Jack Snoeyink is a Professor at the University of North Carolina at Chapel Hill, holding joint appointments in the Department of Computer Science (College of Arts & Sciences) and the School of Data Science and Society. His research focuses on computational geometry, with applications in molecular biology, geographic information systems (GIS), and geometric modeling. His work in computational geometry explores algorithmic design and analysis for problems in solid modeling, computer graphics, and robotics. Key application areas include terrain modeling in GIS, molecular structure validation in biochemistry, and computational topology. He has contributed to output-sensitive algorithms for convex hulls and Voronoi diagrams, and geometric search problems. Articles highlight his expertise in computational geometry, with trends spanning 1999-2000. Topics include contour tree algorithms (SODA'00), watershed extraction (ASPRS'99), and skeleton generation (Crust.pdf). His work bridges theoretical advancements with practical implementations in GIS and structural biology. Jack Snoeyink has collaborated with researchers like Marc van Kreveld, Christopher Gold, and Bettina Speckmann on projects related to Delaunay triangulation, regression depth computation, and geometric assembly problems. He previously served as a program director at the National Science Foundation's CISE division (2015-2018) and co-founded the TRIPODS program for data science foundations.
Prof. Hatice Gunes is a Full Professor of Affective Intelligence and Robotics at the University of Cambridge's Department of Computer Science and Technology, leading the Affective Intelligence and Robotics Lab (AFAR Lab). She holds an EPSRC Fellowship and is an EPSRC Fellow. Her research focuses on multimodal affective and social intelligence for AI systems, particularly embodied agents and robots, integrating Machine Learning, Affective Computing, and Human Nonverbal Behaviour Understanding. Key projects include the CHANSE initiative (2025–2028) for child mental health assessment via social robotics, the EPSRC Fellowship on robotic EQ for wellbeing (2019–2025), and the EU Horizon 2020 WorkingAge project. Prof. Gunes has pioneered systems like the EU SEMAINE project's SAL system, recognized with Best Demo and Paper Awards, and co-founded SensingFeeling, a spin-out company from the Innovate UK Sensing Feeling project. Her work emphasizes ethical AI and fairness, earning awards like the Best Paper Award in Responsible Affective Computing (IEEE ACII'23). She has delivered keynote talks at IEEE FG’19 and ICPR’22 and collaborates with the Department of Psychiatry and wellbeing professionals. Education: PhD in Computer Science from University of Technology Sydney (UTS) under Australian Government IPRS Scholarship Postdoctoral Research at Imperial College London (SEMAINe project) Research Interests: Multimodal affective computing, social robotics for mental wellbeing, fairness in AI systems, human-robot interaction (HRI), and ethical deployment of AI technologies. Her lab develops robots for workplace wellbeing coaching and child mental health assessment, with over 700 media coverages and partnerships with industry and healthcare sectors. Notable Achievements: Runner-up Collaboration Award (2023 VC Research Impact) Better Future Award (2023 Hall of Fame) Finalist RSJ/KROS Interdisciplinary Research Award (2021) Shortlisted Sony Women in Technology Award 2025 Labs & Teams: AFAR Lab drives interdisciplinary research in Cambridge, focusing on socially intelligent robots and AI systems that address critical societal challenges in wellbeing and mental health.
Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.
Subhabrata Sen is an Assistant Professor of Statistics at Harvard University, located in Science Center 713, Cambridge. His research focuses on Applied Probability, Statistics of Networks, Signal Detection, and Machine Learning. He holds a PhD from Stanford University (2017), advised by Amir Dembo and Andrea Montanari, and prior degrees from the Indian Statistical Institute, Kolkata. His work bridges statistical theory, high-dimensional data analysis, and applications in networks and physics-inspired methods. Key contributions include foundational studies on spin glasses, community detection, and causal inference in complex systems. His research often employs mean-field techniques and explores universality principles in estimation problems. Selected awards and recognition are not explicitly mentioned in the provided text. His advising and grants include postdoctoral mentoring at Microsoft Research and MIT (2017-19). He collaborates on projects involving spectral methods, random matrix theory, and multi-layer network analysis. Labs/teams: Active in Harvard's Statistics Department research groups focused on statistical theory and network science. Maintains an academic website with preprints and resources.
