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Nima Shoghi
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portfolio
publications
SLAM Performance on Embedded Robots
Nima Shoghi, Ramyad Hadidi, Hyesoon Kim
Published in Student Research Competition at Embedded System Week (SRC ESWEEK), 2019
Examines the effectiveness of the ORBSLAM2 algorithm on the Raspberry Pi for real-time usage in embedded robots, identifying the Pi's slower performance, but proposing optimizations that nearly quintuple its speed with minimal precision loss, enabling real-time operation.
Neural Network Weight Compression with NNW-BDI
Andrei Bersatti*, Nima Shoghi*, Hyesoon Kim
Published in The International Symposium on Memory Systems, 2020
Introduces NNW-BDI, a specialized memory compression scheme for neural network weights, successfully decreasing memory usage by up to 85% without diminishing inference accuracy.
PISCES: Power-Aware Implementation of SLAM by Customizing Efficient Sparse Algebra
Bahar Asgari, Ramyad Hadidi, Nima Shoghi, Hyesoon Kim
Published in 2020 57th ACM/IEEE Design Automation Conference (DAC), 2020
Introduces Pisces, a method that optimizes power consumption and latency for simultaneous localization and mapping (SLAM). Through using sparse data and reducing memory access, it results in a 2.5 times power reduction and 7.4 times faster execution than other contemporary methods.
Understanding the Software and Hardware Stacks of a General-Purpose Cognitive Drone
Sam Jijina, Adriana Amyette, Nima Shoghi, Ramyad Hadidi, Hyesoon Kim
Published in 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2020
Conducts a detailed analysis of drone operation and efficiency by exploring hardware and software components, using ArduCopter as an example. Optimizing specific aspects of these components can significantly increase drone flight range.
Secure Location-Aware Authentication and Communication for Intelligent Transportation Systems
Nima Shoghi, Ramyad Hadidi, Lee Jaewon, Jun Chen, Arthur Siqueria, Rahul Rajan, Shaan Dhawan, Pooya Shoghi, Hyesoon Kim
Published in arXiv preprint arXiv:2011.08936, 2020
Introduces a unique location-aware protocol for secure communication in Intelligent Transportation Systems, leveraging in situ visual localization (like QR codes) for efficient message verification. The new method is efficient, scalable, and infrastructure-independent, providing a more trustworthy and widely applicable solution than previous approaches.
Quantifying the design-space tradeoffs in autonomous drones
Ramyad Hadidi, Bahar Asgari, Sam Jijina, Adriana Amyette, Nima Shoghi, Hyesoon Kim
Published in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021
Examines the inherent design complexities in autonomous drones—especially the trade-offs between compute, energy, and electromechanical resources—and proposes a systematic exploration of the drone design space. The study emphasizes the benefits of optimizing the SLAM process on FPGA platforms and introduces a customizable, open-source drone.
SmaQ: Smart Quantization for DNN Training by Exploiting Value Clustering
Nima Shoghi*, Andrei Bersatti*, Moinuddin Qureshi, Hyesoon Kim
Published in IEEE Computer Architecture Letters, 2021
Introduces Smart Quantization (SmaQ), a quantization scheme that leverages the normal distribution properties of neural network data structures, leading to a memory usage reduction of up to 6.7x during training, with minimal impact on accuracy.
Transfer learning using attentions across atomic systems with graph neural networks (TAAG)
Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, Siddharth Goyal, Abhishek Das, C Zitnick, Zachary Ulissi
Published in The Journal of Chemical Physics, 2022
Introduces TAAG, a novel attention-based transfer learning approach for Graph Neural Networks which significantly improves performance for out-of-domain datasets and speeds up model training, demonstrating the potential for generalizing important aspects across different atomic system domains.
Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati, Nima Shoghi, Abhishek Das, Brandon Wood, C Zitnick, John Kitchin, Zachary Ulissi
Published in ACS Catalysis, 2022
Examines the challenges and limited generalizability of current machine-learning models for catalyst discovery and discusses the potential advancements brought about by large-scale catalyst data sets like OC20.
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
Published in ACS Catalysis, 2023
Introduces the open source OC22 dataset, containing relaxations across various oxide materials, coverages, and adsorbates, to improve machine learning models for oxide electrocatalysts. The paper also establishes clear benchmarks for future efforts in this area, opens the data and models for community development, and introduces a public leaderboard.
Context-Aware Task Handling in Resource-Constrained Robots with Virtualization
Ramyad Hadidi, Nima Shoghi, Bahar Asgari, Hyesoon Kim
Published in IEEE International Conference on Edge Computing and Communications, 2023
Presents a new context-aware approach for handling tasks in real-time on resource-constrained robots, achieving increased execution speed by integrating a dynamic time-sharing mechanism, event-driven scheduling, and lightweight virtualization.
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
Published in arXiv preprint arXiv:2310.16802, 2023
Introduces Joint Multi-domain Pre-training (JMP), an approach that advances atomic property prediction by training on multiple datasets across diverse chemical domains simultaneously, significantly improving accuracy and setting or matching the state-of-the-art in many tasks.
talks
Attention is All You Need
Published:
Introduces the Transformer, a sequence-to-sequence model by Vaswani et al. (2017) which uses the attention mechanism to learn dependencies between input and output tokens. Goes in-depth into the self-attention and multi-head attention mechanisms and discusses the advantages of the Transformer over recurrent neural networks.
Legal Text Summarization Using Transformer Models
Published:
Develops a transformer-based encoder-decoder architecture for abstractive legal text summarization. Combines PEGASUS’ (from Zhang et al. 2020) pre-training objective with Longformer’s (from Beltagy et al. 2020) dilated attention mechanism to create a model that can handle extremely long input sequences to generate summaries of legal documents. Achieves state-of-the-art summarization performance on the BIGPATENT dataset.
SmaQ: Smart Quantization for DNN Training by Exploiting Value Clustering
Published:
Introduces the Smart Quantization (SmaQ) technique for DNN training. SmaQ is a novel quantization which exploits the observed (normally distributed) value clustering in DNNs to quantize neural network weight, gradient, feature map, gradient map, and optimizer state values. SmaQ is able to reduce memory usage during training by up to 6.7x with no loss in accuracy.
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Published:
Introduces Joint Multi-Domain Pre-training (JMP), a robust supervised pre-training approach which simultaneously trains on data from multiple chemical domains. JMP demonstrates state-of-the-art results on key small molecule, large molecule, and materials datasets and offers insights into the influence of pre-training strategies on fine-tuning.