Members

Mohammad Al-Ani is a cardiologist for advanced heart failure and imaging (TTE, TEE, CMR, CCTA).

Key Research: Cardiomyopathy, extracorporeal membrane oxygenation, hypertrophic cardiomyopathy

Li Chen’s lab focuses on developing deep learning and statistical methods and software for analyzing large-scale multi-omics data, such as genetics, single-cell genomics, and metagenomics.

Key Research: aging and cancer, multi-omics data with imaging, EHR data

Raquel Dias’s lab develops and applies cutting-edge artificial intelligence (AI) methods to examine important research questions across multiple fields of biological sciences, agricultural sciences, and biomedical research.

Key Research: biological sciences, agricultural sciences, biomedical research

Reza Forghani’s research focuses on the use of AI to improve health through enhanced AI-assisted diagnostics, as well as non-interpretative AI applications for improving health care processes, quality, efficiency, and sustainability.

Key Research: precision diagnostic applications, neck oncology, neuroimaging

Xiao Fan’s primary interest lies in the application of AI to medical, biochemical, and genetic questions. She has completed projects on microRNA target prediction, annotation of intrinsically disordered regions in proteins, and prediction of protein crystallization.

Key Research: computational genetics, biochemical, genetic questions

Diego Guarin’s Movement Estimation and Assessment Laboratory at the University of Florida is pioneering the development of a new generation of AI tools for objectively assessing Parkinson’s disease from videos.

Key Research: neural control of movement, neurological conditions and their effect on movement, machine learning and computer vision

Faheem Guirgis’s current and future work is leveraging AI to perform multiomics analysis on ‘omics data from critically patients with Sepsis and ARDS.

Key Research: sepsis, lipid metabolism, clinical trials

Sarah Kim is a practicing pharmacometrician who is passionate about model-informed and AI-powered drug development and is currently leading several computational modeling projects to create and innovate quantitative solutions in healthcare.

Key Research: disease-drug-trial modeling and simulation, AI applications to maximize the use of imaging data in drug development

Mei Liu’s long-term research goal is to develop innovative (AI/ML) methods to support Predictive, Preventive, Personalized, and Participatory (P4) medicine.

Key Research: artificial intelligence, biomedical informatics, machine learning

Alicia Mohr is an active surgeon-scientist who cares for critically ill trauma patients and whose research focuses on the interactions between stress, chronic inflammation, bone marrow dysfunction, and an altered microbiome following traumatic injury.

Key Research: critical care surgery, acute respiratory distress syndrome, anemia

Michael Pizzi is interested in developing machine learning algorithms to identify trends in patient physiologic data to alert clinicians of impending intracranial pressure increases and worsening brain tissue compliance.

Key Research: neurocritical care, acute ischemic stroke,
traumatic brain injury

Louis Scampavia’s team employs High Throughput Screening (HTS) robotics to accelerate the drug discovery process thorough full automation of large-scale screening experiments.

Key Research: biological assays, HTS compatibility, drug lead discovery, HTS robotics

Jinnie Shin has expertise in application of theory-based natural language processing and learning analytics in education research, and she has focused on investigating how to bridge the gap between psychometric analysis and artificial intelligence in education research.

Key Research: educational assessment and measurement, emerging technologies, methodological research, quantitative research

Yu Wang’s research aims to develop AI/ML-based methods for building reliable and trustworthy cyber-physical systems (such as implanted/wearable devices) for healthcare applications.

Key Research: assured autonomy, cyber-physical systems, machine learning, formal methods

James Wynn’s research is focused on the investigation of neonatal-specific innate immune cellular function and inflammatory signaling during sepsis as well as development of novel therapeutic immunomodulatory strategies aimed at improving sepsis outcomes. 

Key Research: neonatal sepsis, pediatrics, neonatal-perinatal medicine

Feifei Xiao’s research involves using machine learning methods to build valid predictive models to resolve public health–related problems, such as using support vector machine methods to predict aphasia severity and specific language measures using multi-modal neuroimaging datasets.

Key Research: bioinformatics, cancer epidemiology, machine learning, statistical genetics

The aim of Jie Xu’s research is to develop integrative, fair, and interpretable approaches that can effectively mine insights from big health data, towards accelerating precision medicine.

Key Research: machine learning and applications, novel computational algorithms, electronic health records (EHRs), medical imaging data

Rui Yin’s research seeks to improve public health at both individual and population levels, providing insights into biological and medical problems with computational approaches.

