UT Dallas CS Ph.D. Grads Find Work in Prestigious Universities, High-Tech Companies, Government, and Top-Tier Research Facilities

When UT Dallas was established in 1961, the institutional emphasis was solely on research. In fact, when the UT Dallas Computer Science program first started in the 70s, it granted only Ph.D. degrees. The UT Dallas Computer Science Department has graduated a total of 333 Ph.D. students, including 37 women, since 2001. Most recently, from the fall of 2018 to summer of 2019, the UT Dallas Computer Science Department has graduated 23 Ph.D. students, six of whom have been women. These graduates have gone on to find jobs in prestigious universities, top-tier research facilities, government, and high-tech companies.

UT Dallas Professors Drs. Latifur Khan, Xiaohu Guo, Ryan McMahan, Cong Liu, Ding-Zhu Du, Weili Wu, Benjamin Raichel, Ravi Prakash, Dan Moldovan, Andi Marcus, Kevin Hamlen, Gopal Gupta, Zhiqiang Lin, Balakrishnan Prabhakaran, Haim Schweitzer, Jorge Cobb, and Rym Zalila-Wenkstern served as faculty supervisors to the 23 UT Dallas Computer Science (CS), Software Engineering (SE), Computer Engineering (CE), and Telecommunication Engineering (TE) doctoral graduates of the 2018-19 academic year. During their time at UT Dallas, Ph.D. students and their faculty advisors work to conduct innovative research and publish their research papers in highly prestigious journals and conferences.

Through the years, graduates of the UT Dallas Computer Science Doctoral Program have accepted jobs at top companies (Adobe, Amazon, Apple, Cisco, Cloudera, Expedia, Fujitsu Labs of America, Facebook, Google, Hewlett Packard, Intel, IBM T.J. Watson, Matterport Microsoft, NetApp, Procter & Gamble, Salesforce, Bank of America, Blue Cross Blue Shield, Samsung, etc.), research facilities (IBM T.J. Watson Research Center, Samsung Research, Walmart Labs), government (Department of Defense, NSA, FBI, etc.), and tenure-track positions at Universities ( University of Texas at San Antonio, UT Dallas, Oklahoma State University, Wayne State University Colorado State University, Clemson, University of New Mexico, University of Delaware, etc.).

The CS Department is committed to producing highly accomplished Ph.D. students who will make important contributions to the field of computer science and software engineering. Towards the end of every semester, the CS Department holds a Ph.D. recruiting event where local area students are invited, and information regarding why one should pursue a Ph.D. degree as well as information about the CS/SE Ph.D. program are shared.

If you would like to learn more about obtaining a Doctor of Philosophy in Computer Science, Software Engineering, Computer Engineering, or the Telecommunications Engineering through the UT Dallas Computer Science Department, please view the following links: CS Ph.D. Program, SE Ph.D. Program, CE Ph.D. Program, and TE Ph.D. Program.

The following constitutes a summary of the recent Ph.D.’s from the UT Dallas Computer Science Doctoral Program:

