Sunnyvale, California

ishihab@iastate.edu

I am a Applied Scientist in Amazon and recently completed my Ph.D. in Computer Science from Iowa State University (October 2025). My research lies at the intersection of artificial intelligence, computer vision, and transportation systems.

Computer Vision Large Language Models Reinforcement Learning Quantum Computing Transportation AI

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🎓 Research Profile

I specialize in developing AI-driven solutions for safety-critical applications, with expertise spanning computer vision, large language models, reinforcement learning, and quantum computing.

Peer-Reviewed

Publications

ICML, CVPR, ACL, BMVC, EMNLP, ECAI

Premier Venues

Multiple

Industry Projects

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📰 Latest News

Aug 20, 2026	4 papers accepted at EMNLP 2026—2 Main Conference papers, EST-PRM and Spectral DPO (arXiv coming soon), and 2 Findings papers, Counterfactual Sensitivity Regularization and Grounded Decoding!
Aug 07, 2026	2 papers accepted at BMVC 2026 (A-rank, 27.9% acceptance rate — 404 of 1,448 submissions)!
Jul 22, 2026	Invited to serve as a Program Committee member for AAAI 2027!
Jun 01, 2026	Serving as Area Chair for ACL ARR May 2026 (EMNLP 2026)!
May 25, 2026	Invited to serve as a reviewer for WACV 2027 and ACL ARR May 2026 (EMNLP 2026)!

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🔬 Research Expertise

Core Technologies

Computer Vision

Crash detection, action localization, traffic analysis

Object Detection Video Analytics Real-time Processing

Large Language Models

Vision-language integration, narrative generation

Vision-LLM Multimodal AI Text Generation

Reinforcement Learning

Autonomous navigation, biomedical optimization

Deep RL Policy Optimization Autonomous Systems

Applied Domains

Transportation AI

ADAS, snowplow navigation, crash prediction

ADAS Traffic Safety IoT DOT

Quantum Computing

QNN architectures, anomaly detection

QNN Network Security Quantum ML

Synthetic Data Generation

CARLA/SUMO simulation, data augmentation

CARLA SUMO Data Synthesis

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📊 Recent Achievements

Publications

ICML (4), EMNLP (4), ACL (3), BMVC (2)

CVPR, ITSC (2), T-ITS, JSR, ECAI, EMNLP 2025 (2)

2025-2026

Iowa DOT

Led AI-based snowplow navigation projects

Crash detection systems

Lead Researcher

Amazon L5

Applied Scientist II

Agentic bot systems

2026 - Present

Amazon

Developed Knowledge Graph frameworks

Novel KGE models

Internship

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💼 Research Impact

Safety-Critical Systems

Developing AI solutions that directly impact public safety through advanced crash detection and prevention systems

Transportation Innovation

Pioneering autonomous navigation systems and real-time traffic analysis for next-generation transportation

Quantum Security

Advancing network security through quantum neural networks with breakthrough anomaly detection capabilities

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🤝 Collaboration Opportunities

Let's Work Together

Research Areas

• Computer vision for safety-critical systems
• LLM applications in transportation
• Quantum machine learning
• Reinforcement learning for real-world applications

Opportunities

Research Collaborations Consulting Projects Academic Partnerships Industry Consulting

Email Me More Details

selected publications

1. EMNLP

   

   EST-PRM: Stress-Testing Process Reward Models Before They Become Load-Bearing

   Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter, and 1 more author

   In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026
2. EMNLP

   

   Spectral DPO: Mitigating Validation Collapse via Heavy-Tailed Regularization

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026
3. EMNLP Findings

   

   Counterfactual Sensitivity Regularization for Trace-Sensitive Reasoning in Large Language Models

   Sanjeda Akter, Ibne Farabi Shihab, and Anuj Sharma

   In Findings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026

   arXiv
4. EMNLP Findings

   

   Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

   Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter, and 1 more author

   In Findings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026
5. BMVC

   

   Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 37th British Machine Vision Conference (BMVC 2026), 2026

   arXiv
6. BMVC

   

   Temporal Zoom Networks: Distance Regression and Continuous Depth for Efficient Action Localization

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 37th British Machine Vision Conference (BMVC 2026), 2026

   arXiv
7. ICML

   

   CalPro: Prior-Aware Evidential Conformal Prediction with Structure-Aware Sensitivity Bounds for Protein Structures

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), 2026

   arXiv
8. ICML

   