Key Research: biomedical informatics, genomics and precision medicine, machine learning and applications, protein language model

Dr. Dasa’s research seeks to use Big Data analytics, Casual Reference and machine Learning coupled with Real Word Evidence (RWE) to explore ways to enrich patient recovery.

Key Research: big data analytics, causal inference, machine learning, electronic health records

At UF, Ivan Ruchkin leads the Trustworthy Engineered Autonomy (TEA) Lab. His research aims to make autonomous systems safer and more trustworthy. He developS techniques, tools, and methodologies for modeling, analyzing, verifying, controlling, and monitoring autonomous robotic and cyber-physical systems.

Key Research: safety chance predictors, conformal calibration, high-dimensional controllers

Dr. Gong’s research interests are centered around the convergence of deep learning, medical imaging, and data science to enhance the diagnosis and treatment monitoring of various diseases, particularly Alzheimer’s disease (AD) and cancer. His work involves developing novel methodologies in medical physics-informed deep learning, leveraging prior information-guided network design, and applying clinical task-driven network training for more accurate and precise results.

Key Research: deep learning, medical imaging, data science

Dr O’Dell is an Associate Professor in the Department of Radiation Oncology in Gainesville. As part of the Therapeutic Medical Physics faculty, he serves as a research mentor for graduate student research. Dr O’Dell oversees the Medical Image and Computational Analysis Lab, which is interested in discovering and applying novel image analysis and image-based computational techniques for improved detection, follow-up, and treatment of cancer.

Key Research: novel image analysis, image-based computational techniques, radiation toxicity

Dr. Singh Ospina is an Associate Professor in the Division of Endocrinology, Diabetes and Metabolism at the University of Florida. She is board certified in Internal Medicine. She was awarded her medical degree from the University of Panama, Panama. She received her Endocrinology, Diabetes and Metabolism fellowship training at the Mayo Clinic, Rochester where she also completed a Master in Clinical and Translational Science.

Key Research: endocrinology, diabetes and metabolism, primary hyperparathyroidism, thyroid nodule

Amelia Winger-Bearskin is an artist who innovates with artificial intelligence in ways that make a positive impact on our community and the environment. She is a Banks Family Preeminence Endowed Chair and Associate Professor of Artificial Intelligence and the Arts, at the Digital Worlds Institute at the University of Florida.  She is the inventor of Honor Native Sky, a project for the U.S. Department of Arts and Culture: Honor Native Land Initiative. She founded Wampum.Codes which is both an award-winning podcast and an ethical framework for software development based on indigenous values of co-creation. 

Key Research: human-centered computing, public health communications, artificial intelligence

Dr. Hochhegger has a distinguished research career in both the domain of securing grants and extensive peer-reviewed publication activity. His main areas of interest are imaging assessment of chronic lung diseases and lung cancer. He has published more than 410 papers with more than 150 colleagues around the world.

Key Research: lung cancer screening, magnetic resonance of the chest, interstitial lung disease

Dr. Newbutt is an Assistant Professor of Advanced Learning Technologies and Director of the Equitable Learning Technology Lab (ELTL) in the Institute for Advanced Learning Technologies. He brings many years’ experience of developing technologies with autistic, and underserved groups. This work began by exploring multimedia programs for deaf communities in the UK. Beyond this, he has also designed and evaluated learning technologies (namely: podcasting and vodcasting) in undergraduate classrooms. 

Key Research: educational technology interventions, educational/instructional design, learning culture and technologies, virtual reality

Mingzhang Yin is an Assistant Professor of Marketing at the Warrington College of Business of University of Florida. His primary research interests are in the intersection of quantitative marketing, Bayesian statistics, probabilistic machine learning, and causal inference. He develops methodologies for probabilistic models, approximate Bayesian inference, and observational studies. He explores marketing applications in online advertisement, personalization, E-commerce and data-driven consumer analysis.

Key Research: intersection of quantitative marketing, Bayesian statistics, probabilistic machine learning, causal inference

Masoud Rouhizadeh, M.A., Prof.MS., M.S., Ph.D., serves as an Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida. Through the University’s AI in the Health Sciences Initiative, he plays central leadership roles as the Lead for the College of Pharmacy’s AI Task Force and as the Lead for the AI Collaboration Hub at the Intelligent Critical Care Center. Additionally, he holds an Adjunct Assistant Professor appointment within the Division of Biomedical Informatics and Data Science at the Johns Hopkins School of Medicine.

Key Research: biomedical informatics, computer science, linguistics, machine learning

Mackenzie Bolen is a graduate student in the biomedical neuroscience Ph.D. program at the University of Florida, College of Medicine. She is interested in identifying the interplay between inflammatory bowel disease (IBD) and Parkinson’s risk via the lens of the immune system.

