Education

• Ph.D. in Electrical and Computer Engineering, University of California, Los Angeles, 2025

  • Thesis: Personalized and Efficient Distributed Machine Learning

  • Committee: Suhas Diggavi (Chair), Quanquan Gu, Lieven Vandenberghe, Lin F. Yang

• M.S. in Electrical and Computer Engineering, University of California, Los Angeles, 2021

• B.Sc. in Electrical and Electronics Engineering (Minor in Economics), Bilkent University, 2019

Industry Experience

• Applied Scientist, Neuron Science Team     Amazon Annapurna Labs, Mar. 2025 – present

  • Developing efficient pretraining algorithms for foundation models, with a focus on system-aware optimizers and low-precision training recipes, and post-training algorithms for kernel generation.

• Applied Scientist Intern, AWS AI     Amazon Web Services, Summer 2023, 2024

  • Worked on stochastic rounding for large-scale low-precision LLM training.
  • Designed MADA, a state-of-the-art meta-optimizer for training LLMs.
  • Worked on constrained Bayesian optimization for distributed training configurations.

Honors and Awards

Amazon Fellow, Amazon Science Hub Advisory Group, 2024

UCLA ECE Departmental Fellowship, 2019–2020

Bilkent University Academic Excellence Award, 2019

Bilkent University Dean’s List, High Honor Student, 2015–2019

Service

• Reviewer: NeurIPS, ICLR, ICML, AISTATS, IEEE JSAC, IEEE TNNLS

• Mentor, Summer Undergraduate Research Programs, UCLA Henry Samueli School of Engineering

Teaching

Teaching Assistant, UCLA ECE, Fall 2020, 2021, 2022

• ECE236A — Linear Programming
• ECE246 — Foundations of Statistical Machine Learning