The Colloquium on Digital Transformation is a series of weekly online talks on how artificial intelligence, machine learning, and big data can lead to scientific breakthroughs with large-scale societal benefit. The summer/fall series focuses on COVID-19 mitigation research. Please register here.
With the emergence of public healthcare crises such as the COVID-19 Pandemic, it is increasingly evident that AI is among the most powerful tools for addressing early detection of diseases, triage, treatment planning, and patient management, among many other pressing healthcare problems. What will it take to build effective machine learning systems for healthcare? This talk will outline our research progress towards answering this question. To this end, I will present emerging technical advances in federated learning, modeling, evaluation, privacy, and trustworthiness. I will also outline how we are bringing these tools to bear to aid in analyzing medical images from COVID-19 patients.
Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo’s research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to neuroscience and biomedical imaging. Koyejo has been the recipient of several awards including a best paper award from the conference on uncertainty in artificial intelligence (UAI), a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organization.
October 1, 1 pm PT/4 pm ET
Improving Fairness & Equity in COVID-19 Policy Applications of Machine Learning
Rayid Ghani, Distinguished Career Professor in the Machine Learning Department and the Heinz College of Information Systems and Public Policy, Carnegie Mellon University
October 8, 1 pm PT/4 pm ET
Solving “Prediction Problems” in Health, from Heart Attacks to COVID-19
Sendhil Mullainathan, Roman University Professor of Computation and Behavioral Science, University of Chicago Booth School of Business
Ziad Obermeyer, Associate Professor of Health Policy and Management, University of California, Berkeley
October 15, 1 pm PT/4 pm ET
COVIDScholar: An NLP Hub for COVID-19 Research Literature
Gerbrand Ceder, Chancellor’s Professor, Department of Materials Science and Engineering, University of California, Berkeley
October 22, 1 pm PT/4 pm ET
Machine Learning-based Design of Proteins, Small Molecules, and Beyond
Jennifer Listgarten, Professor, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
October 29, 1 pm PT/4 pm ET
Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress in Predictive Inference
Emmanuel Candès, Barnum-Simons Chair in Mathematics and Statistics, Professor of Statistics, and Professor, by courtesy, of Electrical Engineering, Stanford University
November 12, 1 pm PT/4 pm ET
Mathematics of Deep Learning
René Vidal, Herschel Seder Professor of Biomedical Engineering and Inaugural Director of the Mathematical Institute for Data Science, Johns Hopkins University
November 19, 1 pm PT/4 pm ET
Mining Diagnostics Sequences for SARS-CoV-2 Using Variation-Aware, Graph-Based Machine Learning Approaches Applied to SARS-CoV-1, SARS-CoV-2, and MERS Datasets
Nancy Amato, Head of the Department of Computer Science and Abel Bliss Professor of Engineering, University of Illinois at Urbana-Champaign
September 17, 1 pm PT/4 pm ET
Impact of Mobility on Epidemic Spread: Some Lessons from NYC and India
Saurabh Amin, Robert N. Noyce Career Development Associate Professor of Civil and Environmental Engineering, MIT
In this talk, we analyze the impact of mobility services (in particular, public transportation systems) on the spread of COVID-19. We also propose testing strategies based on mobility between areas with heterogeneous risk levels to control disease transmission. Our work focuses on two distinct regions: (1) NYC – where a significant number of people rely on the MTA for their daily commute; and (2) Odisha – an eastern state in India which saw a significant influx of migrant workers from COVID hotspots. In both regions, high levels of human mobility and limited testing capacity led to a rapid increase of COVID cases. In the study of NYC, we run panel analysis to evaluate the relative impact of MTA ridership over general mobility on the growth in cases at the zipcode level. We find strong heterogeneities across zip codes, which can be explained by socioeconomic factors and unbalanced testing resources. Importantly, we find that while a higher level of general mobility is associated with an increase in cases, the MTA usage did not lead to additional growth in cases after April. In contrast, the district-level data from Odisha exhibits strong correlation between case growth and volume of incoming migrant workers from high-risk states after May. We construct an optimization model that allows the state health authority to effectively allocate testing resources to worker populations based on the heterogeneous risk levels at their origin states and the local district population. This work is joint with Manxi Wu, Isabel Munoz, Devendra Shelar, and R. Gopalakrishnan.
