5 citations · 15 across the 27 of their papers we have counts for
27 papers
Calibrated Decisions at Scale: Converting Police Crash Narratives into Probabilistic Crash Variables with a System One Model (Jev)
Amir Rafe, Subasish Das
Crash datasets that carry an investigator narrative hold information the coded fields omit. Coding those narratives at scale has been blocked by three obstacles. Frontier large lan…
Building Trustworthy Mental Health Benchmarks on Bluesky: A Validation-Aware Weak-Supervision Framework
Gaurab Chhetri, Anandi Dutta, Subasish Das
Decentralized social media platforms create new opportunities and challenges for computational mental health research because data access, moderation, labeling, and deployment resp…
Toward Auditable and Calibrated AI for Dementia-Related Crash Severity Prediction: A Selective Deferral Framework to Support Human Review
Gaurab Chhetri, Anika Baitullah, Subasish Das
Public crash databases increasingly support automated safety analysis, but crash severity prediction remains difficult to translate into public-sector decision workflows when model…
Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust
Amir Rafe, Subasish Das
Generative artificial intelligence is entering transportation through traveler-facing advisories, synthetic crash-record generation, and policy decision support. Existing governanc…
CHOIR: heterogeneity-aware conformal prediction for crash injury severity across driver safety strata
Amir Rafe, Subasish Das
Transportation agencies increasingly predict crash-injury severity with statistical and machine-learning models, but these models do not state how often their output contains the r…
MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure
Sumit S. Shevtekar, Chandresh K. Maurya, Gourab Sil +1
Powered two-wheeler riders face critical safety challenges in low- and middle-income countries, yet limited studies exist on how cognitive stressors such as Time Pressure influence…
HERMES: Heterogeneous Edge-Relational Multi-Head Embedded SSM Attention for Traffic Conflict Prediction at Signalized Intersections
Md Monzurul Islam, Subasish Das
Surrogate safety measures (SSMs) enable proactive traffic safety assessment, but many existing methods evaluate pairwise interactions independently or flatten multi-agent scenes in…
Socio-Conformal Calibration in Complex Survey Data: Marginal Validity Is Not Enough for Subgroup Reliability
Amir Rafe, Subasish Das
Machine-learning systems used in survey-based social measurement require uncertainty estimates that are reliable across population subgroups, not merely valid in aggregate. We stud…
Coupled-NeuralHP: Directional Temporal Coupling Between AI Innovation Exposure and Public Response
Amir Rafe, Subasish Das
Artificial intelligence innovation exposure and public response co-evolve, but innovation arrives as irregular event streams while response is observed monthly. We introduce Couple…
Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery
Amir Rafe, Subasish Das
Public attitudes toward artificial intelligence are heterogeneous, ordinally measured, and poorly captured by any single dependency graph. Existing ordinal structure learners assum…
SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing
Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury
Personalized search demands the ability to model users' evolving, multi-dimensional information needs; a challenge for systems constrained by static profiles or monolithic retrieva…
WISE: Web Information Satire and Fakeness Evaluation
Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury
Distinguishing fake or untrue news from satire or humor poses a unique challenge due to their overlapping linguistic features and divergent intent. This study develops WISE (Web In…
Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Gaurab Chhetri, Shriyank Somvanshi, Pavan Hebli +2
Post-quantum cryptography (PQC) is moving from evaluation to deployment as NIST finalizes standards for ML-KEM, ML-DSA, and SLH-DSA. This survey maps the space from foundations to…
Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
Shriyank Somvanshi, Pavan Hebli, Gaurab Chhetri +1
This study presents a deep tabular learning framework for predicting crash severity in electric vehicle (EV) collisions using real-world crash data from Texas (2017-2023). After fi…
CognitiveSky: Scalable Sentiment and Narrative Analysis for Decentralized Social Media
Gaurab Chhetri, Anandi Dutta, Subasish Das
The emergence of decentralized social media platforms presents new opportunities and challenges for real-time analysis of public discourse. This study introduces CognitiveSky, an o…
A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm
Gaurab Chhetri, Darrell Anderson, Boniphace Kutela +1
This study presents the first multi-platform sentiment analysis of public opinion on the 15-minute city concept across Twitter, Reddit, and news media. Using compressed transformer…
Model Context Protocols in Adaptive Transport Systems: A Survey
Gaurab Chhetri, Shriyank Somvanshi, Md Monzurul Islam +5
The rapid expansion of interconnected devices, autonomous systems, and AI applications has created severe fragmentation in adaptive transport systems, where diverse protocols and c…
From Tiny Machine Learning to Tiny Deep Learning: A Survey
Shriyank Somvanshi, Md Monzurul Islam, Gaurab Chhetri +8
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counte…
ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity
Mahmuda Sultana Mimi, Md Monzurul Islam, Anannya Ghosh Tusti +2
Understanding the spatial and temporal dynamics of automated vehicle (AV) crash severity is critical for advancing urban mobility safety and infrastructure planning. In this work,…
A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs
Shriyank Somvanshi, Md Monzurul Islam, Syed Aaqib Javed +6
Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems.…
Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes
Shriyank Somvanshi, Anannya Ghosh Tusti, Mahmuda Sultana Mimi +4
The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved i…
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
Gian Antariksa, Rohit Chakraborty, Shriyank Somvanshi +4
Visual object detection utilizing deep learning plays a vital role in computer vision and has extensive applications in transportation engineering. This paper focuses on detecting…
Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet
Shriyank Somvanshi, Rohit Chakraborty, Subasish Das +1
Child bicyclists (14 years and younger) are among the most vulnerable road users, often experiencing severe injuries or fatalities in crashes. This study analyzed 2,394 child bicyc…
Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists
Shriyank Somvanshi, Anannya Ghosh Tusti, Rohit Chakraborty +1
Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. Thi…