Dr. Jingyun Wang is an Assistant Professor in the Department of Computer Science at Durham University. Previously, she held an Assistant Professor position at Kyushu University, Japan. Her primary affiliations include the Centre for Neurodiversity & Development and the Artificial Intelligence and Human Systems Group (AIHS), as well as the Pedagogical Innovation in Computer Science Group (PICS). She is a Fellow of the Higher Education Academy and has led or contributed to research projects funded by JSPS, JST, NICT, Innovate UK, and industry partners. Her research focuses on AI-driven educational technologies, including AI-based feedback systems, computational thinking education, game-based learning, and ontology techniques. She actively contributes to editorial boards (e.g., Computers & Education: Artificial Intelligence ) and serves as a conference chair for AIED, ICCE, and LTLE. Current research includes adaptive learning systems for mathematics education, serious games for cybersecurity training, and visualization tools for e-learning. Her scientific contributions span over 50 peer-reviewed publications, with recent work emphasizing learning analytics, multimodal systems, and digital health interventions. She advises multiple PhD students and mentors in professional recognition pathways. Key projects include developing the BETTER speech training system, the MEMORABLE cybersecurity game framework, and ontology-based language learning platforms.
Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.
Ion Androutsopoulos is a Professor of Artificial Intelligence in the Department of Informatics at Athens University of Economics and Business (AUEB), where he also serves as Head of Department. He is founder and co-director of AUEB's Natural Language Processing Group and an Adjunct Researcher at the Digital Curation Unit and "Archimedes" Research Unit of the Research Centre "Athena". His research spans multiple dimensions of Artificial Intelligence with a focus on Natural Language Processing. Key interests include: Machine learning in NLP, particularly deep learning and large language models Question answering and retrieval augmented generation for document collections Dialog systems for new languages and knowledge domains Sentiment analysis and emotion recognition from text and speech Detecting toxic posts and disinformation online Image-to-text generation for medical diagnostics NLP applications in biomedical, legal, and financial domains His recent publications demonstrate strong activity across medical AI (particularly ImageCLEFmed Caption competitions where his group consistently ranks 1st-2nd), legal NLP (LexGLUE benchmark), financial NLP (EDGAR-CRAWLER), and multilingual challenges. His work shows increasing emphasis on large language models, explainability, and practical applications. Notable awards include: Top 2% scientist worldwide (Stanford University database, 2023) Multiple AUEB Excellent Teaching Awards (2017-18, 2021-22, 2023-24) Three consecutive BioASQ awards (2018-2020) Multiple 1st/2nd place rankings in ImageCLEFmed Caption competitions (2021-2025) He actively organizes major events including the Athens Natural Language Processing Summer School (AthNLP) and SemEval tasks. His group maintains strong industry and research collaborations, particularly in medical AI applications where they've developed systems that generate diagnostic captions from medical images with state-of-the-art performance.
Huazhen Fang is an Associate Professor in the Department of Mechanical Engineering at the University of Kansas School of Engineering, where he joined in 2014. He leads the Information & Smart Systems Laboratory (ISSL) and holds a courtesy appointment in the Department of Electrical Engineering & Computer Science. His research focuses on enabling intelligence for complex systems through information-driven approaches. Dr. Fang received his Ph.D. in Mechanical Engineering from the University of California, San Diego in 2014, following an M.Sc. from the University of Saskatchewan and a B.Sc. in Computer Science & Technology from Northwestern Polytechnic University in China. He was a Visiting Faculty Fellow at Mitsubishi Electric Research Laboratories in 2022. His research interests span Systems and Control, Advanced Battery Management, Energy Storage Systems, and Robotics, with particular focus on system modeling, estimation, control design, machine learning and numerical optimization. Dr. Fang's work has significant applications in energy management, cooperative robotics, and environmental observing systems. His research has been supported by the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. His extensive publication record shows a clear trend toward increasingly sophisticated integration of physics-based modeling with machine learning approaches, particularly in battery management systems and autonomous vehicle control. Recent work demonstrates a growing emphasis on Bayesian inference methods, distributed control architectures, and safety-critical applications of intelligent control systems. Faculty Early Career Award from National Science Foundation (2019) University Scholarly Achievement Award (2024) Miller Professional Development Award (2022) Miller Faculty Scholar Award (2018, 2019, 2023) Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award (2016) Big XII Faculty Fellowship (2015) IEEE Transactions on Transportation Electrification Prize Paper Award (2024) Dr. Fang has successfully mentored numerous graduate students through the Information & Smart Systems Laboratory, with many receiving awards for their research. His research has attracted significant funding from prestigious organizations including the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. He currently serves as an Associate Editor for multiple prestigious journals including Information Sciences, IEEE Transactions on Industrial Electronics, and IEEE Control Systems Letters. The Information & Smart Systems Laboratory (ISSL) under Dr. Fang's leadership has established itself as a center for cutting-edge research in information-driven smart systems. The lab focuses on pushing the frontiers of information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. The lab actively collaborates with industry partners and local communities, emphasizing research that serves societal needs.
