The UT Dallas Computer Science PhD program has been in existence since the mid-70s. The UT Dallas Computer Science Department has graduated a total of 310 PhD students, including 31 women, since 2001. In fact, when the Computer Science program first started in the 70s, it granted only PhD degrees. Most recently, from the fall of 2017 to summer of 2018, the UT Dallas Computer Science Department has graduated 18 PhD students, four of whom have been women. UT Dallas Professors Drs. Murat Kantarcioglu, Latifur Khan, Xiaohu Guo, Ryan McMahan, Cong Liu, Ding-Zhu Du, Alvaro Cardenas, Sanda Harabagiu, Gopal Gupta, Zhiqiang Lin, S. Venkatesan, Balakrishnan Prabhakaran, Ovidiu Daescu, and Haim Schweitzer, served as faculty supervisors to these 18 UT Dallas Computer Science and Software Engineering doctoral graduates of the 2017-18 academic year. These graduates have gone on to find jobs in prestigious universities, top-tier research facilities, government, and high-tech companies.
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, 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 (Colorado State University, Clemson, University of New Mexico, University of Delaware, etc.).
The following includes a recent list of some of the recent PhD graduates, the title of their dissertation, faculty supervisor, accomplishments, and where they are currently working.
- Dr. Ke Xu received her PhD during the fall 2017 under the guidance of Dr. Haim Schweitzer. In her dissertation “Feature Engineering for Data Analytics,” Dr. Xu investigated two important problems in data analytics. The first being feature selection, where she considered both the unsupervised and the supervised case. The second being data privacy, where she proposed a new model and described algorithms that improve data privacy in that model. Dr. Xu’s research interests include machine learning, data protection regression analysis, data processing, and big data. Her research with Dr. Schweitzer has been published in numerous conference publications including IEEE International Conference on Tools with Artificial Intelligence (ICTAI’17), National Conference on Artificial Intelligence (AAAI 2016, 2017, 2018), and others. During her time at UT Dallas, she was asked to take part in in the Computing Research Association (CRA) – Women Grad Cohort Workshop where she participated in a mentoring program for graduate women in computing or related fields. Currently, she is employed by Facebook.
- Dr. Yuan Tian received his PhD during the fall of 2017 under the guidance of Dr. Balakrishnan Prabhakaran. His dissertation titled, “Haptic Rendering In 3D Immersive Virtual Environment,” aims to overcome the emerging challenges in haptic-enabled 3D immersive environments. Specifically, three main research tasks were identified in the dissertation: Bidirectional haptic rendering in 3D Tele-immersion, haptic rendering of 3D streaming deformable surface, and haptic-enabled 3D deformation. Dr. Tian’s research interests include tele-immersion, virtual/mixed reality, haptic rendering, machine learning, physics simulation, and high-performance computing. While at UT Dallas, Dr. Tian worked with Dr. Prabhakaran in his Multimedia Computer Systems Lab researching remote immersive rehabilitation and received the Best Student Paper at the ACM Multimedia 2017 Conference for his paper titled, “H-TIME: Haptic-enabled Tele-Immersive Musculoskeletal Examination.” Their research, 3D Immersive Tele-Rehabilitation Research, also was chosen to be shared with U.S. congress and senate members at Capitol Hill in Washington D.C. Dr. Tian also was chosen by ACM Special Interest Group on Multimedia (SIGMM) as one of ten students to receive a travel scholarship for the ACM 50th Celebration of the Turing Award Ceremony (read more here). His research with Dr. Prabhakaran has been published in numerous conference publications including at the ACM Multimedia Systems Conference (MMyS 2017 & 2015), ACM Multimedia (MM 2017, 2014, 2013), IEEE Haptic Audio-Visual Environments and Games (HAVE 2014 & 2013), IEEE International Symposium on Multimedia (ISM 2014), and more. Currently, Dr. Tian is employed as a research engineer at Midea Robotics, in Silicon Valley, California.
