Graduate

Algorithms & Friends Seminar presents "Modeling the Heterogeneity in COVID-19’s Reproductive Number and Its Impact on Predictive Scenarios"

Topic: 
Modeling the Heterogeneity in COVID-19’s Reproductive Number and Its Impact on Predictive Scenarios
Abstract / Description: 

The correct evaluation of the reproductive number R for COVID-19 — which characterizes the average number of secondary cases generated by each typical primary case— is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modelled as a universal constant for the virus across outbreak clusters and individuals— effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, demographics, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified and lead to inaccurate predictions and/or risk evaluation. From the statistical modeling perspective, the magnitude of the impact of this averaging remains an open question: how can this intrinsic variability be percolated into epidemic models, and how can its impact on uncertainty quantification and predictive scenarios be better quantified? In this talk, we discuss a Bayesian perspective on this question, creating a bridge between the agent-based and compartmental approaches commonly used in the literature. After deriving a Bayesian model that captures at scale the heterogeneity of a population and environmental conditions, we simulate the spread of the epidemic as well as the impact of different social distancing strategies, and highlight the strong impact of this added variability on the reported results. We base our discussion on both synthetic experiments — thereby quantifying of the reliability and the magnitude of the effects — and real COVID-19 data.


 

Hosted by the Algorithms and Friends Seminar

Date and Time: 
Monday, April 5, 2021 - 12:00pm

Probability Seminar: Sharp constants in Khinchin inequalities for type L random variables

Topic: 
Sharp constants in Khinchin inequalities for type L random variables
Abstract / Description: 

I shall discuss sharp moment comparison inequalities (a.k.a. Khinchin inequalities) for weighted sums of i.i.d. random variables of type L (originating in statistical mechanics in Lee-Yang theorems).

Date and Time: 
Monday, April 12, 2021 - 11:00am

SystemX bonus lecture "Human-Robot Interactive Communication and Collaboration"

Topic: 
Human-Robot Interactive Communication and Collaboration
Abstract / Description: 

Autonomous and anthropomorphic robots are poised to play a critical role in manufacturing, healthcare and even our homes in the near future. However, for this vision to become a reality, robots need to efficiently collaborate and physically interact with their human partners. Rather than traditional remote controls and programming languages, adaptive and transparent techniques and interfaces for human-robot collaboration are needed. In particular, robots may need to interpret implicit behavioral cues or explicit instructions and, in turn, generate appropriate responses. In this talk, I will present ongoing work which leverages machine learning (ML), natural language processing and virtual reality to create different modalities for humans and machines to engage in effortless and natural interactions. To this end, I will describe Bayesian Interaction Primitives - an approach for motor skill learning and spatio-temporal modelling in physical human-robot collaboration tasks. Further, I will discuss our recent work on language-conditioned imitation learning and self-supervised learning in interactive tasks. The talk will also cover techniques that enable robots to communicate information back to the human partner via mixed reality projections. To demonstrate these techniques, I will present applications in social robotics and collaborative assembly.

Date and Time: 
Tuesday, April 6, 2021 - 2:00pm

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