Jianjun (Jan) Shi is the Carolyn J. Stewart Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and holds a joint appointment with the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. He previously served as the G. Lawton and Louise G. Johnson Chair Professor of Engineering at the University of Michigan. His research focuses on system informatics and control for manufacturing and service systems, with notable contributions to quality improvement, cyber-physical systems, and data-driven methodologies. B.S. & M.S. in Electrical Engineering, Beijing Institute of Technology (1984–1987) Ph.D. in Mechanical Engineering, University of Michigan (1992) Dr. Shi’s research interests include process modeling, control systems, and quality engineering. He pioneered methodologies for in-process quality improvement and developed advanced frameworks for high-dimensional data analysis in manufacturing. His work integrates statistical methods, machine learning, and system informatics to enhance operational efficiency and product quality. He has published over 150 peer-reviewed papers and secured $19 million+ in research grants from NSF, DOE, and industry partners. His lab, the System Informatics and Control Group, collaborates with automotive, aerospace, and pharmaceutical sectors. Shi leads initiatives such as the Quality Science Center at the Chinese Academy of Sciences and serves on editorial boards of journals like IIE Transactions and ASME Transactions . Recipient of the IIE Albert G. Holzman Distinguished Educator Award (2011) Fellow of INFORMS, ASME, and IIE Academician of the International Academy for Quality Shi advises 26 Ph.D. graduates, many of whom hold faculty positions or leadership roles in industry. His research group’s innovations have been implemented in global manufacturing systems, yielding significant economic impacts. Current work includes 4D printing, cyber-physical system resilience, and federated learning for industrial data.
Sofya Raskhodnikova is a Professor in the Department of Computer Science at Boston University, part of the College of Arts and Sciences. She holds a Ph.D. from MIT and has held positions at Penn State University and postdoctoral fellowships at the Hebrew University of Jerusalem and the Weizmann Institute of Science. Her research focuses on sublinear-time algorithms, data privacy, approximation algorithms, and complexity theory. She is a recipient of the NSF CAREER Award and has contributed significantly to the theoretical foundations of privacy-preserving computation and algorithm design. Education: Ph.D. in Computer Science from MIT (2003), postdoctoral research at Hebrew University of Jerusalem and Weizmann Institute of Science (2003–2006). Visiting positions at UCLA, Harvard University, and the Simons Institute for the Theory of Computing. Research Interests: Sofya’s work bridges theoretical computer science and practical applications, emphasizing algorithms that operate efficiently on large datasets. Key areas include property testing (e.g., monotonicity, sortedness), differential privacy, and sublinear-time algorithms. She explores how algorithms can analyze data while preserving privacy guarantees and minimizing computational resources. Publications: Over 50 peer-reviewed articles in top venues such as STOC, FOCS, and SODA, with recent contributions focusing on dynamic graph algorithms under privacy constraints and robust property testing against adversarial noise. Professional Activities: Editor for ACM Transactions on Computation Theory and Algorithmica ; program committee chair for WOLA 2021 and CSR 2022; active in mentoring initiatives like Sigma Camp and Artemis. Advising & Students: Current advisees include Ephraim Linder and Debanuj Nayak. Notable alumni include Iden Kalemaj (Meta Research) and Nithin Varma (Max Planck Institute). She has supervised over 15 Ph.D. students and postdocs, fostering a collaborative research environment.
Derry Wijaya is an Associate Professor and Program Coordinator for the Data Science Program at Monash University Indonesia. She also co-directs the Monash Data and Democracy Research Hub, focusing on analyzing data's impact on democracy. Previously, she served as an Assistant Professor at Boston University's Department of Computer Science. Her research spans multilingual NLP, low-resource language technologies, and combating AI-driven misinformation. Education: PhD in Language Technologies, Carnegie Mellon University (2013) Postdoctoral Fellowship, University of Pennsylvania (2013–2015) Bachelor's & Master's in Computing, National University of Singapore Research Interests: Improving language model performance via self-consistency and reasoning Analysis of bias, toxicity, and framing in AI outputs Preservation of Indonesian indigenous scripts and languages Development of tools like OpenFraming AI for multilingual framing analysis Recent Work Trends: Her publications (2023–2025) emphasize ethical AI, low-resource language solutions, and social media analysis. Notable contributions include frameworks for metric calibration (MetaMetrics), debiasing generative models, and surveys on Indonesian language technology needs. Awards & Roles: Fulbright Scholarship recipient Serves on program committees for ACL, EMNLP, NeurIPS, and ICLR Co-created OpenFraming AI for multilingual framing analysis Grants & Labs: Leads the Monash Data and Democracy Hub, focusing on tech's societal impact. Active in grant-funded projects preserving Indonesia's linguistic heritage through digitization efforts.