- Dr. M. Solaimani obtained his PhD during the fall of 2017 under the guidance of Dr. Latifur Khan. In his dissertation titled “Design and Development of Real-Time Big Data Analytics Frameworks,” Dr. Solaimani focused on two areas: anomaly detection on structured VMware performance data (e.g., CPU/Memory usage metric, etc.) and text mining for politics in unstructured text data. Dr. Solaimani developed real-time distributed frameworks with ML and NLP techniques. With regard to anomaly detection, Dr. Solaimani implemented an adaptive clustering technique to identify individual anomalies and a Chi-square-based statistical technique to detect group anomalies in real-time. With regards to text mining, Dr. Solaimani developed a real-time framework, SPEC, to capture online news articles in different languages from the web and annotated them using CoreNLP, PETRARCH, and CAMEO dictionary to generate structured political events such as in ‘who-did-what-to-whom’ format. Later, Dr. Solaimani extended this framework to code atrocity events – involving machine-coded, structured data containing perpetrators, action, victims, etc. Finally, Dr. Solaimani developed a novel, distributed, window-based political actor-recommendation framework to discover and recommend new political actors with their possible roles. Dr. Solaimani’s research interests focus on machine learning using Big Data. Currently, he is a Senior Application Architect at Blue Cross and Blue Shield of Illinois, Montana, New Mexico, Oklahoma & Texas, based in Dallas, Texas, where he is designing and leading a team for Data Lake with real-time streaming using cutting edge Big Data technologies.
- Dr. Kenneth Joseph Platz received his PhD during the fall of 2017 under the guidance of Dr. S. Venkatesan. In his dissertation titled “Saturation in Lock-Based Concurrent Data Structures,” Dr. Platz developed two variants of existing lock-based concurrent data structures, a linked list and a skiplist. He then demonstrated how one can unroll these data structures to support multiple keys per node. This was followed by demonstrating how lock-based data structures can saturate, or plateau in performance, at sufficiently high thread counts, depending upon the percentage of write operations applied to that data structure. Dr. Platz then discussed how one can apply a new technique involving group mutual exclusion to provide a lock-based data structure that is resilient to saturation. Platz demonstrated how this technique can be applied to the implementations of linked lists and skiplists to provide the scalable performance to 250 threads and beyond. Dr. Platz’s research interests include concurrent data structures, their memory management techniques, and transactional memory systems. During his time at UT Dallas, he developed and presented computer science classes to high school students as part of UT Dallas’ K-12 CS Outreach Program. Currently, he is employed as a software engineer at NetApp SolidFire in Boulder, Colorado.
- Dr. Ahsanul Haque received his PhD during the fall of 2017 under the supervision of Dr. Latifur Khan. His dissertation was titled, “Semi-Supervised Classification and Novel Class Detection over Data Streams.” His research with Dr. Latifur Khan has been published in numerous conference publications including at the AAAI National Conference on Artificial Intelligence (AAAI 2018 & 2016), ACM Conference on Information and Knowledge Management (CIKM’17), IEEE International Conference on Data Engineering (ICDE’17), and more. His research interests include Big Data and IoT Stream Mining. While at UT Dallas, Dr. Haque was awarded Lars Magnus Ericsson Graduate Fellowship, ECS Louis Beecherl, Jr. Graduate Fellowship for his academic excellence by maintaining a perfect 4.0 GPA. Currently, Dr. Haque is working as a Data Scientist at Microsoft in Redmond, Washington.
- Dr. Zhuo Chen received his PhD during the fall of 2017 under the supervision of Dr. Gopal Gupta. In his dissertation titled “Automating Disease Management Using Answer Set Programming: Heart Failure,” Dr. Chen described a physician advisory system that codes the entire set of clinical practice guidelines for heart failure management using answer set programming (ASP). ASP is a form of declarative programming for building knowledge representation and intelligent reasoning systems. The approach is based on developing reasoning templates that are called knowledge patterns and using these patterns to systematically code the clinical guideline for HF management as ASP rules. The system developed outperformed cardiologists in experiments done on 30 test cases. Dr. Chen’s research interests include logic programming, automated reasoning and knowledge representation. His research has been published in the journal Theory and Practice of Logic Programming Journal as well as other conferences and workshops. Dr. Chen is currently working on his postdoc at UT Dallas where he is working not only on further developing the physician advisory system, but also on other advanced applications of answer set programming to intelligent reasoning.