Saurabh Amin is an Associate Professor in the Department of Civil and Environmental Engineering at MIT. He is affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center at MIT. He received his PhD in Systems Engineering from UC Berkeley (2011), M.S.E. from UT Austin (2004), and B.Tech. from IIT Roorkee (2002). His fields of expertise include stochastic control theory, applied game theory, and optimization in networks. His research focuses on the design of high-confidence monitoring and control algorithms for infrastructure systems. He is a recipient of an NSF CAREER Award, the Google Faculty Research Award, the Robert N. Noyce Professorship, and the Ole Madsen Mentoring Award.
September 10, 1 pm PT/4 pm ET
Evolutionary Adaptations and Spreading Processes in Complex Networks
A common theme among many models for spreading processes in networks is the assumption that the propagating object (e.g., a pathogen, in the context of infectious disease propagation, or a piece of information, in the context of information propagation) is transferred across network nodes without going through any modification. However, in real-life spreading processes, pathogens often evolve in response to changing environments or medical interventions, and information is often modified by individuals before being forwarded. In this talk, we will discuss the effects of such adaptations on spreading processes in complex networks with the aim of revealing their role in determining the threshold, probability, and final size of epidemics, and exploring the interplay between them and the structural properties of the network.
H. Vincent Poor is the Michael Henry Strater University Professor of Electrical Engineering at Princeton University, where he is engaged in research in information theory, machine learning and network science, and their applications in wireless networks, energy systems, and related fields, including recently modeling the spread of the COVID-19 epidemic. He is a member of the National Academy of Engineering and the National Academy of Sciences, and a foreign member of the Chinese Academy of Sciences and the Royal Society. Recognition of his work includes the 2017 IEEE Alexander Graham Medal and honorary doctorates from universities in Asia, Europe, and North America.
September 3, 1 pm PT/4 pm ET
Metapopulation and Age-Structured Epidemic Models for the COVID-19 Pandemic
Zoi Rapti, Associate Professor, Department of Mathematics, University of Illinois at Urbana-Champaign
In this colloquium, Zoi Rapti will first address the issue of parameter identifiability from reported data, such as confirmed cases and deaths. Parameter identifiability analysis is used to determine whether unknown parameters in ordinary differential equation models can be determined from the available data. She will then introduce age-structured and spatial (metapopulation) models for the spread of COVID-19, demonstrating that in some multi-group models that are structured by age, the basic reproductive number of each group is smaller than the basic reproductive number of the entire community. Hence, in most cases, it is not informative to consider separately groups that are known to interact, as this may underestimate the overall severity of the outbreak. In metapopulation models, she will show how traffic data is used in mobility models of epidemic spread. Rapti’s talk represents joint work of a research team that includes Eleftheria Kontou (Civil and Environmental Engineering, University of Illinois, Urbana-Champaign), Yannis Kevrekidis (Chemical and Biomolecular Engineering & Applied Mathematics and Statistics, Johns Hopkins University), and P. G. Kevrekidis (Mathematics and Statistics, University of Massachusetts at Amherst).
Zoi Rapti is an Associate Professor in the Department of Mathematics at the University of Illinois, Urbana-Champaign and the Carle R. Woese Institute for Genomic Biology. Her research focus is on nonlinear dynamics with applications to mathematical biology (DNA denaturation, epidemiology, community assembly, phage-bacteria interactions, data-theory coupling) and physics (Nonlinear Schrodinger-type equations, discrete Klein-Gordon equations). She holds a bachelor’s degree in Mechanical Engineering from the National Technical University of Athens, Greece and a PhD in Mathematics from the University of Massachusetts, Amherst.
August 27, 1 pm PT/4 pm ET
Targeted Dynamic Interventions in Networked Epidemic Models
Asuman Ozdaglar, Distinguished Professor of Engineering Department Head, Electrical Engineering and Computer Science, Deputy Dean of Academics, Schwarzman College of Computing, MIT
Francesca Parise, Assistant Professor, Electrical and Computer Engineering, Cornell University
Epidemic spread models are playing an increasingly central role for policy making in the COVID-19 pandemic. Many of these models consider homogeneous populations, thus failing to capture rich heterogeneities in terms of risk factors, vulnerabilities, economic participation, location, and social interactions. In this colloquium, Asuman Ozdaglar and Francesca Parese present networked SIR models that capture groups of agents with different characteristics and interaction patterns. They then discuss targeted dynamic interventions for testing and lockdown policies that minimize spread of infection while containing social and economic damage. Their focus is on dynamic time-varying policies that adaptively adjust as a function of a community’s infection level.
