AI Engineer · ML Researcher · Applied Mathematics
Building intelligent systems, conducting machine learning research, and sharing knowledge through technology and education — grounded in applied mathematics.
Currently
AI Engineer & ML Researcher
@ Alfa Center
// About
Identity
I come to artificial intelligence from applied mathematics rather than a traditional computer-science path. For me, a model is not a black box to be tuned by trial and error — it is an optimization problem, a probability distribution, a signal to be decomposed and understood. That analytical foundation shapes how I frame problems, choose methods, and reason about why a system works.
As an AI Engineer & ML Researcher at Alfa Center, I build and experiment with machine-learning systems end to end: framing the problem, preparing data, developing and training models, and interpreting results. My focus sits at the intersection of deep learning, signal processing, and healthcare AI — domains where mathematical rigor and real-world impact meet.
Beyond engineering and research, I create educational technology content and teach IELTS — practice that sharpens how I communicate complex ideas clearly. I learn aggressively and continuously, and I am driven by curiosity about how intelligence, both natural and artificial, can be modeled and understood.
// Experience
Alfa Center
Design, build, and research machine-learning systems end to end — from problem framing and data analysis to model development and experimentation.
// Selected Work
A selection of AI, machine-learning, and data projects — from healthcare signal models to applied research.
// Research
My research sits where applied mathematics meets machine learning — methods grounded in optimization, statistics, and signal analysis.
Foundations of intelligent systems and how learning and reasoning can be modeled.
Learning algorithms, generalization, and the statistics that govern them.
Neural architectures and training dynamics for complex, high-dimensional data.
Applying ML to medical signals and data to support diagnosis and care.
Decomposing and analyzing signals — the mathematical bridge to real-world data.
Making model decisions interpretable, transparent, and trustworthy.
Research roadmap
Deep learning on physiological signals (ECG) for diagnosis, grounded in signal processing.
Interpretable models and the optimization theory behind reliable, efficient training.
Graduate research bridging applied mathematics and machine learning for real impact.
“I believe the most durable AI is built on mathematical understanding, not just engineering intuition. Optimization, probability, and signal analysis are not background details — they are the tools that let us reason about why a model works, when it will fail, and how to make it trustworthy.”
// Skills
A mathematics-first stack: foundations underpin the machine-learning and engineering layers above them.
// Content
I share AI, programming, and developer-tools content — and motivation for students — reaching a growing technical audience.
I create educational technology content on AI, programming, and developer tools — alongside motivational content for students. Communicating complex ideas to a broad audience keeps my own thinking sharp and clear.
Topics
// Teaching
I teach IELTS and help students improve their English communication and confidence. Teaching sharpens the skills that make a good researcher and engineer: explaining clearly, mentoring others, and speaking with precision.
Guiding students toward their goals with structured support.
Explaining complex ideas simply, in writing and in speech.
Confident, clear delivery to audiences of every size.
Turning what I learn into resources others can use.
// Open Source
Code, experiments, and consistency — straight from my GitHub.
Murtuja Kayes
@murtuja43AI Engineer & ML Researcher · Applied Mathematics
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