Professor Roy Pea is the David Jacks Professor of Education & Learning Sciences at Stanford University, with a courtesy appointment in Computer Science. He served as Director of the H-STAR Institute (2007-2021) and founded Stanford’s PhD program in Learning Sciences and Technology Design. His research focuses on technology-enhanced learning, social foundations of human learning, and interdisciplinary applications of digital tools. Stanford University, School of Education Graduate School of Education Department Courtesy appointment in Computer Science His work spans complex domains like concussion education, climate change learning, and AI-driven mental health interventions. He co-authored the 2010 National Education Technology Plan and co-edited key texts including Video Research in the Learning Sciences and AI in Education . His NSF-funded LIFE Center (2004-2014) advanced learning science theories. Recent publications address: (1) linguistic framing of concussions and reporting behavior, (2) AI chatbots for mental health, (3) "engineering fiction" to reduce climate change abstractness, and (4) immersive AR/LLM learning experiences. His research integrates data science, psychology, and educational technology. Fellow, American Academy of Arts and Sciences (2019) Inaugural Fellow, International Society of the Learning Sciences (2018) Honorary Doctorate, The Open University (2018) Best Bridging Paper, EDM 2014 LAK13 Best Paper Award (2013) Roy mentors doctoral and master’s students in learning sciences, advising on topics related to technology, cognition, and equity. He contributes to digital education policy through roles on advisory boards for organizations like NSF, NIH, and the Joan Ganz Cooney Center. His patents include methods for digital video analysis and collaborative learning systems.
LEE Mong Li is a Professor of Computer Science at the National University of Singapore (NUS) and serves as Director of the NUS Centre for Trusted Internet and Community. She holds a Ph.D., M.Sc., and B.Sc. (First Class Honours) in Computer Science from NUS, where she was awarded the IEEE Singapore Information Technology Gold Medal as the top Computer Science student in 1989. Her academic career includes a visiting fellowship at the University of Wisconsin-Madison (1999) and consultancy with QUIQ USA (2000). Her research spans Data Management, Spatio-temporal Databases, Biomedical Informatics, and Retinal Image Analysis . She has pioneered work in data cleaning, data fusion, and analysis of semistructured data, with applications in social media analytics and healthcare. Her recent publications demonstrate strong interdisciplinary focus, particularly in AI-driven medical diagnostics including diabetic retinopathy screening and chronic kidney disease detection from retinal images. She co-authored foundational books on 'Designing Semi-structured Database' and 'Temporal and Spatio-Temporal Data Mining'. Her 150+ publications in major database conferences and journals reflect leadership in both theoretical and applied research. Recent work shows significant emphasis on Medical AI applications (retinal analysis, kidney disease prediction) Temporal fact verification systems Misinformation detection in multimodal environments Privacy challenges in large language models Key honors include: Singapore's President Technology Award (2014) for co-inventing an AI system screening eye conditions IEEE Singapore Information Technology Gold Medal (1989) She actively contributes to government-funded multidisciplinary projects building practical deployable systems. Her leadership extends to program committees of prestigious database conferences and directing the NUS Centre for Trusted Internet and Community. She teaches BT5110 Data Management and Warehousing and has co-developed an AI system for diabetic retinopathy screening deployed in Singapore's national teleophthalmology program.
Robert D. Gregg, IV is a Professor of Mechanical Engineering, Robotics, and Electrical & Computer Engineering at the University of Michigan. He serves as Associate Director for Graduate Education at Michigan Robotics and directs the Locomotor Control Systems Laboratory. His research focuses on control systems for wearable robots, prosthetics, and orthotics, emphasizing biomimetic principles and nonlinear control theory. Gregg holds a PhD from the University of Illinois at Urbana-Champaign (2010) and prior academic roles at the University of Texas at Dallas and Northwestern University. Education: PhD, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2010 MS, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2007 BS, Electrical Engineering and Computer Sciences, University of California, Berkeley, 2006 Research Interests: Gregg’s work spans control mechanisms for bipedal locomotion, wearable robotics, nonlinear control theory, and rehabilitation engineering. His lab develops high-performance control systems for prosthetic legs and exoskeletons to enhance mobility for individuals with disabilities. Key areas include energy-efficient control strategies, adaptive impedance systems, and biomechanical modeling of human movement. Grant & Award Highlights: $3M NIH R01 Grant (2023): Modeling and control of agile powered prosthetic legs for varied activities. $1.7M NIH R01 Grant (2021): Modular powered orthoses for broad patient populations. NSF CAREER Award (2017) NIH New Innovator Award (2013) Advising & Labs: Gregg mentors PhD students in robotics and biomechanics, emphasizing independent research and collaborative team environments. His lab supports over 15 researchers and has produced notable alumni like Dr. Cara Welker (University of Colorado Boulder faculty). The lab’s work is supported by NIH, NSF, and industry partnerships. Recent Contributions: Recent work includes phase-variable control for stair climbing, energy shaping methods for exoskeletons, and open-source robotic leg platforms. Gregg also chairs major robotics conferences (e.g., IROS 2023) and advises on clinical translation of wearable robotics.
