Host:

Dr. Gabriel Terejanu (Dr. T)

Time:

F 2:20PM-3:10PM

Office:

SWGN 3A50

Location:

SWGN 2A18 2A31

Email:

terejanu@cec.sc.edu

Aug 21

(1pt)

Speaker: Gabriel Terejanu

Affiliation: CSE, USC

Location: SWGN 2A18

Time: 2:20 - 3:10 PM

Title: Why all the problems look the same to me?

Aug 26

(Wed - Bonus 1pt)

Speaker: Kevin Truman (CEC Dean Candidate)

Affiliation: CME, Dean School of Computing and Engineering, Vice Provost, University of Missouri - Kansas City

Location: Russell House Theater

Time: 2:00 – 3:15 PM

Aug 28

(1pt)

Panel: John Rose, Jianjun Hu, Gabriel Terejanu

Affiliation: CSE, USC

Location: SWGN 2A18

Time: 2:20 - 3:10 PM

Title: Information session on Machine Learning

Abstract: Are you interested in pursuing research or preparing for job opportunities related to Machine Learning and Data Mining? If so please join us for a panel discussion where John Rose, Jianjun Hu, and Gabriel Terejanu will try to address various questions such as: Where do I start? What skills do I need? What classes should I take? What research opportunities are available? … or any other question of interest to you related to this topic.

Sep 04

(2pt)

Speaker: Xiaoyan (Iris) Lin

Affiliation: Statistics, USC

Location: SWGN 2A18

Time: 2:20 - 3:10 PM

Title: A Semiparametric Probit Model for Case 2 Interval-censored Failure Time Data

Abstract: Interval-censored data occur naturally in many fields and the main feature is that the failure time of interest is not observed exactly, but is known to fall within some interval.  In this paper, we propose a semiparametric probit model for analyzing case 2 interval-censored data as an alternative to existing semiparametric models in the literature. Specifically, we propose to approximate the unknown nonparametric nondecreasing function in the probit model with a linear combination of monotone splines, leading to only a finite number of  parameters to estimate. Both maximum likelihood and Bayesian estimation methods are proposed. For each method, regression parameters and the baseline survival function are estimated jointly. The proposed methods make no assumptions about the observation process and can be applicable to any interval-censored data with easy implementation. The methods are evaluated by simulation studies and are illustrated by two real-life interval-censored data applications.

Sep 11

(1pt)

Speaker: Hossein Haj-Hariri (CEC Dean Candidate)

Affiliation: Chair of Mechanical & Aerospace Engineering, University of Virginia

Location: Russell House Theater

Time: 2:00 – 3:15 PM

Sep 18

(1pt)

Speaker: Melur “Ram” Ramasubramaniani (CEC Dean Candidate)

Affiliation: Chair Mechanical Engineering, Clemson University

Location: Russell House Theater

Time: 2:00 – 3:15 PM

Sep 24

(Thu - Bonus 1pt)

Speaker: Joseph Johnson

Affiliation: Physics and Astronomy, USC

Location: Jones Physical Sciences Center, Room 409

Time: 4:15 PM

Title: A Standardization for Numerical Information & The Identification of Dominant Structures in the Numerical Universe

Sep 25

(Fri - Bonus 1pt)

Speaker: Michael Huhns

Affiliation: Computer Science and Engineering, USC

Location: SWGN 3A75

Time: 11:30 AM - 12:30 PM

Title: History and Prospects for the Center for Computational Robotics

Abstract: The Center for Computational Robotics is the outgrowth of two previous research Centers, all with the goal of advancing the capabilities of physical and information systems. The activites of the previous centers can provide a context and perspectives that set the directions for the new Center. This seminar will describe these directions and outline the disciplines needed: from mechanics to ethics.

Sep 25

(1pt)

Speaker: Eric Walker

Affiliation: Chemical Engineering, USC

Location: SWGN 2A31

Time: 2:20 - 3:10 PM

Title: Uncertainty Quantification in Computational Catalysis

Abstract: Catalysts are responsible for 85-90% of products in chemical industries1 and they are expected to play a central role in alternative energy technology.  This work aims to augment the field of computational catalysis which models catalytic reactions using first principles with uncertainty quantification (UQ).  An efficient tool to describe the energetics and structure of atomistic systems is density functional theory (DFT).  DFT proves useful for understanding how catalysts work if DFT results are combined with microkinetic modeling which serves to scale up the atomistic systems to chemical reactors.  However, DFT is inexact in nature due to approximations necessary for computational tractability.  These approximations in DFT cause uncertainty in microkinetic modeling results used for comparison with experiments.  Therefore, reliable model results obtained from DFT should include a quantification of uncertainty.  This work includes a forward problem in which uncertainties in DFT are propagated forward to model results such as turnover frequency (TOF), apparent activation barrier, and reaction orders.  This work also includes inverse problems leading to a model selection.  UQ is performed on a case study using DFT calculations of the water-gas shift reaction (WGS: CO+H_2 O⇌CO_2+H_2).  Uncertainty in model results is accounted and reduced as much as possible by introducing correlations in DFT energies using four separate versions of DFT and a factor analysis to generate a covariance matrix.  Although large uncertainty is encountered, conclusions may be drawn about the atomistic workings of the water-gas shift reaction.

