I am a Ph.D. student in Computer Science at the University of North Texas (UNT) under the supervision of Dr. Yunhe Feng. My research focuses on trustworthy LLMs, agentic AI, MCP, and generative AI for quantum computing. My recent work includes ProMCP, a framework for profiling token flows and latency costs in Model Context Protocol–based LLM agents, accepted to ACL Findings 2026. My research also includes HALO, a retrieval-augmented framework for clinical decision support published at ACM/IEEE CHASE 2025, and Poly-FEVER, a multilingual fact verification benchmark for evaluating hallucinations across large language models. More recently, I have been exploring Generative AI for quantum computing, including multi-framework quantum code generation and execution-aware evaluation. My research has also been recognized through the NSF Student Travel Award for ACM/IEEE CHASE 2025.

I received my M.S. in Computer Science from the UNT with a perfect 4.0 GPA and was honored with the Distinguished Graduate Student Award. I earned my Bachelor's degree in Computer Science and Engineering from Sreenidhi Institute of Science and Technology (SNIST), India.

Beyond research, I bring industry experience as a Support Engineer Intern at Amazon, where I developed diagnostic tools that optimized customer support operations. I also have experience in Oracle Fusion HCM and enterprise cloud systems, along with a strong foundation in cloud computing, data-driven systems, and applied machine learning.

Outside of academics, I enjoy learning new languages, reading novels and manga, and sketching, particularly creating portraits of my favorite characters.

My name is Dawei Gao, and I am a Ph.D. student in Computer Science and Engineering at the University of North Texas, under the supervision of Dr. Yunhe Feng. My current research interests include Trustworthy AI and AI for Biology, with a focus on developing reliable machine learning methods for biomedical and scientific applications.

During my Ph.D. studies, I have been working on AI-driven biomedical research, including using early-stage cellular image data and biological measurements to predict future cellular states and biological changes. This work aims to support more reliable modeling and analysis of complex biological systems.

Before joining UNT, I obtained a Master's degree in Electronic and Information Engineering from the University of Chinese Academy of Sciences. I also hold a Bachelor's degree in Prospecting Technology and Engineering from China University of Petroleum, Beijing.

I primarily program in Python and have experience with C++ and C. In my free time, I enjoy hiking and running, and I have successfully completed a full marathon.

I am a PhD student in the Department of Computer Science and Engineering (CSE) at the University of North Texas (UNT), Denton, TX, where I intend to conduct additional research on Reinforcement Learning in the Responsible AI Lab (RAIL). I am also a UNT alum, having received my undergraduate degree in computer science, and I hold a Master’s degree in Computer Science from the University of Oklahoma. Before entering industry and beginning my academic journey, I served in the United States Marine Corps for 12 years, deploying multiple times and visiting several countries worldwide.

I have industry experience working at L3 Harris Technologies in Arlington, TX, as a software engineer, focusing on modeling & simulation and Communications Navigation and Instrumentation (CNI). After my time at L3, I worked as part of the Pathways Program at NASA’s Johnson Space Center in Houston, TX, where I contributed to the PSION lab, earning co-authorship of a chapter in a major university textbook. I also worked as a member of the Valkyrie team, focusing on virtual reality (VR) and inverse kinematic models for real-time control of humanoid robots.

Currently, I am a Software Engineering Manager at Lockheed Martin (LM) for Advanced Development Programs (ADP), commonly known as Skunk Works. At LM, I have continued my work in modeling and simulation, led DevSecOps teams, and now serve as the Program Manager (PM) for an F-35 Mission Systems Navigation SW team. Additionally, I manage other CNI teams responsible for work in ADP and other programs. My technical skills include programming in Java, C++, Python, and ADA 95, with experience in various other languages as needed. I am proficient in using version control software such as Git, GitHub, GitLab, and BitBucket. I frequently use container solutions like Docker and Podman, manage containers with Kubernetes, and utilize helm charts for Kubernetes management. In the realm of AI and machine learning, I have experience with libraries such as sci-kit, PyTorch, and TensorFlow.

I am Mingchen Li, currently pursuing my Ph.D. in the Department of Computer Science and Engineering (CSE) at UNT. I obtained my Bachelor's degree in Computer Science with AI from the University of Nottingham, China campus and Master's degree in Information Systems at Northeastern University. Before joining the lab, I gained experience in various NLP topics, including BERT fine-tuning for COVID-19 Tweets at Hong Kong HangSeng University, outcome was published in Expert Systems with Application Journal. Additionally, I previously worked as a Software Engineer in China, where I participated in diverse cross-field projects such as deploying the Bayes model on textile machines and handling sensor data in wastewater processing. I am eagerly looking forward to contributing to a more efficient and equitable world with the assistance of AI. Industrial applications combined with AI have the potential to greatly enhance efficiency. I have a strong interest in numerous ML/AI topics and am always open to learning.

