Alimzhan Yerkebulan

My name is Alimzhan Yerkebulan. I was born on March 19, 2002, in Almaty. I graduated from IITU/KazNU with a major in Data Science/Big Data. I work as a programmer at IIСT.

The reason I chose a doctoral program is my desire to expand familiar boundaries and gain a deeper understanding of complex systems. What attracts me most is the opportunity to build, with my own hands, a bridge between rigorous formal models and practical prototypes. That is why I chose the “Information Security” track: it brings together mathematics, software engineering, and experimental design, with results evaluated immediately by measurable metrics. My professional foundation is machine learning and cryptography. I build research pipelines in Python and C and make experiments fully reproducible—from data collection to the automation of computational workflows. To measure the avalanche effect in hash functions, analyze round-by-round diffusion, and estimate error probabilities, I have developed a set of my own tools; next I want to adapt them for testing post-quantum cryptosystems. In science, I am inspired by the combination of proof-driven rigor and engineering intuition: on the one hand—theorems and metrics; on the other—real-world data and noise-robust algorithms.

This motivation leads me to three major directions: post-quantum cryptography, ML for security tasks, and reliable software tooling. In the first, my goal is to model lattice schemes, assess their differential properties using quantitative metrics, and experimentally test their resilience to side channels. In the second, I aim to design models for adaptive authentication and network anomaly detection and to propose methods that reduce false positives. In the third, I plan to assemble benchmark datasets for lab work and an open-source library: algorithms, test scenarios, and evaluation metrics should be stored in one place and structured so others can easily reproduce them. These directions push me to seek new ideas every day: sometimes I turn to formal verification to strengthen the evidential base, sometimes I run visual analyses to understand model behavior, and sometimes I optimize low-level code.

My main goal in the PhD is to develop my own scientific style: to formulate precise questions, turn them into measurable hypotheses, carefully replicate results, and present them briefly and clearly. Over the next three years, I plan to prepare several high-quality publications and build at least two working prototypes—a post-quantum module and an adaptive authentication component. In parallel, I will maintain an open repository and systematize benchmark data, code, and report templates. In the long term, I want to grow my research into a small team and foster a sustainable experimental culture at the intersection of applied cryptanalysis and ML security. For me, this path is not for show but to truly hone my skills and make the acquisition of new knowledge a daily practice.