Ji Hwan Park is an Assistant Professor in the School of Interactive Games and Media at RIT's Golisano College of Computing and Information Sciences (GCCIS). He holds a PhD from Stony Brook University under Prof. Arie Kaufman. His research focuses on accessible data visualization, digital twins, human-AI collaboration, and VR/AR applications. Notable contributions include developing tools for ADHD-friendly visualizations and interactive protein motif identification. He has received funding from the Department of Defense for biomedical research and earned an Honorable Mention at CHI 2024. Current teaching includes courses on game design and advanced algorithms. Research activities span medical imaging analytics (e.g., CMed framework for crowd-sourced diagnostics), climate modeling through Bayesian deep learning, and creative visualization techniques like Graphoto. His work bridges technical innovation with human-centered design principles, particularly in healthcare and neurodivergent accessibility contexts.
Colin Cooper is a Professor of Cancer Genetics at the Norwich Medical School, University of East Anglia. He is also a member of the Metabolic Health and Cancer Studies research groups. His work focuses on genomic evolution, tumor microbiome dynamics, and biomarker development for prostate cancer and musculoskeletal health. Cooper’s recent research explores the interplay between cancer genetics and microbial communities, emphasizing their role in prognosis and treatment outcomes. He investigates clonal evolution in tumors, mutational signatures, and non-invasive diagnostic tools like urinary extracellular vesicles. His collaborations span genomics, microbiology, and clinical applications. 2025: Causes of evolutionary divergence in prostate cancer (Genomics, Precision Medicine) 2024: Applications of urinary extracellular vesicles... (Biomarker Development, Liquid Biopsy) 2023: Caution regarding pan-cancer microbial structure (Methodological Considerations, Microbiome Analysis) In 2022, Cooper received the European Urology Oncology SoMe Award for his contributions. His work is frequently cited and has been featured in media outlets globally, highlighting his impact on cancer and aging research.
Pingfu Fu, PhD, is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University's School of Medicine. He is also a member of the Developmental Therapeutics Program at the Case Comprehensive Cancer Center. His expertise spans biostatistics, mathematics, and computer science, with a focus on cancer research and HIV/AIDS. Dr. Fu advises researchers on study design and statistical methodology for clinical and pre-clinical studies. He teaches courses in survival data analysis and clinical trials, and was recognized as 'Professor of the Year' in 2010 by the Department of Epidemiology and Biostatistics. Education: PhD in Biostatistics (Case Western Reserve University, 2001), MS in Statistics (Case Western Reserve University, 1996), MS in Mathematics (Xiangtan University, 1988), and BS in Mathematics (Jiangxi Normal University, 1984). His research interests include survival analysis, tree-based methods, clinical trials, and statistical applications in medical research. He has co-authored numerous peer-reviewed articles, focusing on cancer disparities, radiomics, and computational pathology. Professional memberships include the American Statistical Association, American Mathematical Society, and American Cancer Society. Dr. Fu holds editorial roles at Reviews on Recent Clinical Trials , Journal of Clinical Oncology , and Journal of the National Cancer Center . His work has addressed mathematical challenges in stochastic processes and resolved statistical issues in study design and tree-based models. Notable contributions include developing risk prediction models for cancer outcomes and advancing interdisciplinary collaborations across oncology, biostatistics, and computer science. His lab focuses on integrating computational methods with clinical data to improve patient outcomes.
Weiyu Xu is a Professor at the University of Iowa, affiliated with both the Department of Electrical and Computer Engineering and the Department of Applied Mathematical and Computational Sciences. He joined the College of Engineering in 2012 and leads the Intelligent Information Processing Lab (IIPL). Ph.D., Electrical Engineering, California Institute of Technology, 2009 M.S., Electrical Engineering, California Institute of Technology, 2006 M.S., Electronic Engineering, Tsinghua University, 2005 B.E., Information Engineering, Beijing University of Posts and Telecommunications, 2002 His research focuses on compressive sensing, information theory, signal processing, network optimization, and deep learning applications in medical imaging and cybersecurity. He has made significant contributions to adversarial attack robustness, distributed optimization algorithms, and medical imaging techniques for OCT segmentation and brachytherapy. Recent publications highlight trends in Adversarial machine learning Medical imaging algorithms Compressed sensing for diagnostics Optimization in wireless communication He has also contributed to federated learning over tree networks and theoretical guarantees for sparse signal recovery. Weiyu Xu's lab, IIPL, integrates deep learning with classical optimization and signal processing. His work bridges foundational theories (e.g., information-theoretic robustness) with real-world applications in healthcare (e.g., cancer treatment planning) and communication systems (e.g., MIMO channel estimation).
