Symposium #1 Grand Challenges in Data Science and Engineering in Healthcare:  Medical Imaging

February 10, 2021 10:00 am – 2:30 pm EST
Moderator: Andrew Laine & Amir Amini
  • Roderic Pettigrew, Ph.D., MD, Texas A&M University
    “Integrating engineering and medicine to address big challenges ”
  • Michael I. Miller, Ph.D., Johns Hopkins University
    “Brain Imaging and Mapping”
  • Cynthia Rudin, Ph.D., Duke University
    “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead”
  • Muyinatu Bell, Ph.D., Johns Hopkins University
    “Ultrasound Image Formation in the Deep Learning Age”
  • Laura Waller, PhD., UC Berkeley
    “End-to-end learning for computational microscopy”
  • Marco Lorenzi, Ph.D., Université Côte d’Azur
    “Biomedical data integration in neurodegenerative disorders: towards in-silico simulation of intervention trials”
  • Hayit Greenspan, Ph.D., Tel Aviv University
    “AI in Medical Imaging: Solving the Medical Data Challenge with Application to COVID-19
  • Eric A. Hoffman, Ph.D., University of Iowa
    “Structural and Functional Lung Phenotyping via Multi-spectral CT”
  • Kristy K Brock, Ph.D., DABR, FAAPM, University of Texas
    “Challenges and Opportunities in the Clinical Translation of AI for Image Guided Cancer Therapy”
Roderic Pettigrew, Ph.D., M.D.

Roderic Pettigrew, Ph.D., M.D.

Texas A&M University

Roderic I. Pettigrew, PhD, MD, serves as Chief Executive Officer (CEO) of Engineering Health (EnHealth) and executive dean for the Engineering Medicine (EnMed) program at Texas A&M University, in partnership with Houston Methodist Hospital. Dr. Pettigrew also holds the endowed Robert A. Welch Chair in Chemistry. EnHealth is the nation’s first comprehensive educational program to fully integrate engineering into all health-related disciplines. EnMed is the nation’s first four-year, fully-integrated engineering and medical education curriculum leading to both a MD and master’s degree in engineering.

An internationally recognized leader in biomedical imaging and bioengineering, Dr. Pettigrew served as the first director for the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at National Institutes of Health (NIH). Prior to his appointment at the NIH, he joined Emory University School of Medicine as a professor of radiology and Georgia Institute of Technology as a professor of bioengineering. Dr. Pettigrew is well-known for pioneering four-dimensional imaging of the cardiovascular system using magnetic resonance imaging (MRI). In addition to his numerous achievements, he is an elected member to both the National Academy of Medicine and the National Academy of Engineering.

After receiving his Bachelor of Science degree in physics from Morehouse College as a Merrill Scholar, Dr. Pettigrew attended Rensselaer Polytechnic Institute, where he earned his Master of Science degree in nuclear science and engineering. Dr. Pettigrew received his PhD in radiation physics at Massachusetts Institute of Technology (MIT) and attained his medical doctorate from Leonard M. Miller School of Medicine at the University of Miami.

Michael I. Miller, Ph.D.

Michael I. Miller, Ph.D.

Johns Hopkins University School of Medicine and Whiting School of Engineering

Michael I. Miller is the Bessie Darling Massey Professor and Director of the Johns Hopkins Department of Biomedical Engineering. He is the co-director of the Kavli Neuroscience Discovery Institute.

An internationally recognized leader in medical imaging and brain mapping, Miller pioneered the field of computational anatomy. His research focuses on the functional and structural characteristics of the human brain in health and disease, including Huntington’s disease, Alzheimer’s disease, dementia, bipolar disorder, schizophrenia, and epilepsy. His lab is currently devising cloud-based methods to build and share libraries of brain images—and the algorithms used to understand them—associated with neuropsychiatric illness.

Miller was appointed as one of 17 inaugural University Gilman Scholars in 2011. He has received numerous other honors, including the Herschel Ruth Seder Professorship and the National Science Foundation Presidential Young Investigator Award. He has co-authored more than 200 peer-reviewed publications, as well as two highly cited textbooks on random point processes and computational anatomy. Miller’s research is highly translational, and he has co-founded four start-up companies in the past decade.

Cynthia Rudin, Ph.D.