Dr. Laura B. Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley. Her work focuses on causal inference, machine learning, and addressing methodological challenges in both randomized trials and observational studies, particularly in global health contexts. She leads collaborations in East Africa, focusing on HIV elimination and community health in rural regions. Her research emphasizes translating academic findings into real-world impact. Education: PhD in Biostatistics, UC Berkeley (2015) MPhil in Computational Biology, University of Cambridge (2009) BS in Applied Mathematics, University of Vermont (2008) Research Interests: Dr. Balzer’s work addresses causal inference in complex settings, including semi-parametric methods, measurement challenges, and dependence structures. Her global health projects target HIV prevention, tuberculosis transmission, and hypertension management in sub-Saharan Africa. She designs interventions like the SEARCH Dynamic Choice model, which offers flexible HIV prevention options, and evaluates community health worker programs. Publications highlight her contributions to HIV/AIDS research, including studies on PrEP uptake, viral suppression in adolescents, and tuberculosis-HIV co-infection. Methodologically, she advances causal inference frameworks to handle missing data and clustered designs. Awards: While no specific awards are listed, her work has been funded by initiatives like the SEARCH trials, reflecting its scientific and public health significance. Advising & Grants: Balzer collaborates with multidisciplinary teams in Uganda and Kenya, focusing on translational research. Her grants support interventions linking statistical innovation to healthcare delivery improvements in resource-limited settings. Labs/Teams: Her research is embedded within global health partnerships, particularly within the SEARCH trials network, which integrates biostatistics with clinical and community-based implementation.
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.
Fatma Deniz is a Full Professor (W3) of Computer Science at Technische Universität Berlin, supported by the Berlin Equal Opportunities Program. She leads the Chair of Language and Communication in Biological and Artificial Systems, and is a member of the Berlin Bernstein Center for Computational Neuroscience. Her roles include membership in TU Berlin's Executive Board and the Berlin University Alliance Steering Committee. She holds a Ph.D. (Dr. rer. nat.) from TU Berlin and a Diploma in Computer Science from Technische Universität München, with research training at Caltech and postdoctoral work at UC Berkeley. Her research focuses on understanding neural mechanisms of language processing, integrating computational neuroscience, cognitive science, and artificial intelligence. Key areas include semantic representation dynamics, cross-modal neural alignment, and language learning in bilingual contexts. She has pioneered studies showing the brain's invariant semantic processing across reading and listening modalities. Her grants include an ERC Starting Grant (2023-2028) for studying language learning shifts and a NSF-BMBF CRCNS grant on bilingual representations. She co-edited The Practice of Reproducible Research: Case Studies in Data Science (UC Press, 2017) and contributed to foundational work on reproducible data science methodologies. She has advised projects in neuroimaging, AI ethics, and computational linguistics, and collaborates with institutions like UCSF and the German Academic Exchange Service. Her lab explores neural correlates of language through fMRI, MEG, and machine learning techniques.
Sarah Ebling is a Full Professor of Language, Technology and Accessibility at the University of Zurich's Faculty of Arts and Social Sciences. She leads the Language, Technology and Accessibility research group within the Institute for Computational Linguistics. Her work focuses on computational linguistics applications for assistive technologies targeting disabilities such as hearing impairments, visual impairments, and cognitive disorders. Key areas include sign language technologies, automatic text simplification, and audio description systems. She directs the large-scale Swiss innovation project 'Inclusive Information and Communication Technologies' (2022-2026, CHF12 million budget) and collaborates on EU H2020 and SNSF Sinergia projects. Education: Holds a doctoral degree (summa cum laude, 2016) from the University of Zurich with research on automatic translation to Swiss German Sign Language. Completed studies in German Linguistics, Computational Linguistics, and English Linguistics at Universities of Zurich and Heidelberg, with research stays in Dublin, Chicago, and Rochester. Research emphasizes multimodal accessibility solutions, including sign language fluency assessment, gesture-based interaction, and AI-driven text adaptation. Current projects explore audio description translation systems (SwissADT), sign language corpus development (SwissSLi), and digital tools for comprehensibility assessment in simplified texts. Her work bridges computational linguistics with ethical considerations in assistive technology deployment. Grants and Leadership: Principal Investigator on major accessibility-focused grants, including the CHF12M Swiss innovation project. Supervises PhD candidates in areas like sign language assessment tools and text simplification algorithms. Active in international collaborations, publishing extensively in computational linguistics and accessibility journals/conferences. Technology Development: Created the 'DigiSpon' benchmark for language sample analysis and developed open-source tools for sign language translation baselines. Her team's innovations include the SignCLIP model connecting text and sign language via contrastive learning, and pose estimation frameworks for sign language recognition.