My primary programming languages are Python, Java, and C#. I hope that my knowledge and programming skills, though limited, will be an asset to the lab. In my free time, you might find me cooking, gaming, or watching anime.

I am a Ph.D. Candidate in Computer Science at the University of North Texas (UNT), where I work as a Graduate Research Assistant in the Responsible AI Lab. My research focuses on efficient AI, hardware-aware machine learning, and accelerated large language model inference.

My current research investigates efficient LLM inference through hardware-aware optimization of transformer attention, including dynamic sparse attention, mixed-precision attention, quantization, and long-context inference acceleration. I design and evaluate efficient inference methods on large-scale GPU and HPC systems, including Polaris and TACC platforms.

I received my M.S. in Data Science with Distinction from the University of Birmingham and my B.S. in Computer Science and Technology with Honours from Xiamen University Malaysia. Before joining UNT, I worked as a storage backend intern at OPPO, where I contributed to CubeFS, a distributed file system for large-scale cloud infrastructure.

I primarily program in Python and have experience with C, C++, Go, CUDA-related development, and large-scale experimental workflows on GPU clusters. Outside of research, I enjoy musicals, museums, tarot, and astrology.

My name is Manikanta Chiranjeevi, and I am pursuing a Master’s degree in Artificial Intelligence at the University of North Texas. I joined the Responsible AI Lab to immerse myself in cutting-edge AI research and contribute to projects that make a tangible impact.

I have experience working on AI and machine learning applications, including LLM based systems and generative AI solutions, and I enjoy connecting academic knowledge with practical implementations. My academic interests include AI system design, and exploring ways to improve AI reliability, usability, and impact across diverse applications.

Skills: Python, machine learning, Gen AI, full stack development(.Net, Angular)

Beyond research, I actively participate in AI meetups and conferences through various professional and academic communities, which allows me to stay engaged with current research trends and industry practices. In my free time, I enjoy playing cricket and badminton, coding, and reading about emerging technology trends.

My name is Thomas Gerrity, I am a graduate student at the University of North Texas with a background in physics and ongoing graduate training in data science. My research interests focus on efficient and responsible AI, particularly in event-driven and risk-sensitive modeling where assumptions, robustness, and failure modes are as important as predictive accuracy.

My recent work includes developing hybrid forecasting pipelines that combine technical models with NLP-based sentiment signals derived from policy and economic events. I am especially interested in how representation choices and inductive biases affect model robustness, generalization, and hallucination behavior in modern AI systems. I have experience working with transformer-based NLP models, classical machine learning methods, and scientific computing workflows.

I am also broadly interested in quantum computing from a representation and long-term efficiency perspective, informed by prior coursework in quantum mechanics, quantum field theory, and nonlinear optics. In my free time, I enjoy cooking, stargazing, reading philosophy, writing, microbrewing, and spending time outdoors.

I am a Master's student in the Department of Data Science at the University of North Texas and a Graduate Research Assistant in the Responsible AI Lab. My research interests lie at the intersection of machine learning, statistical modeling, and deep learning architectures, with a specific focus on leveraging Graph Neural Networks (GNNs) and building models at utmost accuracy. I am dedicated to developing advanced, robust, and ethically aligned AI solutions that address sophisticated real-world data challenges.

Complementing my theoretical foundation, I emphasize building reliable data engineering pipelines and scalable software workflows. My core technical stack includes building predictive pipelines using Python and SQL, managing collaborative environments via Git, and containerizing distributed applications using Docker, Kubernetes, and Apache Beam. I actively focus on designing reproducible MLOps workflows to streamline the integration of data optimization algorithms with lab research infrastructure.

Outside of research, I enjoy exploring professional culinary techniques, experimenting with traditional regional cuisines, and I am highly sports enthusiastic. I have played Volleyball and Cricket at the state level in India for my school team.

My name is Kaicheng Li, and I am pursuing a Master’s degree in Computer Science with a focus on Artificial Intelligence at the University of Southern California. I earned my B.S. in Mathematics (Applied Track) from The Ohio State University, with minors in Computer Information Systems and Game Design. My background bridges mathematics and computer science, with research and practical experience in machine learning, data analysis, and responsible AI. During my undergraduate studies, I strengthened my foundation in discrete mathematics, algorithms, and applied computation using Python, MATLAB, and Mathematica, while also developing projects such as chatbots and robotics control systems. As a research assistant in the Responsible AI Lab, I conducted data-driven analysis and co-authored a publication examining AI’s impact on streaming media. At USC, I am advancing my expertise in artificial intelligence to design reliable, responsible systems that address large-scale real-world challenges, with the long-term goal of contributing to intelligent technologies that integrate mathematical rigor with socially responsible applications.