Husheng Zhou obtained his Ph.D. under the guidance of Dr. Cong Liu during the fall of 2018. Dr. Zhou’s dissertation was titled “Predictable GPGPU Computing In DNN-Driven Autonomous Systems.” In this research, Dr. Zhou proposed GPES, a runtime system that allows GPU executions interruptible and preemptable in a multi-tasking environment. Dr. Zhou investigated the problem of mapping multiple applications implemented using kernel graphs in a heterogeneous system and presented a theoretical framework that formulates this problem as an integer program and a set of practically efficient mapping algorithms. Dr. Zhou’s research interests lie in general-purpose computing on graphics processing units (GPGPU), High-performance computing (HPC), Real-Time and Embedded Systems, and Human-Computer Interaction. While studying at UT Dallas, Dr. Zhou worked as a research assistant in Dr. Cong Liu’s research lab. Zhou also spent a summer interning at the IBM Thomas J. Watson Research Center. Dr. Zhou helped patent Adaptive Parallelism of Task Execution on Machines with Accelerators (US Patent 15/293248, 2016). Currently, Dr. Zhou is working at Bitfusion, a startup company on at-scale heterogeneous computing.
Jing Yuan received her Ph.D. during the Fall of 2018 under the supervision of Drs. Weili Wu and Ding Z. Du. Dr. Yuan’s dissertation was titled “Near Optimal Social Promotion in Online Social Networks.” In her research, Dr. Yuan studied closely related problems in online social networks concerning the issue of active friending, which can be considered as a service provided by the social platforms to stimulate user engagement. Dr. Yuan’s research proves that the problem is NP-hard and propose a discrete semi-differential based algorithm with guaranteed approximation ratio. Dr. Yuan also studied the adaptive version of the discount allocation problem, whose goal is to find a limited set of highly influential seed users to assign proper discounts, such that the number of users who finally adopt the product is maximized. Dr. Yuan proposed a series of adaptive policies with a bounded approximation ratio for the problem. Currently, Dr. Yuan is taking time off to take care of her newborn twins.
Myunghoon Suk obtained his Ph.D. in Computer Engineering during the fall of 2018 while under the supervision of Dr. B. Prabhakaran. His research interests include Human-Computer Interaction, Computer Vision, Machine learning, and the applications of these in the domain of Multimedia. In his dissertation titled, “Study of Real-Time Facial Expression Recognition On Noisy Images and Videos,” Dr. Suk address several problems for real-time facial expression and emotion recognition on smartphone low-power smartphones. While working as a research assistant in Dr. Prabhakaran’s Multimedia and Networking Lab, he researched video-based human motion recognition and facial expression recognition using the Knowledge-Based Hybrid (KBH) method. He also worked as an administrator and content editor working on the ACM SIGMM website. Dr. Suk is currently working as a scientist at Topaz Labs.
Fei Tang obtained his Ph.D. under the guidance of Dr. Ryan McMahan during the Fall of 2018. Dr. Tang’s dissertation was titled “Evaluating Tactile Fidelity of Resolution, Amplitude, And Algorithms For Grid-Based Tactile Sleeve Display.” In this research, several arm-based tactile sleeve displays were developed to investigate how specific characteristics of vibrotactile display design, such as spacing, resolution, amplitude, and tactile-rendering algorithm, can affect the fidelity of a tactile display device and the experiences of its users. Based on the results of Dr. Tang’s research, several design guidelines were proposed to form the best practices for grid-based vibrotactile display designs in a virtual reality (VR) system. While at UT Dallas, Dr. Tang worked with Dr. McMahan in The Future Immersive Virtual Environments (FIVE) Lab performing research on state-of-the-art virtual reality (VR) systems and 3D user interfaces (3DUIs). Dr. Tang’s research interests include Virtual Reality, HCI, and Tactile Display. Dr. Tang’s research with Dr. McMahan has been published in numerous conference publications including the International Conference on Virtual, Augmented and Mixed Reality (VAMR’17), IEEE Virtual Reality Conference (VR’15), Haptic Audio-Visual Environments and Games (HAVE’14), and more. Dr. Tang is currently working as a Software Engineer at Neuro Rehab VR in Fort Worth, Texas. Dr. Tang’s work at Neuro Rehab VR is focused on creating virtual reality devices and applications for medical therapy and neurological rehabilitation.
