Neural Processing Lab (PNPL) at the Oxford Robotics Institute. Their research focuses on neural speech prosthetics, combining machine learning with neuroscience to develop technologies for speech restoration. They hold an honorary fellowship in the Nuffield Department of Clinical Neurosciences and teach in Engineering Science, Computer Science, and Neuroscience, including roles as a stipendiary lecturer in neuroscience and medical teaching at St Peter’s and Oriel Colleges. Jones’ work spans robotics, neural decoding, and endangered language preservation, with a particular emphasis on Hawaiian linguistics. Their lab develops large-scale machine learning methods for neural data analysis and collaborates on interdisciplinary projects involving clinical neuroimaging and AI ethics. Jones is also engaged in cultural preservation efforts through computational linguistics and has published widely on language contact, phonology, and indigenous protocols in technology. Education: Doctoral research in NLP and machine learning at Oxford Neuroscience training at UCL and Oxford Research Interests: Neural prosthetics, speech decoding, small-data machine learning, robotics, and endangered language preservation. Current projects include non-invasive brain-to-text systems, clinical fMRI applications, and computational tools for Hawaiian linguistic analysis. Teaching Contributions: Undergraduate lectures on generative deep learning and robotics, postgraduate supervision in DPhil projects across Engineering, Computer Science, and Neuroscience. Known for interdisciplinary teaching methods integrating neuroscience and AI. Awards & Recognition: No explicit awards listed, but recognized for innovative interdisciplinary research and leadership in neural engineering. Labs & Teams: Leads PNPL, collaborates with Oxford Robotics Institute and Applied Artificial Intelligence Lab. Active in global networks for clinical neuroimaging and indigenous AI ethics.
Hao Peng is an Assistant Professor at the Siebel School of Computing and Data Science, part of the Grainger College of Engineering at the University of Illinois Urbana-Champaign. He holds a B.S. from Peking University (2016) and a Ph.D. from the University of Washington’s Paul G. Allen School of Computer Science & Engineering (2022). His research focuses on Natural Language Processing (NLP) , Machine Learning , Large Language Models (LLMs) , and AI for Science , with particular emphasis on improving LLM efficiency, factuality, and interdisciplinary applications. Recent courses include 'CS 598 PEN - Efficiency in NLP' and 'CS 598 PEN - LLM Post-pretraining.' Hao’s work spans advancing LLM generalization capabilities, mitigating hallucinations, and addressing hardware constraints. In 2024, he co-authored an award-winning paper on 'LM-Infinite,' enabling zero-shot extreme length generalization in LLMs. He collaborates internationally, including with the Hebrew University of Jerusalem under a joint research grant since 2019. He also contributes to Argonne National Laboratory’s AI initiatives through invited lectures. Education : B.S., School of Electronics Engineering and Computer Science, Peking University, 2016 Ph.D., Paul G. Allen School of Computer Science & Engineering, University of Washington, 2022 Key Collaborations : Interdisciplinary research with Hebrew University of Jerusalem (2025) Joint seed grant program with HUJI since 2019 His advising includes graduate student Chi Han, who contributed to the NAACL award-winning work. Grants and seed funding focus on accelerating economic development through tech innovation. No specific lab affiliations are explicitly mentioned, but his research aligns with Argonne’s AI Distinguished Lecture series and open-source platforms like OpenDevin/OpenHands.
Brygg Ullmer is a Professor at Clemson University's School of Computing and Chair of the Human-Centered Computing Division. He leads the Tangible Visualization group, focusing on Tangible User Interfaces (TUIs) Computational Genomics Interactive Computational STEAM Rapid Physical/Electronic Prototyping Computationally-Mediated Art and Design His work bridges physical and digital domains, with applications in K-12 education, high-performance computing, and culturally-rooted design. Research trends from his 15 most recent articles show a focus on Generative AI for cyberphysical systems Shape-changing interfaces Token+Constraint interaction models Genomics data visualization Hybrid tangible-gestural interfaces Multi-display collaboration These span both theoretical and applied work in computer science, biology, and design. Ullmer has held significant roles including Postdoctoral work at Zuse Institute Berlin Associate Professor at Louisiana State University (CCT & Computer Science) Visiting Lecturer at Hong Kong Polytechnic University Contributions to IBM Systems Journal and special editions of Springer's Personal and Ubiquitous Computing He has also co-edited journal special issues and served on conference committees. His scientific contributions include co-invented U.S. patents (6164541, 6263507, 6259441) related to Invisible hyperlinking Audiovisual data browsing Digital video time-shifting These patents reflect early innovations in tangible interface technology that have influenced modern interactive systems.