I am interest in Trading Card games. In my leisure time, I like cooking and watching sports games like soccer, football, and baseball.

Hello, my name is Ben Wilcox and I am an undergraduate Mathematics and Computer Science student. I am fascinated by all things AI, with a particular interest in Model Bias/Reliability, Reinforcement Learning, and Synthetic Data. My whole life I've loved programming, and I've spent the last few years growing my knowledge of Machine Learning and AI.

In my free time, I love to play video games, lift weights, make programs, and learn classical piano pieces. Recently, I made a website for UNT students to update their degree plans, and also created an iOS mobile app that organizations use to manage academics.

I am excited to be a part of the Responsible AI Lab, and I look forward to researching methods to train, analyze, and use AI Models in innovative ways.

Hey there! I am Asmitha Dhamodharan, a Computer Science undergraduate at the University of North Texas.

Recently, I graduated high school in the top 4% of my class with endorsements in STEM and Multidisciplinary Studies. After placing 2nd at the district level for Written Computer Science (in Core Java), I was recognized by the University Interscholastic League (UIL) for my performance at the Regional Academic Meet at Texas Tech University. It was thrilling and humbling to have had the opportunity to represent my school and its computer science department at prestigious events. Impressed by my achievements and leadership, my high school also selected me to receive the Northeast Tarrant Chamber scholarship last year.

Outside of academics, I am an Indian classical dancer and taekwondo athlete! I love doing henna, teaching others, and challenging myself (intellectually and physically). Speaking of teaching, I worked for 1+ years as a Code Sensei at Code Ninjas where I taught game programming to kids using Code Ninjas' game development platform (GDP) and Unity platform. The programming languages used were primarily JavaScript and C#, respectively.

It was purely out of interest that I started working on exploratory data analysis and visualization with Python. Due to my growing curiosity about machine learning, I immersed myself in a foundational course exploring machine learning, AI, and deep learning. I am amazed at the benefits of using AI and hope to balance the ethical uses for it. At the Responsible AI Lab, I look forward to learning more from my seniors and improving my research skillset.

Hello! I’m Krishna Patel, a junior in the Texas Academy of Mathematics and Science (TAMS) at UNT on the Computer Science track. My interests are in AI/ML, particularly reinforcement learning. I’ve developed technical skills in Python, C++, and JavaScript, on top of the rigorous math and CS courses at TAMS, which allow me to solve highly analytical problems.

In my free time, I enjoy running, lifting weights, and playing football. I also love listening to music; if you see me around campus, I’ll most likely have my headphones on. I’ve had a long-standing interest in artificial intelligence and enjoy reading about new trends and learning algorithms.

I’m excited to join the Responsible AI Lab and look forward to contributing to meaningful research that positively impacts people’s lives, while continuing to further my knowledge and passion for machine learning, especially reinforcement learning.

My name is Anish Sandaka, and I am an undergraduate Computer Science student at the University of North Texas. My primary interests include Responsible AI, Generative AI, and building practical AI systems that integrate large language models with real-world data and applications.

I have industry experience through software engineering internships, where I worked with Python-based systems, large language models, LangChain, Retrieval-Augmented Generation (RAG), and backend API workflows. I enjoy designing systems that combine AI models with databases and external tools to produce grounded and reliable outputs.

Through my coursework, projects, and research involvement, I aim to deepen my understanding of trustworthy and efficient AI systems while contributing to applied research in Responsible AI.

My name is Kenshin Kotari, and I am an undergraduate student in Electrical Engineering at the University of North Texas. My main interest is in how AI can help industrial process plants operate more safely and efficiently, and eventually make parts of their operation autonomous. I am also an undergraduate research asistant in the Control Systems Lab (CSL) at UNT, where I am learning about model predictive control (MPC) and economic MPC for complex dynamical systems. In the long term, my goal is to pursue a Ph.D. and work where process control, optimization, and responsible AI meet, with a focus on large-scale "smart plant" systems.

Before coming to UNT, I worked for three years as a field operator at ENEOS Marifu refinery in Japan, rotating through units such as atmospheric distillation and heat-exchanger networks. In that role, I was involved in day-to-day operation, troubleshooting, and several energy-saving efforts, including improving furnace efficiency and reducing fuel-gas consumption. Working so close to real equipment and operators gave me a practical view of both the potential and the risks of automation. These experiences motivate me to explore how reinforcement learning, MPC, and AI safety techniques can be combined to build trustworthy autonomous control systems that improve energy efficiency, safety, and sustainability in the process industries.

My name is Brian Li and I am currently attending Winston Churchill High School, where I am learning advanced mathematics, physics, and computer science. I am passionate about the possibilities of Artificial Intelligence in the rapidly changing world stage and how AI will shape the job industry in the future. I have experience in Python and enjoy writing and reading novels.