AI and Machine Learning

AI and Machine Learning

AI and Machine Learning


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.

Ongoing Research

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.

AI in Biomedical Imaging-Doctor examines CT Scans

Artificial Intelligence in Biomedical Imaging

Research has found that AI tools perform better than doctors in some radiology imaging tasks. We believe that AI tools will expedite time-consuming diagnosis processes and improve accuracy in diagnosis for benefits of the patients. Read more

AI/NLP for EHR Analytics-Cardiologists in scrubs looking at monitor displays

AI/NLP for EHR Analytics

In clinical domains, various types of free texts appear including generic clinical text, discharge summary, insurance claims, clinical notes, lab reports, radiology reports, pathology reports, and clinical trial reports. We can extract information from such text data using natural language processing (NLP) tools and deep learning. Read more

FDA AI Testbed (Close up of female hand with black bracelet touching display of breathing machine. Patient lying in hospital bed on blurred background)

FDA AI Testbed

The overall goal of this project is to develop an infrastructure platform that will facilitate continuous testing and monitoring of diagnostic AI devices operating at real-world clinical practice sites. The platform will also enable data collection for real-world performance evaluation of AI/ML-enabled, computer-aided, detection/characterization/triaging (CAD) devices in radiology and health care applications in clinical practices. Read more

Blockchain stock image


Our investigators are a part of an AIM-AHEAD infrastructure core that will enable a coordinated data and computing infrastructure that enhances the interoperability of large-scale data resources with data that are maintained, governed, and prepared by individual institutions to preserve privacy and autonomy. Below are some of the innovation tools which we are working on building for the program. Read more

AI/ML for Marine Atmospheric Boundary Layer profiles via Satellite Remote Sensing Data Fusion

AI/ML for Marine Atmospheric Boundary Layer profiles via Satellite Remote Sensing Data Fusion

The Planetary Boundary Layer (PBL) is the layer of atmosphere bordering the surface of the earth and represents the greatest importance to human activities.  It is also the most difficult layer of the atmosphere to measure directly. Read more

Intelligently Secured Distributed Solution

Intelligently Secured Distributed Solution

To make their analytical service reliable, the distributed machine learning solutions must secure inter-device data transmission from several cyber threats such as jamming, eavesdropping, denial of service, and so on. However, data transmission among the distributed, densely deployed machine learning agents are more vulnerable, compared to the traditional communication equipment, to cyber-attacks for the following reasons. Read more

Networking with Humans in the Loop

Next-generation networks such as the Internet of Things (IoT), connected and autonomous vehicles (CAVs), social networks, augmented and virtual reality (AR/VR), as well as other wireless connected systems are expected to have enhanced capacity to autonomously process data and operate. However, such intelligent systems still need to work under human intervention, and act fast enough to the irrational and psychological behaviors. The goal of this research direction is to introduce novel networking solutions and enable intelligent devices quickly interpret and response to complex human behavior. Read more


Yelena Yesha, Director, AI + Machine Learning, Institute for Data Science and Computing, University of MiamiYelena 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 Frost 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:


AI + Machine Learning Experts

David Chapman


David R. Chapman, PhD
Associate Professor | Department of Computer Science


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


Mingzhe Chen, PhD
Assistant Professor | Dept. of Electrical and Computer Engineering

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:

  • Machine learning and artificial intelligence (AI) for wireless networks
  • Distributed/Federated learning fundamentals
  • Distributed/Federated learning over real-world wireless networks
  • Virtual reality over wireless networks
  • Unmanned aerial vehicle over wireless networks
  • Age of information

Ljubisa Daba DabicLjubisa Daba Dabic, MSc, Architecture Assoc. AIA; LEED AP BD+C
AMB Architectural Design Studio, LLC

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.



Jamie Deng
Student Researcher

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. DennisStephen J. Dennis
School of Public Policy, University of Maryland, College Park

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 Menon


Sumeet Menon
Student Researcher

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
Mercy Medical Center

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, Keynote Speaker, University of Miami Institute for Data Science and Computing and Miami Herbert Business School, Big Data Conference 2021


Michael Mylrea, PhD
Senior Distinguished Engineer, Cybersecurity (ICS) + Digital Innovation

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: GEPacific Northwest National Lab, the U.S. Departments of Energy and DefenseUS 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 7National Security CouncilNational Science FoundationNational Academy of ScienceIEEENDIAAAAI, 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, Scientist, Ai and Machine Learning, University of Miami Institute for Data Science and Computing


Phuong Nguyen, PhD

Research Associate Professor | Department of Computer Science
and IDSC AI + Machine Learning


Dr. Phuong Nguyen is a Research Associate Professor at the Department of Computer Science and the Frost Institute for Data Science and Computing, and she is affiliated with NSF CARTA at UM. 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 Ogihara, PhD


Mitsunori “Mitsu” Ogihara, PhD
Professor | Department of Computer Science
Director, IDSC Workforce Development & Education
Site Director, NSF University of Miami CARTA


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 CompanionMusic 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).

Babak Saboury, IDSC Visiting Fellow


Babak Saboury, MD, MPH, DABR, DABNM
Lead Radiologist, PET/MRI
NIH Clinical Center

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 Siegel, MD, IDSC Visiting Fellow


Eliot L. Siegel, MD
Professor, Diagnostic Radiology and Nuclear Medicine
University of Maryland | School of Medicine

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, IDSC Visiting Fellow


Mark Wolff, PhD
Advisory Industry Consultant + Chief Health Analytics Strategist
SAS Institute | Global IoT Division

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
highly developed communication and presentation skills.

  • Doctor of Philosophy, Toxicology  |  North Carolina State University, Raleigh, North Carolina
  • Master of Science, Entomology/Toxicology  |  North Carolina State University, Raleigh, North Carolina
  • Bachelor of Science, Biology/Liberal Arts  |  Loyola College, Baltimore, Maryland

Yusen Wu


Yusen Wu, PhD
Assistant Scientist

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.


FDA Visiting Research Scholars

Quian Cao

Quian Cao, PhD | Biomedical Engineering

Visiting Scientist
U.S. Food and Drug Administration

Gene Penello

Gene Penello, PhD, MS | Team Leader, Math Statistician

Office of Biostatistics
Center for Devices and Radiological Health
U.S. Food and Drug Administration

Nicholas Petrick

Nicholas Petrick, PhD | Deputy Director

Division of Imaging, Diagnostics, and Software Reliability (DIDSR)
Office of Science and Engineering Laboratories
Center for Devices and Radiological Health
U.S. Food and Drug Administration


Smriti "Smitty" Prathatpan

Smriti Prathapan, PhD | ORISE Fellow

Division of Imaging, Diagnostics, and Software Reliability (DIDSR)
Office of Science and Engineering Laboratories
Center for Devices and Radiological Health
U.S. Food and Drug Administration


Berkman Sahiner

Berkman Sahiner, PhD | Senior Biomedical Research Scientist

Division of Imaging, Diagnostics, and Software Reliability (DIDSR)
Office of Science and Engineering Laboratories
Center for Devices and Radiological Health
U.S. Food and Drug Administration

Ravi Samala

Ravi Samala, PhD | Staff Fellow

Division of Imaging, Diagnostics, and Software Reliability (DIDSR)
Office of Science and Engineering Laboratories
Center for Devices and Radiological Health
U.S. Food and Drug Administration

Frank Samuelson

Frank W. Samuelson, PhD | Physicist

Center for Devices and Radiological Health
U.S. Food and Drug Administration

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