Kamyar Salahi
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At Stanford, I work on LLMs with Percy Liang. In Summer 2024, I built distributed training infrastructure at Modal Labs. In Summer 2023, I worked on expense automation with LLMs at Ramp. In Summer 2022, I designed low-latency vision models with Snap's Creative Vision research team. In Spring/Summer 2021, I built parts of Dolby Vision. Previously, I worked on 3D reconstruction with Angjoo Kanazawa and Matt Tancik. Before this, I worked in active learning and domain adaptation with Trevor Darrell, precision health at UCLA, and satellite telecomms at MIT.

I am currently a fully-funded research-track MSCS student at Stanford. I studied EECS and Business at UC Berkeley under the M.E.T. and EECS Honors Programs. I'm a Neo Scholar and a Regents' and Chancellor's Scholar.

Google Scholar  /  LinkedIn

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Research

Human progress in the last few centuries has been fueled by high leverage tools that allow us to do more with less. Currently, I'm interested in building systems that enable higher productivity, greater resource efficiency, and tighter iteration cycles.

Nerfstudio: A Framework for Neural Radiance Field Development
Matthew Tancik*, Ethan Weber*, Evonne Ng*, Ruilong Li, Brent Yi, Justin Kerr, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, David McAllister, Angjoo Kanazawa
SIGGRAPH, 2023
Paper  /  Code

Nerfstudio is a modular library enabling end-to-end creation, training, and testing of neural radiance fields.

Rethinking Vision Transformers for MobileNet Size and Speed
Yanyu Li, Ju Hu, Yang Wen, Georgios Evangelidis, Kamyar Salahi, Yanzhi Wang, Sergey Tulyakov, Jian Ren
ICCV, 2023
Paper  /  Code

We build a light-weight vision transformer model for mobile architectures with MobileNet-level latency and size.

Minimax Active Learning
Sayna Ebrahimi, William Gan, Dian Chen, Giscard Biamby, Kamyar Salahi, Michael Laielli, Shizhan Zhu, Trevor Darrell
arXiv Preprint
Paper

We apply a self-supervised minimax entropy approach to data point sampling and model training in active learning.

A wearable freestanding electrochemical sensing system
Yichao Zhao, Bo Wang, Hannaneh Hojaiji, Zhaoqing Wang, Shuyu Lin, Christopher Yeung, Haisong Lin, Peterson Nguyen, Kaili Chiu, Kamyar Salahi, Xuanbing Cheng, Jiawei Tan, Betto Alcitlali Cerrillos, Sam Emaminejad
Science Advances, 2020
Paper

We introduce a strain-isolated biomarker sensing platform through a generalizable and disposable freestanding electrochemical sensing system.

A mediator-free electroenzymatic sensing methodology to mitigate ionic and electroactive interferents' effects for reliable wearable metabolite and nutrient monitoring
Xuanbing Cheng, Bo Wang, Yichao Zhao, Hannaneh Hojaiji, Shuyu Lin, Ryan Shih, Haisong Lin, Stephanie Tamayosa, Brittany Ham, Phoenix Stout, Kamyar Salahi, Zhaoqing Wang, Chuanzhen Zhao, Jiawei Tan, Sam Emaminejad
Advanced Functional Materials, 2020
Paper

We devise a mediator-free sensing interface using platinum nanoparticles with a multiwall carbon nanotube layer to overcome challenges with conventional Prussian Blue enzymatic sensors.

Wearable Chemical Sensors
Bo Wang, Andrew Wilhelm, Aaron Wilhelm, Sanaz Pilehvar, Sina Moshfeghi, Phoenix Stout, Kamyar Salahi, Sam Emaminejad
Wearable Bioelectronics, 2020
Paper

We provide a comprehensive review of the state-of-the-art wearable chemical sensors, including their design, fabrication, and applications.

A Rapid and Low-cost Fabrication and Integration Scheme to Render 3D Microfluidic Architectures for Wearable Biofluid Sampling, Manipulation, and Sensing
Haisong Lin, Yichao Zhao, Shuyu Lin, Bo Wang, Christopher Yeung, Xuanbing Cheng, Zhaoqing Wang, Tianyou Cai, Wenzhuo Yu, Kimber King, Jiawei Tan, Kamyar Salahi, Hannaneh Hojaiji, Sam Emaminejad
Lab on a Chip, 2019
Paper

We develop a CAD-to-microfluidic fabrication workflow to rapidly fabricate and integrate complex 3D microfluidic architectures for wearable biofluid sampling, manipulation, and sensing.

Teaching
CS

CS 229: Machine Learning
Fall 2024

CS 221: Artificial Intelligence
Fall 2024

CS 224N: Natural Language Processing with Deep Learning
Spring 2024

CS 161: Design and Analysis of Algorithms
Winter 2024

CS 236: Deep Generative Models
Fall 2023

EECS

CS 189: Introduction to Machine Learning
Spring 2023

CS 194-26: Intro to Computer Vision and Computational Photography
Fall 2022, Fall 2021

EECS 126: Probability and Random Processes
Spring 2022

CS 70: Discrete Mathematics and Probability Theory
Summer 2021

EECS 16B: Designing Information Devices and Systems II
Spring 2021

Miscellaneous

Accelerating SGD: A theoretical overview of variance reduction and momentum for stochastic gradient methods.

FlexiTrain: Dynamically scaling data parallel ranks in-place for fault tolerance in distributed training workloads.

On-Policy Maximum Entropy Deep RL: Applying max entropy objective from Soft-AC to on-policy deep RL methods.

Quaternions for 3D Rotations: A brief overview of quaternions and their application for representing 3D rotations.


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