Adelle Forth is a Professor in the Department of Psychology at Carleton University, specializing in psychopathy research. Her work examines cognitive and affective processes underlying psychopathic traits, early manifestations in youth, and societal impacts. She has published extensively on violence risk assessment, forensic evaluation tools, and interpersonal dynamics affected by psychopathy. Research Focus: Her studies explore psychopathy's influence on romantic relationships, empathy deficits, humor styles, and vulnerability perceptions. She also validates cross-cultural diagnostic tools like the Spanish PCL:YV and investigates cyber-related deviant behaviors linked to dark personality traits. Professional Service: Dr. Forth serves on editorial boards and has supervised numerous graduate theses. She no longer accepts new students.
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.
Mustafa Bilgic is a Professor and Chair of the Computer Science Department at Illinois Institute of Technology, where he also directs the Master of Artificial Intelligence program and the Machine Learning Laboratory. His research focuses on machine learning, active learning, explainable AI, and probabilistic graphical models, with applications in healthcare, social media analysis, and biomedical engineering. He has received funding from NSF, NIH, and Samsung, among others. Education: PhD in Computer Science, University of Maryland at College Park (2010) M.S. in Computer Science, University of Maryland at College Park (2006) B.S. in Computer Science, University of Texas at Austin (2004, with High Honors and Special Honors) Research Highlights: Dr. Bilgic's work emphasizes AI ethics, algorithm transparency, and interactive machine learning systems. Notable projects include analyzing political news engagement dynamics and developing frameworks for eliminating explanation noise in AI models. His lab explores tools like OrganoID for tracking organoid growth and IDGI for improving model interpretability. Awards: NSF CAREER Award (2014) ACM SIGKDD Best Student Paper Award (2008) Illinois Tech College of Computing Teaching Excellence Award (2021) Teaching and Leadership: Bilgic teaches advanced courses in AI, machine learning, and data mining. He leads initiatives to bridge AI theory and practical applications, emphasizing interdisciplinary collaboration. His administrative roles include overseeing the AI master’s program and fostering innovation in computing education.
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.
Emily Cooper is an Associate Professor of Optometry & Vision Science at the Herbert Wertheim School of Optometry & Vision Science, University of California, Berkeley. She serves as the Chair of the Vision Science PhD Program and is a co-Director of the Center for Innovation in Vision & Optics. Additionally, she is a member of the Helen Wills Neuroscience Institute and a Visiting Faculty Researcher at Google. Dr. Cooper's research focuses on 3D vision, perceptual graphics, AR/VR, computational neuroscience, visual encoding, and display system design. Her work investigates how the visual system processes information to create our perception of the 3D world, with applications in computer graphics, virtual reality, and assistive technologies for people with low vision. Analysis of Dr. Cooper's recent publications (2023-2025) reveals a strong focus on the intersection of vision science and emerging technologies, particularly in augmented reality and assistive vision systems. Her work spans fundamental research on visual perception mechanisms to applied research developing practical technologies for low vision rehabilitation. A significant portion of her recent work addresses visual discomfort in XR displays, perceptual guidelines for AR/VR systems, and innovative approaches to assistive vision technologies that enhance mobility and independence for visually impaired individuals. Dr. Cooper leads an active research laboratory at UC Berkeley's 391 Minor Hall, where she mentors students and collaborators in vision science research. Her lab investigates both basic questions about how vision works and translational questions about improving visual technologies. She has developed perceptual guidelines for optimizing field of view in stereoscopic augmented reality displays and created assistive technologies such as an augmented reality sign-reading assistant for users with reduced vision. Dr. Cooper is also involved in professional activities including co-organizing the Computational Neuroscience: Vision summer course at Cold Spring Harbor Laboratory and working with Community Resources For Science to promote science education.
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.
João Paulo Costeira is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (1995) and was a Visiting Scientist at Carnegie Mellon University's Robotics Institute (1991–1995). His research focuses on Computer Vision, 3D Reconstruction, and Structure from Motion, with contributions to object recognition, robotics, and multimedia analysis. Education: PhD in Electrical and Computer Engineering, IST (1995); Visiting Scientist, CMU Robotics Institute (1991–1995). Roles: Coordinator of the Signal and Image Processing Group (SIPg), Co-director of the Carnegie Mellon|Portugal Dual PhD Program in ECE and Robotics (2007–2018), and Scientific Director of Carnegie Mellon|Portugal (2014–2018). Research Interests: João's work emphasizes 3D reconstruction from video, rigid and non-rigid motion analysis, and applications in robotics and urban surveillance. He has pioneered methods for motion segmentation and robust correspondence problems in computer vision. Publications: His recent work includes advancements in apple counting systems, rotation averaging for robotics, and domain adaptation for traffic density estimation. These contributions highlight his expertise in real-world computer vision challenges. Awards: None explicitly listed. However, his extensive publication record and academic leadership reflect significant scholarly impact. Advising & Grants: Supervised 13 PhD students, many co-advised with CMU faculty. Active in projects like CityCam (vehicle counting) and MultiDrone (robotics collaboration). Funded by FCT, EU, and industry partnerships. Labs/Teams: Leader of the Signal and Image Processing Group (SIPg) at ISR. Involved in NETSyS program for networked systems and robotics.
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.