Ghassan AlRegib is the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. He directs the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), the Center for Energy and Geo Processing (CeGP), and previously led Georgia Tech's MENA initiatives (2015-2018). His research spans machine learning, image processing, and seismic interpretation with real-world applications in autonomous vehicles, medical imaging, and subsurface analysis. His research focuses on trustworthy AI systems through three pillars: enhancing interpretability, improving robustness/generalizability, and tackling domain-specific challenges. Key interests include human-in-the-loop frameworks, uncertainty quantification, explainable AI, and physics-driven learning. The OLIVES lab pioneered modern machine learning applications in seismic interpretation and developed open-source datasets for geological fault analysis. Dr. AlRegib's scientific contributions include over 270 publications, multiple U.S. patents, and leadership roles as Technical Program co-Chair for ICIP 2020/2024. His work demonstrates significant impact through awards like the IEEE Fellow designation (2022) and multiple best paper awards at premier conferences. IEEE Fellow (2022) 2023 EURASIP Best Paper Award 2019 ICIP Best Paper Award 2017 Denning Faculty Award for Global Engagement CSIP Research & Service Awards (2003) He has advised numerous PhD students including Dr. Ashraf Alattar (now Auburn professor) and Dr. Zhiling Long (Kennesaw State faculty). His lab structure emphasizes collaborative teams comprising postdocs, senior/junior PhD students, and undergraduates working on high-impact problems from autonomous systems to medical diagnostics. Current research thrusts include trustworthy neural networks, human-in-the-loop frameworks, and deployment of machine learning in seismic interpretation and ophthalmology.
Prof. Alexander Geissler holds the position of Full Professor of Health Care Management at the School of Medicine (Med-HSG) within the University of St. Gallen. His research focuses on health systems research, health economics, and health policy, with particular emphasis on digital transformation in healthcare and patient-reported outcomes. He has contributed extensively to studies on healthcare quality improvement, public reporting systems, and the integration of artificial intelligence in medical diagnostics and screening programs. His work spans topics like optimizing hospital digital maturity (e.g., German DigitalRadar project), analyzing surgical outcomes (robotic vs. open prostatectomies), and evaluating patient empowerment through quality information. He has pioneered methodologies for interpreting patient-reported outcomes (e.g., EQ-5D-3L) and designing clinical dashboards to enhance care delivery. Recent research highlights include investigating AI applications in breast cancer screening and cost-effectiveness of remote patient monitoring post-joint replacement surgery. Geissler’s publications demonstrate a strong focus on healthcare policy implications, such as hospital capacity planning, payment systems for specialized care, and cross-country comparisons of healthcare transparency initiatives. His work frequently bridges academic rigor with practical policy recommendations, particularly in Switzerland and Germany. Notably, he has addressed low-value care reduction, price sensitivity in healthcare demand, and the socio-demographic factors influencing healthcare utilization. While no specific awards are listed, his prolific research output reflects sustained leadership in health systems analysis. His academic contributions are disseminated through the Alexandria Research Platform and international peer-reviewed journals.
Maria Fällman is a Professor at the Department of Molecular Biology at Umeå University, where she also serves as Deputy Head of Department. She is affiliated with Molecular Infection Medicine Sweden (MIMS), a leading research center for molecular infection medicine in Sweden. Dr. Fällman's research focuses on understanding the molecular mechanisms behind bacterial adaptation to different environments, with particular emphasis on Yersinia pseudotuberculosis and Salmonella enterica Typhimurium. Her group investigates gene regulation critical for establishing and maintaining infections, bacterial stress responses, and the molecular mechanisms of the Type Three Secretion System (T3SS). The lab has developed advanced methods for RNA extraction from complex tissue samples and performs in vivo gene expression analyses. Her publication record shows consistent contributions to understanding bacterial pathogenesis, with recent articles in high-impact journals including Nature Communications, Science, and PLOS Pathogens. Her work spans from fundamental molecular mechanisms of bacterial virulence to computational approaches for analyzing pathogen stress responses. A significant contribution is the PATHOgenex database (http://www.pathogenex.org), containing gene expression data of over 30 human pathogens exposed to different stress conditions. Dr. Fällman leads the Maria Fällman Lab, which has made important discoveries including the finding that sub-lethal doses of Yersinia result in persistent infection in mice with reprogramming of bacterial gene expression. Current projects focus on stress response modeling and deciphering heterogeneous populations of infecting bacteria using single-cell RNA-seq.
