Projects, Research, and Contributions.

Here you can find some of my personal projects, research, and contributions. Here you can check my work around Software Engineering, Data Science/Engineering/Analytics, and Web Development.

  • Enhancing Intraoperative Registration with Neural Radiance Fields: An Exploration of Loss Functions Effects

    Abstract:
    This thesis explores an innovative approach to intraoperative brain registration by utilizing Neural Radiance Fields (NeRFs) as differentiable, implicit representations of brain surface geometry and appearance. Unlike conventional mesh-based techniques, NeRFs allow for direct optimization of camera positions via backpropagation, significantly enhancing alignment accuracy between preoperative and intraoperative imaging.
    We introduce a robust, model-agnostic implementation of neural registration within the nerfstudio framework, overcoming previous limitations regarding customization and adaptability. The primary scientific contribution involves a comprehensive analysis of multiple loss functions—including L1, L2, Structural Similarity Index (SSIM), Normalized Cross-Correlation (NCC), and Mutual Information (MI)—to assess their impact on registration accuracy, convergence rate, and stability.
    Experimental results demonstrate that while L1 loss offers rapid and stable convergence, MI and NCC are notably resilient to NeRF-generated visual artifacts, providing insights crucial for clinical applications. By advancing NeRF-based registration techniques, this work contributes directly toward improving the precision and reliability of image-guided neurosurgical procedures.

    Research
    Neural Radiance Fields
    Neural Registration
    Neurosurgery

    Link to the paper

  • Practicum Report Paper: Evaluating Test Data Quality by Measuring Latent Space Coverage, Density, and Sparsity with Deep Neural Networks

    In this practicum we covered a novel method to assess the quality of test data within neural networks by examining Latent Space Coverage (LSC) and the density/sparsity of the samples generated in this space. Utilizing the Neural Coverage (NLC) fuzzing criterion in an existing fuzzing framework, we applied it to a self-trained LeNet-5 model on the widely-recognized German Traffic Sign Benchmark (GTSRB) dataset. Our findings illuminate the challenges and considerations in high-dimensional spaces when measuring sparsity and density of the latent space, as well as potential solutions for addressing computational resource issues in dealing with complex models.

    Research
    DNN Testing
    Fuzzing
    Latent Space Coverage
    Sparsity & Density
    GTSRB

    Link to the paper

  • Seminar Paper: Protecting the Grid from Wildfire through Data Analytics

    This seminar paper dives deep into the escalating threat of wildfires to power grid infrastructure and local communities, analyzing natural and man-made causes. The paper utilizes data-driven approaches to propose a comprehensive framework for wildfire prevention, early detection, and disaster relief strategies. We spotlight wildfire prediction techniques using physical simulations and real-world data, as well as the use of fault localization as an early warning instrument. The paper explores the role of sensors and data engineering in these processes, focusing on their use in transmission systems. Furthermore, we discuss proactive grid control as a crucial strategy in wildfire scenarios, all while offering an innovative tri-layer solution for serving shut-off areas with alternative power sources. A strong emphasis is placed on using remote sensing technologies like LiDAR for threat identification and establishing response strategies.

    Research
    Data Engineering
    Data Analytics
    Predictive Modeling

    Link to the paper

  • checkBay - Fact Checking NLP System

    With this application our team won a second place at the "TUM.ai AI4Good Hackathon". Our application CheckBay is designed to combat misinformation across all major social networks and beyond by cross-checking all publicly available sources and providing users with relevant ones.

    ML Project
    NLP
    Vector Search
    Hackathon

    Link to the post

  • AI4Good Workshops - Organizer & Lecturer

    AI4Good Workshop by TUM.ai is a two-fold program in a cooperation with the biggest university in Ghana — KNUST. Over the course of two weeks, participants were introduced to the basic principles of artificial intelligence, various application domains and how AI can be used to solve challenges in Ghana.
    For the second week, teams were formed to address a specific real-world problem. At the end of the workshop, the participating teams presented their results and achievements to a jury.

    AI4Good
    AI Education
    Lecturer
    Volunteering

    YouTube Playlist

  • TidyAI - Automating Data Cleaning

    As part of the automated agents hackathon, I designed, developed, and deployed a web application.

    The app focuses on automatically cleaning and exploring the data, as well as designing new features for seamless Machine Learning Development.

    You can test the application here: app.tidyai.tech

    Web Dev & LLM Agents
    LangChain
    GCP
    UX / UI
    Design
    Next.js
    Tailwind CSS
    chatGPT API

    Link to full project description (github, demo, presentation)

  • Portfolio Website

    This website! I built it to showcase my work, to learn Next.js + tailwind, and improve my ux/ui skills.

    Web Dev
    UX / UI
    Next.js
    Tailwind CSS

    podolskyi.io

  • be_certAIn - Career Recommendation Application

    The project involved developing an app that collects user information such as skills, hobbies, and free text input. The data is processed using (at that time) OpenAI's GPT-3 model (now, switched to GTP-3.5), which generates personalized career suggestions based on the input. The app provides explanations of suggested careers. The project aimed to offer a quick and personalized career test with a user-friendly interface, while ensuring data privacy and including a wide range of career options.

    Personal Project
    GPT
    Hackathon

    Link to the pitch deck

  • The Humanitarian Aid Hotline for Ukraine

    "The Humanitarian Aid Hotline" is a humanitarian project developed by a team of dedicated data scientists based in Munich, amidst the backdrop of the Ukrainian crisis. Functioning akin to an emergency hotline, this innovative platform seamlessly connects displaced individuals with suitable relief organizations, optimizing speed and efficiency. Leveraging technology and multilingual volunteers, refugee inquiries are promptly directed to appropriate aid networks. The ultimate vision is to open source the system's code, empowering the creation of similar call centers globally, thus redefining emergency response during crises.

    Software Project
    Data Engineering
    Backend Development
    Volunteering

    Link to the certificate and description