Nice to meet you,
I'm Chris.
CS student at Georgia Tech specializing in AI and Cybersecurity. I build secure, scalable systems and ML-powered applications, bridging research and production with a focus on real-world impact.
How I got into building
My journey started with a curiosity for solving real problems with code. I quickly moved from small ML experiments into full-stack applications: chatbots, investment tools, aviation safety dashboards. Over time I gravitated toward the intersection of AI and security, systems that are not just intelligent, but trustworthy and resilient.
My first research work
In 2023, I built an object detection system to identify bruise damage on fruit using Detectron2: custom-labeled data, fine-tuned across produce types. That led to a full paper on deep learning in orthopedic imaging, achieving 94.2% classification accuracy. One project unlocked the next.
Tools & Technologies
Where I've worked
Teaching Assistant
Part-time
Georgia Institute of TechnologyTA for CS3001 - Computing and Society, which examines the role and impact of information and communication technology in society. Lead recitation sessions on topics like intellectual property, privacy, algorithmic bias, and the societal impact of AI. Provide guidance on technical writing and argument construction during office hours to help students articulate complex ethical dilemmas.
Research Assistant
PURA RecipientGeorgia Institute of TechnologyAwarded the President's Undergraduate Research Award to conduct research under Dr. Ryan Shandler, developing a novel mobile instrument to quantify cyber threat perception without relying on self-reported surveys. Contributing to a DARPA-aligned research effort evaluating how cyberattacks undermine public trust in political institutions. Utilizing ML to perform quantitative analyses on behavioral telemetry, modeling how user interaction traces shift pre and post a simulated cyberattack.
Software Engineer Intern
Full-time
MostEdgeEngineered a RAG pipeline leveraging a private LLM to analyze market trends across SQL databases, improving query response accuracy by 95%. Optimized context handling and memory management across multi-turn interactions, reducing hallucination frequency by 22%. Designed a responsive UI enabling natural language querying with contextual filters, cutting analyst query time by 40%.
Research Assistant
Part-timeGeorgia Institute of TechnologyResearched hallucinations in LLMs and their impact on user decision-making, analyzing over 1,500 responses across GPT, LLaMA, and Mistral. Categorized hallucination types and benchmarked model reliability, achieving a taxonomy that improved evaluation consistency by 30%. Discovered and tested countermeasures including retrieval augmentation and prompt engineering, reducing hallucination rates by up to 18%.
Education & Credentials
B.S. / M.S. in Computer Science
Specialization: Artificial Intelligence and Cybersecurity / Machine Learning
GPA: 3.55 · Dean's List
Certification in Cybersecurity
Two 10-week courses covering threat detection, vulnerability assessment, penetration testing, social engineering, and IPS/IDS log monitoring. Completed with Honors.
Honors · Intermediate + IntroResearch & Writing
Deep Learning in Orthopedic Imaging: Detectron2 for Knee Osteoarthritis Detection and Grading
Nov 27, 2025ASME International Mechanical Engineering Congress and Exposition (IMECE2025)
Applied Detectron2 with a Faster R-CNN ResNet50-FPN backbone to automate KOA detection and severity grading from X-ray images. Achieved 94.2% classification accuracy with strong bounding box performance (AP50: 91.6%). Validated potential for objective OA severity assessment supporting early clinical diagnosis.