Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.
Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.
Enamul Hoque Prince is an Associate Professor and Director of the School of Information Technology at York University. He leads the Intelligent Visualization Lab, funded by the Canada Foundation for Innovation (CFI) and Ontario Research Funds (ORF). He holds a PhD in Computer Science from the University of British Columbia and completed postdoctoral work at Stanford University. His research integrates information visualization, human-computer interaction (HCI), and natural language processing (NLP) to address information overload challenges. Dr. Prince's educational background includes a PhD from UBC, an MSc from Memorial University of Newfoundland, and a BSc from Chittagong University of Engineering & Technology. He has conducted research at institutions like Tableau Software and the Qatar Computing Research Institute and serves on committees for top conferences like ACL and IEEE Vis. His work is supported by grants from NSERC, CFI, and others. Research interests focus on NLP-driven visual analytics, user-adaptive visualization, and accessible interfaces. Notable projects include Evizeon (natural language interfaces for visual analytics), ConVisIT (topic modeling for online conversations), and CIDER (concept-based image search). Publications highlight trends in multimodal systems, chart comprehension, and accessibility. Key awards include the NSERC Discovery Grant (2019) and a Best Paper Honorable Mention at DIS 2021. He supervises graduate and undergraduate students in areas like visualization, NLP, and HCI. Labs and collaborations emphasize interdisciplinary approaches, with the Intelligent Visualization Lab advancing tools for data exploration and user-centered design. Teaching includes courses on design principles and information visualization.
Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.
Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.
Ambuj K. Singh is a Distinguished Professor of Computer Science at the University of California, Santa Barbara (UCSB), with a part-time appointment in the Biomolecular Science and Engineering Program. He holds a PhD from the University of Texas at Austin (1989), an MS from Iowa State University (1984), and a BTech from the Indian Institute of Technology, Kharagpur (1982). His campus affiliations include the Center for Bio-Image Informatics, Information Network Academic Research Center, and IGERT on Network Science. PhD, University of Texas at Austin, 1989 M.S., Iowa State University, 1984 B.Tech., Indian Institute of Technology, Kharagpur, 1982 Research interests span network science, machine learning, and bioinformatics, with a focus on graph-based methodologies. His work addresses: Data-centric modeling of dynamic networks Representation learning and explainability in graph neural networks Network analysis in social systems and biological networks Applications in drug discovery and brain sciences Geometry-preserving distance metrics for data integrity Recent publications highlight advancements in counterfactual explanations, GNN benchmarking, molecular graph pretraining, and self-attention for event detection. Scientific contributions include: Founding Acelot, Inc., an in silico drug discovery company Editorial roles at IEEE Transactions on Knowledge & Data Engineering and BMC Journal of Clinical Bioinformatics NSF-IGERT (2013-2018), ARL-funded Information Networks Academic Research Center (2009-2014), and US Army MURI grants Advising has involved mentoring over 50 graduate/postdoctoral students, including 30+ PhD candidates. He leads a multidisciplinary research group at UCSB and collaborates with off-campus entities like Acelot, Inc.
Nima Mesgarani is an Associate Professor of Electrical Engineering at Columbia Engineering, Columbia University, affiliated with the Sense, Collect and Move Data Committee. His research bridges engineering and neuroscience through reverse-engineering neural signal processing mechanisms, leading to advancements in brain-machine interfaces, neural prosthetics, and speech processing algorithms. He received his PhD in Electrical Engineering from the University of Maryland and completed postdoctoral training at Johns Hopkins University's Center for Language and Speech Processing and UC San Francisco's Neurosurgery Department. Research Focus Professor Mesgarani's lab integrates computational neuroscience and engineering to study acoustic signal processing. Key areas include: Neural decoding of speech and auditory attention in multi-talker environments Development of brain-controlled hearing technologies Novel speech separation and synthesis algorithms inspired by cortical processing Cross-modal learning between auditory and visual systems Applications of large language models in neural signal interpretation Publication Trends Analysis of his 15 most recent articles (2025) reveals dominant themes: neural decoding techniques using intracranial EEG, brain-inspired speech separation models (e.g., Mamba architectures), applications of large language models in auditory neuroscience, cross-modal distillation methods, and clinical translation of audio processing algorithms. A strong emphasis emerges on real-time brain-computer interfaces and noise-robust speech processing. Laboratory and Collaborations Mesgarani directs an interdisciplinary lab developing neurotechnology for hearing restoration. His team collaborates with neurosurgery departments and speech processing centers, focusing on translating theoretical models into clinical brain-machine interfaces. The lab's work has yielded patents for brain-informed speech separation systems and attention-decoding frameworks.
Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.
