Experts
Vanessa Aguiar-Pulido, PhD
Dr. Vanessa Aguiar-Pulido is passionate about problem solving in biomedical research, with a focus on developing tools and algorithms that will impact diagnosis and therapeutics of rare and complex genetic disorders. Her background and interests include big data analytics, machine learning, artificial intelligence, bioinformatics, neuroscience, data mining, ontologies, biomedical data integration, health informatics, epigenetics and omics in general. Her lab is at the interface of Computer Science and Biomedical Sciences, and her research focuses on understanding the underlying genetic causes of monogenic and complex disorders, how these causes can be influenced by environmental factors, and how these findings can be applied to clinical practice. The advent of high-throughput sequencing technologies has ushered in a new era of genetic inquiry, decreasing the economic costs and turnaround time notably. However, with this growth of ‘omic’ data, computational challenges arise. Not only the volume of data is increasing but also the dimensionality, expanding the search space exponentially. As a result, many problems involve such a large set of possible solutions that finding the optimal one in a reasonable amount of time is not feasible. Therefore, developing new approaches for ‘intelligent’ data analysis is necessary. The work of Dr. Aguiar-Pulido shows how artificial intelligence can be applied to identify potential genetic risk factors in complex genetic disorders (i.e., those that require genetic predisposition and environmental factors for the patient to develop the disease). As part of her research, she has developed methods based on evolutionary computation and strategies using machine learning for candidate gene analysis and identification. In her most recent work, she devised a comprehensive approach that uses machine learning (more specifically, embedded feature selection) to pinpoint biological pathways affected in structural birth defects employing ethnically diverse cohorts. Approaches such as the latter are fundamental to further human disease research in the age of precision medicine. |
David R. Chapman, PhD
Dr. David Chapman has completed his B.S. from the University of Maryland, Baltimore County (UMBC) in 2008, his M.S. from UMBC in 2010, and his Ph.D. from UMBC in 2012. Subsequently Dr. Chapman completed a Postdoctoral fellowship at Columbia University’s Lamont Doherty Earth Observatory, where he studied statistical forecasting methods for the El Niño Southern Oscillation (ENSO). Dr. Chapman has had three years of industry experience with Oceaneering International, where he developed a novel dynamic programming algorithm for edge detection for the purposes of subsea robotic navigation. Dr. Chapman’s present research emphasizes computer vision and image processing algorithms primarily with applications to medical imaging analytics. Recent research results include a novel semi-supervised image classification algorithm with applications to lung cancer screening as well as COVID19 classification, a novel algorithm for CT-scan image deep denoising, as well as an approach for domain invariant feature learning to reduce algorithmic bias for X-ray image screening for Tuberculosis. Dr. Chapman is actively collaborating with RAD-AID international (they bring radiology to low-resource areas), Carestream, Inc. (a worldwide provider of X-ray imaging systems), and the U.S. Dept. of Homeland Security, to create the largest ever medical imaging dataset for Tuberculosis screening. Dr. Chapman is also collaborating with RAD-AID international and Google foundation to address and overcome issues of algorithmic bias that may affect clinical translation of diagnostic screening to Low and Middle Income Countries. Furthermore, Dr. Chapman is collaborating with the National Alliance against Disparities in Public Health (NADPH) to improve AI infrastructure for Minority Serving Institutions. Finally, Dr. Chapman is working on the development of core computer vision and machine learning algorithms including a novel methods for semi-supervised learning and edge detection. |
Mingzhe Chen, PhD Dr. Mingzhe Chen is currently an Assistant Professor in the Department of Electrical and Computer Engineering at University of Miami. He received his Ph.D. degree from Beijing University of Posts and Telecommunications, Beijing, China, in 2019. From 2016 to 2019, he was a Visiting Researcher at the Department of Electrical and Computer Engineering, Virginia Tech, working with Prof. Walid Saad. From 2019 to 2021, he was a Postdoctoral Research Associate with the Department of Electrical and Computer Engineering at Princeton University, working with Prof. H. Vincent Poor. In 2022, he worked as an AI Researcher at Ericsson Research, USA. His research interests include:
|
|
Shai Cohen has taught in Spain (Pamplona, Montenegro, Granada) and Italy (Torino), and is now in the beautiful city of Miami. Some of the classes he’s taught include Spanish and Hebrew Languages and Literatures, Culture, Civilization, Cultural Marketing, Linguistics, and a large spectrum of topics applied in different classes as part of the curriculum. In addition, he engages in different related social and cultural activities in collaboration with educational centers, and is directing a Theater Workshop. Shai’s involved in the Digital Humanities is for a better dissemination of information and collaboration. His classes are always being accompanied by the use and the creation of digital materials and their dissemination. This is complemented with extracurricular activities that foments the students’ engagement with the language from a multicultural approach. Specialties: Spanish History and Civilization, Spanish Golden Age, Spanish Peninsular Literature, Sephardic History, Literature and Culture, Judeosephardic Language, Languages Learning (Spanish and Hebrew), Hebrew Language and Culture, Multicultural Encounters, Digital Humanities.
