We are delighted to welcome Laura Grabowski, Emmett Tomai and Yang Liu

to join our faculty as assistant professors in Fall 2009. No department can thrive without continuing to add new faculty, and the addition of these three outstanding scholars will both deepen and broa-den the department’s strengths in teaching and research.

Laura is our alumnus and expects her Ph.D. in Computer Science from Michigan State University this May. As a graduate researcher in the Digital Evolution Laboratory and Evolving Intelligence Project at MSU, Laura is investigating the evolution of simple computational intelligence, such as simple navigation behaviors and memory use.  Her research affiliations at MSU have also included the Embodied Intelligence Laboratory, MSU's Integrated Graduate Education and Research Training (IGERT) program in Sequential Decision-Making in Cognitive Science, and the Cognitive Science Specialization.  She is a Michigan State University Fellow, and was an IGERT Associate Fellow in 2006-2007.  Her work has been presented at the Genetic and Evolutionary Computation Conference (GECCO), the International Conference on Development and Learning (ICDL), International Conference on Autonomic Computing (ICAC), and Evolution 2008.  She won the award for Best Paper at the Michigan Celebration of Women in Computing 2007.

Emmett is receiving his Ph.D. in Computer Sci-ence from Northwestern University this coming May. His research interests include  practical natural language understanding, among others.  He received B.S. degrees in both Electrical and Computer Engineering from Northwestern in 1997 where he also worked as a network engineer for campus-wide computing and a web developer for the Optimization Technology Center.  After graduation, he worked for Braun Consulting in Chicago, IL including projects for MasterCard, Intl. and Motorola, Inc. before leaving in 1999 to  start his own consulting business.  As an independent consultant he did several projects for Chicago-area businesses and Northwestern University research groups.  He has been in graduate school at Northwestern since 2002.

Yang  is anticipating his Ph.D. in Computer Science this coming May from Texas A&M University. His research has been focused on designing feasible algorithms for problems with small parameters, algorithm engineering, and query optimization problems in Database Systems. He has published in journals such as the Journal of ACM, Journal of Computer and System Sciences and Algorithmica, and in conferences such as STOC, WADS, and IWPEC. Yang Liu received his B.S in Electrical Engineering in 1997 from Zhejiang University in China, and his M.S. in Electrical Engineering in 2005 from Rose-Hulman Institute of Technology. He is a Graduate Teaching Academy Fellow at Texas A&M University.

Dr. Bin Fu Won the Prestigious NSF CARRER Award. Congratulations!

Dr. Bin Fu’s winning proposal is “CAREER: Theories and Applications of Efficient Separator and Randomization”, 2009 April 1 to 2014 March 31, with $409157.  

Dr. Chen and Dr. Fu at the luncheon	Dr. Wendy Lawrence-Fowler and Dr. Bin Fu

NSF states that “The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation's most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations. Such activities should build a firm foundation for a lifetime of leadership in integrating education and research.”

The goal of Dr. Fu's project is to study efficient separator and randomization theories and technologies with applications to algorithm design. Separator and randomization are basic tools of designing algorithms for many applications problems. A separator is often used to decompose a difficult computational problem into smaller and easier ones, and then to solve the problem with a divide and conquer strategy. Randomization is widely used to speed up computation by sampling a small number of cases from a large number of possibilities. In order to attack challenging computational problems, such as protein 3D structure prediction, more efficient decomposition methods are expected to be developed. His project unifies the two approaches of separators and randomization because of their close connection. This unified approach helps the development of sublinear time algorithms, which are usually based on random sampling.  

