My primary coding language, used extensively in research, internships, and personal projects.

My preferred deep learning framework. Proficient also with torchvision, torchaudio, and torchsummary.

ViT, Swin, DeiT, ResNet, U-Net, MAE; SSL (MoCo, DINOv2).

BERT, GPT, T5, RoBERTa; fine-tuning, prompt engineering, in-context learning, RAG, instruction tuning.

Highly skilled in containerizing applications for edge AI deployment and simulation workflows.

Skilled in database queries, schema design, and relational DB management, primarily from my load forecasting internship.

Proficient in version control. Mostly use GitHub, but also have experience in GitLab.

Robust experience, especially in HPC contexts (CUDA, MPI, OpenMP).

Proficient in shell scripting and command-line workflows; using it as the main OS for AI projects (through VMs).

Working with large-scale pre-trained models and fine-tuning (few-shot and zero-shot).

Combining text, vision, audio, and other modalities (e.g., CLIP, Flamingo, Speech-to-Text).

Experience with ONNX, TensorFlow, Keras, JAX, and OpenVINO—especially converting models among frameworks.

Distributed Data Parallel for large-scale deep learning on multiple nodes.

Tracking experiments, metrics, and visualizations.

Forecasting and modeling techniques (especially in the electricity market).

GPU parallel computing for accelerating numerical workloads (in both Python and C++).

Techniques to reduce model size and compute load (e.g., EfficientViT, DynamicViT, NViT).

Accelerating training without performance impacts.

Reducing model precision for inference efficiency (edge deployment).

Proficient in scheduling and managing compute-cluster jobs on HPC systems.

Message Passing Interface for parallel computing on distributed-memory systems (in Python and C++).

Profiling and optimizing resource usage for high-performance computations.

Highly skilled in numerical methods, simulations, and control engineering (including Simulink).

Experience with HTML, CSS, and JavaScript (e.g., this website, advanced data visualizations).

Proficient in working with virtual machines via SSH (at EPFL, ESA, etc.).

Experience with deploying and scaling applications on GCP, primarily from my load forecasting internship.

Proficient in SentinelHub API, MajorTOM, and Copernicus Browser.

Experience with GDAL, Raster, GeoTIFF, GeoPandas, PyProj, Folium, etc.

Knowledge of solar energy generation, heat transfer, green hydrogen, desalination, and more.

Experience with market dynamics, pricing models, and trading of electric power.

Gained experience during a research project (soon to be published) on multi-agent Bayesian optimization.