Saifeng Ni obtained her Ph.D. during the Fall of 2018 while working under the supervision of Dr. Xiaohu Guo in the Computer Graphics and Animation Lab. In her dissertation titled, “Variational Volumetric Meshing,” Dr. Ni discussed variational-based methods to help tackle mesh generation problems, i.e., within an energy-optimization framework. An energy that inhibits small heights is proposed to suppress almost all badly-shaped elements through tetrahedral meshing. By iteratively optimizing vertex positions and mesh connectivity, slivers are harshly suppressed even in anisotropic tetrahedral meshing. In addition, a particle-based field alignment framework is introduced. Notably, a Gaussian Hole Kernel is constructed and associated with each particle to constrain the formation of the desired one-ring structure aligned with the frame field. The minimization of the sum of Gaussian hole kernels induces an inter-particle potential energy whose minimization encourages particles to have the desired layout. A cubic one-ring structure leads to high-quality, hexahedral-dominant meshing. The one-ring structures of the BCC and FCC lattice lead to high-quality, field-aligned tetrahedral meshing. This is the first time both Riemannian distance alignment and direction alignment problems have been considered in tetrahedral meshing. In addition, field-aligned, tetrahedral meshing better preserves the rotation geometry and also creates better anisotropic meshes. Dr. Ni has had her research published in various conference and journal publications including the Eurographics Symposium on Geometry Processing (SGP’18), International Conference on Geometric Modeling and Processing 2017 (GMP’17), 7th International ICST Conference, (WICON’13), IEEE Vehicular Technology Conference (VTC’11), and more. Over the summer, Dr. Ni took part in an internship at Samsung Research America (SRA) in Dallas, Texas, where she worked on 3D Face Reconstruction. During one of their research competitions, Dr. Ni received best poster award (3rd place). Dr. Saifeng Ni is currently working at Samsung Research America as a Senior Research Engineer.
Kanchan Anil Bahirat obtained her Ph.D. in Computer Science (Interactive Computing) under the supervision of Dr. Balakrishnan Prabhakaran during the fall of 2018. In her dissertation titled “On 3D Content Manipulation: Simplification, Modification and Authentication,” Dr. Bahirat presents a series of novel approaches for 3D content manipulation mainly focusing on making it effective across different platforms and reliable for various sensitive applications. While obtaining her Ph.D., Dr. Bahirat worked as a research assistant in Dr. Prabhakaran’s Multimedia Systems Lab where she worked on the 3D Tele-Immersion (3DTI) project, which uses RGB-D data from multiple Microsoft Kinects to generate a 3D model that can interact with remote participants or objects in the virtual environment. The project involved various tasks such as the real-time mesh generation, mesh simplification, depth segmentation, skeleton identification, intrinsic and extrinsic camera calibration, merging multiple meshes, texture blending and real-time transmission of 3D data. Her research interests include Computer Vision and Graphics, Virtual, and Augmented Reality. Digital Image Processing, and machine learning with a special focus on 3D Tele-immersion, Virtual Reality for Healthcare domain applications, virtual rehabilitation, training, and gaming. Dr. Bahirat currently works as a Computer Vision Researcher at Vicarious AI in Union City, California.
Justin Sahs received his Ph.D. during the fall of 2018 under the supervision of Drs. Bhavani Thuraisingham and Latifur Khan. In his dissertation titled “Bayesian Nonparametric Probabilistic Methods In Machine Learning,” he focused on furthering the developing the field of Bayesian Nonparametrics. Initially, Dr. Sahs introduced a novel nonparametric model that takes advantage of a representation theorem about arrays whose column and row order is unimportant. Subsequently, he developed an inference algorithm for this model and evaluated it experimentally. Later, Dr. Sahs considered the classification of streaming data whose distribution evolves over time. Dr. Sahs then introduced a novel nonparametric model that finds and exploits a dynamic hierarchical structure underlying the data. Dr. Sahs presented an algorithm for inference in this model and showed experimental results before extending the streaming model to handle the emergence of novel and recurrent classes and evaluate the extended model experimentally. Dr. Sahs research interests lie in Big Data Analytics for Security. Currently, Dr. Sahs is working on a postdoctorate at Rice University in Houston, Texas.