|
|
Ljubisa Daba Dabic, AAIA, an innovative architect with experience in healthcare and smart city programs is a partner in AMB Architectural Design Studio LLC in Rockville, Maryland, Dabic is a LEED accredited professional (AP) who has designed a chain of healthcare facilities across the United States, Canada, the United Kingdom, and Germany. He has also led the design and renovation projects of the Embassy of Macedonia, and the Embassy of Bosnia and Herzegovina in United States. Dabic’s research at IDSC will focus on combining architectural innovation with digital health solutions in the context of smart cities. In collaboration with other IDSC colleagues, Dabic will contribute his architectural engineering expertise toward achieving the vision of University of Miami’s leadership in the smart city program, Yesha added. He will also build IDSC’s international collaborators through partnerships with international academic institutions and private industry.
|
Jia “Jamie” Deng Jamie Deng’s career began as a CPA/Analyst after graduating from the University of Auckland in New Zealand in 2003 with a Bachelor of Commerce degree in Finance and International Business. Jamie worked for some time in Shenzhen before returning to the University of Auckland to receive a Bachelor of Science in Computer Science with Honors in 2019. After moving to California in 2019, he attended the University of California at Santa Cruz and received a Master of Science in Computer Science in 2020. Currently a PhD student at UM, he is working on NLP (natural language processing) and computer vision with the IDSC AI and Machine Learning team under Dr. Yelena Yesha.
|
Pierre-François d’Haese Dr. d’Haese is a serial entrepreneur and faculty at the intersection of data analytics and medicine. Most recently, Dr. d’Haese serves as the CEO of Neurotargeting and as the Director of Digital Health and Analytics at the Rockefeller Neuroscience Institute (RNI), an institution focused on the study of human memory and memory diseases. Dr. d’Haese defined the Institute’s digital health vision and analytics, leading a team of multidisciplinary engineers and scientists to build the next generation of AI-driven clinical diagnosis and intervention tools. Dr. d’Haese is an Associate Professor in NeuroRadiology and Computer Science and Elec. Engineering. Prior to the RNI, Dr. d’Haese started and advised numerous MedTech startups while pursuing an academic path in parallel. Among other projects, Dr. d’Haese built cloud-based medical imaging analytics pipelines to foster discoveries for neurodegenerative disease across academic centers, published on neurodegenerative pathologies such as Parkinson’s or focused ultrasound brain-blood-barrier opening for Alzheimer’s, and developed novel neurosurgical software systems for computer-assisted intervention for DBS and Epilepsy.
|
|
Before joining the University of Miami, Stephen Dennis conducted research and participated in the development of next-generation analytic and computing technologies and applications as a senior researcher at the University of Maryland at College Park. He led and served as a team member contributing to the vision, strategy, research activities, and partnerships that contribute to institutional growth. In this role, he coalesced and leveraged extensive innovation experiences and networks, technical skills, and collaborations that added value to the research ecosystem and yielded scalable results for the emerging technology marketplace. In addition, he formulated and maintained highly effective partnerships with internal/external organizations to develop effective and repeatable engagements that were scalable in practice, and represented the organization to nonprofit, industry, academia, government, and foreign government partners as required to create, mature, and leverage effective relationships that served common goals and derived mutual benefit. Currently, Mr. Dennis teaches Advanced Topics in Policy: Homeland Security as a graduate student instructor and subject matter expert regarding the history and formulation, execution, and frameworks for homeland security policymaking. Beginning in Fall 2021, he also began serving as a Senior Consultant for the Center for Innovative Technology supporting the Chief Technology office in the development of capabilities that support State of Virginia decision-making, including the Governor and Legislature, regarding a spectrum of emerging technology innovations and related economic development activities. He also serves as a subject-matter expert advising the development of information-sharing capabilities and internet of things architectures related to a wide variety of state, regional, and local smart city applications. From January 2020 to January 2021, Mr. Dennis served as the Director, Department of Homeland Security (DHS), Science and Technology Directorate, Advanced Computing Technology Centers, and as the DHS Science and Technology Representative to the White House Office of Science and Technology Policy and the National Security Council’s National Strategic Computing Initiative. He began at the DHS in 2007 as the Technical Director for the Advanced Research Projects Agency, advancing to Advanced Research Projects Agency Innovation Director in 2012. In 2016, he became the Director for the DHS Science and Technology Directorate, Data Analytics Technology Center. Mr. Dennis has a BS in Computer Engineering from Clemson University, an MS in Electrical Engineering and MBA from the University of Maryland, College Park. He received the Presidential Rank Award for Meritorious Service—Career Service (2017), the DHS Secretary Unity of Effort Award—Cross-cuting Analytics (2016), the DHS Secretary Meritorious Service Medal—Delivering Mission Capability (2015), the DHS S&T Undersecretary Award for Building Partnerships (2014), and the DHS S&T Undersecretary Award for Innovation (2012). |
|
Kareem Diab, PhD
|
|
Carter Falkenber, MS Carter is a computer science student pursuing a bachelor and master of science in computer science and a minor in mathematics (May 2025). His area of interest is machine learning and AI for solving cutting-edge problems. His goal is to use AI as a tool to drive innovation, empower society, and improve people’s lives.
