Lingyu Kong, Nima Shoghi, Guoxiang Hu, Pan Li, Victor Fung

Introduces MatterTune, a modular platform that enables fine-tuning of pre-trained atomistic foundation models for materials science applications.

Nima Shoghi, Adeesh Kolluru, John Kitchin, Zachary Ulissi, C. Lawrence Zitnick, Brandon Wood

Introduces Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that leverages diverse data to advance atomic property prediction across chemical domains, achieving state-of-the-art performance on 34 out of 40 downstream tasks.

Nima Shoghi, Pooya Shoghi, Anuroop Sriram, Abhishek Das

Introduces Distributional Mixture of Experts (DMoE), a robust method for molecular property regression that outperforms baselines on multiple datasets and architectures.

Ramyad Hadidi, Nima Shoghi, Bahar Asgari, Hyesoon Kim

Develops a fast context-aware technique that enables resource-constrained robots to handle multiple tasks simultaneously with improved timeliness, demonstrating a 42% speedup in execution time compared to standard scheduling approaches.

Richard Tran, Janice Lan, ..., Nima Shoghi, ..., C. Lawrence Zitnick

Introduces the Open Catalyst 2022 (OC22) dataset, consisting of 62,331 DFT relaxations, to accelerate machine learning for oxide electrocatalysts and establish benchmarks for the field.

Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, Siddharth Goyal, Abhishek Das, C. Lawrence Zitnick, Zachary Ulissi

Introduces a transfer learning approach using Graph Neural Networks to generalize models across domains in molecular and catalyst discovery, reducing the need for large, domain-specific datasets.

Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati, Nima Shoghi, Abhishek Das, Brandon Wood, C. Lawrence Zitnick, John Kitchin, Zachary Ulissi

Discusses the challenges and potential of developing generalizable machine learning models for catalyst discovery, highlighting the importance of large-scale datasets like the Open Catalyst 2020 Data set (OC20).

Nima Shoghi, Andrei Bersatti, Moinuddin Qureshi, Hyesoon Kim

Introduces SmaQ, a quantization scheme that leverages the normal distribution of neural network data structures to efficiently quantize them, addressing the memory bottleneck in single-machine training of deep networks.

Ramyad Hadidi, Bahar Asgari, Sam Jijina, Adriana Amyette, Nima Shoghi, Hyesoon Kim

Formalizes the subsystems of autonomous drones and quantifies the complex tradeoffs in their design space to enable optimized solutions for diverse applications.

Nima Shoghi, Andrei Bersatti, Hyesoon Kim

Introduces NNW-BDI, a neural network weight compression scheme that reduces memory usage by up to 85% without sacrificing inference accuracy on an MNIST classification task.

Bahar Asgari, Ramyad Hadidi, Nima Shoghi, Hyesoon Kim

Introduces Pisces, a power-aware SLAM implementation that consumes 2.5x less power and executes 7.4x faster than the state of the art by customizing efficient sparse algebra on FPGAs.

Nima Shoghi, Ramyad Hadidi, Lee Jaewon, Jun Chen, Arthur Siqueria, Rahul Rajan, Shaan Dhawan, Pooya Shoghi, Hyesoon Kim

Introduces a scalable, infrastructure-independent, location-aware authentication protocol for intelligent transportation systems, providing trustworthy communication and efficient sender localization using visual authentication beacons.

Sam Jijina, Adriana Amyette, Nima Shoghi, Ramyad Hadidi, Hyesoon Kim

Conducts an in-depth analysis of the hardware and software components of autonomous drones, characterizing the performance of the ArduCopter flight stack and providing insights to optimize flight controllers and increase drone range.

Nima Shoghi, Ramyad Hadidi, Hyesoon Kim

Demonstrates the feasibility of running ORB-SLAM2 in real-time on the Raspberry Pi 3B+ for embedded robots through optimizations that achieved a 5x speedup with minor impact on accuracy.