👋 Volodymyr Vakhniuk
I am specializing in Deep Learning and its applications in Computer Vision and Language Processing. I am primarily focusing on Generative AI - fantastic blend of advanced math with practical applications.
Explore My Projects
Explore my GitHub, where I implement and play with cool machine-learning models and more!
👉 My GitHub 👈⭐ Selected Projects ⭐
Survey on Visual Reasoning Methods
V. Vakhniuk, A. Sarkar
Recent deep learning methods proved to surpass human-level performance on many detection, recognition, and tracking tasks. Despite these advancements, the reasoning abilities of deep learning methods are quite distant from those of humans, signaling the importance of enhancing AI models with reasoning capabilities. This project conducts a survey/tutorial on current visual reasoning approaches, focusing on Visual Question Answering (VQA), exploring various methodologies, including graph-based, neural symbolic, and large pretrained vision-language models, discussing their applications and effectiveness.
STAC: Leveraging Spatio-Temporal Data Associations
V. Vakhniuk, A. Sarkar, R. Gupta
This project proposes STAC, an efficient cross-cameras surveillance system leveraging spatio-temporal associations to provide real-time analytics and inference under constrained network environments. STAC integrates omni-scale feature learning for accurate people reidentification, combined with frame filtering and state-of-the-art compression, optimizing video transmission cost while maintaining high accuracy for real-time inference.
Linear Transformation Visualizer
V. Vakhniuk, M. Parovyi
This project was created in the context of the 2021 Summer of Math Exposition (SoME1) announced by Grant Sanderson (3blue1brown). It implements an easy-to-use tool that students who start learning about linear algebra can use to ease their understanding of the subject.
🧑🎓 Education
Master's in Computer Science
August 2022 - December 2023
Bachelor's in Computer Science
August 2018 - May 2022
Relevant Courses
Computer Science (UIUC)
- CS 543 Computer Vision
- CS 444 Deep Learning for Compt Visn
- CS 598 Deep Generative & Dyn. Models
- CS 412 Introduction to Data Mining
- CS 450 Numerical Analysis
Math (UIUC)
- STAT 510 Mathematical Statistics
- MATH 564 Applied Stochastic Processes
- ECE 563 Information Theory
- MATH 540 Real Analysis
Computer Science (UIC)
- CS 418 Introduction to Data Science
- CS 421 Natural Language Processing
- CS 412 Intro to Machine Learning
Math (UIC)
- STAT 381 Applied Statistical Methods
- MATH 310 Applied Linear Algebra
- MATH 414 Analysis II
- MATH 330 Abstract Algebra
- MATH 313 Analysis I
Continuous Learning
My passion for knowledge doesn't stop at formal education. I currently audit:
- Partial Differential Equations
- Statistical Physics
To deepen my understanding of energy-based models — a significant historical thread in machine learning — and diffusion models, which represent the state of the art in image generative models.
To greatly boost 🚀 my learning, I am actively employing the following study techniques:
- Mindmaps
- Inquiry-Based Learning
- Pre-study
- Feynman Technique
- Flowmodoro
- Active Recall
🛠️ Relevant Tools
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