Asuman Ozdaglar is MIT’s MathWorks Professor of Electrical Engineering and Computer Science, Department Head of the Electrical Engineering and Computer Science Department, and Deputy Dean of Academics at the Schwarzman College of Computing. She is affiliated with Laboratory for Information & Decision Systems and the Operations Research Center. Her research focuses on problems that arise in the analysis and optimization of large-scale dynamic multi-agent networked systems including communication networks, transportation networks, and social and economic networks.
Francesca Parise was named Assistant Professor of Electrical and Computer Engineering at Cornell University in July 2020. She previously served as a postdoctoral researcher at the Laboratory for Information and Decision Systems at MIT. She was recognized as an EECS rising star in 2017, and is the recipient of the Guglielmo Marin Award from the Istituto Veneto di Scienze, Lettere ed Arti, the SNSF Early Postdoc Fellowship, the SNSF Advanced Postdoc Fellowship, and the ETH Medal for her doctoral work.
August 20, 1 pm PT/4 pm ET
Lessons from COVID-19: Efficiency vs. Resilience
Moshe Y. Vardi, University Professor, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice University
In both computer science and economics, efficiency is a cherished property. In computer science, the field of algorithms is almost solely focused on their efficiency. In economics, the main advantage of the free market is that it promises “economic efficiency.” A major lesson from COVID-19 is that both fields have over-emphasized efficiency and under-emphasized resilience. Professor Vardi argues that resilience is a more important property than efficiency and discusses how the two fields can broaden their focus to make resilience a primary consideration. He will include a technical example, showing how we can shift the focus in formal reasoning from efficiency to resilience.
Moshe Y. Vardi is a University Professor and the Karen Ostrum George Distinguished Service Professor in Computational Engineering at Rice University. He is the recipient of three IBM Outstanding Innovation Awards, the ACM SIGACT Goedel Prize, the ACM Kanellakis Award, the ACM SIGMOD Codd Award, the Blaise Pascal Medal, the IEEE Computer Society Goode Award, the EATCS Distinguished Achievements Award, the Southeastern Universities Research Association’s Distinguished Scientist Award, and the ACM SIGLOG Church Award. He is the author and co-author of over 600 papers and the books Reasoning about Knowledge and Finite Model Theory and Its Applications. He is a Fellow of the American Association for the Advancement of Science, the American Mathematical Society, the Association for Computing Machinery, the American Association for Artificial Intelligence, the European Association for Theoretical Computer Science, the Institute for Electrical and Electronic Engineers, and the Society for Industrial and Applied Mathematics. He is a member of the US National Academy of Engineering and National Academy of Science, the American Academy of Arts and Science, the European Academy of Science, and Academia Europaea. He holds six honorary doctorates. He is currently a Senior Editor of the Communications of the ACM, after having served for a decade as Editor-in-Chief.
August 13, 1 pm PT/4 pm ET
Predictive and Prescriptive Analytics for the COVID-19 Pandemic
Dimitris Bertsimas, Associate Dean of Business Analytics & Boeing Professor of Operations Research, MIT Sloan School of Management
The COVID-19 pandemic creates unprecedented challenges for healthcare providers and policymakers. How to triage patients when healthcare resources are limited? Whom to test? And how to design social distancing policies to contain the disease and its socioeconomic impact? Dimitris Bertsimas and Alexandre Jacquillat of MIT Sloan School of Management believe that analytics can provide an answer and have collected comprehensive data from hundreds of clinical studies, case counts, and hospital collaborations at www.covidanalytics.io. This colloquium will present their epidemiological model of the disease’s dynamics, a machine-learning model of mortality risk, and a resource allocation model. Specifically, it will address: How can we predict admissions in intensive care units using machine learning? How does COVID-19 impact different demographic and socioeconomic populations? How does mobility impact the disease’s spread, and how to optimize social distancing policies? And how to augment COVID-19 tests with data-driven warnings that identify high-risk subjects? Bertsimas will present a new machine learning model for predicting being COVID-positive and mortality using data from over 40 hospitals around the world, along with high-performance computing (using the C3 AI suite), and advanced machine learning and artificial intelligence. He will summarize his research group’s end-to-end ML/AI methods, spanning epidemiological modeling (to model the disease’s spread), machine learning (to predict ICU admissions and test results), causal inference (to investigate disparities across populations), and optimal control (to support social distancing guidelines), as well as a new optimization model for allocating vaccines to minimize deaths.
