Kimon Drakopoulos
Assistant Professor of Data Sciences and Operations
USC Marshall School of Business

Deploying a Data-Driven COVID-19 Screening Policy at the Greek Border

Abstract

In collaboration with the Greek government, we designed and deployed a nation-wide COVID-19 screening protocol for travelers to Greece. The goals of the protocol were to combine limited demographic information about arriving travelers with screening results from recently tested travelers to i) judiciously allocate Greece’s limited testing budget to identify asymptomatic, infected travelers and ii) quickly identify hotspots and spikes in other nations to inform immigration/border policies in real-time. This talk details i) the operations of our designed system (including border screening, database management, closed-loop feedback, and liasing with contact-tracing teams) ii) a novel, batched, contextual bandit algorithm tailored to the unique features of this problem and iii) an empirical assessment of the benefits of the deployed system from the summer/fall 2020, showing that targeted testing based on traveler’s features essentially doubles the effectiveness compared to random testing and static greylisting. That is, in a country with daily budget of 7500 tests, targeting is as effective as Radom sampling with 14,850 tests, a number that at the time was effectively the testing capacity of the whole country. Furthermore, the resulting estimates of the true, unbiased positivity rate within the traveling population, guarantees a 3-4 week early warning for upcoming outbreaks, hence enabling effective and timely greylisting decisions. (Joint work with V. Gupta, H. Bastani)

Biography

Kimon Drakopoulos is an Assistant Professor of Data Sciences and Operations at USC Marshall School of Business, where he researches complex networked systems, information design and information economics. He completed his Ph.D. in the Laboratory for Information and Decision Systems at MIT, focusing on the analysis and control of contagion within networks. His current research revolves around controlling contagion, epidemic or informational as well as the use of information as a lever to improve operational outcomes in the context of testing allocation, fake news propagation and belief polarization.

Zico Kolter
Associate Professor of Computer Science
CMU School of Computer Science

Deep Equilibrium Models: One (Implicit) Layer is All You Need

Abstract

Does deep learning actually need to be deep? In this talk, I will present some of our recent and ongoing work on Deep Equilibrium (DEQ) Models, an approach that demonstrates we can achieve most of the benefits of modern deep learning systems using very shallow models, but ones which are defined implicitly via finding a fixed point of a nonlinear dynamical system. I will show that these methods can achieve results on par with the state of the art in domains spanning large-scale language modeling, image classification, and semantic segmentation, while requiring less memory and simplifying architectures substantially. I will also highlight some recent work analyzing the theoretical properties of these systems, where we show that certain classes of DEQ models are guaranteed to have a unique fixed point, easily-controlled Lipschitz constants, and efficient algorithms for finding the equilibria. I will conclude by discussing ongoing work and future directions for these classes of models.

Biography

Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University, and also serves as chief scientist of AI research for the Bosch Center for Artificial Intelligence. His work spans the intersection of machine learning and optimization, with a large focus on developing more robust and rigorous methods in deep learning. In addition, he has worked in a number of application areas, highlighted by work on sustainability and smart energy systems. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and best paper awards at NeurIPS, ICML (honorable mention), IJCAI, KDD, and PESGM.