Chao Wang received his Ph.D. in Computer Graphics and Vision during the spring of 2019 while under the supervision of Dr. Xiaohu Guo. In his dissertation titled “Optimization of Pose, Texture and Geometry In 3d Reconstruction with Consumer Depth Cameras,” Dr. Wang focused on improving the robustness of camera tracking in online RGB-D reconstruction process, as well as optimizing camera pose, face texture and geometry quality of 3D models in the offline RGB-D reconstruction with consumer depth cameras. Dr. Wang’s research interests include Computer Graphics and Vision, specifically on 3D model processing, indoor environment, scanning, and 3D reconstruction. Currently, Dr. Wang is working as a computer vision Engineer at Matterport in Sunnyvale, California where he works on 3D reconstruction of indoor and outdoor scenes, image processing and understanding, 3D Scene understanding with deep learning, and 3D Model processing, which includes mesh creation, denoising, decimation, rendering, texture mapping, etc.
Wenhao Wang obtained his Ph.D. under the supervision of Dr. Kevin Hamlen during the spring of 2019. In his dissertation titled “Source-Free, Component-Driven Software Security Hardening,” Dr. Wang proposed the ﬁrst Control Flow Integrity system to successfully harden multiple, large (millions of lines) binary Windows COTS software without sources. It implements a prototype for Microsoft COM (largest production component-based architecture in the world) with low overhead. His dissertation was supported in part by the Office of Naval Research (ONR), the Air Force Office of Scientiﬁc Research (AFOSR) under Young Investigator Program (YIP) award, the National Security Agency (NSA), the National Science Foundation (NSF) under CAREER award, and NSF Industry-University Collaborative Research Center (IUCRC) awards from Raytheon Company and Lockheed-Martin. Dr. Wang’s research interests lie in language-based security, software security, web security, and mobile security. While at UT Dallas, he worked as a research assistant to Dr. Kevin Hamlen and spent time as a research intern at Visa. Currently, Dr. Wang is working as a Software Engineer at Google in Mountain View, California.
Krishna Kadiyala obtained her Ph.D. in Telecommunication engineering during the spring of 2019 while under the supervision of Dr. Jorge Cobb. Dr. Kadiyala’s research interests include Computer Networks, Wireless Sensor Networks (WSNs), Software Defined Networks (SDN), and computer architecture. In her dissertation titled “Applications of Software Defined Networking In A Service Provider Environment,” Dr. Kadiyala explores, identifies, and implements use case scenarios of hybrid networks, in which, a few SDN devices co-exist within the traditional network architecture. During her time at UT Dallas, Dr. Kadiyala received two Best Paper Awards, one from the 2018 Netsoft Performance Issues in Virtualized Environments and Software Defined Networking Conference and the other from the 2017 IEEE Network Functions Virtualization and Software Defined Networks (NFV-SDN) Conference. She is currently employed as an assistant professor at Rollins College in Orlando, Florida.
Behnam Torabi obtained his Ph.D. during the spring of 2019 under the supervision of Dr. Rym Zalila-Wenkstern. While at UT Dallas, Dr. Torabi worked with Dr. Rym Zalila-Wenkstern as a research assistant in the UT Dallas Multi-Agent & Visualization Systems (MAVS) Lab researching Intelligent Transportation Systems. In his dissertation titled”DALI: A Collaborative, Agent-Based Traffic Signal Timing System,” Dr. Torabi presents DALI (Distributed, Agent-Based Traffic Lights), a smart collaborative traffic signal timing system. With DALI, intersection controller agents communicate with each other through direct links and do not have a supervising unit to oversee the coordination. Existing intersection controllers are augmented with intelligent agents which become the “brains” of the controllers. The agents analyze the traffic data, communicate with each other directly, and work together to execute a timing strategy that reduces congestion. The deployment of DALI on Waterview Parkway in the City of Richardson shows that delay was reduced by 40% on average. For more details, click here. Dr. Torabi was a finalist in the innovators’ category of the Tech Titans 2019 Awards, which is a group that recognizes the elite in North Texas technology, specifically individuals currently transforming the high-tech industry. Dr. Torabi also received the Smart 50 Award 2019 in the mobility category; The award recognizes the 50 most influential smart-city projects in the world, Smart Cities Connect Conference, Denver, CO, April 2019. Dr. Torabi has had various paper publications in several conferences including the flagship IEEE Intelligent Transportation Systems and JAAMAS (Journal of Agents and Multi-Agent Systems), the most prestigious journal in the Multi-Agent Systems field. The full list of publications is available. Dr. Torabi has two patents filed one for “Collaborative, Intelligent Traffic Signal Timing System” and the other for “Collaborative, Distributed Agent-Based Traffic Control System and Its Method of Use.” Dr. Torabi is currently employed as a Software engineering consultant. He still presently working at the MAVs Lab where is working on the commercialization of the technology developed as part of his Ph.D.
Afshin Taghavi Nasrabadi obtained his Ph.D. while under the supervision of Dr. Ravi Prakash during the Spring of 2019. While studying for his Ph.D. at UT Dallas, Dr. Nasrabadi researched multimedia, video encoding, video streaming, and computer networks. In his dissertation titled “Improving Quality of Experience For HTTP Adaptive Video Streaming: From Legacy To 360◦ Videos,” Dr. Nasrabadi proposed a hybrid adaptation method called LAAVS, which employs both Scalable Video Coding (SVC) and regular encoded video to improve user quality of experience (QoE) while mitigating the bandwidth and HTTP signaling overheads of SVC. Experimental results using real-world bandwidth traces show that LAAVS can perform better than state-of-the-art HTTP Adaptive Streaming (HAS) solutions. LAAVS uses enhancement layers occasionally to reduce rebuffering while streaming high-quality video. While working towards his Ph.D., Nasrabadi won the Louis Beecherl, Jr. Graduate Fellowship for the 2018-2019 academic year, and received Honorable mention for Best Poster at IEEE VR 2017. Dr. Nasrabadi currently Works as a Media Processing Engineer at Apple in the San Francisco Bay Area.
Swair Rajesh Shah obtained his Ph.D. with a focus on Artificial Intelligence and Machine learning during the spring of 2019 while under the guidance of Dr. Haim Schweitzer. In his dissertation titled “Feature Selection and Extraction – Algorithms and Applications,” Dr. Shah addressed a hybrid problem that combines feature selection and extraction. Feature selection is an essential process in statistics and machine learning while feature extraction is another dimensionality reduction process that finds a small set of features to approximate a given data set. In his dissertation, Dr. Shah proposes an algorithm to solve the hybrid problem optimally by using heuristic search methods inspired by the classic A* search algorithm. Currently, Dr. Shah is working as an Algorithm Engineer at Mode.ai in Palo Alto, California.
Chenglin Fan received his Ph.D. in Computer Science during the spring of 2019 while under the supervision of Dr. Benjamin Raichel. Dr. Fan’s research interests lie broadly in Algorithms and Data Science, with a focus on geometry. His thesis titled “Metric Violation and Similarity” combined his two principal publications during his Ph.D. studies. The first publication, in the Symposium on Computational Geometry (SoCG), introduces and gives an algorithm to compute the similarity between a pair of curves, generalizing the standard Frechet distance. The second publication, in the Symposium on Discrete Algorithms (SODA), introduces and gives various results for Metric Violation Distance, where the goal is to minimally modify a data set to make it metric. SoCG is the top conference on Computational Geometry and SODA is one of the top 3 general algorithms conferences, and so the publication of these papers constitutes significant achievements. Dr. Fan has also published in other conferences, such as WADS and MFCS, and currently has several other manuscripts in submission. Dr. Fan will begin a postdoctorate in algorithms this fall in Paris, France.
Linrui Zhang obtained his Ph.D. in computer science during the spring of 2019 while under the supervision of Dr. Dan Moldovan. During his time studying in the UT Dallas Computer Science Department, Dr. Zhang worked as a research assistant in the Human Language Technology Research Institute. Dr. Zhang’s research focused on Deep Learning for Natural Language Processing (NLP). Many of the projects upon which he worked concentrated primarily on developing Deep Learning techniques for text understanding. Dr. Zhang’s research interests lie in Natural Language Processing with Deep Learning. In his dissertation titled “Neural Network Models for Text Understanding,” Dr. Zhang studied Deep Learning techniques, including multi-task learning, transfer learning, and multi-lingual learning and applied them to solve a couple of text understanding tasks, such as Semantic Textual Similarity, Textual Entailment, and Semantic Relation Extraction. Dr. Zhang is currently a research scientist at Lymba Corporation.
Kevin Parag Desai received his Ph.D. during the spring of 2019 while under the supervision of Dr. Balakrishnan Prabhakaran. While studying for his Ph.D., Dr. Desai was a research assistant in Dr. Prabhakaran’s Multimedia Systems Lab. Dr. Desai was also the co-founder and president of the UT Dallas Grads of Computer Science organization and worked a summer as an Interaction and Experiences Research intern in Facebook Reality Labs, where he worked in the hand tracking team within Oculus, developing different interactions and experiences for hands-on Virtual Reality and Augmented Reality devices as well as conducted user studies to test them. Dr. Desai’s research interests include 3D Computer Vision, Virtual and Augmented Reality, Human-Computer Interaction, Multimedia Systems, Computer Graphics, Deep Learning and Machine Learning with applications in domains of health-care, rehabilitation, virtual training, and gaming. In his dissertation titled “Quantifying Experience and Task Performance in 3D Serious Games,” Dr. Desai focused on the problem of quantifying experience and task performance in 3D serious games. Currently, Dr. Desai is an Assistant Professor in Practice at the UT San Antonio Department of Computer Science.
