Keynote Speakers

Eva Lee
Virginia C. and Joseph C. Mello Chair and Professor,
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech, USA

Doheon Lee
Director of Bio-Synergy Research Center and Professor,
Korea Advanced Institute of Science and Technology, Korea

Luonan Chen
Professor, Excutive director,
Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China


Abstract: Modeling and Evaluating Intervention Options and Strategies for COVID-19 Containment: A Biological-Behavioral-Logistics Computation Decision Framework

SARs, bird flu, H1N1, Ebola crisis in W. Africa, Zika and current SARS-CoV-2 underscore the critical importance of emergency response and medical preparedness. Such needs are wide-spread as globalization and air transportation facilitate rapid disease spread across the world. Computational modeling of infectious disease outbreaks and epidemics offer insights in propagation patterns and facilitate policy makers to synthesize potential interventions. Current models include inclined decay with an exponential adjustment, SEIR (susceptible, exposed, infectious, recovered) compartmental model, discrete time stochastic processes, and transmission tree. While many of these models incorporate contact tracing to predict spread pattern, key elements on optimal usage of scarce resources and effective and efficient process performance (e.g., diagnostics and screening, non-pharmaceutical interventions, trained personnel/robots for treatment, decontamination) have not been included. This is particularly critical in the fight of COVID-19 containment due to lack of testing kits and the prevalence of asymptomatic transmission, and the long period of hospitalization required by severely sick patients.

This work focuses on designing a system computational decision modeling framework that simultaneously i) captures disease spread characteristics, ii) incorporates day-to-day hospital and home care processes and resource usage, iii) explores non-pharmaceutical intervention, social and human behavior and iv) allows for system optimization to minimize infection and mortality under time and labor constraints.

Prof. Eva Lee, H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, also serves as the Director of the Center for Operations Research in Medicine and HealthCare. She is also a Senior Research Professor at the Atlanta VA Medical Center. Dr. Lee works in the area of mathematical programming and large-scale computational algorithms with a primary emphasis on medical/healthcare decision analysis and logistics operations management. She tackles challenging problems in health systems and biomedicine through systems modeling, algorithm and software design, and decision theory analysis. Specific research areas include health risk prediction, early disease prediction and diagnosis, optimal treatment strategies and drug delivery, healthcare outcome analysis and treatment prediction, public health and medical preparedness, large-scale healthcare/medical decision analysis and quality improvement. Dr. Lee's research in logistics focuses on large-scale optimization and algorithmic advances for optimal operations planning and resource allocation. She has developed decision support systems for inventory control; large-scale truck dispatching, scheduling, and transportation logistics; telecommunications; portfolio investment; and emergency treatment response and facility layout and planning. Dr. Lee was awarded a NSF/NATO postdoctoral fellowship on Scientific Computing, and a postdoctoral fellowship from Konrad-Zuse-Zentrum Informationstechnik Berlin in 1995 for Parallel Computation. In 1996, she received the NSF Presidential Young Investigator Award for research on integer programming and parallel algorithms and their applications to medical diagnosis and cancer treatment. She was the first OR/IE recipient for the prestigious Whitaker Foundation Biomedical Grant for Young Investigators, awarded for her work on a novel approach for combining biological imaging and optimal treatment design for prostate cancer. In 2004, she was selected as one of the Extraordinary Women Engineers. In 2005, she received the INFORMS Pierskalla award for research excellence in HealthCare and Management Science for her work on emergency response and planning, large-scale prophylaxis dispensing, and resource allocation for bioterrorism and infectious disease outbreaks. In 2006, she was chosen by the American Mathematical Society as the representative mathematician to speak and discuss individually with congressional leaders about her research advances in the medical and healthcare domain, and about the importance of mathematics in scientific advances. Together, Lee and Dr. Marco Zaider from Memorial Sloan-Kettering Cancer Center were named winners of the 2007 Franz Edelman award for their work on using operations research to advance cancer therapeutics. Lee has received seven patents for innovative medical systems and devices. Her research has been featured and discussed in numerous news media articles, including articles in the New York Times, London Times, Urology Times, Atlanta Business Chronicle, and Homeland Security IAIP Directorate Daily Report. Her cancer research was featured in a TV science news segment for Discoveries and Breakthroughs, Inside Science, Curing Prostate Cancer, broadcast by television stations nationwide.


