6 citations · 11 across the 8 of their papers we have counts for
10 papers
Adaptive Computation with Conformal Certification
Shehtab Zaman, Aneesh Rangnekar, Nancy Lee +1
Scientific imaging data involving medical (e.g., 3D radiographic, microscopic images) and astronomical sensors pose different challenges from natural images. One key challenge is t…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Score-based Idempotent Distillation of Diffusion Models
Shehtab Zaman, Chengyan Liu, Kenneth Chiu
Idempotent generative networks (IGNs) are a new line of generative models based on idempotent mapping to a target manifold. IGNs support both single-and multi-step generation, allo…
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun +10
Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules…
PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval
Ryuma Nakahata, Shehtab Zaman, Mingyuan Zhang +2
Ptychography is a computational method of microscopy that recovers high-resolution transmission images of samples from a series of diffraction patterns. While conventional phase re…
STRIDE: Structure-guided Generation for Inverse Design of Molecules
Shehtab Zaman, Denis Akhiyarov, Mauricio Araya-Polo +1
Machine learning and especially deep learning has had an increasing impact on molecule and materials design. In particular, given the growing access to an abundance of high-quality…
ParticleGrid: Enabling Deep Learning using 3D Representation of Materials
Shehtab Zaman, Ethan Ferguson, Cecile Pereira +3
From AlexNet to Inception, autoencoders to diffusion models, the development of novel and powerful deep learning models and learning algorithms has proceeded at breakneck speeds. I…
Graph Neural Network for Metal Organic Framework Potential Energy Approximation
Shehtab Zaman, Christopher Owen, Kenneth Chiu +1
Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. MOFs play an important role in industrial applications such as gas separation,…
Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway
Angel Beltre, Shehtab Zaman, Kenneth Chiu +3
Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocati…
Real-Space Visualization of Quantum Phase Transition by Network Topology
Shehtab Zaman, Wei-Cheng Lee
We demonstrate that with appropriate quantum correlation function, a real-space network model can be constructed to study the phase transitions in quantum systems. For the three-di…