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.
Sumit Chopra is an Associate Professor at the Grossman School of Medicine , affiliated with the Department of Radiology at New York University. His work focuses on integrating machine learning and artificial intelligence with medical imaging to enhance diagnostic accuracy and clinical decision-making. Research interests include: Deep learning applications in prostate cancer imaging and MRI reconstruction Development of open-access medical imaging datasets (e.g., FastMRI Prostate) Improving biopsy decision strategies via representation learning AI-driven Alzheimer's disease risk prediction using electronic health records Advancements in radiologic assessment for pancreatic cystic lesions Email: Sumit.Chopra@nyulangone.org
Dr Vu Minh Hieu Phan is a Research Fellow at the Australian Institute for Machine Learning , University of Adelaide. His work focuses on foundational models, multimodal learning, and medical image analysis, leveraging deep learning and large language models. Research Interests : Medical Image Analysis, Vision-Language Models, Generative AI, Semantic Segmentation, Continual Learning, Knowledge Distillation. Key Venues : CVPR, ACL, EMNLP, IJCAI, MICCAI, NeurIPS, TPAMI, and IJCV. Notable Contributions include advancements in multimodal learning for medical imaging, explainable AI frameworks, and efficient knowledge distillation techniques. He serves as a reviewer for top-tier journals and conferences. Email : vu.minhhieu.phan@adelaide.edu.au
Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.
Dr. Ulas Bagci is an Associate Professor at Northwestern University's Feinberg School of Medicine, Department of Radiology. He holds courtesy appointments in Biomedical Engineering (BME), Electrical and Computer Engineering (ECE) at Northwestern, and Computer Science at the University of Central Florida. As the director of the Machine and Hybrid Intelligence Lab, his research focuses on AI and machine learning applications in biomedical and clinical imaging. Education: BS: Bilkent University (2003) MS: Koç University (2005) Fellow: University of Pennsylvania (2009) PhD: University of Nottingham (2010) ISTP Fellow: NIH (2012) Research Interests: Dr. Bagci’s work spans artificial intelligence, machine learning, and their integration into medical imaging workflows. His lab develops algorithms for tumor segmentation, radiomics analysis, and ethical AI frameworks in healthcare. Notable projects include large-scale MRI segmentation of cirrhotic livers and predictive models for clinical outcomes in oncology and cardiology. Publications: His recent work emphasizes AI-driven solutions for challenges in radiology, including lung disease detection, pulmonary embolism mortality prediction, and ethical considerations in foundational AI models. His articles reflect a focus on bridging clinical needs with advanced computational methods. Lab & Affiliations: The Machine and Hybrid Intelligence Lab collaborates with the Robert H. Lurie Comprehensive Cancer Center. Research themes include federated learning, medical image synthesis, and AI ethics in clinical decision-making.
Ardo van den Hout is a Professor of Statistics at the Department of Statistical Science, University College London. He holds a PhD in Social Statistics from Utrecht University (2004) and has previously worked at the MRC Biostatistics Unit in Cambridge. His research focuses on advanced statistical methodologies including longitudinal data analysis, survival analysis, multi-state models, and applications in aging research and public health. He has authored influential works such as Multi-state Survival Models for Interval-censored Data (2017). Research Interests Development and application of multi-state models for complex health data Survival analysis techniques for interval-censored and longitudinal datasets Methodological advancements in cognitive decline and disease progression modeling Integration of socio-economic factors in health expectancy analysis Key Contributions Pioneered penalized likelihood approaches for multi-state models Developed frameworks for estimating life expectancies in health and disease Advanced methods for handling missing/misclassified data in longitudinal studies Awards Recipient of the Gopal Kanji Prize 2012 for outstanding contributions to statistics Professional Activities Maintains an active research program with collaborations across biostatistics, epidemiology, and health economics. Supervises doctoral students focusing on statistical methodologies with real-world health applications. His work frequently addresses critical questions in aging populations, cancer research, and public health policy.
