AI Researcher & Engineer
Avishek
Shrabon
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
Building towards
meaningful AI research
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 — finishing the degree in 3.5 years. 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.
Since mid-2022 I have worked as a private tutor, teaching Physics, Chemistry, Biology, Mathematics, Higher Mathematics, and ICT to students from Grade 8 through 12. Maintaining 3–4 students simultaneously — five days a week — throughout my degree taught me how to communicate complex ideas clearly, manage my time under pressure, and stay financially independent while pursuing research.
Journey
How I got
here
The honest story of how someone with no ML background taught themselves federated learning and produced original research — in six weeks, mostly alone.
Self-directed learning under pressure is the most important skill a researcher can have. I found that out the hard way — and I'm grateful for it.
No ML background. A thesis to write.
In early 2025, I was assigned a federated learning thesis with three teammates — two of whom had completed Machine Learning and CVPR courses. I had taken neither. My only formal AI exposure was a survey course covering search algorithms. I didn't know what a forward pass was, what backpropagation meant, or how Knowledge Distillation worked.
From zero to research-ready.
I spent roughly six weeks building my foundation from scratch — working through Google, YouTube, research papers, and the Flower federated learning framework to understand the field. I mapped out the terminology, the architecture patterns, the math behind gradient flow and distillation. By the end I could read papers, understand the tradeoffs, and think about the problem independently.
Leading the technical development.
My two most technically experienced teammates were doing their internships simultaneously and couldn't dedicate time to the thesis. I was left as the sole technical contributor. Rather than wait, I moved forward — designing the entire pipeline, making architectural decisions, and writing most of the codebase independently.
A novel approach, proposed from scratch.
I proposed combining gradient-based client clustering with a Knowledge Distillation setup to handle heterogeneous data and client distributions in federated environments. I presented this to our supervisor, who approved the direction. The approach became the core contribution of the paper.
A+ and a paper approaching publication.
The thesis was graded A+. The paper is currently being refined for conference submission. The experience taught me that self-sufficiency is not just a skill — it's a research necessity. A PhD environment will have no shortage of moments where you have to figure things out alone. I already know I can.
Research
Active work in AI systems
Adaptive Federated Learning with Heterogeneous Data and Client Distributions
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 refinement toward conference submission.
Hidden Knowledge Elicitation in Large Language Models
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.
Targeting submission within approximately two months. Positioned within the growing literature on LLM introspection and probing.
Projects
Software and hardware that ships and works
Church Event Ledger & Ticket Reconciliation System
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 — averaging 15–20 minutes per session. The software centralised all transactions in real time, reducing close-out time to under 5 minutes across five consecutive events.
Built under time pressure, used in production, by real people. Currently being refined with additional features.
Proximity Sensor Aid for the Visually Impaired
A wearable proximity detection system designed to assist blind users in navigating their environment. Built around an Arduino Nano with an HC-SR04 ultrasonic sensor for mid-range detection and an experimental Time-of-Flight sensor for close-range precision. The device provided haptic and audio feedback on obstacle proximity.
Identified key limitations honestly: the prototype was too bulky for practical daily use, and AI-based object recognition — the intended next step — proved cost-prohibitive at the time. A clear improvement path exists.
Darkness-Triggered Street Light Automation
An automatic street lighting system using a light-dependent resistor to detect ambient darkness and trigger relay-controlled lighting. Designed as an energy-efficient alternative to timer-based systems, activating only when environmental conditions require it.
Coursework project demonstrating practical application of sensor-based automation and circuit design.
Hotel Management System
A full-featured hotel management system built as coursework, handling room booking, guest records, billing, and occupancy tracking. Emphasis was on high-logic system design — the primary goal of the course.
Group project. Source code lost to a system reset — a lesson in version control that led directly to consistent Git usage afterward.
Skills
Tools of the trade
∗ 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 taught myself federated learning from scratch, carried a thesis team alone, and produced original research without the prerequisite coursework. 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
Let's connect
Whether you're a professor, researcher, collaborator, or just someone who found my work interesting — I'd love to hear from you.
Send a message
Have a research opportunity or just want to talk? Drop a note below.