|
|
Sumeet Menon, PhD Dr. Sumeet Menon is a Research Assistant at University of Maryland, Baltimore County. He worked with Accenture as a BI developer and has worked as a research assistant with Dr. Mikhail Gofman from California State University, Fullerton. His fields of interest are Machine Learning, Deep Learning, and Computer Aided Diagnosis. Sumeet developed a Mean Teacher and Transfer Learning generative model to generate synthetic COVID-19 x-rays, implemented a 3D CNN based deep learning model to detect cancer nodules in CT scans, and worked on semi-supervised learning and on an active learning model for classification using entropy minimization (published paper in the SPIE conference). He is working on generative approaches like Cycle GAN on domain transfer problems. In a collaborative effort along with IBM Research, Sumeet represented UMBC at the RSNA’2019 conference where he presented the software used to generate radiology reports and collected feedback from radiologists, which would be used for hypothesis testing. Also, he developed a module for the website as a web developer.
|
|
Michael Anthony Morris, MD MS Dr. Michael Morris is a Maryland clinician-scientist whose work focuses on advanced diagnostic imaging. Dr. Morris’s clinical practice includes diagnostic radiology and nuclear medicine, oncoradiology, theranostics, and cancer imaging Informatics. He serves on the medical staff at Mercy Medical Center, a private academic-affiliated hospital and cancer referral center for the State of Maryland and surrounding regions.
In his research work, Dr. Morris has conducted numerous studies and co-authored multiple publications, including “New Technology and Clinical Applications of Nanomedicine,” and “Nanomedicine and Drug Delivery.” His academic interests include oncoradiology, molecular and hybrid imaging, and imaging informatics with various projects at his host institution, and in collaboration with the National Institutes of Health (NIH), Baltimore VA Medical Center, University of Maryland Baltimore County (UMBC), Johns Hopkins University, and other organizations.
Dr. Morris graduated from Johns Hopkins University with bachelor’s and master’s degrees in molecular and cellular biology, as well as exposure to “multi-omics” in biological systems. He then served as a team member on the initial U.S. Food and Drug Administration (FDA) clinical trial for an intraoperative diagnostic tool, which helped spark his interest in quantitative approaches to medical diagnostics. After earning his medical degree at the University of Maryland, he completed an internship in the joint Mercy Medical Center/University of Maryland Medical Center program in internal medicine, and his residency in diagnostic radiology at the University of Maryland Medical Center in the department of diagnostic radiology and nuclear medicine, where he also completed his nuclear medicine training.
|
Michael Mylrea, PhD Dr. Michael Mylrea is a Distinguished Fellow for Industrial Cybersecurity at IDSC, and a Senior Distinguished Engineer at Resilience, one of the fast-growing technology companies in the US focused on disrupting medicine with innovation. At Resilience, Michael is leading security architecture and design efforts with various disruptive solutions, from Digital Twin to Privacy Preserving Zero Trust platforms. Dr. Mylrea has +18 years of cybersecurity experience developing innovative solutions and holds +14 cyber and blockchain patents. He led one of the first and largest federally funded blockchain projects that help introduce blockchain tech to the national lab system. He launched and led a successful ethical hacking cybersecurity company and has held CISO, CTO and senior technical positions in industry, government, including, but not limited to: GE, Pacific Northwest National Lab, the U.S. Departments of Energy and Defense, US Cyber Consequences Unit, Harvard’s Berkman Klein Center, and Cyber Team 7. Michael has helped influence various standards, regulations, and technology developments through participation in Group of 7, National Security Council, National Science Foundation, National Academy of Science, IEEE, NDIA, AAAI, and NIST panels, consortium, advisor boards (Tenable, CyManII, CARTA, Rocky Mountain Institute, EC Council and World Business Angels Investment Forum). He frequently keynotes large conferences and workshops such as RSA and IoT World. His work has appeared in news, journal articles, television, and congressional testimony and is frequently cited in technical, industry, and government publications. Dr. Mylrea is a National Science Foundation CyberCorps Scholar alum, completing his Doctorate at George Washington University (GWU) on Cybersecurity. Michael is a recipient of a number of distinguished awards (Fulbright Scholarship, NSF CyberCorps, Rosenthal Fellowship, FDD National Security Fellowship, Top 99 Future Leaders Award). Michael speaks Hebrew, Arabic, Spanish, and Portuguese and is proficient in auditing various computer languages.
|
Phuong Nguyen, PhD
Dr. Phuong Nguyen is a Scientist, Artificial Intelligence & Machine Learning in the Frost Institute for Data Science and Computing (IDSC) at the University of Miami/UHealth. She is also a Research Assistant Professor at the Department of Computer Science and Electrical Engineering at University of Maryland Baltimore County (UMBC) and an affiliated faculty at the NSF, Center for Accelerated Real Time Analytics (CARTA). She received PhD in Computer Science from UMBC in 2012. Her research interests are artificial intelligence, federated learning, distributed computing, and blockchain technologies with applications to multi-sensor data fusion, climate modeling, medical imaging, and digital health. Dr. Nguyen has led the development of AI-based Computer Aided Diagnosis using an active semi-supervised learning algorithm that is able to assist Radiologists in diagnosis of early lung cancer using Computed Tomography images. She is currently collaborating with The Center for Vascular Research, University of Maryland School of Medicine to develop AI-based analytic models for Venous Thromboembolism (VTE) Risk Assessment using patient Electronic Medical Records (EMR). Dr. Nguyen have published Machine Learning emulation of Planetary Boundary Layer and Microphysics parameterizations to speed up Weather Research and Forecasting (WRF) model using Neural Network Architecture Search. Previously, she was a Faculty Research Assistant at University of Maryland College Park and a Guest Researcher at National Institute of Standards and Technology where she made a contribution to develop segmentation algorithms and analytics/visualization tools for analyzing large microscopy images of Cell Biology. Dr. Nguyen mentored Undergraduate and Graduate Students. Her over forty academic peer-reviewed articles, talks, and book chapter have been distributed by prominent workshops, conference proceedings, and journals. In addition, she is served as Graduate Committee Member and IEEE/Springer conferences/journals’ Committee Member. |
Mitsunori “Mitsu” Ogihara, PhD
Dr. Mitsunori Ogihara joined the University of Miami in 2007 as Professor in the Department of Computer Science and as Program Director of the Big Data Analytics & Data Mining Program for the Center for Computational Science (now IDSC). More recently, he served as Associate Dean for Digital Library Innovation in the College of Arts and Sciences. He is currently the Director of IDSC Workforce Development and Education, and Site Director for NSF University of Miami CARTA. Dr. Ogihara holds secondary appointments in the Department of Electrical and Computer Engineering in the College of Engineering and the Department of Music Media and Industry in the Frost School of Music. Dr. Ogihara obtained his PhD in Information Sciences from the Tokyo Institute of Technology in 1993. From 1994 to 2007, Dr. Ogihara was a Computer Science faculty member at the University of Rochester, where he was promoted to Associate Professor with tenure in 1998, and to Full Professor in 2002. He also served as Chair of the Department from 1999 to 2007. His research interests are in data mining, information retrieval, network traffic data analysis, program behavior analysis, molecular computation, and music information retrieval. A prolific scholar, Dr. Ogihara has authored/co-authored three books The Complexity Theory Companion, Music Data Mining, and Fundamentals of Java Programming, and the author of more than 200 peer-reviewed research papers. Many papers by Dr. Ogihara are through interdisciplinary collaborations. His articles appear in journals and conferences that cover many fields, including psychology, implementation science, library science, chemistry, biology, and digital humanities. He is currently serving as Editor-in-Chief for the Theory of Computing Systems Journal (Springer) and on the editorial board for the International Journal of Foundations of Computer Science (World Scientific). |
Alex Pissinou Makki
|
|
Babak Saboury, MD, MPH, DABR, DABNM Dr. Babak Saboury is a radiologist and nuclear medicine physician, dual-board certified by ABR and ABNM, and board eligible in clinical informatics by ABPM with extensive clinical expertise in oncoradiology, particularly MRI and PET (Magnetic Resonance Imaging and Positron Emission Tomography) image interpretation, as well as targeted radionuclide therapy. Dr. Saboury is a physician-scientist in the Department of Radiology and Imaging Sciences at the NIH Clinical Center. He joined the University of Pennsylvania (UPenn) in 2009 and during his four-year tenure there, he gained advanced expertise in modern molecular imaging with a focus on Positron Emission Tomography (PET) and novel quantitative techniques