Computer Vision · Vision-Language Models · NLP
CS undergraduate and INSAIT research scholar working on vision-language models for satellite imagery and 3D scenes; currently an NLP intern at Graphwise, with a deep interest in aviation safety, originally from Lyaskovets, Bulgaria.
I am an undergraduate research scholar at INSAIT and Sofia University, advised by Dr. Danda Paudel, working on remote-sensing vision-language models and spatial reasoning in 3D scenes.
I am also an NLP research intern at Graphwise. Previously, I worked on quantitative ML at Man Group and on synthetic-image detection, adversarial robustness, and interpretability.
Outside research, I am interested in aviation safety and aircraft design — especially the Airbus A350. I also follow interesting aircraft on Flightradar24: digitally for now, plane spotting soon.
Sofia, Bulgaria
What I am exploring
Adapting vision-language models to satellite imagery using scalable supervision derived from OpenStreetMap.
With Mario Markov, Stefan Maria Ailuro, and Mohammad Mahdi · Advised by Dr. Danda Paudel
Connecting 3D point clouds with language representations for structured spatial reasoning and scene understanding.
With Runyi Yang · Advised by Dr. Danda Paudel
Where I have worked
Conducting NLP research on extracting and structuring information from text.
Lecturing and mentoring students preparing for the International Olympiad in Artificial Intelligence (IOAI).
Doing research at INSAIT in the area of Computer Vision — mostly working on 3D vision processing and Vision-Language models.
Conducted Quant ML research, focusing on developing and evaluating machine-learning models for systematic trading and signal forecasting.
Peer-reviewed work & preprints
A self-contained adaptation framework that uses OpenStreetMap data to generate geographic supervision without manual labels or a stronger external teacher.
A balanced 200K-image benchmark spanning four content types and multiple open and proprietary generators, with strong contrastive-learning baselines.
An empirical study of how adversarial training changes learned representations and makes computer-vision model decisions more interpretable.
Things I have built
A secure, bilingual platform for machine-learning competitions, built for Bulgarian AI Olympiad selection and national contests. It runs notebook and Python submissions in hardened Docker sandboxes and provides live leaderboards, double-blind jury review, custom evaluators, and distributed workers.
A dependency-aware C++ expression engine with polymorphic tokenization and two-stack infix evaluation.
A modular C++ audio workstation with timeline editing, file I/O, and an extensible effects architecture.
A 144 GB, 200K-image dataset for synthetic-image detection, balanced across photos, paintings, faces, and uncategorized imagery. It supports both real-versus-synthetic classification and generator identification across open and proprietary image generators.
A PyTorch implementation of gradient caching for scaling computer-vision contrastive learning beyond GPU memory limits. It reproduces large-batch gradients from smaller chunks and supports SimCLR, SupCon, SelfCon, mixed precision, and distributed training.
PyTorch training and evaluation tools for studying how adversarial robustness affects model interpretability, with SHAP, Integrated Gradients, representation inversion, and class-specific visualization workflows.
Academic background
Bachelor of Computer Science
Recognition
Research & engineering