Yi Li obtained her Ph.D. in the spring of 2019 while under the supervision of Dr. Weili Wu. In her dissertation titled “Content Spread and User Relations in Social Computing,” Dr. Li studied the negative content spread on social networks such as rumors, misinformation, arguments, and even cyberbullying messages from the views of information spread and user relations. Dr. Li has joined the University of Texas at Tyler as a tenure-track assistant professor.
Rittika Shamsuddin received her Ph.D. in Computer Science with a focus on Data Science during the spring of 2019 while under the supervision of Dr. Balakrishnan Prabhakaran. While at UT Dallas, Dr. Shamsuddin worked as a research assistant for Dr. Prabhakaran in the Multimedia Systems lab in collaboration with Dr. Amit Sawant from UT Southwestern Medical Center. She worked on various projects including automatic annotation of abdominal tumor motion, improving accuracy for predicting (online) changes in abdominal tumor motion, and characterizing individual variations in patients. She received the International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Student Travel Award at the 4th IEEE International Conference. In her dissertation titled “Analyzing and Synthesizing Healthcare Time Series Data For Decision-Support,” Dr. Shamsuddin used the Respiration Induced Tumor Motion or RITM dataset to present three diﬀerent case studies, where she analyzed the datasets using unsupervised machine learning techniques to provide: (A) patient similarity as a solution to handle lack of control dataset and variability present, (B) a summary of the dataset as low dimensional proﬁles of the patients, and (C) annotation of the dataset via computational characterization of medically relevant patterns. This fall, Dr. Shamsuddin joined the Department of Computer Science at Oklahoma State University as a tenure-track Assistant Professor.
Zheng Dong obtained his Ph.D. during the spring of 2019 while under the supervision of Dr. Cong Liu. Dr. Dong’s dissertation titled “Efficient Scheduling And Analysis Techniques For Supporting,” focused on developing efficient real-time scheduling algorithms and timing validation techniques to enhance the computing capability of safety-critical embedded systems (often called cyber-physical systems – CPS) powered by homogeneous or heterogeneous multicore platforms. His research covers real-time scheduling theory, with a special focus on utilization-based schedulability analysis for different real-time task models, such as suspending task model, gang task model, DAG task model, pipeline task model, and stochastic task model, as well as some practical issues. CPS can be adequately solved using real-time scheduling theory. During the past few years, he has developed several scheduling techniques to analyze CPS in different scenarios, such as Data-analytics driven Cyber-Physical Systems, IoT, wireless sensor network and mobile edge computing, providing predictable latency performance to end-users. Besides his main research interests in CPS, his research work also covers network coding theory. This fall, Dr. Dong joined the Department of Computer Science at Wayne State University as a tenure-track Assistant Professor.
Chengyuan Lai obtained his Ph.D. during the summer of 2019 while under the supervision of Dr. Ryan McMahan. In his dissertation titled “3D Travel Techniques for Virtual Reality Cyberlearning Systems,” Dr. Lai investigated how to make VR cyberlearning systems more effective and efficient by proposing a standardized methodology to evaluate 3D travel techniques in order to guide the design of VR cyberlearning systems. The goal of his dissertation was to provide a systematic approach of evaluating 3D travel techniques, gain a better understanding of how 3D travel interaction design affects cyberlearning systems in terms of effectiveness and usability, and develop guidelines for selecting 3D travel techniques for VR learning systems. While working towards his Ph.D. at UT Dallas, Dr. Lai worked primarily as a research assistant in Dr. McMahan’s Future Immersive Virtual Environment (FIVE) lab where he studied virtual reality and training research. During that time, he worked on several key topics with Dr. McMahan, including investigating the vertical locomotion techniques and its usage in the mining industry. He also developed an application for high school teaching using the VR technique. Dr. Lai is a VR/AR researcher and developer and works to leverage his research and programming skills to make XR technologies better facilitate people’s everyday life. Dr. Lai is currently working as a Software Engineer at Neuro Rehab VR in Fort Worth, Texas.
