Seminars


The department hosts a Research Seminar Series, which is open to undergraduate and graduate students; we strongly encourage you to attend the seminars because they are very helpful to identify research interests, and are a chance to meet faculty and other students.

Stay connected with the department, visit the following links:  Calendar,  Student Organizations, and Scholarships, Awards, and Opportunities to locate opportunities available to you as UTEP CS students.

 

Upcoming Seminars

Time to Gather Stones

Dr. Vladik Kreinovich
Friday, March 15th 10:00- 11:00 AM
Business Building Room 302

http://www.cs.utep.edu/vladik/cs5354.19/syllabus.html

 

TIME TO GATHER STONES. Many heuristic methods have been developed in intelligent computing. Researchers have proposed many new exciting ideas. Some of them work well, some don’t work so well. And promising techniques — that work well — often benefit from trial-and-error tuning. It is great to know and use all these techniques, but it is also time to analyze why some technique work well and some don’t. Following the Biblical analogy, we have gone through the time when we cast away stones in all directions, when we developed numerous seemingly unrelated ideas. It is now time to gather stones, time to try to find the common patterns behind the successful ideas. Hopefully, in the future, this analysis will help to replace the time-consuming trial-and-error optimization with more efficient techniques.

CASE STUDIES. In this class, we will mainly concentrate on three classes of empirically successful semi-heuristic methods that do not yet have a full theoretical explanation:

* fuzzy techniques, techniques for translating expert knowledge described in terms of imprecise (“fuzzy”) natural-language words like “small” into precise numerical strategies;

* neural networks (in particular, deep neural networks), techniques for learning a dependence from examples; and

* quantum computing, techniques that use quantum effects to make computations faster and more reliable.

 

 


Past Seminars

Toward fluent collaboration in human-robot teams

Dr. Tariq Iqbal, MIT
February 11, 4:30-5:30
Classroom Building room 305

Robots currently have the capacity to help people in several fields, including health care, assisted living, and manufacturing, where the robots must share physical space and actively interact with people in teams. The performance of these teams depends upon how fluently all team members can jointly perform their tasks. In order to successfully act within a group, a robot requires the ability to monitor other members’ actions, model interaction dynamics, anticipate future actions, and adapt its own plans accordingly. To achieve that, I develop human-team inspired algorithms for robots to fluently coordinate and collaborate with people in complex, real-world environments by modeling how people interact among themselves in teams and by utilizing that knowledge to inform robots’ actions.

In this talk, I will present algorithms to measure the degree of coordination in groups and approaches to extend these understandings by robots to enable fluent collaboration with people. I will first describe a non-linear method to measure group coordination, which takes multiple types of discrete, task-level events into consideration. Building on this method, I will present two anticipation algorithms to predict the timings of future actions in teams. Finally, I will describe a fast online activity segmentation algorithm which enables fluent human-robot collaboration.

Tariq Iqbal is a postdoctoral associate in the Interactive Robotics Group at MIT. He received his Ph.D. from the University of California San Diego, where he was a member of the Contextual Robotics Institute and the Healthcare Robotics Lab. His research focuses on developing algorithms for robots to solve problems in complex, real-world environments, which enable robots to perceive, anticipate, adapt, and fluently collaborate with people in teams.

 

Toward building an automated bioinformatician: Parameter advising for improved scientific discovery

 Dr. Dan DeBlasio, Carnegie-Mellon University
Thursday, Feb 14, 4:30-5:30 Classroom Building, Room C305

Modern scientific software has a large number of tunable parameters that need to be adjusted to ensure computational performance and accuracy of the results. When these parameter choices are made incorrectly we may overlook significant results or falsely report insignificant ones. Optimizing the parameter choices for one input may not provide an assignment that’s good for another, so this parameter optimization process typically needs to be repeated for each new piece of data. Standard machine learning methods for solving this problem need to repeatedly run the software which may not be suitable in practice. Because of the time consumption required to optimize parameters and the possible loss of accuracy that can result when chosen incorrectly, the default parameter vector that are provided by the tool developer is often used. These defaults are designed to work well on average, but most interesting cases are rarely “average”.