Allison Sullivan is an Assistant Professor of Computer Science at The University of Texas at Arlington (UTA), affiliated with the Software Engineering Research Center (SERC) and the College of Engineering. She holds a PhD in Software Engineering from The University of Texas at Austin (2017) and previously served as an Assistant Professor at North Carolina A&T State University (2018-2020). Her research focuses on software reliability through automated engineering techniques and formal methods, supported by NSF and DoD grants. Education: PhD in Software Engineering (UT Austin, 2017), MS (UT Austin, 2014), BS (UT Dallas, 2012). Research interests include automated test generation, mutation testing, program synthesis, and formal verification of autonomous systems. Key areas span Model-Based Testing, First-Order Logic, and SAT/SMT solvers. Publications reflect her work in Alloy-based tools (e.g., AUnit, MuAlloy), mutation testing frameworks, and educational outreach. Notable awards include NSF CAREER (2024), UTA Rising Star (2024), and Outstanding Early Career Faculty (2025). She advises multiple graduate and undergraduate students, teaches courses in algorithms, software testing, and formal methods, and serves on program committees for conferences like ASE, MODELS, and ISSRE. Her broader impacts include K-12 outreach, curriculum development, and mentoring through initiatives like Google Faculty In Residence and AMIE Design Challenge. Labs/Teams: Leads the SCOPE lab at UTA, focusing on program correctness verification.
Andrea Esposito is a Research Fellow at the University of Bari Aldo Moro, affiliated with the IVU Laboratory (Interaction, Visualization, Usability & UX). He holds a Ph.D. in Computer Science (Artificial Intelligence) from the same institution, with visiting research stints at Ludwig-Maximilians Universität (LMU) Munich and Eusoft S.r.l. His work focuses on Human-Centered Artificial Intelligence, emphasizing explainability, human-AI collaboration, and ethical frameworks. He contributed to projects like the ISO 25000-certified 'eGLU-Box PA' and the 'SERENE' platform for UX evaluation. Education: B.Sc. in Computer Science and Digital Communication (University of Bari Aldo Moro), M.Sc. in Computer Science (Artificial Intelligence) (same university). Research interests include Human-Computer Interaction, XAI, and AI applications in healthcare. He collaborates with the European Humane-AI network and is active in ACM SIGCHI, SIGAI, and related organizations. Teaching roles include assisting in Human-Computer Interaction courses, focusing on UCD sprints. Labs/Teams: Core member of IVU Lab, contributing to interdisciplinary AI research.
Yi Bao is an Associate Professor at Stevens Institute of Technology, affiliated with the Department of Civil, Environmental and Ocean Engineering within the Charles V. Schaefer, Jr. School of Engineering and Science. His research group focuses on developing advanced sensors, materials, and machine intelligence algorithms for intelligent, resilient, and sustainable civil, energy, and environmental systems. Key areas include ultra-high-performance concrete (UHPC), fiber optic sensors, machine learning for infrastructure monitoring, and sustainable material design. Recent work emphasizes AI-driven material optimization, automated structural health monitoring, and environmental impact assessments. His publications span topics such as UHPC crack reduction, microplastic characterization via AI, and high-speed railway safety. He leads studies integrating robotics and computer vision for infrastructure inspection, and his group explores low-carbon concrete solutions and digital twin technologies for bridge decks. Collaborations highlight interdisciplinary approaches to address challenges in smart infrastructure, renewable energy systems, and material sustainability. Notably, his team has developed knowledge graph-guided design frameworks for UHPC and ECC materials, leveraging machine learning to predict properties and discover physicochemical reactions. Current projects include real-time corrosion monitoring of pipelines and fatigue life assessment of orthotropic steel decks using deep learning.
Shaowei Wang is an Assistant Professor in the Department of Computer Science at the University of Manitoba's Faculty of Science. His research focuses on software engineering and data mining, aiming to develop algorithms that leverage big software data (e.g., code repositories, developer social media) for efficient and effective software development. Key interests include recommendation systems, data-driven software engineering, software debugging, program comprehension, and secure software development. He leads the Mamba Lab, which explores these areas through empirical studies and tool development. His work spans topics such as large language model (LLM) applications in code analysis, vulnerability detection, graph fairness, and automated evaluation frameworks for API-oriented code generation. Recent studies address challenges like input order bias in LLMs, silent vulnerability fixes, and fair graph learning. He has contributed to over 50 peer-reviewed publications, emphasizing practical and theoretical advancements in software engineering and data science. Teaching interests include software engineering, data-driven software engineering, and data mining. His research has been recognized through collaborations with industry and academic partners, though no specific awards are listed. Advising and grant details are not explicitly mentioned in available texts.
