AI Researcher & Engineer
BSc in Computer Science & Engineering. Researching federated learning systems and LLM knowledge elicitation. Seeking a fully-funded Masters or PhD abroad to push the boundaries of AI.
About
I'm a CS graduate from American International University Bangladesh, where I completed my BSc in Computer Science and Engineering with a CGPA of 3.74. My academic journey has been defined by a deep curiosity about how intelligent systems learn, communicate, and scale.
My research sits at the intersection of distributed machine learning and large language model behavior — two areas I believe will define the next decade of AI development. I'm driven by problems that are both theoretically rich and practically meaningful.
Beyond research, I build software that solves real problems. I believe that good engineering and good science reinforce each other — the discipline of shipping code makes you a more rigorous thinker.
Research
Addresses one of federated learning's most persistent challenges: real-world deployments where clients hold statistically heterogeneous data and differ in computational capacity. The approach combines gradient-based client clustering with a Knowledge Distillation framework, enabling models to generalize across non-IID distributions without sacrificing client privacy or convergence stability.
Originally completed as part of the BSc CSE programme (graded A+), this work is undergoing further refinement toward publication.
Investigates the gap between what LLMs know and what they express. Large language models encode vast latent knowledge that standard prompting fails to surface. This work explores systematic methods to elicit this hidden knowledge — with implications for model interpretability, alignment, and evaluation.
This paper targets submission within approximately two months and positions itself within the growing literature on LLM introspection and probing.
Projects
A practical ledger application built in three days to manage ticket sales and financial reconciliation for a church revival meeting. Before this tool, end-of-night accounting required manual tallying across multiple counters — a process averaging 15–20 minutes per session. The software centralised all ticket transactions in real time, reducing close-out time to under 5 minutes across five consecutive events.
This is the kind of software that only gets built when you understand the problem from the inside. It's not a side project — it was used in production, by real people, under time pressure. Currently being refined with additional features.
Several practical software ideas currently in development. Each will be built to solve real problems — not to pad a portfolio.
Skills
∗ Surface-level familiarity
// Goals
"I want to do research that matters — and build the skills to keep doing it."
I'm actively seeking a fully-funded Masters, PhD, or joint programme abroad in AI or Machine Learning. My goal is to join a research group where I can contribute meaningfully from day one — not just as a student, but as a collaborator.
My research background in federated learning and LLM knowledge elicitation gives me a foundation to build on. I'm particularly interested in labs working on efficient and distributed learning systems, LLM interpretability, and the alignment between model capabilities and knowledge representation.
I come from Bangladesh, where access to computational resources and research mentorship is limited — but that constraint has taught me to be resourceful, rigorous, and deeply motivated. I'm not looking for a comfortable path. I'm looking for the right environment to grow into the researcher I know I can be.
If you're a professor or researcher whose work intersects with mine, I'd genuinely love to connect.
Contact
Whether you're a professor, researcher, collaborator, or just someone who found my work interesting — I'd love to hear from you.
Have a research opportunity or just want to talk? Drop a note below.