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AI Engineer · ML Researcher · Applied Mathematics

Murtuja Kayes

>AI Engineer

Building intelligent systems, conducting machine learning research, and sharing knowledge through technology and education — grounded in applied mathematics.

Murtuja Kayes
Open to roles

Currently

AI Engineer & ML Researcher

@ Alfa Center

0+AI Projects
0K+Video Reach
0+Social Audience
MultipleResearch Initiatives

// About

The mathematician's approach to AI.

Identity

AI EngineerML ResearcherApplied MathematicsHealthcare AIEducator
Currently
AI Engineer & ML Researcher @ Alfa Center
Studying
Applied Mathematics, Kazakhstan
Focus
Deep Learning · Signal Processing · Healthcare AI
Mindset
Research-driven · Continuous learning

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.

Mathematics first — engineering and research follow from it.

// Experience

Where I build and research

  • AI Engineer & ML Researcher

    2024 — Present

    Alfa Center

    Design, build, and research machine-learning systems end to end — from problem framing and data analysis to model development and experimentation.

    • Conduct research-oriented machine-learning work, translating analytical and mathematical methods into working systems.
    • Develop and train deep-learning models, including signal-based architectures for healthcare applications.
    • Run data analysis and experimentation workflows: preprocessing, feature engineering, evaluation, and iteration.
    • Solve technical problems across the ML lifecycle, balancing mathematical rigor with engineering pragmatism.
    PyTorchPythonDeep LearningResearchSignal Processing

// Selected Work

Projects

A selection of AI, machine-learning, and data projects — from healthcare signal models to applied research.

ECG-Based Myocardial Infarction Detection

Deep learning for automated cardiac diagnosis from ECG signals.

PythonPyTorchDeep LearningSignal Processing

Lung CT Scan Analyzer (3D)

AI-powered lung CT analysis with interactive 3D visualization.

PythonDeep LearningMedical Imaging3D Visualization

ZhanAI

A full-stack AI application, from ML core to web interface.

PythonTypeScriptDockerMachine Learning

Breast Cancer Detection

Machine-learning classification for breast cancer detection.

PythonJupyterScikit-LearnMachine Learning

// Research

Research interests & direction

My research sits where applied mathematics meets machine learning — methods grounded in optimization, statistics, and signal analysis.

Artificial Intelligence

Foundations of intelligent systems and how learning and reasoning can be modeled.

Machine Learning

Learning algorithms, generalization, and the statistics that govern them.

Deep Learning

Neural architectures and training dynamics for complex, high-dimensional data.

Healthcare AI

Applying ML to medical signals and data to support diagnosis and care.

Signal Processing

Decomposing and analyzing signals — the mathematical bridge to real-world data.

Explainable AI

Making model decisions interpretable, transparent, and trustworthy.

Research roadmap

Now

Healthcare signal models

Deep learning on physiological signals (ECG) for diagnosis, grounded in signal processing.

Near-term

Explainability & optimization

Interpretable models and the optimization theory behind reliable, efficient training.

Future

Research at scale

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.

— Research philosophy

// Skills

Technical toolkit

A mathematics-first stack: foundations underpin the machine-learning and engineering layers above them.

Mathematical Foundations

Core differentiator
  • Optimization
  • Statistics
  • Linear Algebra
  • Signal Processing
  • Mathematical Modeling

Machine Learning

  • Deep Learning
  • Data Science
  • NumPy
  • Pandas
  • Matplotlib

AI / ML Stack

  • Python
  • PyTorch
  • TensorFlow
  • Scikit-Learn

Development

  • React
  • Next.js
  • TypeScript
  • Tailwind CSS

Tools

  • Git
  • GitHub
  • Linux
  • Docker
0+
AI Projects
0K+
Video Views
0+
Social Audience
Multiple
Research Initiatives

// Content

Tech content creator

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

AIGitHubProgrammingDeveloper ToolsTechnology

// Teaching

IELTS Instructor & Mentor

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.

Mentorship

Guiding students toward their goals with structured support.

Communication

Explaining complex ideas simply, in writing and in speech.

Public Speaking

Confident, clear delivery to audiences of every size.

Knowledge Sharing

Turning what I learn into resources others can use.

// Open Source

Building in public

Code, experiments, and consistency — straight from my GitHub.

MK

Murtuja Kayes

@murtuja43

AI Engineer & ML Researcher · Applied Mathematics

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PythonTypeScript

// Contact

Let's build something

Open to AI internships, ML engineering roles, research assistantships, and graduate scholarships. Reach out — I respond to every message.

Open to AI internships, research collaborations & scholarships.

murtujakayes@gmail.com