Gilad Morad

© Gilad Morad 2026. All rights reserved.

Fall 2026

Started at Stanford University

Stanford University

Began my undergraduate studies as part of the Class of 2030.

2026-07-08

STATe-of-Thoughts accepted to COLM 2026

COLM — Conference on Language Modeling (Main Track)

Structured Action Templates for Tree-of-Thoughts — accepted as main-track paper at COLM 2026 (decision July 8, 2026).

Jul 2026

Launched Gilad Does

Started creating content: 30-second reels across calisthenics, building AI products, moving to Stanford, and life philosophy — each backed by a deep-dive page on GiladDoes.com with the full story and resources.

Dec 2025

Admitted to Stanford University

Stanford University

Accepted for undergraduate studies at Stanford University, starting Fall 2026.

Oct 2025

TOEFL: 109

TOEFL score of 109.

Aug 2025

SAT: 1510 (770 Math, 740 Reading & Writing)

SAT score of 1510 — 770 Math, 740 Reading & Writing.

2025

Toyota Israel Earth Prize (₪1M) — Core Contributor · Featured in Ynet & Israel Hayom

Citizens for Clean Air / Toyota Israel

CFCA won for environmental waste-site mapping, quantifying that 76% of airborne carcinogens stem from illegal trash burning. Served as a core contributor. Work featured on Ynet and Israel Hayom.

Aug 2024 — Present

Researcher

Technion / Princeton University

Collaborated with Zach Bamberger (PhD, Technion; former Google Brain) and Till Sanger (Princeton ORFE) on LLM reasoning research. Third author; paper planned for COLM 2026.

Aug 2024 — Present

Volunteer Developer

Citizens for Clean Air

Built interactive heatmap visualization and API for 12,000+ citizen-submitted reports on illegal trash burning and pollution. Tool underpinned a February 2025 Knesset policy proposal calling for more investment in pollution monitoring stations. Featured on Ynet and Israel Hayom.

2024

"Creative Thinking" Award Finalist

Recognized as a finalist for innovative approach to improving research methodologies.

Jun 2022 — Present

Data Scientist & Product Manager

Led data science and product management initiatives. Built data pipelines, conducted statistical analyses, and drove product decisions using ML models and experimentation frameworks.