The decomposition methods are studied from low dimensional geometric spaces to high dimensional spaces and to general graphs and to algebraic computation. Proving the existence of a separator will get new insights into combinatorial nature of a given problem. Finding a separator efficiently is also an interesting algorithmic problem itself and often uses randomized methods. On the other hand, the research on complexity theory related to randomness is a part of his project, which includes the research about some lower bounds for randomization methods and the limitation of derandomization. A potential application of separator theory is protein folding prediction. A more efficient decomposition method will bring faster algorithm for this significant problem in science. As his project combines decomposition with randomization, the results of the research make new contributions to the core area of computation theory and discover applications in the field of bioinformatics. Education is an integral part of his project. Minority graduate students and female graduate students are involved in the protein 3D structure related algorithm design and web-server implementation. A randomized computation course for both undergraduate and graduate students is developed.  

Dr. Bin Fu received his Ph.D. from Yale University in 1998. He also studied at Princeton University. He joined the UTPA faculty in Fall 2006.  Prior to that, he completed three and a half years of teaching as an Assistant Professor at the University of New Orleans and five years of teaching as a Lecturer at the Beijing Computer Institute. 

Dr. Bin Fu is a scholar in several areas of computer science including bioinformatics (protein folding), algorithms (width-bounded separator theory, Rocchio's relevance feedback algorithm analysis, and Abelian group factorization), complexity theory, and molecular computing. He has made significant contributions to the above areas of computer science. 

Dr. Fu has pioneered and established the width-bound geometric separator theory and its applications in a variety of areas including approximation algorithms, bioinformatics, etc. This theory is profoundly different from the existing well-known separator theories and is more precise and robust in dealing a large array of algorithm design problems.  

For the first time since early 1970s, he has, along with Dr. Zhixiang Chen, given the first rigorous analysis about Rocchio’s similarity-based relevance feed back algorithm, the most popular, fundamental query reformation algorithms in information retrieval. Thus, the long standing challenging problem of complexity analysis of this fundamental algorithm is settled.  

His research group’s protein search software outperforms the best software in the area. The service provided by their software is open for public access via his UTPA web site.  

His research with his collaborators on randomized and approximation algorithm design and analysis has led to a number of significant discoveries, including the sublinear time randomized algorithm to find separators in high dimensional space and inapproximability results for several bioinformatics problems such exemplar break point problem.  

His major scientific contributions before joining UTPA include: (1) The separation of EXP from P_{n^{1-\epsilon}(SPARE). This is a breakthrough in the area of computational complexity. He used Kolmogorov complexity to masterfully carry out the need complex analysis. (2) The settlement of a challenging open problem in structural complexity theory: NC^{PL}_{1} = PL and NC^{PP}_{1} = PP. This result leads to improvement on a series of results about probabilistic complexity classes. (3) The separation of NE from P_{n^{o(1)}-T}(NP). (4) He has also made contributions to DNA computing in his Ph.D. dissertation at Yale University. 

Congratulations to Jorge S. Hernandez and Mr. David Egle

The following have been selected to be honored for the Engineering Week representing the Department of Computer Science.

Mr. Jorge S. Hernandez has been selected as the most outstanding student by the department faculty.

Mr. David Egle has been selected by the ACM Student selection as Most Outstanding Faculty.

Again, congratulations from all of us in the Department of Computer Science.

WALMART Leadership in Technology and Innovation Scholarship

Who we are

The Department of Computer Science is a part of the School of Engineering and Computer Science at the University of Texas - Pan American. We are located in Edinburg, Texas, close to South Padre Island and Mexico. The University of Texas - Pan American is a component of the University of Texas System with an enrollment of around 18,000 students.

The Department of Computer Science offers an CAC/ABET accredited Bachelor of Science in Computer Science (BSCS) degree with a major in computer science. The Department of Computer Science offers a joint Computer Engineering Degree with the Electrical Engineering Department. A Bachelor of Science (BS) degree with a major in Computer Science with a required minor field is also offered. The BS degree is not accredited by CAC/ABET.  The Department also offers an 18-hour minor in computer science. 

The department offers Master's degrees in Computer Science and Information Technology.

Please come by the department offices on the third floor of the Engineering building and discuss the opportunity of studying Computer Science with our department.