In this talk, I will describe my first steps in automatically learning the correct program configuration for biological applications using a framework we call “Parameter Advising”. To apply this framework to the problem of multiple sequence alignment we developed an accuracy estimator, called Facet, to help choose alignments since no ground truth is available in practice. When we use Facet for advising on the Opal aligner we boost accuracy by 14.6% on the hardest-to-align benchmarks. For the reference-based transcript assembly problem, when applying parameter advising to the Scallop assembler we see an increase in accuracy of 28.9%. The framework is general and can be extended to other problems in computational biology and beyond. I will discuss possible areas where parameter advising could be used to automatically learn to run complex analysis software

Biography

Dan DeBlasio is currently a Lane Fellow of the Computational Biology Department in the School of Computer Science at Carnegie Mellon University where he works in Carl Kingsford’s group.

He received his PhD in Computer Science from the University of Arizona in 2016 under John Kececioglu. He holds an MS and BS in Computer Science from the University of Central Florida

working with Shaojie Zhang. He recently published a book on his work titled “Parameter Advising for Multiple Sequence Alignment”. Dan also recently finished a two year appointment to

the Board of Directors of the International Society for Computational Biology and is an advisor to the ISCB Student Council where he has held several roles.

 

 

Relativistic Effects Can Be Used to Achieve a Universal Square-Root (Or Even Faster) Computation Speedup

Dr. Vladik Kreinovich
Friday, February 1, 10:00am – 11:00am
CCSB G.0208

In this talk, we show that special relativity phenomenon can be used to reduce computation time of any algorithm from T to square root of T. For this purpose, we keep computers where they are, but the whole civilization starts moving around the computer – at an increasing speed, reaching speeds close to the speed of light. A similar square-root speedup can be achieved if we place ourselves near a growing black hole. Combining the two schemes can lead to an even faster speedup: from time T to the 4-th order root of T.

 

Artificial Intelligence Approaches for Wickedly Hard National Security Problems

Dr. David Tauritz
Monday, February 4, 4:30-5:30
CLRB 305

Abstract:

Many national security problems are wickedly hard in that they map to computational problem classes which are intractable. This seminar aims to illuminate how artificial intelligence approaches can be created to address these problems and produce useful solutions. In particular, two promising approaches will be discussed, namely (I) computational game theory employing coevolutionary algorithms for identifying high-consequence adversarial strategies and corresponding defense strategies, and (II) hyper-heuristics employing evolutionary computation for the automated design of algorithms tailored for high-performance on targeted problem classes.

 

The first approach will be illustrated with the Coevolving Attacker and Defender Strategies for Large Infrastructure Networks (CEADS-LIN) project funded by Los Alamos National Laboratory (LANL) via the LANL/S&T Cyber Security Sciences Institute (CSSI) [https://web.mst.edu/~tauritzd/CSSI/]. This project focuses on coevolving attacker & defender strategies for enterprise computer networks. A proof of concept for operationalizing cyber security R&D from this project demonstrated in simulation that coevolution is capable of implementing a computational game theory solution for adversarial models of network security. Currently a high-fidelity emulation framework with intelligent attacker and defender agents is being developed with as end goal to provide a fully automated solution for identifying high-impact attacks and corresponding defenses.

 

The second approach will be illustrated with the Scalable Automated Tailoring of SAT Solvers project funded by Sandia National Laboratories with supplemental funding from the Computer Research Association’s Committee on the Status of Women in Computing Research (CRA-W), and with the Network Algorithm Generating Application (NAGA) project funded via CSSI. These projects show how hyper-heuristics can be employed to create algorithms targeting arbitrary but specific problem classes for repeated problem solving where high a priori computation costs can be amortized over many problem class instances.

 

Bio:

Daniel Tauritz is an Associate Professor & Associate Chair in the Department of Computer Science at the Missouri University of Science and Technology (S&T), a University Contract Scientist for Sandia National Laboratories, a University Collaboration Scientist at Los Alamos National Laboratory (LANL), the founding director of S&T’s Natural Computation Laboratory, and founding academic director of the LANL/S&T Cyber Security Sciences Institute. He received his Ph.D. in 2002 from Leiden University for Adaptive Information Filtering employing a novel type of evolutionary algorithm. His research interests focus on artificial intelligence approaches to complex real-world problem solving with an emphasis on national security problems in areas such as cyber security, cyber physical systems, critical infrastructure protection, and program understanding. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.