- Dr. Khaled Al-Naami obtained his PhD under the supervision of Dr. Latifur Khan during the fall of 2017. His dissertation, “Enhancing Cyber Security with Encrypted Traffic Fingerprinting,” focused on utilizing bi-directional dependence of network traffic for website fingerprinting. During his time at UT Dallas, he has published numerous research papers including at IEEE International Conference on Big Data (BigData’17), Annual Computer Security Applications Conference (ACSAC’16), IEEE Symposium Series on Computational Intelligence (SSCI’15), and others. Currently, Dr. Khaled Al-Naami is working as a Senior Software Engineer at Salesforce in Seattle, Washington.
- Dr. Junia Valente obtained her PhD under the supervision of Dr. Alvaro Cardenas during the spring of 2018. Dr. Valente’s dissertation was titled, “Vulnerability Trends In IoT Devices And New Sensor-Assisted Security Protections.” The ﬁrst part of her dissertation summarized security and privacy practices in popular consumer devices (smart children’s toys, consumer drones, surveillance systems, smart home devices, and voice-enabled personal assistant devices), deployment patterns that emerge among them, and vulnerabilities found in them. The second part of her dissertation focused on how IoT devices can be secured by fundamentally new security paradigms. In particular, Valente focused on the security of camera surveillance systems and proposed a new way to verify the integrity and freshness of the video feed by sending visual challenges to the area monitored by the camera. Valente’s work illustrated the unique cyber-physical properties that sensor devices can leverage in their cyber-security defenses. Her research interests include Internet of Things (IoT) security and cyber-physical systems (CPS) security. She has reported various vulnerabilities in IoT devices, including CVE-2015-8287 and CVE-2017-3209 on surveillance systems and consumer drones. Her work has been covered by Forbes and Threatpost Security news (click here to learn more). During her time at UT Dallas, Dr. Valente published numerous research papers including in highly prestigious conferences such as ACM CCS’16, ACSAC’15, and AAMAS’13. She also won numerous awards: best poster at WiCyS’18, best dome at AAMAS’13, best paper at (ADS’12) Symposium, Best Overall Paper Award at SpringSim’12. She is the recipient of a Google Internet of Things (IoT) Technology Research Award. Her entire list of awards, honors, and publications can be viewed by visiting her website. In addition to her studies, Valente has participated at hackathons with tech leaders and engineers from the Bay Area to build advocacy tools intersecting public policy and tech: Text4Reform and FWDnow. In addition, she is committed to efforts to increasing and retaining the participation of women in tech. Likewise, Valente is involved with the music program at UT Dallas: as a flutist with the UT Dallas Pep Band and in the past as a violist at the UTD Orchestra under the direction of the late Mr. Arkady Fomin. Dr. Valente currently working on her postdoc at UT Dallas in Dr. Alvaro Cardenas’ research group.
- Dr. Ahmad Mustafa obtained his PhD during the spring of 2018 under the supervision of Dr. Latifur Khan. His dissertation “Novel Class Detection and Cross-Lingual Duplicate Detection over Online Data Stream,” focused on solving concept-drift and concept-evolution challenges in news data streams by using ensemble-based classification models. Dr. Mustafa’s research interests include Data Stream Mining, Natural Language Processing, Cyber Security, Big Data Analytics, Deep Learning, Python, and R. His research with Dr. Khan has been published in various conference publications including IEEE International Conference on Big Data (2017), IEEE International Conference on Intelligence and Security Informatics (ISI’16), Annual Conference on Computer Security Applications (ACSAC ’16), IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom’14), and more. Currently, Dr. Mustafa is working at Samsung in Amman, Jordan.