Key Research: neurodegeneration, immunology, peripheral biomarkers

Dr. Ou’s research is inspired by the community-centered informed hazard-oriented decision-making, which lead to the development of high fidelity and computational efficient regional-scale simulation platform. Her research experience includes physical and surrogate modeling of complex civil infrastructure systems, machine learning, information theory, advanced experimental testing, model updating, and the integration of data analytics with physical-based modeling.

Key Research: structural engineering, sustainable construction engineering, community resilience

Dipankar Gupta is a board-certified physician specializing in pediatrics, pediatric cardiology, and pediatric critical care medicine. He received his medical degree from the University College of Medical Sciences at the University of Delhi in India. He then pursued pediatric residencies at Safdarjang Hospital at the University of Delhi and at Brookdale University Hospital and Medical Center in Brooklyn.

Key Research: pediatrics, pediatric cardiology, congenital heart disease, pediatric heart surgery

Dr. Naveen Baskaran is an internist in Gainesville, Florida and is affiliated with UF Health Shands Hospital. He received his medical degree from Madras Medical College and has been in practice between 11-20 years. Dr. Naveen Baskaran has expertise in treating inpatient dementia, diabetes, chronic diabetes, among other conditions.

Key Research: AI in cardiovascular medicine, applied clinical informatics, biomedical informatics, cardiometabolic disease

Dipankar Gupta completed medical school at the Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER) in Puducherri. He was a Rhodes scholar at Oxford University in England, where he earned a master’s degree in applied mathematics and a research doctorate in mathematical biology. He received his clinical training in internal medicine through rotations in various hospitals in the British National Health Service and completed a general neurology residency at the Institute of Neurological Sciences in Glasgow. He was fellowship trained in epilepsy and EEG at the Cleveland Clinic.
Dipankar Gupta’s career has been devoted to the care of patients with epilepsy, along with research in the science surrounding epilepsy. His publications have appeared in multiple peer-reviewed journals, and his work has been funded by the National Institute of Neurological Disorders and Stroke, as well as the American Epilepsy Society, among others.

Key Research: epilepsy imaging, cortical neurophysiology, brain dynamics

Dr. Hu is currently serving as a Research Assistant Professor in the Department of Medicine at the College of Medicine and the Intelligent Clinical Care Center (IC3) at the University of Florida. His academic journey began with a bachelor’s degree in Electronic Information Engineering, followed by a Master’s in Computer Science. He earned a Ph.D. in Biomedical Engineering from the University of Florida in 2019. His research focuses on the application of advanced multimodal signal processing, machine learning, and AI to explore the complexities of cognitive function and address impairments in neurological and psychiatric disorders. Through this work, he aims to drive innovations in clinical decision-making, precision medicine, and patient care.

Key Research: advanced multimodal signal processing, machine learning, AI with cognitive function

Dr. Zarrinpar is a professor at UF in the Division of Transplantation and Hepatobiliary Surgery, specializing in the treatment of liver cancer, liver disease, liver injuries, bile duct cancer, and bile duct injuries, primarily through surgery or liver transplantation. Currently, his lab is dedicated to applying the technology they develop to address practical surgical and medical challenges, with an emphasis on delivering the right drug or treatment to the right patient, in the right amounts, and at the right times.

Key Research: transplant surgery, bile duct obstruction, choledocholithiasis

Dr. Graim received her Ph.D. in 2016 from the University of California, Santa Cruz, advised by Professor Josh Stuart. She did postdoctoral research at Princeton University and the Flatiron Institute, Simons Foundation, working with Professor Olga Troyanskaya. Dr. Graim’s research operates at the confluence of life science research and computer science. Her lab develops machine learning models that integrate diverse large-scale genomics data to address key questions in human health and disease. These models effectively probe complex biological networks to answer questions arising from basic science and translational research. Dr. Graim’s overarching goal is to map the mechanisms of human diseases and to enable development of personalized therapies.

Key Research: bioinformatics, computational biology, machine learning, data science and informatics

Dr. Jon Kim, DVM, PhD is an Assistant Professor in the Department of Small Animal Clinical Sciences, College of Veterinary Medicine at the University of Florida. He earned his DVM from Konkuk University in South Korea. He received his Ph.D in Veterinary Pathology from the same school, where he established veterinary and comparative oncology research. He completed a post-doctoral work at University of Minnesota and Masonic Cancer Center, and established independent comparative oncology research program. As a faculty member of the UF AI Initiative, Dr. Kim focuses on developing novel diagnostic and clinical applications in the fields of comparative oncology and translational medicine by utilizing AI and machine learning.