Xin Lu is the John M. and Mary Jo Boler Collegiate Associate Professor in the Department of Biological Sciences at the University of Notre Dame. She is a full member of the Harper Cancer Research Institute (HCRI), the Boler-Parseghian Center for Rare and Neglected Diseases (CRND), and the Tumor Microenvironment and Metastasis Program at the Indiana University Simon Comprehensive Cancer Center. Her research spans tumor immunology, immunotherapy, metastasis, and multi-omics, with a focus on prostate, breast, and rare cancers. Ph.D. in Molecular Biology, Princeton University (2004–2010) B.S. in Biological Sciences, Tsinghua University, China (2000–2004) Postdoctoral Fellow, Dana-Farber Cancer Institute and M.D. Anderson Cancer Center (2010–2016) Assistant to Associate Professor, University of Notre Dame (2017–Present) Dr. Lu's research investigates the molecular and cellular mechanisms of tumor-immune crosstalk, particularly the role of myeloid-derived suppressor cells (MDSCs) and neutrophils in promoting immunotherapy resistance. Her lab uses genetically engineered mouse models, functional genomics, single-cell and spatial transcriptomics, and high-throughput screening to uncover novel therapeutic targets. A major focus is on how cancer-cell-intrinsic oncogenic signaling shapes the immunosuppressive tumor microenvironment. Her recent publications reveal mechanisms such as Acod1-mediated ferroptosis resistance in neutrophils, Pygo2-driven immunosuppression in prostate cancer, and the efficacy of ketogenic diets in overcoming checkpoint blockade resistance. These studies span multiple disciplines including cancer biology, immunology, metabolism, epigenetics, and bioengineering, reflecting a highly integrative approach to immuno-oncology. Jane Coffin Childs Postdoctoral Fellow John M. and Mary Jo Boler Faculty Appointment Cluster Chair, Cellular & Molecular Biology, IBMS PhD Program Junior Chair, Boler-Parseghian Center for Rare and Neglected Diseases Dr. Lu mentors a diverse team of graduate students, postdoctoral fellows, and undergraduates. Her lab is actively recruiting and has secured funding from federal agencies and private foundations. She collaborates with chemists, bioengineers, and bioinformaticians to develop novel therapeutics, including antibody-drug conjugates, CAR-NK cells, and small-molecule inhibitors. Her lab also develops innovative platforms like mini-tumor chips for immunotherapy evaluation. Her lab maintains affiliations with multiple interdisciplinary centers including the Warren Family Center for Drug Discovery, Eck Institute for Global Health, and Berthiaume Institute for Precision Health, underscoring her collaborative and translational research vision.
Simon Brewster serves as Senior Research Fellow and Consultant Urological Surgeon at the University of Oxford's Hertford College, where he has been Clinical Tutor since 2002 after serving as Lecturer (2002-2011). His clinical work focuses on prostate cancer at Oxford University Hospitals NHS Trust, delivering exam-focused bedside teaching for Years 4-6 medical students. Education: BSc in Anatomy (1st class), Charing Cross Hospital Medical School, 1983 MBBS (Hons. Pathology), Charing Cross Hospital Medical School, 1986 FRCS exams and MD research, Bristol (1988-1998) Brewster's research program demonstrates sequential evolution from basic science (1998-2007) investigating IGF1R targeting and Wnt signaling in prostate cancer to clinical translation (2011-2022) evaluating multiparametric MRI integration in diagnostic pathways and active surveillance protocols. His work bridges laboratory discoveries with patient-centered outcomes, particularly examining how imaging advancements impact treatment decisions and quality of life. The multidisciplinary nature of his clinical research involves close collaboration between urologists, oncologists, radiologists, and pathologists to optimize prostate cancer management. Analysis of his 15 most recent publications reveals three dominant themes: (1) MRI-driven diagnostic refinement including pre-biopsy MRI implementation and active surveillance reclassification, (2) comparative treatment efficacy studies evaluating focal therapy versus radical prostatectomy, and (3) national guideline development through NICE recommendations. His work consistently addresses real-world clinical challenges in prostate cancer management, with strong emphasis on patient satisfaction metrics and practical implementation within the UK healthcare system. Scientific Awards: BJUI Article of the Week (November 18, 2018) for MRI impact on active surveillance As an educator, Brewster has co-supervised four higher research degrees and served as Clinical Supervisor for junior surgical trainees since 1998, including Intercollegiate ISCP Educational Supervisor role since 2009. His grant portfolio includes the UK PART trial (focal therapy vs radical prostatectomy, PI Prof Richard Bryant) and Oxfordshire MRI implementation study (2015-2019). He chairs the Hertford College/Vaughan Williams Prize for Excellence in Clinical Medicine. He leads a multidisciplinary research consortium including Dr. Val Macaulay (basic science) and Prof. W. Bodmer (Wnt pathway research), while coordinating clinical activities through Oxford University Hospitals NHS Trust. His work directly informs national prostate cancer pathways through British Association of Urological Surgeons committees and NICE guideline development.