- Dr. Harish Babu Arunachalam received his PhD during the spring of 2018 while under the supervision of Dr. Ovidiu Daescu. His dissertation was titled, “Computational Methods for Histo-Pathological Whole Slide Image Analysis of Osteosarcoma.” Dr. Arunachalam research on Osteosarcoma focuses on developing image-analysis and machine-learning techniques to successfully predict tumor necrosis in histopathology image datasets (digitized glass-slides). The methods use whole slide images (WSIs) – high-resolution images consisting of more than 109 pixels, supporting up to 40X magniﬁcation. The novel contributions of this research include, (1) building an automated image-analysis pipeline for Osteosarcoma, (2) creation of tumor-prediction maps from image-tiles, (3) design of an end-to-end necrosis detection tool, and (4) image-tile annotation and gross-image area-computation tools. The outcomes of this research will play a vital role in building novel, automated methods for Osteosarcoma and save valuable time of pathologists by reducing the time-consuming tumor necrosis estimation process. Dr. Arunachalam’s research interests lie in data science, medical image processing, machine-learning, deep-learning natural language processing, artificial intelligence, and genomics. Dr. Arunachalam received the Outstanding Teaching Assistant Award from the UT Dallas Department of Computer Science in May ’18. Currently, Dr. Harish Babu Arunachalam is employed as a Senior Big Data Engineer at Verizon in Dallas, Texas.
- Dr. Vishal Karande received his PhD under the supervision of Dr. Latifur Khan and Dr. Zhiqiang Lin during the spring of 2018. In his dissertation titled “Protecting User Applications Using Trusted Computing,” Dr. Karande proposed secure architectures for video game anomaly detection using trusted hardware. His research interests lie at the intersection of software security, trusted computing, and machine learning.” His research with Dr. Latifur Khan has been published in numerous conference publications including IEEE International Conference on Information Reuse and Integration (IRI’18), European Symposium on Research in Computer Security (ESORICS’17), IEEE Symposium Series on Computational Intelligence (SSCI’15), Workshop on Automated Decision Making for Active Cyber Defense (SafeConfig ’17), and others. During his time at UT Dallas, he received the Outstanding Teaching Assistant Award in Fall of 2016 and an ECS Graduate Fellowship by maintaining a perfect 4.0 GPA. Dr. Karande will be joining Google in Mountain View, California, in the August 2018, where he will be working with the Google Cloud Data team.
- Dr. Travis Goodwin received his PhD under the supervision of Dr. Sanda Harabagiu in the Human Language Technology Research Institute (HLTRI) during the spring of 2018. In Dr. Goodwin’s dissertation titled, “Medical Question Answering and Patient Cohort Retrieval,” Dr. Goodwin presented research that unlocked knowledge encoded in clinical texts by automatically (1) identifying clinical texts relevant to a specific information need and (2) reasoning about the information encoded in clinical text to answer medical questions posed in natural language. Moreover, Goodwin’s dissertation presented a number of approaches for overcoming some of the most significant complexities of processing electronic health records. Dr. Goodwin presented new approaches for (1) modeling the temporal aspects of electronic health records; (2) inferring underspecified information and recovering missing sections of records; and (3) applying machine learning to learn an optimal set of relevance criteria for a specific set of information needs and collection of clinical texts. Combined, this work demonstrated the importance of harnessing the natural language content of electronic health records and highlighted the promise of medical question answering and patient cohort retrieval for enabling more informed patient care and improved patient outcomes. Dr. Goodwin’s research occupies the intersection of natural language processing (NLP), information retrieval (IR), and medical informatics. Dr. Goodwin and Dr. Sanda Harabagiu, received the Homer R. Warner Award at the 2017 American Medical Informatics Association’s (AMIA) Annual Symposium for their paper, “Inferring Clinical Correlations from EEG Reports with Deep Neural Learning,” in which Dr. Goodwin was the lead author. The paper presented a novel method for automatically extracting and analyzing the clinical correlations between findings documented in a neurological report and the overall clinical picture of the patient, which helps to mitigate misdiagnoses and improve patient care. This is the second year in a row that Goodwin received a prestigious award at a conference. In 2016, the paper “Medical Question Answering for Clinical Decision Support,” co-authored with Harabagiu, received the Best Student Paper Award from the ACM International Conference on Information and Knowledge Management. Goodwin earned his bachelor’s and master’s degrees in computer science from UT Dallas. Currently, Dr. Goodwin is a Postdoctoral Research Fellow at the National Library of Medicine (NLM) in Bethesda, Maryland.
