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Events for October 10, 2018
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Computer Science General Faculty Meeting
Wed, Oct 10, 2018 @ 12:00 AM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Simone Silvetti and Laura Nenzi
Wed, Oct 10, 2018 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Simone Silvetti (Numerical Methods Group) and Laura Nenzi (TU Wien),
Talk Title: Talk1: Combining Active learning optimization and Temporal logic for parameter synthesis and falsification of Complex Systems Talk 2: System Design of Stochastic Models using Robustness of Temporal Properties
Series: Computer Science Colloquium jointly with CCI-MHI Cyber-Physical Systems Seminar
Abstract: We are pleased to announce two talks during this colloquium.
Talk 1: Combining Active learning optimization and Temporal logic for parameter synthesis and falsification of Complex Systems
In this talk, we discuss the combination of Active Learning Optimization and temporal logic to the falsification and parameter synthesis of complex dynamical systems. First, we introduce Gaussian Processes and an active learning approach aimed to falsify a black box model with time-dependent functional inputs. Second, we introduce a technique also base on Gaussian Processes, named Smoothed Model Checking, which is able to estimate the probability that a stochastic system satisfies a temporal logic formula. We leverage this estimation ability and an active learning approach to find regions of the parameter space where the model satisfies a temporal logic formula with probability greater (or less) than a given threshold.
Talk 2: System Design of Stochastic Models using Robustness of Temporal Properties
In the last years, researchers from the verification community have proposed several notions of robustness for temporal logic providing suitable definitions of distance between a trajectory of a (deterministic) dynamical system and the boundaries of the set of trajectories satisfying the property of interest. In this talk, we present an extension of this notion of robustness to stochastic systems, showing that this naturally leads to a distribution of robustness degrees. Then, we show how to exploit this notion to address the system design problem, where the goal is to optimise some control parameters of a stochastic model in order to maximise robustness of the desired specifications. The key idea is to use a learning algorithm to estimate the dependence of the average robustness of a qualitative formula over the model parameters. A powerful and provably convergent machine learning method, namely the Gaussian Process - Upper Confidence Bound (GP-UCB) algorithm is use to improve the parameter optimisation. Finally, we demonstrate the applicability of our method on a number of case studies and ongoing works.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in OHE 100D, seats will be first come first serve.
Biography: Talk 1: Simone Silvetti is a researcher and developer at the Numerical Methods Group of Esteco SpA, Italy. He received a Ph.D. in Computer Science from the University of Udine in 2018 and a MSc in Mathematics from the University of Rome in 2012. His research focuses on the application of machine learning to quantitative formal methods and optimization.
Talk 2: Since 2017, Laura Nenzi is a research assistant at the TU Wien. She received a Ph.D in Computer Science from IMT Lucca, in 2016. In December 2018, she will join the University of Trieste as Assistant Professor. Her research interests include: spatio-temporal logics, statistical verification routines for uncertain models and combination of formal methods with machine learning techniques.
Host: Jyotirmoy Deshmukh and Paul Bogdan
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CAIS Seminar: Dr. Pinar Keskinocak (Georgia Tech) - Quantitative Models for Decision-Support in Healthcare Applications
Wed, Oct 10, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Pinar Keskinocak, Georgia Institute of Technology
Talk Title: Quantitative Models for Decision-Support in Healthcare Applications
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: With the goal of improving patient outcomes, efficiency, and effectiveness, quantitative models are increasingly used for decision-support in healthcare. In this presentation Dr. Keskinocak will discuss a few applications from organ transplant, vaccination, screening, and workforce allocation decisions.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Pinar Keskinocak is the William W. George Chair and Professor in the School of Industrial and Systems Engineering, and co-founder and Director of the Center for Health and Humanitarian Systems at Georgia Tech. She also serves as the College of Engineering ADVANCE Professor. Her research focuses on the applications of quantitative methods to have a positive impact in society, particularly in healthcare and humanitarian systems. She has worked on projects with a variety of governmental and non-governmental organizations, and healthcare providers, including American Red Cross, CDC, Children's Healthcare of Atlanta, Emory Healthcare, and Task Force for Global Health.
Host: Dr. Milind Tambe and Dr. Sze-chuan Suen
Location: Mark Taper Hall Of Humanities (THH) - 301
Audiences: Everyone Is Invited
Contact: Computer Science Department