Kyumin Lee is an Associate Professor in the Computer Science Department at Worcester Polytechnic Institute (WPI), with affiliations to both the Data Science Program and Artificial Intelligence Program. He maintains his office in Unity Hall 363 and leads the Infolab research group at WPI. Dr. Lee earned his BS from Kyonggi University in 2005, MS from Sungkyunkwan University in 2007, and PhD from Texas A&M University in 2013. Prior to joining WPI, he served as an assistant professor at Utah State University from 2013 to 2017. His primary research interests focus on social computing, artificial intelligence, machine learning, natural language processing, and information retrieval within large-scale systems like the Web and social media. A significant portion of his work addresses threats to these systems, developing methods to mitigate negative behaviors such as misinformation and hate speech. He also explores positive applications of these technologies, including recommender systems and natural language understanding. Notably, his work on AI for social good, particularly disrupting wildlife trafficking networks, has received substantial recognition including a $2 million grant from the National Science Foundation and the Paul G. Allen Family Foundation. Analysis of his recent publications (2023-2025) reveals a strong focus on recommendation systems, graph neural networks, large language models, and applications for social good. His work demonstrates a consistent trajectory toward addressing both theoretical challenges in AI and practical societal problems, with particular emphasis on context-aware systems, contrastive learning approaches, and multi-modal representations. Google Faculty Research Award (2013) NSF CAREER Award (2016) ACM CIKM Test of Time Award (2020) ACM SIGIR Test of Time Award - Honorable Mention (2022) Air Force Research Lab Summer Faculty Fellow (2022) WPI Recognition of Faculty Achievement (2018, 2025) Nominee for Outstanding Academic Advisor of the Year (2019, 2024, 2025) Dr. Lee has demonstrated exceptional commitment to student mentorship, evidenced by multiple nominations for Outstanding Academic Advisor of the Year. His research has been supported by prestigious grants including the NSF CAREER Award and a $2 million grant for wildlife trafficking research. He actively serves the academic community through program committee memberships for top conferences including SIGIR, WSDM, ACL, and AAAI. As the leader of Infolab at WPI, Dr. Lee directs research efforts focused on social computing, AI, and information retrieval. His lab has produced significant work on detecting misinformation, hate speech, and wildlife trafficking activities on social media platforms, while also developing next-generation recommendation systems and natural language understanding techniques.
Nurit Kirshenbaum is an Assistant Professor in the Information and Computer Sciences department at the University of Hawaii. She is affiliated with the Laboratory for Advanced Visualizations and Applications (LAVA) and the Hawaii Creativity and Technology (HiCat) lab. Her work focuses on Human-Computer Interaction , Tangible User Interfaces , and Data Visualization . Education: PhD in Computer Science, University of Hawaii (2016-2021) M.Sc. in Computer Science, University of Hawaii (2014-2016) M.Sc. in Interactive Media, Quinnipiac University (2012-2014) B.Sc. in Electrical Engineering, Technion - Israel Institute of Technology (1997-2001) Research Interests: Physical Visualization : Encoding data into physical objects (e.g., terrain models with projected data) SAGE3 : Reimagining collaboration tools for hybrid meetings with AI integration PEPA : Bendable interactive cards exploring novel interaction gestures Real-Time Drone Visualization : Streaming environmental data for microbiome research Awards : Best Software Innovation Award (UIST 2015) for ShowFlow , later renamed Set&Motion Advising and Labs: LAVA Lab: Focused on scalable displays and collaborative sense-making HiCat Lab: Exploring creativity-technology intersections Current Teaching: ICS-664: Advanced Human-Computer Interaction ICS/DATA/ACM-484: Data Visualization
Thomas Naselaris is an Associate Professor at the University of Minnesota, specializing in cognitive neuroscience and neuroimaging. His research focuses on decoding brain activity using advanced fMRI techniques and machine learning models, particularly in understanding visual perception, mental imagery, and memory. He leads the development of benchmark datasets like the Natural Scenes Dataset (NSD) and NSD-Imagery, which have become foundational for studying human visual cortex dynamics. His work integrates computational methods with neuroimaging to uncover how the brain represents and processes complex visual information. Notable contributions include reconstructing seen images from fMRI data and analyzing neural representations across different brain regions. He collaborates on large-scale studies involving ultra-high-field MRI and interdisciplinary approaches combining electrophysiology (iEEG) with fMRI. Key research themes include the role of generative models in visual processing, signal-to-noise dynamics in neural responses, and systems consolidation in memory. His grants include collaborative research proposals funded by CRCNS, focusing on evaluating machine learning architectures using benchmark datasets. Ongoing projects aim to bridge cognitive neuroscience with artificial intelligence, emphasizing interpretable models and scalable brain mapping techniques.
Dr. Ian Wassell is a University Senior Lecturer at the University of Cambridge's Department of Computer Science and Technology. He holds a PhD from the University of Southampton (1990) and BSc/BEng degrees from the University of Loughborough (1983). His research focuses on wireless communications, sensor networks, and electromagnetic propagation modeling. He has authored over 200 publications since 1999 and supervised 23 PhD and 6 MPhil students. Currently, he leads wireless communications research in the Digital Technology Group and is a Fellow of Churchill College. His research interests include broadband fixed wireless access (FWA), cooperative networks, MIMO systems, compressive sensing, and AI-driven radio propagation models. He teaches Digital Electronics and Hardware Practical Classes at the undergraduate level. Key contributions include developing data-driven propagation models, deep learning applications in wireless networks, and optimizing heterogeneous radio access networks. Dr. Wassell's work integrates theoretical advancements with practical systems, addressing challenges in high-speed rail communications, indoor/outdoor detection, and 6G IoT opportunities. His research bridges signal processing, machine learning, and wireless network design, emphasizing real-world deployment in infrastructure like tunnels and vehicles.