Dr. Jeewanie Jayasinghe Arachchige is a Lecturer in the Department of Computer Science at Vrije Universiteit Amsterdam, Faculty of Science. She teaches undergraduate courses including Bachelor Project Computer Science, Professional Development, and Software Engineering Processes for the academic year 2024–2025. Her research focuses on process mining , healthcare informatics , and data security . She applies process mining to analyze healthcare pathways and subpopulation treatment variations, develops explainable AI frameworks for predictive analytics, and examines data governance in emerging architectures like Data Lakehouses. Her work intersects legal informatics, particularly formalizing Sri Lankan civil court processes using ontology engineering. Recent publications highlight trends in balancing simplicity and complexity in process modeling, Industry 4.0 healthcare applications, and cybersecurity in model-driven web development. She has contributed to over 20 peer-reviewed articles since 2006, spanning topics from service-oriented architectures to value network analysis. Her teaching and research emphasize practical applications of IT in healthcare, legal systems, and enterprise environments. No ancillary activities are currently recorded.
Susan A. Murphy is the Mallinckrodt Professor of Statistics and of Computer Science at Harvard University, with affiliations to the Kempner Institute. She leads the Statistical Reinforcement Learning Lab, focusing on developing algorithms to inform sequential decision-making in health, particularly for Just-in-Time Adaptive Interventions (JITAIs) and micro-randomized trials (MRTs). Her work is funded by NIH institutes, including NIDA, NHLBI, and NIBIB. Dr. Murphy has been awarded a MacArthur Fellowship (2013) and is a member of the National Academy of Medicine (2014) and the National Academy of Sciences (2016). Her research integrates statistical methods with computer science techniques to optimize mobile health interventions. She collaborates with d3Lab and mDOT on projects like HeartSteps and Sense2Stop, evaluating real-time treatment policies. Notable contributions include advancing MRT designs, sample size calculations, and reinforcement learning algorithms for personalized healthcare. Dr. Murphy advises a large team of postdocs, graduate students, and undergraduates, many of whom hold academic and industry roles globally. She emphasizes engagement in digital interventions, balancing personalization with ethical considerations. Her lab’s work spans algorithm development, clinical trial design, and causal inference, aiming to improve health outcomes through adaptive interventions.
Hyun (Michel) Koo is a Professor at the University of Pennsylvania School of Dental Medicine , with affiliations in the Department of Orthodontics , Division of Community Oral Health , and Division of Pediatric Dentistry . As Co-Founder and Co-Director of the Center for Innovation & Precision Dentistry (CiPD) , he leads interdisciplinary efforts merging bioengineering, nanotechnology, and oral health research. Education : DDS and PhD Research Focus : Biofilms, bacterial-fungal interactions, and nanotechnology for oral disease prevention Leadership : Co-Director of CiPD; key roles in training programs like NIDCR-sponsored R90 and T90/R90 Dr. Koo’s research explores biofilm mechanisms in oral infectious diseases, particularly childhood caries, through engineering methods and microrobotics . His team developed micron-scale robots for automated biofilm eradication and FDA-approved nanoparticles for caries prevention. Collaborations with Penn Engineering, including Dr. Daeyeon Lee and Dr. Kacy Cullen, emphasize translational approaches. The 15 most recent publications highlight his work in nanorobotics , interkingdom biofilms , and precision diagnostics . Articles span 2025–2024 and address topics like adaptive micromotors , biofilm matrix degradation , and single-cell microbial interactions . These emphasize his focus on targeted therapies and biofilm microenvironment engineering . Key Awards : Elected Fellow, American Association for the Advancement of Science (AAAS) IADR Distinguished Scientist Award for innovative dental research Dr. Koo trains next-generation researchers through the CiPD NIDCR T90/R90 Postdoctoral Training Program , mentoring fellows like Smruti Nair (ACE2 Chewing Gum development) and Zhi Ren (K99 awardee). His work intersects with Penn Health-Tech, CT3N , and Penn Institute for Biomedical Informatics , fostering transdisciplinary innovation.
Dr. Daniel Dutton is an Associate Professor in the Department of Community Health and Epidemiology at Dalhousie University's Faculty of Medicine, based at the Dalhousie Medicine New Brunswick campus in Saint John. His research focuses on how social environments influence health outcomes and the role of government policies in mitigating these impacts, particularly in areas like poverty, homelessness, and health economics. He holds adjunct roles at the University of New Brunswick (Sociology) and is the Scientific Director of APPTA, a hub bridging aging research and policy. He teaches courses in epidemiology, population health, and statistics. Education: PhD in Community Health Sciences (University of Calgary) MA in Economics (University of Calgary) BA Honours in Economics (Queen’s University) Research Interests: Dr. Dutton examines population-level health determinants, government policy effectiveness, and socioeconomic disparities. His work employs large datasets and econometric models to analyze topics such as homelessness incidence, healthcare cost savings through preventive measures, and the distributional impacts of public spending. Key Contributions: Scientific Director of the APPTA aging and technology hub. Co-founder of the IMPART research collaborative. Author of influential studies on Housing First programs, opioid prescribing patterns, and the fiscal benefits of guaranteed annual income policies. Advising & Grants: Supervises multiple MSc students and has collaborated on grants analyzing healthcare utilization in New Brunswick and Alberta. His work often intersects with policy-making, emphasizing actionable insights from quantitative research. Labs & Teams: Leads the Dutton Research Group, which uses mascot Odin the Saint Bernard to foster team cohesion. Active in interdisciplinary collaborations across public health, sociology, and economics.
Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.
Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.
Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.
