Optuna Experts in Germany

• Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
• Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
• Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment

• Developed end-to-end reproducible ML pipelines (PyTorch) with data versioning (DVC), experiment tracking (MLflow), automated testing (PyTest), and CI/CD across all training workflows.
• Scaled Vision Transformer and CNN training across NVIDIA A100 GPU clusters (CUDA, DDP, SLURM); applied hyperparameter optimization (W&B Sweeps) to reduce training overhead and identify optimal configurations.
• Developed a real-time 3D human motion generation system (ViT, VQ-VAE, SMPL-X/PIXIE) for personality-conditioned avatar synthesis; achieved state-of-the-art FID = 6.15 and P-FID = 10.31 on the UDIVA benchmark.
• Validated model expressiveness through structured user studies, achieving 86% accuracy in distinguishing extroverted vs. introverted avatar behaviors.
• Optimized inference pipelines by deploying PyTorch models via TensorRT and ONNX Runtime into native C++ code; benchmarked performance.

Technical audit for scientific software.

• Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
• Independently designing analytics solutions with Python, SQL, etc.
• Designing and implementing ETLs and data pipelines
• Creating and maintaining APIs
• Independently applying CI/CD, testing, and version control
• Data modeling
• Model development and optimization
• Anomaly detection with AI
• Predictive analytics

Used technologies:

• Snowflake
• Fabric
• Azure Synapse Analytics
• Azure DataFactory
• Azure Data Lake
• Azure DevOps
• Databricks
• Spark
• CI/CD
• SQL Database
• Python
• Power Platform

• Developed and optimized LSTM-RNN and Decoder Transformer models to predict vehicle trajectory during target loss events in Adaptive Cruise Control systems, achieving 20% improved predictive accuracy over baseline models.
• Engineered novel data preprocessing pipeline from real road campaign data, processing multi-sensor time series data, generating 300+ training snippets.
• Implemented Bayesian hyperparameter optimization and applied physical constraints to prevent model run-away behavior, resulting in 30% smoother acceleration profiles.
• Extended existing patented technology for AI-assisted ACC function improvements, building upon foundational work to enhance network performance.
• Tools: Python, TensorFlow, Keras, Optuna, CarMaker

• Innovative prompt engineer with expertise in generating and refining prompts specifically for computer science-related images.
• Proficient in developing responses that enhance machine learning models' understanding of visual data in the computer science domain.

• Focused on exploring the intersection of Music and AI.

• Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
• Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
• Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS