Adam Yala is an Assistant Professor of Computational Precision Health, Statistics, and Electrical Engineering and Computer Science at UC Berkeley and UCSF. He is also the Founder & CEO of Voio Inc., a company focused on clinical translation of AI tools. PhD in Computer Science from MIT (2022) His research lies at the intersection of Machine Learning and Precision Medicine, with a focus on robust AI tools for clinical deployment, personalized screening policies, and private data sharing. Current work includes multi-modal imaging analysis, decision guarantees in clinical workflows, and prospective trials in oncology and radiology. Recent publications highlight advancements in AI for cancer risk prediction, vision-language models in healthcare, and data privacy techniques. Tools like Mirai are implemented in 66 hospitals across 30 countries. Bakar Fellows Spark Award (2024) Eppy Award: Investigative Reporting (2022) Falling Walls Finalist: Life Science (2022) NSF Fellowship (2016) He advises PhD students in AI-driven healthcare and collaborates with hospital systems globally. His lab emphasizes clinical translation of machine learning methods in radiology and oncology.
Shu Yang is an Associate Professor of Statistics at North Carolina State University (NC State), specializing in causal inference, missing data analysis, and biostatistics. She holds a Ph.D. in Applied Mathematics and Statistics from Iowa State University and has held roles including Postdoctoral Fellow at Harvard University and Assistant Professor at NC State. Her research focuses on developing statistical methods for observational and clinical studies, particularly in healthcare and environmental applications. Education: Ph.D. in Applied Mathematics and Statistics from Iowa State University (2014) B.Sc. in Mathematics and Applied Mathematics from Beijing Normal University (2009) Research Interests: Dr. Yang’s work addresses challenges in causal inference, including longitudinal data analysis, missing data imputation, and high-dimensional statistics. She applies these methods to environmental health, cardiovascular diseases, HIV infection, and cancer research. Her team also explores spatial statistics and data integration techniques. Awards: 2025: Think, Collaborate & Do Ideation Award 2024: COPSS Emerging Leader Award, Cavell Brownie Mentoring Award 2022: University Faculty Scholar 2018: Ralph E. Powe Junior Faculty Enhancement Award Grants & Advising: She leads funded projects on causal inference methods in environmental health, sepsis detection, and marine protected areas. She advises over 20 Ph.D. students and postdocs, focusing on causal methods, data integration, and healthcare analytics.
Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.
Dr. Manisha Kulkarni is a Professor at the School of Epidemiology and Public Health, University of Ottawa, where she holds the University Research Chair in Climate Change and Emerging Diseases. She is also the Director of the INSIGHT (Interdisciplinary Spatial Informatics for Global Health) research lab and serves as Scientific Director of the Canadian Lyme Disease Research Network (TickNet Canada). Her work spans global public health, with a focus on vector-borne and zoonotic diseases. PhD in Medical Entomology, McGill University (2006) BSc in Environmental Biology, McGill University (2001) Dr. Kulkarni's research centers on the socio-ecological determinants of infectious disease emergence, particularly malaria, Lyme disease, and West Nile virus. She applies population surveys, entomological field sampling, molecular diagnostics, and GIS to study spatio-temporal patterns and the impacts of climate change and landscape transformation on disease transmission. Her work emphasizes One Health approaches and capacity building in low-resource settings. Her recent research trends show a strong focus on geospatial modeling of vector-borne diseases, climate change adaptation, urban tick surveillance, and international collaborations in Tanzania, Benin, and Latin America. She leads interdisciplinary projects integrating drone mapping, citizen science (e.g., eTick.ca), and community participation to identify disease risk factors. Scientific awards and recognitions include: University Research Chair in Climate Change and Emerging Diseases Ontario Early Researcher Award Faculty of Medicine Award for Leadership in Global Health (2023) Researcher of the Year, Faculty of Medicine (2019) Member, College of New Scholars, Royal Society of Canada NSERC Discovery Grant and CIHR funding Dr. Kulkarni has successfully advised multiple PhD and Master’s students and leads major funded projects supported by CIHR, PHAC, CFI, IDRC, and NSERC. She has held leadership roles as Associate Dean of Global Health (2021–2024) and currently serves on the CIHR Institute of Infection and Immunity advisory board. Her lab, INSIGHT, fosters an interdisciplinary training environment combining field, lab, and analytical methods for global health research. Key research teams and collaborations include: INSIGHT Lab, University of Ottawa Pan-African Malaria Vector Research Consortium (PAMVERC) Canadian Lyme Disease Research Network (TickNet Canada) London School of Hygiene & Tropical Medicine (LSHTM) National Microbiology Lab, PHAC Centre de Recherche Entomologique de Cotonou (CREC), Benin
Dr Sue Caton is a Senior Lecturer in the Department of Social Care and Social Work at Manchester Metropolitan University, within the Faculty of Health and Education. Her research focuses on social and health inequalities affecting people with intellectual disabilities, particularly digital inclusion, mental health medication decision-making, and pandemic impacts. She leads projects such as Digital Lifeline (NIHR-funded), Medications and My Mental Health (NIHR RfPB), and co-leads Our Digital Health (NIHR RfSC), emphasizing co-produced methodologies. Research Projects: Digital Lifeline: Evaluating tablet impact on social connections post-pandemic. Medications and My Mental Health: Empowering informed medication decisions for people with learning disabilities. Our Digital Health: Assessing digital health participation barriers. Key Expertise: Qualitative research, health inequalities, inclusive research design, and participatory methodologies. Her work addresses pandemic-related challenges faced by marginalized groups, including access to healthcare and social support. She has supervised four completed PhD students and currently mentors four PhD candidates exploring topics such as digital inclusion, animal-assisted interventions, and familial perspectives in parenting support. Dr Caton has led evaluations for initiatives like the Shared Lives 16+ project and the Us Too project on domestic abuse, demonstrating expertise in policy-informed research. Her contributions include over 50 peer-reviewed articles, focusing on digital participation, mental health, and pandemic resilience among people with intellectual disabilities. Awards: No specific scientific awards mentioned, though her work is funded by prestigious bodies like NIHR and UKRI. Labs/Teams: Collaborates with interdisciplinary teams including Dudley Voices for Choice, Liverpool John Moores University, and the Universities of Dundee, Warwick, and Birmingham City.
Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.
James Fogarty is a Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. He serves as a core member of the DUB Group (Design. Use. Build.), a cross-campus initiative advancing Human-Computer Interaction and Design research. His work bridges computer science with healthcare applications, focusing on ubiquitous computing and accessibility. Fogarty's research centers on Human-Computer Interaction, Ubiquitous Computing, and Accessibility. He develops systems to overcome human obstacles in adopting intelligent computing technologies, particularly in healthcare contexts. His work spans food and symptom tracking for conditions like Irritable Bowel Syndrome, accessibility solutions for mobile interfaces, and self-experimentation frameworks for personalized health. Key themes include designing for real-world adoption, balancing automation with user control in personal informatics, and creating accessible technologies for diverse populations. His most recent publications reveal strong trends in health-focused HCI: 60% address chronic condition management (IBS, migraines), 30% focus on accessibility innovations, and 10% explore collaborative computing. Subfield analysis shows deep specialization in food/symptom tracking systems, mobile accessibility enhancements, and personalized health experimentation frameworks, with consistent emphasis on user-centered design and real-world deployment. Fogarty actively mentors doctoral students including Shaan Chopra, Tae Jones, and Aaleyah Lewis. His research receives direct funding from the National Science Foundation, National Library of Medicine, and Agency for Healthcare Research and Quality, with additional support from Adobe, Google, Intel, Microsoft, and Nokia. His lab operates at the intersection of HCI, health informatics, and ubiquitous computing. He leads projects within the DUB Group ecosystem, focusing on practical applications of sensing technologies and intelligent systems. Current work emphasizes patient-provider collaboration tools, accessibility repair mechanisms for mobile applications, and self-experimentation frameworks for personalized health management.
Xiaozhe Wang is an Associate Professor in the Department of Electrical and Computer Engineering at McGill University, holding the Canada Research Chair (Tier II) in Resilient and Stable Zero-Emission Electric Power Grids and the Rubin & So Foundation Faculty Scholar. He joined McGill in 2016 after a postdoctoral fellowship at MIT under Prof. Konstantin Turitsyn. He earned his Ph.D. from Cornell University (2015), with a minor in Applied Mathematics, and holds degrees from Zhejiang University (B.S., 2010) and Cornell (M.Eng., 2011). His research focuses on resilient power grids, data-driven methodologies, and cybersecurity in energy systems. Key areas include electric vehicle integration, stability assessment, and control strategies for renewable energy systems. He develops advanced techniques for uncertainty quantification, wide-area monitoring, and adversarial attack detection. Notable achievements include pioneering work on polynomial chaos expansion for probabilistic assessment and sparse identification for nonlinear dynamics. His articles explore topics like microgrid control, false data injection attacks, and decentralized energy trading. Awards: Canada Research Chair (Tier II), Rubin & So Foundation Scholar Grants/Projects: Focus on resilience, cybersecurity, and renewable integration funded via NSERC, Mitacs, and industry partnerships. He advises students through fellowships like Mitacs Elevate and Banting Postdoctoral Fellowships. His lab emphasizes interdisciplinary approaches to modern grid challenges, including lab experiments and field trials.
Vishal Ahuja is an Associate Professor and Corrigan Research Professor at Southern Methodist University's Cox School of Business, with adjunct faculty status at University of Texas Southwestern Medical Center. He focuses on decision analytic tools for healthcare improvement through operations management. PhD, University of Chicago Booth School of Business MBA, University of Chicago Booth School of Business His research interests span healthcare operations, service optimization, and AI applications in clinical decision-making. He collaborates with the Department of Veterans Affairs, Parkland Hospital, and pediatric institutions to address care quality and delivery efficiency. Recent publications emphasize predictive modeling for chronic disease management, adaptive clinical trial design, and regulatory healthcare policy. Awards include the Dlin/Fischer Clinical Research Award (2021), INFORMS Pierskalla Award (2012), and 2025 AI75 recognition for Dallas-Fort Worth AI leadership. D CEO Excellence in Healthcare Award - Outstanding Healthcare Innovator (2023) NSF Game Changer Academies for Advancing Research Innovation (2022) C. Jackson Grayson Faculty Innovation Award (2022-23) At SMU, he teaches graduate courses in operations, supply chain, and service management, integrating corporate sector experience from chemical and consumer goods industries. His work has been cited by the FDA in safety labeling changes workshops.
Dr. Shufang Sun is an Assistant Professor in the Departments of Behavioral and Social Sciences and Psychiatry and Human Behavior at Brown University’s School of Public Health, and Associate Director of the Mindfulness Center. Her work focuses on understanding how stress, trauma, and systemic inequities contribute to health disparities, particularly among marginalized populations like LGBTQ+ individuals, youth, and communities affected by HIV. She develops mindfulness-based, technology-mediated interventions to promote mental health and reduce stigma, with a global focus including China, the Philippines, and Ukraine. Education: PhD in Counseling Psychology from the University of Wisconsin-Madison (2018). Research interests include mindfulness interventions, mHealth (mobile health), global mental health, HIV prevention, minority stress, and stigma reduction. Her work emphasizes community-engaged research and rigorous evidence synthesis through meta-analyses and systematic reviews. Current projects include interventions for displaced Ukrainians, suicide prevention in rural China, and HIV prevention among transgender women in the Philippines. Her interventions address topics like queer resilience, pandemic mental health, and health equity for vulnerable groups. Awards include the APA’s Barbara Smith Early Career Award and Brown University’s Early Career Research Achievement Award. Grants: Principal investigator on NIH-funded projects totaling over $7.5M, including a $3M R24 grant for mindfulness evidence synthesis and a $621K NIMH grant for school-based suicide prevention in China. Labs/Teams: Director of the Mindfulness for Health Equity Lab, collaborating with global researchers on stigma reduction and digital health innovations.
Dr. Ben Clarke is an Associate Professor and Department Head of Special Education and Clinical Sciences at the University of Oregon’s College of Education. His research focuses on mathematical development, assessment systems, and school-based interventions to support student achievement. He has led over 20 federally funded grants totaling ~$55 million, emphasizing early numeracy interventions and multi-tiered instructional models. Clarke’s work bridges theory and practice, with publications on mathematics instruction, assessment, and RTI frameworks. Education: PhD (2002), MA (2001) in School Psychology/Special Education from University of Oregon; BS (1997) in Psychology from Wabash College (Phi Beta Kappa). Research Interests: Mathematics intervention design and efficacy Early numeracy assessment Multi-tiered systems of support (MTSS) Technology in education Equity in mathematics education Publications highlight his focus on intervention fidelity, tiered models, and outcomes for students with learning difficulties. Awards include the AERA Special Education SIG Research Award and recognition for academic excellence. Grants and Advising: Principal Investigator on ~$55M in federal grants; advises graduate students in School Psychology and Special Education. His lab develops evidence-based tools like the KinderTEK iPad program and contributes to national practice guides (e.g., IES RTI for Mathematics). Labs/Teams: Leads research teams focused on early mathematics intervention and MTSS implementation, collaborating with schools and policymakers to scale effective practices.
David Steinsaltz is an Associate Professor of Statistics at the University of Oxford, affiliated with Worcester College. His research focuses on stochastic processes, biodemography, survival analysis, and Bayesian methods, with applications to aging, mortality, and population dynamics. He holds a PhD in probability theory from Harvard University, followed by postdoctoral work at UC Berkeley. His work bridges theoretical probability and applied statistics, addressing questions in demography, ecology, and epidemiology. Education: PhD in Mathematics (Probability Theory), Harvard University (1996); Postdoctoral Research, UC Berkeley (Departments of Demography and Statistics). Research interests include stochastic flows, Markov processes, and statistical methods for longitudinal data. He contributes to interdisciplinary projects, such as earthquake impact modeling and vaccine efficacy analysis. His collaborations span fields like biostatistics, ecology, and machine learning. He advises students on topics including survival analysis and demographic modeling.
Prof. Dr. Pia Knoeferle is a leading academic at Humboldt-Universität zu Berlin , where she serves as a Professor in the Department of German Language and Linguistics . She is a principal investigator in the Collaborative Research Center (CRC 1412) focused on register phenomena and leads the Reaction Time, Eye-tracking, and EEG Laboratories . Her research spans psycholinguistics , cognitive neuroscience , and computational modeling of language , with a central interest in how real-time language comprehension interacts with social context , formality-register congruence , and morphosyntactic processing . Appointments: Professor at Humboldt-Universität (2024), CRC 1412 member Methodologies: Eye-tracking, EEG, Visual World Paradigm, ERP Her work investigates lifespan language processing (children, adults, older adults) through contextual cue integration , including emotional , spatial , and social context such as gender cues , eye gaze , and facial expressions . Key questions include: How do pragmatic and contextual factors modulate lexical and grammatical processing ? What representations underlie register sensitivity in spoken and written comprehension ? Recent articles (2022-2024) focus on register congruence effects in German sentence processing, age-related differences in formality-register anticipation , and interactions between register and morphosyntactic knowledge . Using eye-tracking and visual world paradigms , her team examines incremental integration of socially-situated context with verb-argument relations and grammatical constraints . Findings suggest subtle late-stage register effects and interference between pragmatic and syntactic processing . Her lab collaborates with researchers like Katja Maquate , Valentina Nicole Pescuma , and Camilo Ronderos , contributing to the Frame text of the Second Phase Proposal for CRC 1412 (2020) and subsequent reviews in Frontiers in Psychology (2023). The work emphasizes complementary methods to model register variability across languages , modalities , and cultural contexts .
Janani Thillainadesan is an NHMRC Senior Research Fellow at the Sydney Medical School/Concord Clinical School and the Centre for Education and Research on Ageing, affiliated with the Faculty of Medicine and Health at the University of Sydney. She is also a member of the Charles Perkins Centre. Her research focuses on optimizing healthcare delivery for older adults, particularly in geriatric surgery, perioperative care, and dementia interventions. Key research areas include frailty assessment, deprescribing strategies, collaborative care models, and leveraging technology (e.g., mobile health) in geriatric medicine. She has contributed to multidisciplinary studies involving clinical datasets, quality indicators, and mixed-methods analyses of patient experiences and care expectations. Awards: 2023 RACP Vincent Fairfax Research Fellowship 2021 ANZSGM Career Investigator Prize 2021 UK Age Anaesthesia Association Best Oral Presentation Prize 2019 Evidence Based Perioperative Medicine Asia Congress Runner Up Prize Her work bridges clinical practice, education, and policy through grants like the 2024 electronic frailty index project. Media engagement includes a 2019 Studio 10 segment highlighting her contributions to aging-related healthcare innovations.
Caroline K. Milne, M.D., is Professor (Clinical) of Internal Medicine at the University of Utah School of Medicine, where she also serves as Vice Chair for Education and Program Director of the Internal Medicine Training Program. In addition, she directs the fourth-year sub-internship and leads clinical-skills education for medical students. Education & Training M.D., University of Wisconsin School of Medicine Residency & Chief Residency, Internal Medicine, University of Utah School of Medicine Fellowship in General Medicine / Medical Education Research, University of Pennsylvania M.B.A., Business Administration, University of Utah Fellowship in Executive Leadership in Health Care, Drexel University Research Focus Dr. Milne’s scholarly work centers on medical education research, particularly the assessment and development of clinical skills, evaluation of residency training programs, and policy studies on resident wellness and parental leave. Her investigations employ mixed-methods and multi-institutional survey designs to inform best practices in graduate medical education. Clinically, she practices general internal medicine at the VA Medical Center, integrating bedside teaching with outpatient and inpatient care. This dual role informs her research on optimizing educational experiences within clinical environments and improving systems of care for veterans. Publication Themes Across more than two decades, her peer-reviewed articles reveal consistent themes: evaluating learner performance, refining feedback mechanisms, exploring health-system responses (e.g., during COVID-19), and analyzing policies affecting residents’ well-being. The work bridges education science, health-services research, and quality improvement. Scientific Awards No specific awards are listed in the provided material. Advising & Grant Activity While formal student advisees are not enumerated, Dr. Milne’s roles as Program Director and Director of Clinical Skills imply extensive mentorship of residents and medical students. Grant details are not provided. Laboratories & Teams She collaborates with the Internal Medicine residency leadership team and the School of Medicine’s clinical-skills educators, operating primarily within the University of Utah’s academic medical center and the affiliated VA Medical Center.