Abstract: Accelerating Drug Discovery with an AI-Based Virtual Human System CODA

Formidable complexity of systemic human physiology often give rises of unintended effects of therapeutic compounds during the drug development processes or even after the drug approvals. Though the beneficial unintended effects could lead opportunities of repositioning drugs, the harmful effects might put critical hurdles against successful drug development. We have been developing a virtual human system, CODA, which can explore functional effects of therapeutic compounds in the systemic level. CODA integrates three types of physiological knowledge from public structured databases, literature, and in-house experiments into a unified format of physiological interactions. More than ten public databases including KEGG, GO, and CTD have been transformed; around 25 million PUBMED abstracts have been text-mined; and more than 5,000 in-house novel findings have been incorporated. We have also developed two types of analysis on the CODA knowledge repository. Given therapeutic compounds of interest, CODA can identify possible phenotypic effects in the systemic level. When therapeutic compounds and their observed functional effects are given, CODA can enumerate possible effect paths encompassing molecular, functional, and disease level interactions. We have been testing CODA by applying it to various tasks including drug repositioning, drug-drug interactions, and side effect prediction with known benchmark datasets. Though we are enriching CODA with more knowledge sources and more sophisticated analysis techniques, the current version is already providing unique analysis capabilities and one of the most comprehensive information for drug discovery.

Prof. Doheon Lee is Professor in the Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology and Director of Bio-Synergy National Research Center, Korea. He also serves as the President of Korean Society for Bioinformatics. Doheon Lee received the B.S., M.S., and Ph.D. degrees in computer science from Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1990, 1992, and 1995, respectively. He was a visiting professor of Stanford University, Indiana University, Translational Genomics Research Institute (TGEN) and Univ. of Texas at Austin, USA. Currently, he is a professor in Department of Bio and Brain Engineering, KAIST, and the director of Bio-Synergy Research Center (BSRC), a Korean national project where over 30 principal investigators are collaborating for natural product bioinformatics and systems biology. He was an Associate Editor for ACM Transactions on Internet Technology for nine years. He is also serving Scientific Reports, International Journal of Data Mining in Bioinformatics, and Healthcare Informatics Research as an Editorial Board Member. He is a co-founder of ACM International Workshop on Data and Text Mining for Biomedical Informatics. He has published over 200 academic papers in bioinformatics, medical informatics, neuroinformatics, and data mining.


Abstract: Network biomarker for quantifying regular state of a biological system, and dynamic network biomarker for quantifying critical state of a biological system

We defined two new types of biomarkers to quantify the states of biological systems based on network, in contrast to the traditional molecular biomarkers. Network biomarker is constructed to quantify regular state of a biological system, while dynamic network biomarker is to quantify the critical state or tipping point of a biological system. (1) Network biomarker (NB) is a subnetwork or network module, which is composed of a number of associations or regulations between molecules (or variables), rather than simply a number of molecules. Those associations (the second-order statistics) in the module are formed collectively as a biomarker, thus robustly and accurately quantifying the regular state of a biological system, completely different from the concentrations or densities of conventional molecular biomarkers (the first-order statistics). (2) Dynamic network biomarker (DNB) is a subnetwork or module, and is also composed of a number of associations or regulations between molecules but with three statistical conditions (in terms of variances and covariances), which are actually a number of strongly and collectively fluctuated molecules in the network. Theoretically, DNB is able to quantify the critical state or the tipping point of a biological system, thereby serving as a general early-warning signal to indicate an imminent state transition. A number of real datas are provided to validate the effectiveness of NB and DNB.

Prof. Luonan Chen received BS degree in the Electrical Engineering, from Huazhong University of Science and Technology, and the M.E. and Ph.D. degrees in the electrical engineering, from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences. He was the founding director of Institute of Systems Biology, Shanghai University. He was elected as the founding president of Computational Systems Biology Society of OR China, and Chair of Technical Committee of Systems Biology at IEEE SMC Society.In recent years, he published over 350 journal papers and two monographs (books) in the area of bioinformatics, nonlinear dynamics and machine learning.