Jay Pujara is a Research Associate Professor in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering and the Information Sciences Institute (ISI). He directs the Center on Knowledge Graphs and leads research in artificial intelligence, specializing in knowledge graph construction, scalable machine learning, and probabilistic models. Education : PhD in Computer Science (University of Maryland, 2016), MS and BS in Computer Science from Carnegie Mellon University (2005 and 2004), with minors in Robotics, Mathematical Sciences, and Logic & Computation. Research Interests : His work focuses on probabilistic models for dynamic data, knowledge graph construction, entity resolution, and applications in NLP and social network analysis. He emphasizes scalable algorithms and real-world impact in domains like finance, climate science, and healthcare. Awards : Includes the SWSA Ten-Year Award (2023), Best Paper Awards at IUI 2019 and ISWC 2013, and grants totaling over $9M from DARPA, NSF, and industry partners. Grants & Mentorship : Principal Investigator on projects like "Artificial Domain-Understanding and Collaborative Agency" (DARPA) and "Explainable and Robust AI Agents" (NSF). Mentored over 50 students in PhD, MS, and undergraduate programs, focusing on knowledge graphs, NLP, and machine learning. Labs & Teams : Leads ISI’s Knowledge Graph and Neurosymbolic AI teams, coordinating the Open Knowledge Network (OKN) and tools like KGTK. Active in academic service, including roles on PhD admissions committees and ISI’s Space Management Committee.
Qiongxiu Li is a Tenure-Track Assistant Professor in the Cyber Security group at Aalborg University's Copenhagen campus, part of the Technical Faculty of IT and Design. Her research focuses on cybersecurity, distributed optimization, privacy/security, and federated learning. She has authored/co-authored 38 papers in top-tier venues including IEEE Transactions on Information Forensics and Security, ICLR, and EUSIPCO. Education: PhD in Privacy and Security from Aalborg University (2018-2021). Notable achievements include winning the EUSIPCO 2020 3MT Contest and co-delivering a tutorial on privacy-preserving distributed optimization at EUSIPCO 2024. She actively reviews for conferences like NeurIPS, ICLR, and journals such as TPAMI and TIFS. Research Themes: Privacy-preserving distributed algorithms, federated learning security, differential privacy, and adversarial machine learning. Recent Trends: Focus on securing AI systems (e.g., LLM vulnerabilities, federated clustering privacy), quantization for privacy, and theoretical bounds in decentralized learning. Awards: 2020 EUSIPCO 3MT Winner (outstanding finalist in EURASIP's annual doctoral research competition). Grants/Projects: Co-PI of the AI:SECURITY project (2025-2029) addressing AI security threats like phishing and malicious actors. Labs/Teams: Leads the Cyber Security group at Aalborg's Copenhagen campus, focusing on theoretical and applied research in secure distributed systems.
Dr. Nour Moustafa is an Associate Professor and ARC DECRA Fellow at the School of Systems & Computing (SysCom) , University of New South Wales (UNSW) Canberra , Australia. He leads the Intelligent Security Group and focuses on developing AI/ML-driven cybersecurity frameworks for smart systems. Educated at Helwan University (BSc/MSc in Information Systems) and UNSW (PhD in Cybersecurity). Research Interests include intrusion detection, threat intelligence, privacy preservation, digital forensics, and cyber resilience, with methodologies spanning statistical analysis , machine learning , and deep learning applied to IoT , Edge/Cloud , and Industrial IoT environments. His work emphasizes federated learning for privacy preservation, blockchain for secure AI, and digital twins for network self-healing. Notable contributions include the TON-IoT , Bot-IoT , and UNSW-NB15 datasets for cybersecurity evaluation. Scientific Awards : 2020 Spitfire Memorial Defence Fellowship ACM Distinguished Speaker IEEE Senior Member He has served as guest associate editor for IEEE Transactions journals and held leadership roles in conferences like IEEE TrustCom . His research bridges academia and industry, with over 75 publications in top-tier venues.
Dr. Difan Zou is an Assistant Professor in the Department of Computer Science at the University of Hong Kong's School of Computing and Data Science. He holds a PhD in Computer Science from UCLA and degrees in Applied Physics and Electrical Engineering from the University of Science and Technology of China (USTC). His research focuses on machine learning theory, optimization, and learning structured data such as time-series and graph data, with an emphasis on understanding deep learning's theoretical underpinnings like optimization trajectories and generalization properties. Dr. Zou's academic background includes a B.S. from USTC's School of Gifted Young (Applied Physics) and a M.S. in Electrical Engineering from the same institution. His work bridges theoretical foundations and practical applications, addressing challenges in adversarial robustness, algorithm design for deep neural networks, and explainable machine learning systems in healthcare and finance. His research projects aim to establish rigorous frameworks for deep learning optimization, develop efficient training algorithms, and integrate conventional statistical models with machine learning for improved interpretability. He has received the Bloomberg Data Science Ph.D. Fellowship and has contributed to top-tier conferences like ICML, NeurIPS, and ICLR.