- Dr. Mustafa Amir Faisal received his PhD under the guidance of Dr. Alvaro Cardenas during the spring of 2018. Mustafa’s research interests lie at the intersection of machine learning and security and privacy in industrial control systems, and the smart grid. His work focuses on deep packet inspection of industrial control protocols, tracking the communication patterns of controllers, servers, and field devices for security purposes. In his dissertation titled,“Modeling Behavior of Industrial Control System Protocols for Intrusion Detection,“ Dr. Faisal studied two modeling techniques, Discrete-time Markov Chain (DTMC) and Deterministic Finite Automata (DFA), for communication between Human Machine Interface (HMI) and Programmable Logic Control (PLC) using various protocols like Modbus TCP, EtherNet/IP, DNP3, etc. Dr. Faisal currently works as a data scientist at Procter & Gamble in Mason, Ohio.
- Dr. Carlos Alfredo Barreto received his PhD under the guidance of Dr. Alvaro Cardenas during the spring of 2018. Dr. Barreto research focused on the security of critical infrastructure (applied mainly the electricity system). Unlike traditional information security, this research is not focused on protecting information, but protecting physical processes from cybernetic attacks, which can cause physical harm. Dr. Barreto utilized game theory and control theory to build models of systems, analyze possible security issues, and devise protection schemes to handle cyber risk. In his dissertation titled, “Role of economic policies in the security of critical infrastructures,” Dr. Barreto investigated how economic policies affect the security of critical infrastructures. First, Barreto illustrated the importance of economic incentives, showing how policies designed to protect systems have the opposite effect. Second, he analyzed attacks that leverage the market’s infrastructure to manipulate the demand of users. Third, Barreto investigated the optimal allocation of resources to protect systems against cyber threats that evolve over time. Dr. Barreto determined that uncertainties in the system’s state to make insurance more attractive as a risk management tool, however, the defenders need incentives to purchase cyber insurance. Moreover, insurance can improve the investment in either prevention or detection, though policies with indemnity subsidies and unlimited coverage nonetheless can introduce perverse incentives that degrade the investments in security. His research with Dr. Cardenas has been published in various conference and journal publications including the IEEE Conference on Control Technology and Applications (CCTA’17), Annual Workshop on the Economics of Information Security (WEIS’16), IEEE Conference on Decision and Control (CDC 2015 & 2013), IEEE American Control Conference (ACC’15), IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS’14), and more. Currently, Dr. Barreto is a Postdoctoral Scholar at Vanderbilt University.
- Dr. Guangmo Tong received his PhD during the summer of 2018. Dr. Tong earned his PhD while under the supervision of Dr. Ding-Zhu Du and Dr. Cong Liu (co-adviser) while working in the Data Communication and Data Management Lab at UT Dallas. In his dissertation titled “Optimization Problems in Social Networks,” Dr. Tong analyzed problems emerging from modern online social systems from the perspective of information diffusion. Based on various information diffusion models, Dr. Tong studied several problems including viral marketing, online friending, rumor blocking and others. Dr. Guangmo Tong’s research with his advisers has been published in numerous journal and conference publications including IEEE International Conference on Computer Communications (INFOCOM 2017& 2016), IEEE Transactions on Computational Social Systems (TCSS’16 & 18), IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS’15), IEEE Transactions on Parallel and Distributed Systems (TPDS’15), ACM International Conference on Embedded Software (EMSOFT’14), IEEE/ACM Transactions on Networking (TON’17), and more. Dr. Guangmo Tong’s research interests include Big Data analysis, social network analysis, and resource allocation, and scheduling. This fall, Dr. Tong will be joining the University of Delaware as a tenure-track assistant professor.
