Poster for the 2014 UNT Graduate Exhibition in the Computer Science and Information Technology category. This poster discusses data mining techniques for predicting breast cancer survivability among women in the United States.

Article investigates the strength-ductility synergy of high-entropy alloys (HEAs).

This article proposes a novel preconditioned and accelerated Gauss–Siedel algorithm referred to as Symmetric Successive Overrelaxation Preconditioned Gauss-Seidel (SSORGS) to address the signal detection challenges associated with massive MIMO technology.

Data management plan for the grant, "REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach." This Research Experiences for Undergraduates (REU) Site Program at the University of North Texas will enhance the knowledge and research skills of a diverse cohort of undergraduate students through empowering, innovative, and interdisciplinary research experiences in developing Deep Learning applications and systems. The program aims to 1) expose undergraduate students to real-world and cutting-edge research focused on accelerated deep learning through combined hardware and software development; 2) encourage more undergraduate students to continue their academic careers and seek graduate degrees in computer science, computer engineering, and related disciplines; 3) develop research skills and improve communication and collaborative skills in undergraduate students.

Article reviewing the modeling and verification of products derived from biomass pyrolysis and discussing the possible solutions towards more accurate modeling of biomass pyrolysis.

Data management plan for the grant, "REU Site: TaMaLe - Testing and Machine Learning for Context-Driven Systems: Research Experience for Undergraduates." TaMaLe (Testing and Machine Learning for Context-Driven Systems), a renewal Research Experience for Undergraduates (REU) Site at University of North Texas, engages 10 undergraduate students for 10 weeks with problems in the context-driven system domain. The students explore research problems to improve the reliability and security of context-driven systems. Context-driven systems, such as mobile apps, face constant streams of input from both users and context changes in their environments. Users interact with apps through touch and speech interfaces. These systems also respond to context events that occur in their environments such as changes to network connection, battery level, screen orientation, and more. The combined explosion of possible user events and context event sequences pose new challenges that require cost effective testing solutions. Students and mentors in this REU program work in small teams to develop and empirically evaluate new software testing techniques for context-driven systems using strategies such as reinforcement learning and combinatorial-based techniques.

Data management plan for the grant, "CAREER: Manufacturing of Mechanically Stable Nanoporous Ceramic Structures Via Selective Infiltration of Polymer Templates." This Faculty Early Career Development (CAREER) grant from the National Science Foundation supports fundamental research to elucidate a new strategy of manufacturing nanoporous ceramic structures with controllable structure and composition and programmable mechanical stability. The specific goal of this research is to discover processing-structure-property relationships in ceramic coatings and heterostructures by providing fundamental insights on the mechanism of liquid phase swelling-based infiltration of spin-coated polymer templates with inorganic precursors and defining the rules that control the resulting structure and, thus, access to various materials surfaces and interfaces.