Oct 02

GAMECOCK SYMPOSIUM

Oct 09

CANCELED DUE TO BAD WEATHER

Oct 16

(2pt)

Speaker: Dylan Shell

Affiliation: CSE, Texas A&M University

Location: Faculty Lounge

Time: 2:20 - 3:10 PM

Title:

Robots at the End of their Tether

Abstract:

There are lots of practical reasons why one might attach a tether to a mobile robot (providing power from off-board sources, high-speed communication to a base-station, etc.) but, since the tether constrains the motion of the robot, doing so makes the problem of moving the robot trickier than it would be otherwise. This talk will explore the motion planning problem for a planar robot connected via a cable to a fixed point in R^2. I'll describe how to visualize the configuration space manifold for such a robot, showing that it has regularity which can be used to produce a neat representation. This representation describes the manifold via (1) a discrete structure that characterizes the

cable's position (2) an element within a single continuous cell. Further, when the tether has a constraint on its curvature, I'll show how Dubins’s theory of curves can be combined with work on planning with topological constraints to concisely represent the configuration space manifold, resulting in a data-structure that facilitates search for optimal paths.

Bio:

Dylan Shell is a computer scientist with broad interests. He's an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, where he runs a laboratory focused on robotics and artificial intelligence. His research group aims to synthesize and analyze complex, intelligent behavior in distributed systems that exploit their physical embedding to interact with the physical world in a variety of ways.  He has published papers on multi-robot task allocation, robotics for emergency scenarios, biologically inspired multiple robot systems, multi-robot routing, estimation of group-level swarm properties, minimalist manipulation, rigid-body simulation and contact models, human-robot interaction, and robotic theatre. His work has been funded by DARPA and the NSF; and he has been the recipient of the Montague Teaching award, the George Bekey Service award, and the NSF Career.

Oct 23

NO CLASS (FALL BREAK)

Oct 30

(2pt)

Speaker: Brian Williams

Affiliation: Los Alamos National Laboratory

Location: SWGN 2A31

Time: 2:20 - 3:10 PM

Title: Computational Enhancements to Bayesian Design of Experiments Using Gaussian Processes

Abstract: Bayesian design of experiments is a methodology for incorporating prior information into the design phase of an experiment. Unfortunately, the typical Bayesian approach to designing experiments is both numerically and analytically intractable without additional assumptions or approximations. In this paper we discuss how Gaussian processes can be used to help alleviate the numerical issues associated with Bayesian design of experiments. We provide examples drawn from accelerated life testing and probabilistic calibration of

model parameters and compare our results with large sample methods.

Bio: Brian Williams has been a Technical Staff Member at the Los Alamos National Laboratory (LANL) since 2003. He is a member of the Statistical Sciences Group. Previously he was associate statistician at the RAND Corporation (2000-2003). He is contributing to the development and implementation of statistical methods for the design and analysis of computer experiments, focusing on the technical areas of sequential optimization, global sensitivity analysis, calibration,predictive maturity assessment and rare event inference for computer models. He is currently working on the development of technology for efficient Bayesian experimental design and implementation of uncertainty quantification methods supporting applications in nondestructive assay. Dr. Williams coauthored a 2003 book entitled The Design and Analysis of Computer Experiments with Thomas J. Santner and William I. Notz of The Ohio State University. In 2015, he was elected Fellow of the American Statistical Association for fundamental methodological contributions to the statistical design of experiments involving computer simulators and the analysis of data from such experiments including uncertainty quantification, for excellence in leadership of uncertainty quantification in critical federal programs, for excellence in collaborative research, and for service to the American Statistical Association. His research interests include experimental design, computer experiments, Bayesian inference, spatial statistics, statistical computing, and uncertainty quantification. He holds the Ph.D. in statistics from The Ohio State University.

Nov 06

NO CLASS (ATTEND CONFERENCE - ACM SIGSPATIAL)

Nov 13

(1pt)

Speaker: Gregory Gay

Affiliation: CSE, USC

Location: SWGN 2A31

Time: 2:20 - 3:10 PM

Abstract: Software development for critical software systems is still a costly, time consuming, and error prone process. Software testing is our key method of finding faults in systems and building evidence that a system if fit to be released. However, a major limitation is that developers lack proper guidance in two key areas: (a) whether or not the tests they have produced are effective and (b) when they can stop writing additional tests. Various test adequacy metrics have been proposed to alleviate these issues, commonly by measuring the coverage of structural elements of the program under test—such as individual statements, branches of control flow, or Boolean conditions. When coverage has been satisfied, the product is considered adequately tested.

The idea of measuring coverage of the program is simple, but compelling: unless code is executed, faults will never be found. Unfortunately, although unexecuted code will never reveal faults, the converse is not true - executing code does not ensure that faults are revealed. How code is executed is more important than whether it has been executed. In this talk, we will discuss ongoing efforts to define new test adequacy metrics, and how those metrics can be used as the basis for powerful algorithms that automatically generate tests cases for a system.

Nov 20

NO CLASS (seminar talk canceled)

Nov 27

NO CLASS (THANKSGIVING RECESS)

Dec 04

(1pt)

Speaker: Jenay Beer

Affiliation: CSE, USC

Location: SWGN 2A31

Time: 2:20 - 3:10 PM

Title: Assistive Robotics and Technology

Abstract: Maintaining one’s independence is a primary goal of older adults and a key component to successful aging and aging-in-place. Technology has the potential to help older adults maintain their independence.  In this presentation, Dr. Beer will discuss current and future technology aids, such as robotics, home sensors, and smart homes.  For assistive technology to be successful, it is important that the older adult user finds the technology to be simple, user friendly, and useful – a field of study called user-centered design!  We will discuss what makes technology user-friendly, how technology might be integrated into the home or healthcare setting, and where the field is headed.