- Dr. Swarup Chandra received his PhD while working under the supervision of Dr. Latifur Khan and Dr. Zhiqiang Lin. During the summer of 2018, He has successfully defended his dissertation titled “Scalability and Security of Multistream Classification.” His research interests are in the areas of machine learning and security. Specifically, his current research focuses on supervised learning in non-stationary domains under limited availability of labeled data, with applications in image processing and text analytics. Particular to security problems in cloud analytics, he has worked with applications using a trusted execution environment, and adversarial attacks. He has published numerous research papers including at National Conference on Artificial Intelligence (AAAI’18), European Symposium on Research in Computer Security (ESORICS’17), ACM Conference on Information and Knowledge Management (CIKM’17), IEEE International Conference on Data Mining (ICDM’16), and more. Dr. Chandra will be joining Hewlett Packard Enterprise (HPE) as a Research Scientist in Palo Alto, California.
- Dr. Yasmeen Alufaisan received her PhD under the guidance of Dr. Murat Kantarcioglu during the summer of 2018. Dr. Alufaisan’s research interests include Data Mining and Transparency. In her dissertation titled “Towards Algorithmic Accountability in Data Mining,” Dr. Alufaisan focused on four desired aspects of accountability in data mining. These aspects are: reliability, discrimination-awareness, transparency, and privacy. Dr. Alufaisan explains, “We first investigate the reliability of using data mining techniques to predict various individual traits. We then measure the impact of various adversarial attacks on the prediction accuracy of data mining models. We also propose countermeasures that can reduce the effectiveness of these attacks. Secondly, we develop two techniques to measure discrimination of a black-box model (i.e., without knowing the data mining model details) as a result of data bias or algorithmic weakness. Data bias is investigated further by introducing artificial bias to the dataset under consideration. After that, we develop transparency models to help unmask the incomprehensible reasoning made by any data mining\machine learning models. We look into the transparency of both white-box (i.e., when we know the model details) and black-box machine learning models. For white-box transparency, we propose an Instance-based Transparency model (IT) that provides simple explanations by using a novel rule selection technique. For the black-box transparency, we introduce the Reverse Engineering Approximate Learning (REAL) Model that outputs a decision tree to interpret any black-box classifier. Finally, we extensively study the trade-off between privacy and transparency. We introduce a novel privacy model that can prevent the inference of individuals’ sensitive information when disclosing a transparency model.” Her research with Dr. Murat Kantarcioglu has been published in various conference publications and journals including Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD’18), IEEE International Conference on Collaboration and Internet Computing (CIC 2017 & 2016), IEEE International Conference on Intelligence and Security Informatics (ISI’17), Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD’18), and more. She gave birth to beautiful twin girls during the last year of her PhD and finished her PhD on time while also simultaneously publishing papers. Dr. Alufaisan has a recently got a position at Prince Mohammad Bin Fahd University in Saudi Arabia.
- Dr. Fei Tang obtained his PhD under the guidance of Dr. Ryan McMahan during the summer 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 certain 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 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.
- Dr. Saifeng Ni obtained her PhD during the summer 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 in 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. Especially, 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 problem have been considered in tetrahedral meshing. Also, field-aligned, tetrahedral meshing better preserves the rotation geometry and also creates better anisotropic meshes. Dr. Ni has had her research published in various conferences and journals 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 has been working 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.
If you would like to learn more about obtaining a Doctor of Philosophy in Computer Science or Software Engineering through the UT Dallas Computer Science Department, please click here for more information about the Computer Science PhD Program or click here for more information about the Software Engineering PhD program.
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 2,800 bachelors-degree students, more than 1,000 master’s students, 190 Ph.D. students, 52 tenure-track faculty members and 41 full-time senior lecturers, as of Fall 2018. 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.