Hi I am Mona! I am a computer scientist (AI concentration) in training, Stanford student, undergraduate researcher and AI instructor passionate about helping humanity be around for long and prosper... . I am advised by Prof. Alex Aiken and Prof. Fei-Fei Li at Stanford Vision and Learning Lab, and also serve as leadership for multiple student organization on campus including CS+Social Good and Stanford Public Interest Technology Lab.


My immediate research interests include theory of mind, robotics, reinforcement learning and computer vision. However, I endeavor to work on my personal three-fold goals as described below:

First, I want to commit to pursuing integrity and moral education through taking multiple ethics and philosophy classes, participation in campus life where my peers discuss ethics of technology, personal studies and self-reflection. Second, I want to pursue a depth of understanding in math and computer science through my course work and auditing related material. Third, I want to be actively involved with the research community in forms that can have immediate benefits such as the BEHAVIOR project, or through working on applications of AI to my fields of interest such as genetics and law.

My long-term goals in pursuing computing research involve simultaneously working towards increasing my capacity to assist with ethically-development Artificial Intelligence in the long term, pursuing a PhD in the field, and making short term contributions that empower fellow researchers and make my field of interest more accessible and less expensive.

You can reach me at monaavr[at]stanford.edu!


  • [August 2022] I was accepted into the Above and Beyond Computer Science Program at Meta 💻

  • [September 2022] Our paper was accepted to CoRL 2022 💻

  • [June 2022] started working on my Curis project advised by prof. Mark Horowitz; Accelarating DeepConsensus at Inference 📜

  • [June 2022] started working on my Curis project advised by prof. Silvio Savarese; Deep Learning for generating 3D Procedural Materials from Images. 📜


BEHAVIOR-1K: A Benchmark for Embodied AI with 1,000 Everyday Activities and Realistic Simulation

Conference on Robot Learning (CoRL) 2022

Chengshu Li, Cem Gokmen, Gabrael Levine, Roberto Martín-Martín, Sanjana Srivastava, Chen Wang, Josiah Wong, Ruohan Zhang, Michael Lingelbach, Jiankai Sun, Mona Anvari, Minjune Hwang, Manasi Sharma, Arman Aydin, Dhruva Bansal, Samuel Hunter, Kyu-Young Kim, Alan Lou, Caleb R Matthews, Ivan Villa-Renteria, Jerry Huayang Tang, Claire Tang, Fei Xia, Silvio Savarese, Hyowon Gweon, Karen Liu, Jiajun Wu, Li Fei-Fei
[paper] [project page]

Accelarating DeepConsensus at Inference

Undergraduate Research in Computer Science(CURIS) Poster Session[Oral Presentation]

Mona Anvari, Sneha Goenka, Pi-Chuan Chang, Andrew Carroll, Mark Horowitz

Deep Learning for Generating 3D Procedural Materials from Images

Undergraduate Research in Computer Science(CURIS) Poster Session[Oral Presentation]

Mona Anvari, Lyne Tchapmi, Silvio Savarese

Class Projects

Recent Advances in Reinforcement Learning from Unlabeled Videos

Class Project for CS 333: Algorithms for Interactive Robotics

In this class project for CS333: Algorithms for Interactive Robotics taught by professor Dorsa Sadigh, I surveyed 10 most notable papers on the topic of robotic agents learning from unlabeled demonstrations.I structured this survey into subsections based on different possible modalities for reinforcement learning including policy learning, reward learning and a combination of action and reward learning from in-the-wild videos.

Are Transformers Robust at Solving Math Word Problems?

Class Project for CS229: Machine Learning

The goal of this project was to find out if transformers can be robust to solving MWP problems as previous work suggested they were not. I performed experiments using multiple transformer models to find patterns in the ways that transformers fail to solve MWPs. Based on our findings, I curated an augmented version of the SVAMP dataset to investigate this question.


CS 21SI: AI for Social Good [2020, 2021, 2022]

Teaching Team Lead: During the course of taking CS 21SI Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). The aim of the class is to empower students to apply these techniques outside of the classroom. The class focuses on techniques from machine learning and deep learning, including regression, support vector machines (SVMs), neural networks, convolutional neural networks (CNNs), transformers and recurrent neural networks (RNNs). The course alternates between lectures on machine learning theory and discussions with invited speakers, who will challenge students to apply techniques in their social good domains. Students complete weekly coding assignments reinforcing machine learning concepts and applications.

AI for ALL [Summer 2022]

NLP Research Mentor

Inspirit AI [2021]


MDUSD STEM and Robotics Camp[Summer 2019]

Coding and Robotics Coach

Course Work


Math 113: Linear Algebra and Matrix theory

Math 151: Introduction to probability Theory

Introduction to Reinforcement Learning with David Silver

CS 224N: Natural Language Processing with Deep Learning

for a Grade:

CS 231N: Convolutional Neural Networks for Visual Recognition

CS 182: Ethics, Public Policy, and Technological Change

CS 229: Machine Learning

CS 107: Computer Organization Systems

CS 161: Design and Analysis of Algorithms

TAPS 104: Intermediate Improvisation

COMPLIT 207: Why is Climate Change Unbelievable? Interdisciplinary Approaches to Environmental Action

PHIL70: Justice

CS 221: Artificial Intelligence: Principles and Techniques

CS109: Introduction To Probability for Computer Scientists

PWR2: Design Thinking: Bringing d.thinking to Research, Writing Presentation+

CS 103 : Mathematical Foundations of Computing

CS 181W: Computers, Ethics, and Public Policy

Psyc 45: Introduction to Learning and memory

Psyc 124: Brain Plasticity

Math 51: Linear Algebra and Differential Calculus of Several Variables

CS80Q : Race and Gender in Silicon Valley

Math 21: Linear Algebra and Differential Calculus

CS106B: Programming Abstractions in C++

CS333: Algorithms for Interactive Robotics


  • [Summer 2022] CURIS Summer Intern at VLSI Group

  • [Summer 2021] CURIS Summer Intern at SVL

  • [Summer 2021] Epilog Developer Intern at CodeX, The Stanford Center for Legal Informatics

  • [Summer 2020] CURIS Summer Intern at SVL


  • [2022 - present] Associated Students of Stanford University (ASSU) student mental health and wellness committee

  • [2022 - present] Vice President of CS + Social Good Student Org

  • [2021 - 2022] Social chair of CS + Social Good Student Org

  • [2021 - present] VP of Partnerships and Outreach at Stanford PIT Lab

  • [2021 - 2022] Public Interest Technology - UN Student Assistant

  • [2021] Code in Place Section Leader

  • [2020-2021] Organizer at Stanford Existential Risk Initiative


Accelerated Automation and Designing the Future of Labor

Intersect: The Stanford Journal of Science, Technology, and Society

Mona Anvari [article]

Stories of the workers left behind in a crisis

The Stanford Daily

Mona Anvari, Luke Miller

Website by AT