Cynthia Rudin, Ph.D.

Duke University

Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is interpretable machine learning. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award and a fellow of the American Statistical Association and the Institute of Mathematical Statistics.

Muyinatu Bell, Ph.D.

Muyinatu Bell, Ph.D.

Johns Hopkins University

Muyinatu Bell is an Assistant Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science at Johns Hopkins University, where she founded and directs the Photoacoustic and Ultrasonic Systems Engineering (PULSE) Lab. Dr. Bell earned a B.S. degree in Mechanical Engineering (biomedical engineering minor) from Massachusetts Institute of Technology (2006), received a Ph.D. degree in Biomedical Engineering from Duke University (2012), conducted research abroad as a Whitaker International Fellow at the Institute of Cancer Research and Royal Marsden Hospital in the United Kingdom (2009-2010), and completed a postdoctoral fellowship with the Engineering Research Center for Computer-Integrated Surgical Systems and Technology at Johns Hopkins University (2016).  Dr. Bell is Associate Editor-in-Chief of IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (T-UFFC) and holds patents for short-lag spatial coherence beamforming and photoacoustic-guided surgery. Dr. Bell’s a multiple awards and honors include MIT Technology Review’s Innovator Under 35 Award (2016), the NSF CAREER Award (2018), the NIH Trailblazer Award (2018), the Alfred P. Sloan Research Fellowship (2019), the ORAU Ralph E. Powe Jr. Faculty Enhancement Award (2019), and Maryland’s Outstanding Young Engineer Award (2019). She most recently received the inaugural IEEE UFFC Star Ambassador Lectureship Award (2020) and the SPIE Early Career Achievement Award (2021).

Laura Waller, Ph.D.

Laura Waller, Ph.D.

UC Berkeley

Laura Waller is the Ted Van Duzer Associate Professor of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, a Senior Fellow at the Berkeley Institute of Data Science, and affiliated with the UCB/UCSF Bioengineering Graduate Group. She received B.S., M.Eng. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT) in 2004, 2005 and 2010, and was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012. She is a Packard Fellow for Science & Engineering, Moore Foundation Data-driven Investigator, Bakar Fellow, OSA Fellow and Chan-Zuckerberg Biohub Investigator. She has received the Carol D. Soc Distinguished Graduate Mentoring Award, Agilent Early Career Professor Award (Finalist), NSF CAREER Award and the SPIE Early Career Achievement Award.

Marco Lorenzi, Ph.D.

Marco Lorenzi, Ph.D.

Université Côte d’Azur

Marco Lorenzi is a tenured research scientist (CR) at Université Côte d’Azur, Inria Sophia Antipolis. His research interest is in the development of statistical and machine learning methods for the analysis of large-scale and heterogeneous biomedical data. Current research topics include Bayesian modeling and uncertainty quantification, time-series analysis, latent variable models, and federated learning.

Hayit Greenspan, Ph.D.

Hayit Greenspan, Ph.D.

Tel Aviv University

Hayit Greenspan is a Full Professor of Biomedical Engineering in the Faculty of Engineering, Tel-Aviv University. Dr. Greenspan received the B.S. and M.S. degrees in Electrical Engineering (EE) from the Technion, and the Ph.D. degree in EE from CALTECH – California Institute of Technology. She was a Postdoc with the CS Division at U.C. Berkeley following which she joined Tel-Aviv University.  From 2008 until 2011, she was a visiting Professor at Stanford University, Department of Radiology, Faculty of Medicine. She was also a visiting researcher at IBM Research in the Multi-modal Mining for Healthcare group, in Almaden CA.

Dr. Greenspan has over 150 publications in leading international journals and conferences and has received several awards and patents. She is member of several journal and conference program committees, including SPIE medical imaging, IEEE_ISBI and MICCAI.  She served as an Associate Editor for the IEEE Trans on Medical Imaging (TMI) journal.  In 2016 she was the Lead Co-editor for a Special issue on Deep Learning in Medical Imaging in IEEE TMI. In 2017 she Co-edited an Elsevier Academic Press book on Deep learning for Medical Image Analysis. Recently she was titled as one of the Top-30 Women AI leaders in Drug Discovery and Advanced Healthcare, by Deep Knowledge Analytics 

Eric A. Hoffman, Ph.D.

