LSU Computer Science graduate (Summa Cum Laude, GPA 4.267) and incoming MSCS student at UNC-Chapel Hill, building reliable software and practical AI systems.
I'm a software engineer and AI researcher focused on machine learning systems, applied NLP, and production software delivery across academic and industry projects.
I recently graduated from LSU with a BS in Computer Science and a Minor in Mathematics, and I am starting the MSCS program at UNC-Chapel Hill. I focus on high-impact technical outcomes: improving model performance, shipping maintainable code, and translating research into systems that support real users.
BS Computer Science (Software Engineering)
Minor in Mathematics
Louisiana State University (2026)
GPA: 4.267
Incoming MSCS, UNC-Chapel Hill
AI/ML and NLP systems
LLM evaluation and retrieval
Programming-language intelligence
AI/LLM course instruction support
LSU Stamps Scholarship Recipient
LSU Distinguished Researcher
Tau Beta Pi Engineering Honors Society Member
LSU CXC Distinguished Communicator
LSU Tiger Twelve Member
College Honors, Summa Cum Laude
LSU McLaughlin Medalist
Prototyped a Meta Quest 3 desktop environment focused on active work sessions, including gesture-driven controls and usability validation with early testers.
Built a security-focused Blazor app with LDAP authentication, role-based access controls, and encrypted messaging for enterprise collaboration scenarios.
Implemented a full compiler in Java using JLex and CUP, covering lexical analysis, parsing, semantic analysis, and code generation for an extended ANSI C specification.
Presented system architecture, deployment approach, and impact metrics for LSU's campus AI assistant.
Citation: Alam I, Nguyen J. MikeGPT: Enhancing LSU with AI. LSU Discover Day Undergraduate Research and Creativity Conference. 2025 April 25; Louisiana State University, Baton Rouge, Louisiana.
Technical lecture segment covering retrieval diagnostics, grounding quality, and evaluation strategy.
If embed playback is blocked, watch on YouTube.
Conference presentation on Programming Encoder Classification Analysis Network (PECAN) and language identification results.
Citation: Alam I. PECAN: Programming Encoder Classification Analysis Network. Stanford Research Conference. 2026 April; Stanford University, Stanford, CA.
Overview of model architecture, dataset scale, and performance outcomes for language identification research.
Read the summary →Documentation of retrieval workflows, governance, and safety controls used in production releases.
Dive into the architecture →Reach out for software engineering roles, AI/ML collaborations, or research conversations.