Tuğba Dalyan is an Associate Professor in the Department of Computer Engineering at Istanbul Bilgi University, Faculty of Engineering and Natural Sciences. She holds a Ph.D. in Computer Engineering from Yıldız Technical University (2014), an MSc from Kocaeli University (2007), and dual BSc degrees in Mathematics and Computer Science and Business Administration (Minor) from Istanbul Bilgi University (2003). She has been a faculty member since 2016 and previously served as a Teaching Staff member and Research Assistant at the same institution. Her research focuses on Natural Language Processing , Machine Learning , Deep Learning , Text Mining , Data Science , and Big Data Analytics . Her work spans computational linguistics, sentiment analysis, author profiling, machine translation, and smart systems. She has led and contributed to numerous research projects, particularly in AI-driven urban solutions and health technologies. The most recent publications show a strong trend in Turkish NLP, zero-shot classification, multimodal AI (image captioning), emotional robotics, and decision support systems using fuzzy logic. Her work combines theoretical rigor with practical applications in smart cities, education, and healthcare. Best Paper Award , CICLing 2012 TÜBİTAK 2209-A student project awards (2022–2024) Horizon2020 Eşik Üstü Ödülü , MIMOSCSA 2024 TÜBİTAK 2242 competition: 2nd and 3rd place (2016, 2018) She has advised numerous student research projects, many of which have received national recognition. She has directed multiple TÜBİTAK and institutional research grants, including projects on smart homes, blockchain crowdfunding, mental health, and AI for social polarization. Her leadership roles include Head of Department, Vice Dean, and Director of Graduate Programs. Tuğba Dalyan leads research in AI and NLP with a strong emphasis on Turkish language technologies. She is involved in interdisciplinary teams working on emotional robots, smart city platforms, and citizen science ecosystems. Her lab activities focus on neural networks, text analysis, and intelligent systems development.
Howard Forman is a Professor of Radiology and Biomedical Imaging at Yale School of Medicine, with secondary appointments in Public Health (Health Policy), Management, and Economics. He is fully joint in the School of Management and holds affiliations with the Institute for Social and Policy Studies. He serves as Director of the MD/MBA Program, the Executive MBA Healthcare Focus Area, and the Health Care Management Program at the Yale School of Public Health. He is also the Faculty Director of Finance in the Department of Radiology and an active clinician at Yale New Haven Hospital’s Emergency Department, where he serves as deputy operational chief for Radiology. Professor, Radiology & Biomedical Imaging, Yale School of Medicine Professor, School of Management Professor, Economics Professor, Health Policy & Management Director, MD/MBA Program Director, Health Care Management Program (YSPH) Faculty Director of Finance, Radiology Department Dr. Forman’s research centers on health economics, healthcare policy, quality improvement, and radiology administration. His interests include healthcare financing, cost analysis, health systems reform, and the application of AI and large language models in radiology reporting. He has extensively studied patient access to imaging reports, clinician staffing, and end-of-life cancer care. His recent work explores how AI can improve reporting accuracy while addressing ethical concerns like racial bias. His recent publications reflect a strong trend in leveraging artificial intelligence to enhance radiology workflows and patient engagement, while maintaining a focus on equity and policy implications. Articles in Radiology , JAMA Network Open , and Clinical Imaging demonstrate his leadership at the intersection of medicine, technology, and policy. Healthcare Track Teaching Award – 2025, 2023, 2017 Regent's Award – 2019 Leah Lowenstein Award – 2019 Distinguished Student Mentoring Award – 2013 Fellow, Society for Advanced Body Imaging – 2003 Dr. Forman is a dedicated educator and mentor, having founded and led multiple interdisciplinary programs. He has been instrumental in shaping health policy curricula and promoting evidence-based public health communication. He co-hosts the popular Health & Veritas podcast with Harlan Krumholz, contributing to public discourse on healthcare. He has served on editorial boards for journals including American Journal of Roentgenology and Clinical Imaging , and is actively involved in professional societies such as the Radiologic Society of North America.
Professor Emanuele (Manuel) Trucco is the NRP Chair of Computational Vision in the Department of Computing, School of Science and Engineering, at the University of Dundee. He holds additional roles as an Honorary Clinical Researcher at NHS Tayside and formerly served as an Adjunct Professor at the Chinese Academy of Sciences from 2018 to 2021. He earned his MSc and PhD in Electronic Engineering from the University of Genoa, Italy, in 1984 and 1990, respectively. His research focuses on medical image and data analysis, particularly in retinal imaging, using machine and deep learning techniques. He is co-director of VAMPIRE, a major international initiative in retinal image analysis, which supports biomarker studies in cardiovascular disease, diabetes, dementia, and neurodegenerative disorders. His recent work includes AI-driven tools for predicting dementia from brain scans and cardiovascular risk from retinal images. The latest publications reflect strong trends in applying deep learning to retinal and brain imaging for early disease detection, with themes centered on AI in healthcare, precision medicine, and non-invasive diagnostics. FRSA (Fellow of the Royal Society of Arts) FIAPR (Fellow of the International Association for Pattern Recognition) Professor Trucco has led and co-led significant research projects funded by EPSRC, NIHR, and EU programs. He has supervised PhD students through industry-sponsored studentships (e.g., OPTOS, NIDEK, Toshiba) and collaborated with institutions such as the Universities of Edinburgh and Liverpool. His research is supported by extensive industrial partnerships including Canon Medical, Epipole plc, and NIDEK. He is a key member of the UK Biobank Eye and Vision Consortium and co-director of the VAMPIRE initiative, a collaborative effort between the Universities of Dundee and Edinburgh focused on retinal image analysis. His work also involves participation in major research networks such as the Academic Health Science Partnership in Tayside.
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.