Eric A. Hoffman, Ph.D.

University of Iowa

 Eric A. Hoffman, PhD is a professor of radiology, medicine and biomedical engineering at the University of Iowa. He is the director of the Advanced Pulmonary Physiomic Imaging Laboratory (APPIL) in the Department of Radiology and the director of the Iowa Comprehensive Lung Imaging Center (I-Clic) at the University of Iowa. He received his Ph.D. in Physiology from the University of Minnesota / Mayo Graduate School of Medicine in 1981 and remained on staff at the Mayo Clinic where he was a member of the team which Developed the Dynamic Spatial Reconstructor (DSR), a one of a kind CT scanner which was able to gather up to 240 contiguous CT sections of the body every 1/60 second. Throughout his career he has used advanced imaging methodologies to study basic respiratory physiology centered largely on mechanisms of ventilation and perfusion heterogeneity and regional lung mechanics. Dr. Hoffman moved from the Mayo Clinic in 1987 to head the Cardiothoracic Imaging Research Center in the Department of Radiology at the University of Pennsylvania and then moved in 1992 to his current position at the University of Iowa. Most recently, in addition to continuing basic physiologic research of the lung, he has established a combination of single and multi-spectral multidetector row spiral CT imaging (Siemens SOMATOM Force) methodology to objectively follow human lung pathology and pathophysiology with a particular emphasis on inflammatory lung diseases. Dr. Hoffman is the author of more than 570 journal articles and 20 book chapters in the field of dynamic volumetric imaging of the lung and heart and served for 5 years as the founding chair of the Physiology and Function from Multidimensional Imaging sessions of the SPIE Medical Imaging conference. Dr. Hoffman was inducted into the college of fellows of the American Institute for Medical and Biological Engineering in March of 2000 and was elected into the FleischnerSociety in May 2005.  He received the 2013 John West award for Outstanding Contributions to the Field of Functional Pulmonary Imaging from the IWPFI, the 2014 Joseph R Rodarte Award for Scientific Distinction from the Respiratory Structure and Function Assembly of the American Thoracic Society and the 2018 Alton Ochsner Award “relating smoking and disease.” He is a fellow of the European Respiratory Society and the American Thoracic Society. Dr. Hoffman’s laboratory is dedicated to the use of advanced imaging methodologies for the exploration of normal and pathologic physiology of the lung and serves as the Radiology Center for numerous large multi-center studies utilizing imaging to phenotype the lung as a component of the study (including SPIROMICS, MESA Lung, SARP, PreCISE, British Lung Foundation Early COPD).

Kristy K Brock, Ph.D., DABR, FAAPM

Kristy K Brock, Ph.D., DABR, FAAPM

University of Texas

Kristy K. Brock received her PhD in Nuclear Engineering and Radiological Sciences from the University of Michigan in 2003. After receiving her PhD, she joined the faculty at the University of Toronto (Radiation Medicine Program, Princess Margaret Hospital) and subsequently the faculty at the University of Michigan (Department of Radiation Oncology). She is currently a Professor with tenure in the Department of Imaging Physics at the University of Texas MD Anderson Cancer Center, where she is the Director for the Image-Guided Cancer Therapy Research Program. Her research has focused on image guided therapy, where she has developed a biomechanical model-based deformable image registration algorithm to integrate imaging into treatment planning, delivery, and response assessment as well as to understand and validate imaging signals through correlative pathology. Her algorithm was licensed by RaySearch Laboratories and was incorporated into their commercially available radiation therapy treatment planning system.

She is board certified by the American Board of Radiology in Therapeutic Medical Physics and holds a joint appointment with the Department of Radiation Physics at MD Anderson. Dr. Brock has published over 100 papers in peer-reviewed journals, is the Editor of the book ‘Image Processing in Radiation Therapy’ and has been the PI/co-PI on 21 peer-reviewed, industry, and institutional grants. She currently serves as the Vice Chair of Science Council for the American Association of Physicists in Medicine as well as Vice Chair of the Big Data Subcommittee. In addition, she is the Chair of the Promoting Science through Research and Training Committee of the American Society of Radiation Oncology and serves on the Program Committee for the SPIE Medical Imaging – Image-guided Procedures, Robotic Interventions and Modeling Conference Program.