   What Reward Structure Enables Efficient Sparse-Reward RL? A Proof-of-Concept with Policy-Aware Matrix Completion

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), 2026

   arXiv
9. ICML

   

   CGRiC: Compositional Risk Certification for Structured LLM Outputs

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), 2026
10. ICML

    

    Certificate-Guided Pruning for Stochastic Lipschitz Optimization

    Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

    In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), 2026

    arXiv
11. T-ITS

    Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems

    Sanjeda Akter, Ibne Farabi Shihab, and Anuj Sharma

    IEEE Transactions on Intelligent Transportation Systems, 2026

    arXiv
12. CVPR

    

    LLM-Guided Probabilistic Fusion for Label-Efficient Document Layout Analysis

    Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

    arXiv preprint arXiv:2511.08903, 2026

    Accepted to IEEE/CVF CVPR 2026

    arXiv
13. ACL

    

    Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning

    Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

    In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), 2026

    arXiv
14. ACL

    

    Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics

    Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

    In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), 2026

    arXiv

15. 

    HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems

    Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

    In ECAI 2025 – 27th European Conference on Artificial Intelligence, 2025

    DOI

16. 

    Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments

    Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

    In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Nov 2025

    Abs DOI

    As the deployment of AI models shifts towards edge devices, developing efficient sequence models has become critical. State-space models (SSMs), particularly Mamba, have emerged as strong rivals to Transformers due to their linear-time complexity and impressive performance across a range of tasks. However, their large parameter counts still hinder their use in resource-constrained environments. To address this, we propose a novel unstructured pruning framework specifically tailored for Mamba, achieving up to 70% parameter reduction with only a 3–9% drop in performance. Unlike pruning techniques designed for Transformers, our approach leverages Mamba’s unique recurrent dynamics by incorporating pruning based on both weight and gradient importance to preserve critical parameters, a gradual pruning schedule to maintain model stability, and a global strategy to optimize parameter allocation across the model. Extensive experiments on the WikiText-103, Long Range Arena, and ETT benchmarks demonstrate significant efficiency gains, including 1.77× faster inference and a 46% reduction in memory usage. Our component analysis confirms Mamba’s robustness to pruning, highlighting the framework’s potential for enabling practical deployment while underscoring the need for careful evaluation to avoid introducing biases in sensitive applications.

17. 

    Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains

    Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

    In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Nov 2025

    Abs DOI

    Integrating large language models (LLMs) as action proposers in reinforcement learning (RL) significantly boosts performance in text-based environments but incurs prohibitive computational costs. We introduce a cache-efficient framework for Bayesian RL that leverages LLM-derived action suggestions, drastically reducing these costs while maintaining near-optimal performance. Our approach features an adaptive caching mechanism, optimized via meta-learning based on policy performance, to enable efficient inference across text-based games (e.g., TextWorld, ALFWorld) and robotic control tasks (e.g., MuJoCo, MetaWorld). This framework achieves a 3.8×–4.7× reduction in LLM queries and 4.0×–12.0× lower median latencies (85–93ms on consumer hardware), while retaining 96–98% of the uncached policy’s performance. We provide theoretical guarantees on the reliability of cached decisions with Kullback-Leibler (KL) divergence bounds, which are validated empirically by high success rates (90.4–95.6%) in complex text environments. For offline RL, our proposed CQL-Prior variant improves performance by 14–29% and reduces training time by 38–40%. Evaluations across eight diverse tasks demonstrate the framework’s generalizability and practicality for resource-constrained settings, making LLM-guided RL a viable and accessible approach for both text-based and robotic applications.

18. 

    Quantum-driven Zero Trust Architecture with Dynamic Anomaly Detection in 7G Technology: A Neural Network Approach

    Shakil Ahmed, Ibne Farabi Shihab, and Ashfaq Khokhar

    Measurement: Digitalization, Nov 2025

    DOI
19. CVPR

    

    DeepLocalization: Using Change Point Detection for Temporal Action Localization

    Mohammed Shaiqur Rahman, Ibne Farabi Shihab, Lu Chu, and 1 more author

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Nov 2024

    DOI
20. ITSC

    

    Robust and Precise Sidewalk Detection with Ensemble Learning: Enhancing Road Safety and Facilitating Curb Space Management

    Ibne Farabi Shihab, Sudesh Ramesh Bhagat, and Anuj Sharma

    In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Nov 2023

    DOI

Feel free to reach out for research collaborations or discussions.