for the development of translational imaging biomarkers as the Director of the Quantitative Imaging Biomarker Laboratory of Abass Alavi. In 2014, he joined The University of Maryland as a combined radiology and nuclear medicine track resident physician training with visionaries in both fields, Eliot Siegel and Vaskin Dilsizian, and stayed there as an attending radiologist and nuclear medicine physician until 2019 when he accepted his clinical appointment at the NIH. His clinical residency at the University of Maryland exposed him to the full breadth and depth of clinical radiology and nuclear medicine sharpening his expertise and skills as a physician. This rigorous training prepared him to address the most complicated aspects of clinical radiology and nuclear medicine. On the other hand, working at UPenn with the world-renowned pioneers of molecular imaging and PET carefully attuned his scientific mind. He is the author of more than 60 peer-reviewed papers, with more than 100 presentations at national and international meetings. Dr. Saboury is a Professor of Computer Science and Electrical Engineering (adjunct) at the University of Maryland, Baltimore County (UMBC). As an oncoradiologist, nuclear medicine, and clinical informatics physician, he is the lead radiologist for PET/MRI and the Chief Clinical Data Science Officer for RADIS. |
|
Eliot L. Siegel, MD Dr. Eliot Siegel is Professor and Vice-Chair at the University of Maryland School of Medicine, Department of Diagnostic Radiology, as well as Chief of Radiology and Nuclear Medicine for the Veterans Affairs Maryland Healthcare System. He is the Director of the Maryland Imaging Research Technologies Laboratory and has adjunct appointments as Professor of Bioengineering at the University of Maryland College Park, and, as Professor of Computer Science at the University of Maryland Baltimore County (UMBC). Dr. Siegel was responsible for the NCI’s National Cancer Image Archive and served as Workspace Lead of the National Cancer Institute’s caBIG In Vivo Imaging Workspace. He has been named as Radiology Researcher and Radiology Educator of the year by his peers as well as one of the Top Ten radiologists. Under his leadership, the VA Maryland Healthcare System became the first filmless healthcare enterprise in the world. He has written over 200 articles and book chapters about PACS (Picture Archiving and Communication Systems) and digital imaging, and has edited six books on the topic, including Filmless Radiology and Security Issues in the Digital Medical Enterprise. He has made more than 1,000 presentations throughout the world on a broad range of topics involving computer applications in imaging and medicine. Dr. Siegel served as symposium chairman for the Society of Photo-optical and Industrial Engineers (SPIE) Medical Imaging Meeting for three years, and is currently serving on the board of directors of the Society of Computer Applications in Radiology (now Society for Imaging Informatics in Medicine). He is a fellow of the American College of Radiology and of the Society of Imaging Informatics in Medicine. |
|
Mark Wolff, PhD Dr. Mark Wolff has over 25 years of experience in the healthcare, life science, and software industries as a scientist and analyst working in the U.S., Europe and Asia, having held a variety of research and leadership positions in academia, government, and industry. Recognized as an accomplished practitioner and thought leader in the development and application of advanced/predictive analytics, machine learning, artificial intelligence, and data visualization to solve complex problems in research, development, and commercialization in the pharmaceutical, medical device, and healthcare industries. his current work focuses on the development and application of machine learning approaches to streaming sensor/IoT data in support of improving health outcomes and safety and the design of intelligent decision support systems for clinical development, care delivery, and digital health initiatives. Dr. Wolff is a sought-after speaker, writer, and consultant by industry, academia, and government, with
|
Yusen Wu, PhD Dr. Yusen Wu is an Assistant Scientist at the University of Miami Frost Institute for Data Science and Computing. He received his PhD in Computer Science Department from the University of Maryland, Baltimore County in 2022. During his graduate studies, he was one of UMBC CARTA members researching Permissioned Blockchain, Data Security, Edge Computing, and Byzantine Fault Tolerance. Before joining UMBC, he had two years of industry experience as a software engineer on the Distributed Systems. |
Shai Cohen, PhD
Ljubisa Daba Dabic, MSc, Architecture Assoc. AIA; LEED AP BD+C
Stephen J. Dennis, MS
