5 citations · 10 across the 11 of their papers we have counts for
13 papers
Learning and Predicting Patent Technology Reuse Trajectories from Emergence-Time Signals
Ayham Yousef, Qiang Ye, Qiang Cheng
Forecasting how a newly emerged patent technology will be reused is central to technology intelligence, but reuse-pattern labels do not exist in advance: they must be constructed f…
OmicSync: Reliability-Aware Spatial Multi-Omics Clustering with Evidence-Constrained LLM Reasoning
Rabeya Tus Sadia, Qiang Ye, Qiang Cheng
Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discovery methods provide only cluste…
STRIDE: Reasoning-Aware Training for Time Series Forecasting
Md Atik Ahamed, Mihir Parmar, Palash Goyal +4
Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LLMs directly to temporal data i…
TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems
Md Atik Ahamed, Mihir Parmar, Palash Goyal +7
We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely…
CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction
Rabeya Tus Sadia, Qiang Ye, Qiang Cheng
Accurate prediction of RNA-associated interactions is essential for understanding cellular regulation and advancing drug discovery. While Biological Large Language Models (BioLLMs)…
MolSnap: Snap-Fast Molecular Generation with Latent Variational Mean Flow
Md Atik Ahamed, Qiang Ye, Qiang Cheng
Molecular generation conditioned on textual descriptions is a fundamental task in computational chemistry and drug discovery. Existing methods often struggle to simultaneously ensu…
RefiDiff: Progressive Refinement Diffusion for Efficient Missing Data Imputation
Md Atik Ahamed, Qiang Ye, Qiang Cheng
Missing values in high-dimensional, mixed-type datasets pose significant challenges for data imputation, particularly under Missing Not At Random (MNAR) mechanisms. Existing method…
Mol-CADiff: Causality-Aware Autoregressive Diffusion for Molecule Generation
Md Atik Ahamed, Qiang Ye, Qiang Cheng
The design of novel molecules with desired properties is a key challenge in drug discovery and materials science. Traditional methods rely on trial-and-error, while recent deep lea…
CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation
Rabeya Tus Sadia, Md Atik Ahamed, Qiang Cheng
The integration of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data is crucial for understanding gene expression in spatial context. Existing methods fo…
GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture
Md Atik Ahamed, Andrew Cheng, Qiang Ye +1
Graph Neural Networks (GNNs) have demonstrated remarkable success in various applications, yet they often struggle to capture long-range dependencies (LRD) effectively. This paper…
TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification
Md Atik Ahamed, Qiang Cheng
Multivariate time series classification (TSC) is critical for various applications in fields such as healthcare and finance. While various approaches for TSC have been explored, im…
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
Md Atik Ahamed, Qiang Cheng
Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficie…
MambaTab: A Plug-and-Play Model for Learning Tabular Data
Md Atik Ahamed, Qiang Cheng
Despite the prevalence of images and texts in machine learning, tabular data remains widely used across various domains. Existing deep learning models, such as convolutional neural…