Pietro Liò is a Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he is also a member of the Artificial Intelligence group. His work bridges computer science and biomedical applications, with a strong focus on advancing AI methods for healthcare. Research Interests: His research is centered on developing Artificial Intelligence and Computational Biology models to unravel the complexity of diseases and support personalized and precision medicine. A current emphasis is on Graph Neural Network modeling, leveraging topological data structures to represent biological and medical systems. His interdisciplinary background enables innovative approaches at the intersection of machine learning and life sciences. The trends in his research, though no specific articles are listed, indicate a strong focus on AI-driven biomedical discovery, particularly using deep learning on structured data for health applications. This includes modeling biological networks, disease mechanisms, and patient-specific conditions through advanced neural architectures. Scientific Awards: As a principal investigator and academic leader, Pietro Liò likely supervises PhD and postdoctoral researchers and secures research grants in AI for health, though specific advisees and funding details are not mentioned in the text. His dual PhD background and affiliation with a leading AI group suggest a robust research program with significant grant involvement. He is associated with AI research activities at Cambridge, including seminars and software development, possibly contributing to or leading a research lab or initiative focused on AI applications in biology and medicine, though no formal lab name is provided beyond the general AI group membership.
Luís B. Elvas is an Assistant Professor at ISCTE-University Institute of Lisbon's Department of Social and Business Sciences (SINTRA) and a Research Assistant at ISTAR-Iscte Research Center. He holds qualifications including a Technical Specialization in TensorFlow for AI (Coursera, 2021) and certifications in IoT/Blockchain from ISCTE and cybersecurity from Palo Alto Networks. His research spans artificial intelligence, healthcare informatics, smart cities, and blockchain, with applied work in medical imaging, data sharing, and urban analytics. Research interests include: Healthcare AI : Developing deep learning models for cardiac diagnostics, medical imaging analysis, and blockchain-based health data systems Smart Cities : Implementing IoT solutions for urban mobility optimization, disaster management, and sustainable transportation Data Science : Creating predictive analytics frameworks for clinical decision support and urban planning His publications demonstrate a strong focus on AI-driven healthcare solutions (67% of recent works) and smart city technologies (33%), with emerging interests in blockchain and NLP. Research consistently targets real-world applications in clinical settings and urban environments. Awards: Award for best internship, Order of Engineers (2022) Distinction for best internship, Order of Engineers (2021) He leads/contributes to multiple EU research consortia including AMR-EDUCare (antimicrobial resistance education), NEEM (e-health in Nepal), and Blockchain.PT. Coordinates the IEEE Computational Intelligence Society Student Branch Chapter at ISCTE and developed the ManagiDiTH master's program in digital health transformation.
Jiaoyan Chen is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester. She previously held roles as a Lecturer at Manchester, a Senior Researcher at the University of Oxford, and a Postdoctoral Fellow at Heidelberg University. Education: PhD and Bachelor's in Computer Science and Technology from Zhejiang University (2016 and 2011), with a visiting PhD stint at Zurich University's Department of Informatics. Research Interests: Integrating knowledge graphs and ontologies with machine learning and large language models (LLMs), focusing on semantic embeddings, knowledge curation, and explainable AI systems. Publication Trends show emphasis on ontology embeddings (e.g., OWL2Vec*), LLM evaluation with knowledge graphs, and hybrid neural-symbolic reasoning. Her work bridges structured knowledge and modern AI through projects like OntoEm and ConCur . Current Research Team includes postdoctoral researchers, PhD students, and externally co-supervised associates. She actively recruits PhD candidates in areas like Retrieval-Augmented Generation and LLM Explainability , with projects funded by EPSRC and international consortia. Grants & Leadership: EPSRC New Investigator Award (2023-2026) Manchester-Melbourne-Toronto Research Fund (2024-2026) EPSRC ConCur Project (2021-2025) Professional Service: Associate Editor, Transactions on Graph Data and Knowledge EPSRC Peer Review College member OAEI Track Co-organizer at ISWC
Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.