Ambarish Kulkarni is an Assistant Professor in the Department of Chemical Engineering at the University of California, Davis. His research focuses on multi-scale molecular modeling, data science for materials discovery, catalysis, and separations. He combines quantum chemistry methods (e.g., wave function theory, density functional theory) with classical simulations and machine learning to design novel materials for applications in catalysis, energy storage, and environmental remediation. Specific areas of interest include methane activation, CO 2 capture, and heterogeneous electrocatalysis. His work bridges theory and experiment, collaborating with experimental groups to validate computational findings. Notable projects include: Developing catalysts with atomically dispersed metals for enhanced reactivity Designing zeolite materials for selective chemical transformations Creating machine learning workflows to accelerate material discovery Recent research highlights the role of water in CO 2 adsorption mechanisms, the dynamic behavior of confined nanoparticles, and redox-cycling phenomena in zeolite-embedded catalysts. His computational tools like the Multiscale Atomic Zeolite Simulation Environment (MAZE) enable detailed analysis of complex material behaviors. No scientific awards are explicitly listed in the provided information. His advising activities and grants are not detailed in the current data, but his extensive publication record indicates active research collaboration and funding support.
Indrani Bhattacharya, PhD, is an Assistant Professor in the Department of Biomedical Data Science and the Center for Precision Health and Artificial Intelligence (CPHAI) at Dartmouth College's Geisel School of Medicine. Her research focuses on developing human-centered AI systems for healthcare, particularly in multimodal medical imaging and behavioral health analytics. She holds a BS in Electrical Engineering from Jadavpur University (India), and MS/PhD from Rensselaer Polytechnic Institute (USA). Postdoctoral training at Stanford University's Department of Radiology further specialized her in biomedical imaging informatics. Research interests include: Integrating imaging and non-imaging data for precision medicine AI-driven prostate cancer detection/classification Multimodal behavior estimation for doctor-patient interactions Privacy-preserving sensor systems for group interaction analysis Her work bridges computer vision, medicine, and social science, with recent breakthroughs in MRI-ultrasound fusion AI outperforming radiologist interpretations in multi-center studies. Active in AI ethics and translational research, she leads teams developing clinical decision support tools for oncology and behavioral health. Key career milestones include: Postdoctoral scholar at Stanford University School of Medicine (2016-2021) Academic research staff at Stanford Radiology (2021-2022) Founding member of Dartmouth CPHAI precision health initiatives Labs/Teams: Leads the Biomedical AI for Healthcare group at Dartmouth, collaborating with Stanford and industry partners on AI-driven diagnostic systems.
Ali Bilgin is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Arizona's College of Engineering. He also holds associate professor appointments in Biomedical Engineering, the BIO5 Institute, and Medical Imaging, and is a member of the Graduate Faculty. His work bridges engineering and medical applications, particularly in signal and image processing. His educational background includes: PhD in Electrical Engineering, University of Arizona, 2002 MS in Electrical Engineering, San Diego State University, 1995 BS in Electronics and Telecommunications Engineering, Istanbul Technical University, 1992 Dr. Bilgin's research focuses on signal and image processing , with key applications in image and video coding, data compression, and magnetic resonance imaging (MRI) . His work integrates theoretical advances with practical biomedical applications. Teaching interests include digital signal processing, linear algebra, probability theory, and machine learning in image processing. With over 250 research papers and 13 granted patents, his scholarly output reflects sustained contributions to engineering and imaging sciences. Though specific articles are not listed, his editorial roles and publication volume indicate leadership in signal and image processing domains, particularly in compression and medical imaging. His scientific recognition includes multiple teaching awards from the UA College of Engineering, notably being named Most Supportive Senior Faculty . Most Supportive Senior Faculty, UA College of Engineering Dr. Bilgin has served as an associate editor for several top IEEE journals, including IEEE Signal Processing Letters (2010–2012), IEEE Transactions on Image Processing (until 2014), and IEEE Transactions on Computational Imaging (2014–2019), reflecting his standing in the academic community. While no specific grants or students are listed, his extensive publication record and interdisciplinary affiliations suggest active mentorship and funded research. He is affiliated with the BIO5 Institute, indicating participation in collaborative, interdisciplinary research teams focused on health and bioscience innovation.
