ML engineer

I'm a multidisciplinary ML engineer with a background in Computational Neuroscience, AI, and creative software development. I enjoy building systems that explore the intersection of brains, code, and tools for insight. Trained as a scientist, I combine strong academic/research skills with hands-on engineering across disciplines. I enjoy making things that serve real needs, whether it's for science/education, safety, health, efficiency, or everyday utility. If something can make life better for a lot of people, I'm excited to build it.

Areas of expertise

I design intelligent tools:

AI & ML – generative models, custom training loops, prototyping, evaluation (PyTorch, Hugging Face)
software engineering – GUI development, webscraping, web development, debugging, deployment, version control (Git), environment management (Conda, Pip, Shell), remote workflows (SSH)
data & backend systems – pipelines, scraping, automation, dashboards
neuro-inspired modeling – decoding/encoding models, inductive biases, foveated vision, eye movement prediction, behavioral experiments, human-computer interaction, receptive field mapping, fMRI/MUA signal analysis
creative tools – browser demos, interactive experiments, game development (Unity & C#)
academic skills – peer-reviewed writing, experimental design, literature reviews
languages – Python, C#, C++, MATLAB, SQL, LaTeX, HTML

Highlighted projects

Inverse Receptive Field
End-to-end image reconstruction from brain activity using attention-based receptive field modeling

transformer architecture · attention modules · PyTorch · neural decoding · CNN · spatial modeling · feature weighting · interpretability

MonkeySee (NeurIPS)
Reconstructing natural images from macaque brain data

machine learning · CNN · GAN · PyTorch · temporal dynamics · image data · OpenCV · preprocessing · Git · SSH · Conda

Brain2Pix
Reconstructing video frames from human brain activity

video reconstruction · generative models · CNN · MXNet · Python · dynamic signals · OpenCV · evaluation

Adversarial Attention
Adversarial image generation for both ML models and the human visual system

model ensembles · PyTorch · saliency maps · behavioral experiment design · eye tracking · infrared camera · EyeLink · MATLAB · statistics · GUIs · user interfaces · model optimization · plotting

ClimberSoap
Creative climbing-inspired soaps

product design · entrepreneurship · branding · web hosting · e-commerce

MyPinger
Tracking item prices across the web

web scraping · automation · C++ · Selenium

👉 for more projects, visit the projects page

📚 Academic papers

Le, L., Ambrogioni, L., Seeliger, K., Güçlütürk, Y., van Gerven, M., & Güçlü, U. (2022). Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity. Frontiers in Neuroscience, 16, 1684. [link]

Le, L., Kimman, N., Dado, T., Seeliger, K., Papale, P., Lozano, A., & Roelfsema, P. (2025). Neural encoding with affine feature response transforms. arXiv:2501.03741. [link]

Le, L., Dado, T., Seeliger, K., Papale, P., Lozano, A., Roelfsema, P., & Güçlütürk, Y. (2025). Inverse receptive field attention for naturalistic image reconstruction from the brain. arXiv:2501.03051. [link]

Le, L., Papale, P., Seeliger, K., Lozano, A., Dado, T., Wang, F., & Roelfsema, P. (2024). MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity. NeurIPS. [link]

Le, L. (2020). Adversarial images steering human attention (Master’s thesis). Radboud University.

Le, L. (2018). Biological motion for visual cortex induced phosphenes (Undergraduate thesis).