Navigating investigations in oceans of data and making discoveries is possible only with the use of efficient algorithms. As data grows in volume, velocity, variety, and veracity, so does the demand for efficiency in data science applications. Artificial Intelligence (AI) and Machine Learning (ML), will streamline algorithms based on algorithmic and methodological developments from the field of AI. The computational methods of AI aim to mimic human intelligence but also exceed human capabilities, assisting humans in decision making and in solving complex problems.
The numerous branches of AI encompass ML, natural language understanding (spoken and written), computer vision, data mining, human-computer interfaces, data visualization, and, more recently, deep learning. In all these areas, data plays a crucial role as algorithmic innovations are developed from within while taking inspiration from mathematics, statistics, and physics. The applications of these major branches of AI are broad, reaching far beyond the traditional realm of science and engineering, and ushering in crucial advances in medicine, the social sciences, business, and even the arts and the humanities.
Through IDSC, the University is developing an AI testbed that gives scientists access to state-of-the-art AI/ML tools and a dedicated high-performance computing infrastructure, incorporating human experts in the loop for developing, evaluating, and addressing the most challenging AI/ML application problems.
- Artificial Intelligence in Biomedical Imaging
- AI/NLP for EHR Analytics
- FDA AI Testbed
- NIH AIM-AHEAD Program
- AI/ML for Marine Atmospheric Boundary Layer profiles via Satellite Remote Sensing Data Fusion
- Intelligently Secured Distributed Solution
- Networking with Human in the Loop
Yelena Yesha, PhD
Knight Foundation Endowed Chair of Data Science and AI
Director, IDSC AI + Machine Learning
IDSC Innovation Officer and Head, International Relations
Professor, Department of Computer Science, College of Arts and Science
Professor, Department of Radiology, Miller School of Medicine
Founding Director, NSF CARTA
|About Dr. Yesha
At the University of Miami, Dr. Yelena Yesha is the Knight Foundation Endowed Chair of Data Science and AI at the Institute for Data Science and Computing (IDSC). At IDSC, Dr. Yesha is also the Innovation Officer and Head of International Relations. In this role, Dr. Yesha assists faculty in engaging government and industrial partners to collaborate with the University and consults with faculty on developing research ideas into innovations.
Dr. Yesha was the Founding Director of the National Science Foundation Center for Accelerated Real Time Analytics (CARTA), an NSF-funded Industry/University Cooperative Research Center (I/UCRC) that aims to develop long-term partnerships among industry, academia, and government. CARTA partners with Rutgers University New Brunswick, North Carolina State University, the University of Maryland Baltimore County (UMBC), Tel Aviv University, and the University of Miami.
Dr. Yesha received her B.Sc. degrees in Computer Science and in Applied Mathematics from York University, Toronto, Canada, and her M.Sc. degree and Ph.D. degree in Computer Science from The Ohio State University She has published 11 books as author or editor, and more than 200 papers in prestigious refereed journals and refereed conference proceedings, and she has been awarded external funding in a total amount exceeding $50 million dollars. She is currently working with leading industrial companies and government agencies on new innovative technology in the areas of blockchains, cybersecurity, and big data analytics with applications to electronic commerce, climate change, and digital healthcare. Dr. Yesha is also a Fellow of the IBM Centre for Advanced Studies.
Forbes magazine highlighted Dr. Yesha’s accomplishments in technology in a two-part profile:
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:
Ljubisa Daba Dabic, MSc, Architecture Assoc. AIA; LEED AP BD+C
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’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, Jamie 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, Jamie is working on NLP (natural language processing) and computer vision with the IDSC AI and Machine Learning team under Dr. Yelena Yesha.
Stephen J. Dennis
Stephen Dennis conducts research and participates 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 leads and serves as a team member contributing to the vision, strategy, research activities, and partnerships that contribute to institutional growth. In this role, he coalesces and leverages extensive innovation experiences and networks, technical skills, and collaborations that add value to the research ecosystem and yield scalable results for the emerging technology marketplace. In addition, he formulates and maintains highly effective partnerships with internal/external organizations to develop effective and repeatable engagements that are scalable in practice, and represents the organization to nonprofit, industry, academia, government, and foreign government partners as required to create, mature, and leverage effective relationships that serve common goals and derive 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).