Shreyas Sanjeev Gokhale obtained his Ph.D. during the summer of 2019 while under the supervision of Dr. Neeraj Mittal. His research interests include Distributed Computing, Concurrency Techniques, and Concurrent Data Structures. In his dissertation titled “Advanced Concurrency Techniques for Concurrent Data Structures,” Dr. Gokhale presented algorithms for the Group Mutual Exclusion (GME) problem, which can be used as an advanced concurrency technique to increase the performance of software built on concurrent data structures. Dr. Gokhale is currently a member of the technical staff at Datrium Inc. where he will be working on projects that are in the domain of file systems, storage, and replication technology.
Elmer Salazar obtained his Ph.D. during the summer of 2019 while under the supervision of Dr. Gopal Gupta. In his dissertation titled “NAF-Based Logic Semantics: Proof-Theoretic Generalization and Non-Ground Extension,” Dr. Salazar studied the semantics of logic programs. Dr. Salazar’s research interests include Computational Logic in Artificial Intelligence and Interaction between Computation Logic and Neural Networks. While working towards his Ph.D., Dr. Salazar had numerous research papers published in various journals and conferences, most notably four papers published in the Theory and Practice of Logic Programming Journal. Currently, Dr. Salazar is working for the UT Dallas Computer Science Department as an Assistant Professor of Instruction.
Oscar Javier Chaparro obtained his Ph.D. during the summer of 2019 while under the supervision of Dr. Andi Marcus. Dr. Chaparro’s research interests include software maintenance and evolution, program comprehension, code refactoring, code quality, and developer’s productivity. The goal of his research is to improve how users report and how developers triage and solve software bugs. His current research aims at improving the quality of bug report information and leveraging this information for better bug triaging and resolution. In his dissertation titled “Automated Analysis of Bug Descriptions to Support Bug Reporting and Resolution,” Dr. Chaparro proposed automatic techniques to identify, verify, and leverage the observed behavior, the expected behavior, and the steps to reproduce from bug descriptions, for generating automated feedback to reporters about problems in their bug descriptions, and for improving the accuracy of bug resolution tasks. Specifically, he proposed techniques and present empirical results on (A) discovering discourse patterns used by reporters to describe bugs, (B) automatically detecting missing information in bug descriptions, (C) automatically assessing the quality of the steps to reproduce provided in such descriptions, and (D) combining bug descriptions and query reformulation to improve automated bug localization and duplicate bug report detection. While working towards his Ph.D., Dr. Chapparo had various papers published in multiple conferences and journals including the ACM Joint Meeting on the Foundations of Software Engineering, IEEE International Conference on Software Analysis, Evolution, and Reengineering, and IEEE International Conference on Software Maintenance and Evolution. His published papers have received numerous awards including the ACM SIGSOFT Distinguished paper award for the paper titled “Assessing the Quality of the Steps to Reproduce in Bug Reports,” which was co-authored by Drs. Andrian Marcus, Vincent Ng, Carlos Bernal-Cárdenas, Jing Lu, Kevin Moran, Massimiliano Di Penta, and Denys Poshyvanyk, and the IEEE TCSE Distinguished Paper Award for his work on the paper titled Using Observed Behavior to Reformulate Queries during Text Retrieval-based Bug Localization” which was co-authored by Drs. Andrian Marcus and Juan Manuel Florez. Dr. Chapparo will be joining the Computer Science Department at the College of William and Mary this fall as a tenure-track Assistant Professor.

ABOUT THE UT DALLAS COMPUTER SCIENCE DEPARTMENT

The UT Dallas Computer Science program is one of the largest Computer Science departments in the United States with over 3,315 bachelors-degree students, more than 1,110 master’s students, 165 Ph.D. students, 52 tenure-track faculty members, and 44 full-time senior lecturers, as of Fall 2019. With The University of Texas at Dallas’ unique history of starting as a graduate institution first, the CS Department is built on a legacy of valuing innovative research and providing advanced training for software engineers and computer scientists.

Past Ph.D. Stories:

Learn about the 2017-2018 Ph.D. Graduates

Learn about the 2016-2017 Ph.D. Graduates

Learn about the 2015-2016 Ph.D. Graduates