Currently joining OSU's IE&M department under Professor Akash Deep, focusing on meta-learning,
optimization, machine learning, and data-driven decision making for real-world infrastructure.
When I'm not training models, I'm on a trail drawn to wide skies, dense forests,
and the quiet that comes from moving through nature.
Building AI systems that are not just powerful but transparent, reliable, and fair.
Meta-Learning
Instead of training a single model from scratch to solve one specific task, meta-learning exposes the algorithm to a broad distribution of tasks.
XAI & Trustworthy AI
Developing interpretable ML models and explanation methods to make AI decisions transparent and auditable by humans.
Optimization
Designing algorithms and mathematical programs for complex decision problems from scheduling and routing to resource allocation under uncertainty.
AI for Infrastructure
Applying computer vision and deep learning to transportation networks, bridge inspection, health sector, and smart cities enabling real-time, reliable infrastructure monitoring.
Background
Education
Ph.D. in Industrial Engineering & Management
Oklahoma State University · Starting Aug 2026
Advisor: Professor Akash Deep · Focus: Optimization, ML, Data-Driven Decision Making
M.S. in Data Science GPA 3.79
University of New Haven · Dec 2025
Tagliatela College of Engineering · West Haven, CT
B.S. in Computer Science GPA 4.0
University of Wolverhampton · June 2023
Wolverhampton, UK · FYP: Deep Learning for Breast Cancer Detection
"In every walk with nature, one receives far more than he seeks."
— John Muir
Scholarly Work
Research & Publications
My research spans computer vision, explainable AI, and infrastructure monitoring.
Below is a record of publications, presentations, and preprints.
Peer-Reviewed
Publications
2026
Automated Concrete Bridge Deck Inspection Using Unmanned Aerial System–Collected Data: A Deep Learning Approach
Khatry, K., Elmi, S., & Samsami, R.
ASCE Open: Multidisciplinary Journal of Civil Engineering, 4(1)
Improved crack detection accuracy by 4% across multiple benchmarks by developing a custom Residual Vision Transformer (RvT) model in PyTorch for UAV-captured bridge imagery.
Enhanced model interpretability by integrating LIME-based explainable AI methods to visualize prediction-relevant regions for infrastructure defect detection.
Reduced false negatives by performing EDA on 40,000+ high-resolution images using OpenCV, Pandas, and NumPy.
Contributed to 4 accepted papers: ASCE Journal, TRB 2026, CRC 2026, and 1 under submission.
Undergraduate ResearcherSep 2022 – June 2023
University of Wolverhampton (Herald College) · Kathmandu, Nepal
Developed and compared three CNN architectures (LeNet, ResNet50, VGG16) for breast cancer detection from histopathological images.
Implemented data augmentation to address class imbalance, improving model generalization and robustness.
Industry Experience
Analytics EngineerJan 2026 – July 2026
Downtown Eveningx Soup Kitchen · New Haven, CT
Conducted exploratory data analysis to identify trends and provide actionable insights supporting organizational decision-making.
Designed and maintained data pipelines to collect, clean, and transform operational and donor data from multiplesources.
Graduate AssistantMar 2024 – Dec 2025
Graduate Admissions · University of New Haven, CT
Developed Power BI dashboards with custom DAX measures and predictive analytics, reducing data interpretation time across departments.
Managed communication workflows with hundreds of prospective students regarding admissions and program details.
Data Analyst InternJan 2025 – May 2025
Downtown Evening Soup Kitchen · New Haven, CT
Automated data workflows using Python, Zapier, and Excel macros, integrating 4 business systems and reducing reporting time.
Implemented data validation protocols with Cognito Forms and MSSQL, improving accuracy across 30,000+ monthly records.
Associate Data EngineerJuly 2023 – Dec 2023
Krispcall Pte. Ltd · Singapore
Developed and optimized ETL pipelines, increasing data processing speeds for faster BI decisions.
Analyzed user behavior using Mixpanel and Python scripts, identifying 20+ fraud instances and enhancing platform security.
Selected Projects
FinRAG: Retrieval-Augmented Generation for Financial Analysis
Multi-source RAG system combining 289,642 documents from Reddit, historical stock data, and financial literature.
Benchmarked Mistral-7B, Phi-3-Mini, and Llama-3.1-8B on domain-specific financial query generation.
Data Engineering Pipeline with AWS & Apache Spark
Scalable ETL pipeline using AWS Lambda and Apache Spark for distributed analysis of large-scale YouTube dataset.
Get in Touch
Let's Build Something Together
I'm open to research collaborations, academic discussions, and connecting with fellow scholars.
Whether you're working on AI systems, infrastructure problems, or just want to chat about optimization —
I'd love to hear from you.