Sumeet is Computer Science student at University of Maryland, Baltimore County. He worked for 18 months with Accenture as a BI developer and has worked as a research assistant with Dr. Mikhail Gofman from California State University, Fullerton.
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 the University of Miami 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 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).
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.
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.
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 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.
FDA Visiting Research Scholars
Quian Cao, PhD | Biomedical Engineering
Gene Penello, PhD, MS | Team Leader, Math Statistician
Nicholas Petrick, PhD | Deputy Director
Smriti Prathapan, PhD | ORISE Fellow
Berkman Sahiner, PhD | Senior Biomedical Research Scientist
Ravi Samala, PhD | Staff Fellow
Frank W. Samuelson, PhD | Physicist
News + Events
- Dr. Yelena Yesha speaks at Data Science Conference on 10/10
- Experts Explore Technology’s Potential to Bolster Democracy and Education at eMerge
- The U Recognizes 10 Women in Technology at eMerge Americas 2022
- Dr. Yelena Yesha Featured Speaker at FLAIRS-35 5/15-18
- UM at eMerge 4/18+19 Showcases Innovation and Recognizes Women in Tech
- Catch the Replay: Take Action! Bias in Technology with Yelena Yesha
- Dr. Yelena Yesha to Speak on Trusted AI at “Hack the Port 22” in FTL Monday 3/21
- Yelena Yesha: Keynote Speaker at Take Action! Bias in Technology 3/17
- Drs. Yelena Yesha and Pierre-François d’Haese Speak at FDA AI/ML Meeting 11/29
- Dr. Yelena Yesha spoke at 9/28 State of the University Town Hall Panel on Innovation
- NIH Funds New Consortium Aimed at Advancing Health Equity and Researcher Diversity
- Women in Academia Looks at Female Endowed Chairs
- Dr. Yesha speaks at NANS Neuromodulation Society Meeting 7/15-17
- Dr. Yelena Yesha Named First Knight Foundation Chair of Data Science and AI
- Using Data and Digital Tools to Improve Health and Well-Being
- Catch the Replay: Smart Cities MIAMI 2021 Reimagines Healthcare
- Catch the Replay: Meet a Data Scientist with Yelena Yesha
- Using Blockchain to Empower Digital Government
- Yelena Yesha Receives IEEE Lifetime Achievement Award
- Yelena Yesha Lecture 2/5/2020 on Medical Imaging
- Computational Approaches to Understanding Interaction and Development
- IEEE Computer Journal: Challenges and Issues in Data Science Education
- Frontiers in Psychology: Remote Data Collection During a Pandemic
- Early Interaction: New Approaches
- Remote Data Collection During a Pandemic: A New Approach for Assessing and Coding Multisensory Attention Skills in Infants and Young Children
- Recommendations for the Safe, Effective Use of Adaptive CDS in the US Healthcare System
- Yearbook of Medical Informatics: Ethics in Health Informatics
- Inference From Complex Networks: Role of Symmetry and Applicability to Images
- iDEC: Indexable Distance Estimating Codes for Approximate Nearest Neighbor Search
- Balancing Risks and Benefits of Artificial Intelligence in the Health Sector
- Hoshi: A Japanese Morphological Adorner for TEI XML
- Stigma, Biomarkers, and Algorithmic Bias: Recommendations for Precision Behavioral Health with AI
- Long-Duration Waveform Descriptive Grammar for Consumer Electronics Design, Diagnosis, and Validation
- Machine and Deep Learning Approaches for Cancer Drug Repurposing
- Scaling Up Heterogeneous Waveform Clustering for Long-Duration Monitoring Signal Acquisition, Analysis, and Interaction
- Ren, Ogihara, and Johnson Present Papers at IEEE 2019 in Beijing
- The “Dark” Energy Between Sonic Partials: Parametrics Estimation and Analysis of Weak Spectral Components for Musical Sound Analysis and Synthesis
- A New Auditory Image for Social Media: Moving Towards Correlation of Spectrographic Analysis and Interpretation with Audience Perception
- Multi-Scale Auralization for Multimedia Analytical Feature Interaction
- Imaging Tumor Oxidative Stress with Surface Enhanced Raman Scattering Gold Nanoparticles