Dimitris Bertsimas is currently the Boeing Professor of Operations Research and the Associate Dean of business Analytics at the Sloan School of Management at the Massachusetts Institute of Technology. His research interests include machine learning, optimization, and their applications in health care. He has co-authored more than 250 scientific papers and five graduate level textbooks. He is currently Editor in Chief of INFORMS Journal on Optimization and former Area Editor of Management Science in Optimization and of Operations Research in Financial Engineering. He has supervised 76 doctoral students and is currently supervising 25 others. He is a member of the National Academy of Engineering and he has received numerous research awards including the John von Neumann Theory Prize for fundamental contributions in Operations Research and Management Science and the INFORMS President Award for significant impact in society, both in 2019. Since March, 2020 he has led a group of 30 doctoral students, postdocs, and professors to study multiple aspects of COVID-19. These efforts are detailed at COVID Analytics. He has co-founded several companies over the years including Dynamic Ideas, a financial services company, sold to American Express in 2002, D2 Hawkeye, sold to Verisk in 2009, Benefit Sciences, ReClaim, and Savvi Financial.
August 6, 1 pm PT/4 pm ET
Optimal Targeted Lockdowns for COVID-19 in a Multi-Group SIR Model
Daron Acemoglu, Institute Professor Department of Economics, Massachusetts Institute of Technology
This colloquium will investigate targeted lockdowns using a multi-group SIR model, in which infection, hospitalization, and fatality rates vary among groups—in particular among young, middle-aged, and old patients. The model—developed by PI Daron Acemoglu with MIT Economics Professors Victor Chernozhukov, Iván Werning, and Michael Whinston—enables a tractable quantitative analysis of optimal policy. For baseline parameter values for the COVID-19 pandemic as applied to the United States, Daron Acemoglu and his colleagues find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group. Intuitively, a strict and long lockdown for the most vulnerable group both reduces infections and enables less strict lockdowns for the lower risk groups. The colloquium also will investigate: the impacts of group distancing, testing, and contract tracing; the matching technology; and the expected arrival time of a vaccine for optimal policies. Overall, Acemoglu’s model indicates targeted policies combined with measures that reduce interactions among groups and increase testing and isolation of the infected can minimize both economic losses and deaths.
Daron Acemoglu is Institute Professor at MIT and an elected fellow of the National Academy of Sciences, the Turkish Academy of Sciences, the American Academy of Arts and Sciences, the Econometric Society, the European Economic Association, and the Society of Labor Economists. He is the author of five books, including Introduction to Modern Economic Growth, Why Nations Fail: Power, Prosperity, and Poverty (joint with James A. Robinson), and The Narrow Corridor: States, Societies, and the Fate of Liberty (with James A. Robinson). His academic work covers a wide range of areas, including political economy, economic development, economic growth, inequality, labor economics and economics of networks. He has received the inaugural T. W. Shultz Prize from the University of Chicago in 2004, and the inaugural Sherwin Rosen Award for outstanding contribution to labor economics in 2004, Distinguished Science Award from the Turkish Sciences Association in 2006, the John von Neumann Award, Rajk College, Budapest in 2007, the Carnegie Fellowship in 2017, the Jean-Jacques Laffont Prize in 2018, and the Global Economy Prize in 2019.
July 30, 1 pm PT/4 pm ET
Networked Epidemiology Models for COVID-19 Analysis and Control
Carolyn Beck, Professor of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign; Tamer Bașar, the Swanlund Endowed Chair and CAS Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign; and Rebecca Smith, Associate Professor, Department of Pathobiology, University of Illinois College of Veterinary Medicine
Spread of epidemics over large populations has been an important research area for several centuries, studied by epidemiologists, statisticians, mathematicians, and more recently data scientists. Over the past eight months or so, the science of epidemics has accelerated at an exponential rate due to the global threat caused by COVID-19. In addition, for quite some time, mathematical models of epidemics have been developed to help predict spread and inform policy makers as to what types of containment measures might be effective. In this lecture, Carolyn Beck, Tamer Bașar, and Rebecca Smith will introduce several mathematical models within a networking (graph-theoretic) framework and discuss their work as well as others’ in: analyzing stability (or instability) of the equilibrium states (endemic and disease-free equilibria); optimally determining curing rates (through antidote control techniques); optimally modifying the network structure so as to mitigate spread; and developing algorithms to assimilate real- time testing data into networked epidemiological models. The speakers will discuss the plans of their project team—also comprised of Prashant Mehta (PI) and Matthew West of the University of Illinois at Urbana-Champaign and Philip E. Paré of Purdue University—in the development of models, algorithms, and software tools to support the state-level PCR (polymerase chain reaction) and serological testing efforts.
