Nima Shoghi

I’m an AI researcher with a background in computer science and machine learning. I earned my B.S. and M.S. degrees in Computer Science from Georgia Tech, and I am currently pursuing my PhD in Machine Learning at the School of Computational Science and Engineering at Georgia Tech, focusing on Deep Learning for Scientific Applications. During my undergraduate and master’s studies, I conducted research at the High Performance Computer Architecture Lab, where I focused on accelerating ML training and inference. I recently concluded my two-year AI residency at Meta AI’s FAIR Chemistry team, where I worked on developing large pre-trained models, trained on a large mixture of available chemical data across multiple different domains, for general-purpose chemical property prediction. I am particularly interested in the application of large-scale machine learning techniques to problems in science and engineering.

My CV is available here.

Recent Updates

[Jan 2024] Our paper on large-scale diverse pre-training for chemical property prediction has been accepted to ICLR 2024! Please visit our webpage for more information, including an interactive visualization of its embeddings!

Education

Ph.D. in Machine Learning (School of Computational Science and Engineering), Georgia Institute of Technology, 2024 - Present
- Advisors: Dr. Victor Fung and Dr. Pan Li
- Research Focus: Deep Learning for Scientific Applications (e.g., Chemistry, Climate Science, etc.)
M.S. with Highest Honors in Computer Science (Machine Learning Specialization), Georgia Institute of Technology, 2020 - 2021
B.S. with High Honors in Computer Science (Machine Learning and Devices Threads), Georgia Institute of Technology, 2015 - 2019
International Baccalaureate Diploma, Druid Hills High School, 2011 - 2015

Work experience

Temporary Research Staff at the High Performance Computer Architecture Lab at Georgia Tech, Dec 2023 - May 2024
- Working on efficient inference strategies for pre-trained image diffusion models, with a focus on generating diverse, high-quality images.
- Advisors: Dr. Hyesoon Kim and Dr. Stefano Petrangeli
AI Resident at Meta Fundamental AI Research (FAIR), Aug 2021 - Aug 2023
- Worked on the Open Catalyst Project on the FAIR Chemistry team, focusing on the development of large-scale pre-training methods for chemical property prediction.
- Advisors: Dr. Larry Zitnick and Dr. Abhishek Das
Research Assistant at High Performance Computer Architecture Lab at Georgia Tech, May 2019 - May 2021
- Developed software-level and hardware-level techniques for accelerating deep learning training and inference.
- Advisors: Dr. Hyesoon Kim and Dr. Moinuddin Qureshi
Graduate Teaching Assistant at Georgia Institute of Technology, Aug 2020 - May 2021
- CS 4510: Automata and Complexity, Spring 2021, Taught by Dr. Zvi Galil
- CS 4510: Automata and Complexity, Fall 2020, Taught by Dr. Merrick Furst
Student Research Assistant at the Cyber Forensics Innovation (CyFI) Lab at Georgia Tech, Jan 2020 - Aug 2020
- Developed GNN-based ML models to analyze social media data for detecting incoming cyber attacks.
- Advisor: Dr. Maria Konte
Software Engineering Intern at Ciena, May 2017 - May 2018
- Developed software to interface with network devices and maintained CI/CD pipelines for build processes.

Skills

Data Science and Machine Learning:
- Proficient in Python data science libraries: NumPy, Pandas, Matplotlib, and Seaborn.
- Extensive experience with deep learning libraries: PyTorch and JAX.
- Experience with high-performance computing (HPC) and distributed (e.g., 128+ GPUs) training.
Programming and Development:
- Proficient in Python, C, C++, Rust, C#, and JavaScript/TypeScript
- Experience with test-driven development, including unit tests, integration tests, and end-to-end tests.
- In-depth knowledge of virtualization, containers, and Docker/Podman/Singularity.

Publications

(* denotes equal contribution)

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Nima Shoghi, Adeesh Kolluru, John Kitchin, Zachary Ulissi, C. Zitnick, Brandon Wood, arXiv preprint arXiv:2310.16802, 2023.

Context-Aware Task Handling in Resource-Constrained Robots with Virtualization

Ramyad Hadidi, Nima Shoghi, Bahar Asgari, Hyesoon Kim, IEEE International Conference on Edge Computing and Communications, 2023.

The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts

Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Félix Therrien, Jehad Abed, Oleksandr Voznyy, Edward Sargent, Zachary Ulissi, C. Zitnick, ACS Catalysis, 2023.

Nima Shoghi

Nima Shoghi

Recent Updates

Education

Work experience

Skills

Publications

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Context-Aware Task Handling in Resource-Constrained Robots with Virtualization

The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts

Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

SmaQ: Smart Quantization for DNN Training by Exploiting Value Clustering

Quantifying the design-space tradeoffs in autonomous drones

Secure Location-Aware Authentication and Communication for Intelligent Transportation Systems

Understanding the Software and Hardware Stacks of a General-Purpose Cognitive Drone

PISCES: Power-Aware Implementation of SLAM by Customizing Efficient Sparse Algebra

Neural Network Weight Compression with NNW-BDI

SLAM Performance on Embedded Robots

Talks

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

SmaQ: Smart Quantization for DNN Training by Exploiting Value Clustering

Legal Text Summarization Using Transformer Models

Attention is All You Need