Daniel Yamin

Tel Aviv, Israel

I’m a Senior Software Engineer with 10+ years building scalable systems, and a Computational Neuroscience researcher studying how the brain encodes memory. I enjoy bridging the gap between cutting-edge research and production-grade software.

What I Do

Software Engineering

Currently at Franklin by QIAGEN, developing algorithms for clinical genomics. Previously built DeFi risk platforms at Chaos Labs, IoT security at Microsoft Azure, and real-time anomaly detection at Allot. Stack: Python, Go, TypeScript, AWS, Azure, Docker, Kubernetes View Full CV →

Research

My MSc research at Tel Aviv University (Yuval Nir Lab) developed a novel method to measure memory through eye movements — no verbal reports needed. Published in Nature Communications Psychology. See Publications →

Featured Research

Our MEGA paradigm (Memory Episode Gaze Anticipation) uses eye tracking + machine learning to quantify memory retrieval. This breakthrough enables memory assessment in patients who cannot speak, infants, and even animals.

“Even when subjects said they didn’t remember, their gaze direction showed they did.”

The research was featured on Channel 13 News and covered by Times of Israel, Neuroscience News, and more.

Media Coverage →

Open Source & Projects

I maintain several open-source projects including the MEGA research implementation, tools for LLM analysis, and API clients for trading platforms.

View Repositories → Research Projects →

Education

MSc Computational Neuroscience — Tel Aviv University (GPA 98)
BSc Computer Science & Cognitive Science — Open University of Israel (GPA 92, Honors)

Let’s Connect

selected publications

Commun Psychol
Anticipatory eye gaze as a marker of memory

Flavio Jean Schmidig, Daniel Yamin, Omer Sharon, and 4 more authors

Communications Psychology, Aug 2025

Abs DOI Bib HTML

Human memory is typically studied by direct questioning, and the recollection of events is investigated through verbal reports. Using the MEGA (Memory Episode Gaze Anticipation) paradigm, we show that monitoring anticipatory gaze using eye tracking can quantify memory retrieval without verbal report. Upon repeated viewing of movie clips, eye gaze patterns anticipating salient events can quantify their memory traces seconds before these events appear on the screen. Machine learning-based classification can identify whether a given viewing is associated with memory for the event based on single-trial data of gaze features.
@article{schmidig2025anticipatory, title = {Anticipatory eye gaze as a marker of memory}, author = {Schmidig, Flavio Jean and Yamin, Daniel and Sharon, Omer and Nadu, Yoav and Nir, Jonathan and Ranganath, Charan and Nir, Yuval}, journal = {Communications Psychology}, volume = {3}, pages = {122}, year = {2025}, month = aug, publisher = {Nature Portfolio}, doi = {10.1038/s44271-025-00305-7}, url = {https://www.nature.com/articles/s44271-025-00305-7}, }