Tamer Başar has been with the University of Illinois at Urbana-Champaign since 1981, where he holds the Swanlund Endowed Chair, CAS Professor of Electrical and Computer Engineering, Professor at Coordinated Science Laboratory and Information Trust Institute, and Affiliate Professor, Mechanical Science and Engineering. He is also Director of the Center for Advanced Study. He is a member of the NAE, and Fellow of IEEE, IFAC, and SIAM. He has received several awards and recognitions over the years, including the highest awards of IEEE CSS, IFAC, AACC, and ISDG, the IEEE Control Systems Technical Field Award, and a number of international honorary doctorates and professorships. He has over 900 publications in systems, control, communications, optimization, and dynamic games, including six books. His current research interests include stochastic teams, games, and networks; multi-agent systems and learning; data-driven distributed optimization; spread of information and epidemics; security and trust; energy systems; and cyber-physical systems.
Carolyn L Beck is a Professor at the University of Illinois at Urbana-Champaign in Industrial and Enterprise Systems Engineering, with affiliate appointments in Electrical and Computer Engineering, Mechanical Science and Engineering, and the Coordinated Science Lab. She currently serves on the Carle Illinois College of Medicine Admissions Committee. She has held visiting positions at KTH Royal Institute of Technology in Stockholm, Stanford University, and Lund University in Sweden. She has received national research awards, including the NSF CAREER Award and the ONR Young Investigator Award as well as local teaching awards. Her main research interests lie in the study of modeling and analysis of networked control systems and include the development of model approximation methods, network inference and aggregation, and distributed optimization and control methods.
Rebecca L. Smith is an Associate Professor in the Department of Pathobiology at the University of Illinois College of Veterinary Medicine, the Carl R. Woese Institute for Genomic Biology, and the National Center for Supercomputing Applications. Her research focuses on the understanding and use of complex data, including longitudinal datasets and heterogeneous data collection, for prediction and modeling of infectious diseases. Her current research focuses on understanding the impact of data collection on propagation of errors and uncertainty through prediction.
July 23, 1 pm PT/4 pm ET
Can Targeted Closures Reduce Economic Loss and Control COVID-19 Spread?
John Birge, Hobart W. Williams Distinguished Service Professor of Operations Management, University of Chicago Booth School of Business
In New York City, could the use of available data on individuals’ movements, the level of economic activity in different neighborhoods, and knowledge about the epidemic lead to decisions that control COVID-19 and substantially lower economic losses than citywide closures? Could targeted closures work? John Birge with his research colleagues Ozan Candogan of University of Chicago and Yiding Feng of Northwestern University are developing a spatial epidemic spread model for multiple New York City neighborhoods, whose residents fall into five COVID-19 related categories: 1) susceptible, 2) exposed, 3) infected clinical, 4) infected subclinical, and 5) recovered. Putting themselves in the position of city planners who seek a framework for policies that induce the lowest economic costs, Birge, Candogan, and Feng focus on areas with small and large numbers of infections, commuting patterns across them, and how infections in one neighborhood can trigger infections in others. The research team’s initial results indicate that targeted closures can achieve the same policy goals for disease prevention at substantially lower economic losses. In addition, they find that coordination with neighboring counties is paramount. Contrary to what might be expected, and due to the spatial aspect of the epidemic spread, they argue that New York City neighborhoods with higher levels of infections should not necessarily be the ones exposed to the most stringent economic closure measures.
John Birge is the Hobart W. Williams Distinguished Service Professor of Operations Management at the University of Chicago Booth School of Business. He studies mathematical modeling of systems under uncertainty with applications in energy, finance, health care, manufacturing, public policy, and transportation. He is an INFORMS Fellow, MSOM Society Distinguished Fellow, member of the US National Academy of Engineering, and Editor-in-Chief of Operations Research. He is also former dean of engineering at Northwestern University, professor and chair of industrial and operations engineering at the University of Michigan, and holds a bachelor’s degree in mathematics from Princeton University and a master’s and Ph.D. in operations research from Stanford University.
July 16, 1 pm PT/4 pm ET
Using AI Techniques for Detection and Containment of COVID-19 and Emerging Diseases
Alberto Sangiovanni-Vincentelli, Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Science, UC Berkeley
When using AI methods to tackle the COVID-19 pandemic, distribution shifts from test data to training data create challenges. These shifts often lead to degraded performance, presenting challenges when deploying machine-learning models in healthcare AI and autonomous driving applications. In the first part of this talk, Professor Sangiovanni-Vincentelli will review the risks brought by incipient diseases and domain mismatch–two notable types of distribution shifts–to high-stake decision-making scenarios. To address the challenges, his research team has devised approaches that leverage the uncertainty information from ensemble learners and domain randomization. Their theoretical and empirical results show that these approaches produce classifiers that are more robust against distribution shifts. For COVID-19 research, the sparsity of emerging disease data, especially at the initial outbreak of the epidemic, represents another challenge in using AI to detect and contain disease spread. In the second part of the talk, Professor Sangiovanni-Vincentelli will present his research team’s plan for tackling this challenge by building upon the above-mentioned techniques and incorporating them into state-of-the-art, human-AI collaborative methods. This is a joint investigation with Geoff Tison of UCSF and Yuxin Chen of the University of Chicago.
Alberto Sangiovanni-Vincentelli is the Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Science at University of California, Berkeley, where he has served on faculty since 1975. He is Special Advisor to the Dean of Engineering for Entrepreneurship and Chair of the Faculty Advisors to Berkeley’s accelerator, SkyDeck. His many honors include the IEEE-EDAA Kaufman Award for pioneering contributions to electronic design automation (EDA) and the IEEE/RSE Maxwell Medal “for groundbreaking contributions that have had an exceptional impact on the development of electronics and electrical engineering or related fields.” He co-founded Cadence and Synopsys, two leading EDA companies with combined NASDAQ valuation of close to $60 billion. He is a board member of Cadence, KPIT, ISEO, ExpertSystem, Cogisen, Cy4gate; has consulted for companies worldwide, including Intel, IBM, ST, Mercedes, BMW, UTC, GM; and is International Advisory Council Chair of the Milano Innovation District. He is a member of the NAE, a IEEE and ACM Fellow, and holds an Honorary Doctorate from Aalborg University and KTH. He has published over 1,000 papers and 19 books with an h-index of 117 (Google Scholar) and graduated over 100 doctorate students.
July 9, 1 pm PT/4 pm ET
Translating AI Research in Breast Cancer Imaging to COVID-19
Maryellen Giger, A.N. Pritzker Professor of Radiology at The University of Chicago
The COVID-19 pandemic presents a pressing public health need for computational techniques to augment the interpretation of medical images in their role for: surveillance and early detection of COVID-19 resurgence via monitoring of medical imaging data; detection, triaging, and differential diagnosis of COVID-19 patients; and prognosis, including prediction and monitoring of response, for use in patient management. While thoracic imaging, including chest radiography and computed tomography, are being re-examined for their role in patient management, the limitations for improved interpretation are partially due to the qualitative interpretation of the images. Professor Giger and her colleagues at University of Chicago and Argonne National Laboratory aim to develop machine intelligence methods to aid in the interrogation of medical images from COVID-19 patients. They draw on decades of AI development of medical images to quantify and explain the COVID-19 presentation on imaging, specifically through machine learning methods of interrogating cancer on multimodality breast images for “virtual biopsies.”
Maryellen L. Giger is the A.N. Pritzker Professor of Radiology and Medical Physics at the University of Chicago. For decades, she has worked on computer-aided diagnosis/machine learning/deep learning in medical imaging and cancer diagnosis and management. Her AI research in breast cancer for risk assessment, diagnosis, prognosis, and therapeutic response has yielded various translated components, and she is using these “virtual biopsies” in imaging-genomics association studies. Giger is a former president of AAPM and of SPIE, and is the Editor-in-Chief of the Journal of Medical Imaging. She is a member of the National Academy of Engineering; Fellow of AAPM, AIMBE, SPIE, SBMR, IEEE, and IAMBE; and was cofounder, equity holder, and scientific advisor of Quantitative Insights (now Qlarity Imaging), which produces QuantX, the first FDA-cleared, machine-learning driven CADx system.