152 citations · 306 across the 37 of their papers we have counts for
14 papers · 1 filter
Exploiting the Experts: Unauthorized Compression in MoE-LLMs
Pinaki Prasad Guha Neogi, Ahmad Mohammadshirazi, Dheeraj Kulshrestha +1
Mixture-of-Experts (MoE) architectures are increasingly adopted in large language models (LLMs) for their scalability and efficiency. However, their modular structure introduces a…
InsightBuild: LLM-Powered Causal Reasoning in Smart Building Systems
Pinaki Prasad Guha Neogi, Ahmad Mohammadshirazi, Rajiv Ramnath
Smart buildings generate vast streams of sensor and control data, but facility managers often lack clear explanations for anomalous energy usage. We propose InsightBuild, a two-sta…
ALCo-FM: Adaptive Long-Context Foundation Model for Accident Prediction
Pinaki Prasad Guha Neogi, Ahmad Mohammadshirazi, Rajiv Ramnath
Traffic accidents are rare, yet high-impact events that require long-context multimodal reasoning for accurate risk forecasting. In this paper, we introduce ALCo-FM, a unified adap…
PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Rajiv Ramnath
Time series forecasting often demands a trade-off between accuracy and efficiency. While recent Transformer models have improved forecasting capabilities, they come with high compu…
Scalable Deep Metric Learning on Attributed Graphs
Xiang Li, Gagan Agrawal, Ruoming Jin +1
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based…
Federated Contrastive Learning of Graph-Level Representations
Xiang Li, Gagan Agrawal, Rajiv Ramnath +1
Graph-level representations (and clustering/classification based on these representations) are required in a variety of applications. Examples include identifying malicious network…
Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques
Noushin Behboudi, Sobhan Moosavi, Rajiv Ramnath
Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper…
Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous Data
Biplob Biswas, Rajiv Ramnath
Expansion-enhanced sparse lexical representation improves information retrieval (IR) by minimizing vocabulary mismatch problems during lexical matching. In this paper, we explore t…
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
Amin Karimi Monsefi, Pouya Shiri, Ahmad Mohammadshirazi +4
Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash…
Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations
Ahmad Mohammadshirazi, Aida Nadafian, Amin Karimi Monsefi +2
Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and tempera…
Judge Me in Context: A Telematics-Based Driving Risk Prediction Framework in Presence of Weak Risk Labels
Sobhan Moosavi, Rajiv Ramnath
Driving risk prediction has been a topic of much research over the past few decades to minimize driving risk and increase safety. The use of demographic information in risk predict…
Will there be a construction? Predicting road constructions based on heterogeneous spatiotemporal data
Amin Karimi Monsefi, Sobhan Moosavi, Rajiv Ramnath
Road construction projects maintain transportation infrastructures. These projects range from the short-term (e.g., resurfacing or fixing potholes) to the long-term (e.g., adding a…
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Xiang Li, Dong Li, Ruoming Jin +2
Web-based interactions can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successful…
Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights
Sobhan Moosavi, Mohammad Hossein Samavatian, Srinivasan Parthasarathy +2
Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using s…