Key Research: fundamental mechanisms of cancers, comparative oncology, artificial intelligence, multi-omics

Colleen Gutman, MD is an assistant professor of pediatric emergency medicine at UF. Dr. Gutman’s research is focused on clinician communication in the pediatric emergency department. She is an affiliate faculty member with the Pediatric Emergency Care Applied Research Network STELAR node and serves as an Assistant Editor for the Joint Commission Journal of Quality and Patient Safety. Dr. Gutman is currently funded by a K23 from the National Institutes of Health.

Key Research: adolescent development, pediatric emergency medicine, pediatrics

As a professor of electrical and computer engineering, with expertise in computer architecture, system on chip design, FPGA, hardware software co-design, Professor Bobda investigates efficient architecture for artificial intelligence (AI) with a broad range of applications, including medical (surgical robotics, fall prevention, Parkinson progress assessment, etc., human-robot collaboration, and autonomous robots.

Key Research: computing systems, AI hardware, computer vision, surgical robotics, Parkinson assessment

Anu Sharma’s research is focused on developing and applying precision medicine in the care of young adults with type 2 diabetes. She currently works alongside AI faculty on preliminary data to support R01 applications in this field.

Key Research: type 2 diabetes, young adults, precision medicine

Guoshuai Cai’s group develops AI, statistical and bioinformatics approaches to analyze omics data — including bulk, single-cell and spatial transcriptomics and epigenetics data. He is leading the Surgery Genomics Core to provide optimized and customized platforms and pipelines of sequencing and tailored data analysis empowered by AI.

Key Research: genomics, sequencing, sepsis, vascular diseases, cancer

As an immunologist and structural biologist, David’s research intersects with medical AI, focusing on drug discovery and diagnostics. He leverages AI to identify and select drug candidates for treating cancer, autoimmune diseases, and infectious diseases. Using the HiPerGator supercomputer, I developed a pipeline that streamlines candidate identification and development of new therapeutics. This interdisciplinary approach, combining AI with biomedical sciences, enhances the efficiency of treatment strategies across various disease areas.

Key Research: drug discovery, immunology, structural biology

Dr. Devika Das is a board-certified hematologist and medical oncologist at UF Health with 12 years of experience in academia and community oncology practice. Her goal is to apply both research and science toward providing equitable lung cancer care, and she is committed to offering the highest quality care for patients, just as she would want for herself and her loved ones.

Key Research: lung cancer screening, immunotherapy toxicity, clinical trials

Dr. Qianqian Song is a tenure-track Assistant Professor in the College of Medicine, Department of Health Outcomes & Biomedical Informatics at the University of Florida. She also serves as the Director of Translational Bioinformatics in the UFHCC Cancer Informatics Shared Resource (CISR) and the UF CTSI Biomedical Informatics program.

Key Research: graph artificial intelligence, multi-modal data integration, patient characterization

With extensive experience in laboratory medicine, Dr. Maximo Marin brings a deep understanding of clinical assay strengths and limitations, clinical interpretations, laboratory technology, workflow processes, and data analysis to improve hospital operations. His research team, which includes a pre-med undergraduate, a computer science graduate student, a pathology resident, and a post-doctoral fellow in laboratory medicine, has collaborated for nearly two years. Their current projects involve using machine learning to optimize ADAMTS13 testing for thrombotic thrombocytopenic purpura and an artificial neural network to enhance lab result accuracy when samples present common interferences.

Key Research: thrombotic microangiopathy, thrombotic thrombocytopenic purpura, coagulation

Dr. Quevedo is an ssistant professor in the department of obstetrics and gynecology at the University of Florida. She has contributed to the growth of endometriosis quality surgical access and received the Surgical Review Corporation’s designation of Surgeon of Excellence in Minimally Invasive Gynecologic Surgery at UF Health’s Center of Excellence in Minimally Invasive Gynecologic Surgery. Dr. Quevedo is also the the recipient of the Robert A. Winn Diversity in Clinical Trials Award, fostering her growth in community-engaged participatory research. Her passion for interdisciplinary collaborations includes working with AI engineers through the internally funded Rapid AI Prototyping and Development for Patient Safety (RAPiDS) program. Through RAPiDS, her team is applying UF’s Artificial Intelligence infrastructure to decrease the time to diagnosis and address disparities for patients with endometriosis within the healthcare system.

Key Research: endometriosis, chronic pain, multi-omics