Вадим

Vadym Tunik Email : [відкрити контакти](див. вище в блоці «контактна інформація»)
Mobile : [відкрити контакти](див. вище в блоці «контактна інформація»)

Experience
Huawei R&D Kyiv, Ukraine
•
RnD Software Engineer Jan 2024- Present
◦ Designed a state-of-the-art machine learning-driven methodology utilizing PyTorch/SciPy to
predict thermal performance from high-fidelity CFD Simulation data with 99.1% acc. for cooling system
optimization.
◦ Developed thermal engineering software leveraging Python, PySide6, and pymoo, which accelerated
the design of thermally efficient liquid cooling systems for microelectronic chips by 4x faster.
◦ Led a multi-disciplinary team of six during a critical transition period, bridging developers and
scientists to guarantee the on-time completion of the department’s top project of the year.

Education
Mälardalen University Västerås, Sweden
•
Master of Mathematics (Erasmus+)

Taras Shevchenko National University of Kyiv Kyiv, Ukraine
•
Master of Statistics

Taras Shevchenko National University of Kyiv Kyiv, Ukraine
•
Bachelor of Computer Mathematics (Honors Diploma)
◦ GPA: 3.87/4.00
◦ Program Focus: fundamental math, advanced algorithms, machine learning.
◦ Qualification: researcher in the field of data analysis.

Projects
• AVE - AI teacher assistant: Designed an AI-driven platform for STEM teachers to automate
personalized feedback and grading for handwritten student assignment images.
• MoA+: Mixture of Autoencoders with Varying Concentrations for Enhanced Image Clustering. This
architecture’s primary contribution is solving the critical problem of expert dominance. Demonstrated
effectiveness on clustering mel-spectrograms of real-world military action sounds (presentation, code).
• Heat Transfer Simulation: Developed a high-performance 2D heat equation solver, parallelizing the core
computation with MPI for execution on distributed memory systems using C++ and Python. (code).

Certifications
• High-Dimensional Probability for Data Science by Roman Vershynin.
• Deep Learning Specialization by DeepLearning.AI.
• Machine Learning Specialization by Stanford University & DeepLearning.AI.

Hard skills
• Languages: Python, C/C++, Kotlin, SQL.
• Core CS: DS & Algorithms, OOP, Machine learning, Deep learning, Computer Vision.
• Core Math: Optimization, Clustering, Probability theory, Statistics, Computational geometry.
• Technologies & Frameworks: TensorFlow, PyTorch, NumPy, SciPy, Scikit-learn, Matplotlib, Pandas,
PySide6, Ceres, pymoo, OpenCV, MPI, async, Git.