👋 Hi there! I’m Md Asif Bin Syed, a Sr. Supply Chain Data Analyst at The Home Depot, the world’s leading home improvement retailer. Leading the development of offline reinforcement learning agents that reduce delivery failures by 4.5% ($6.5M retained revenue) and building ML Model for operational forecasting.
I am a researcher specializing in reinforcement learning, generative AI, and deep learning across domains such as marine surveillance, medical diagnosis, supply chain optimization, and time series forecasting.
I have published in venues including ICML Workshop’25, NeurIPS Workshop’25, IEEE conferences, with contributions in time series foundation models, diversity quantification, and physics-informed neural networks.
📄 View my complete publications →
2025 -Working in The Home Depot leveraging machine learning and reinforcement learning to optimize our delivery network - using predictive models to forecast delivery times, route optimization algorithms to determine the most efficient delivery paths, and reinforcement learning to dynamically adjust delivery schedules based on real-time conditions.
2024 -
leverage machine learning in Volvo to optimize the load for carrier and forecasting the demand for inboud deliveries
2023 -
🆕 What's New
- Nov 2024: Promoted to Sr. data analyst in supply chain at The Home Depot
- Oct 2024: Published a journal in Journal of Marine Science and Engineering: "Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data."
- Oct 2024: Awarded my first associate of the month by The Home Depot for discovering an opportunity to save around 2 M dollars by reducing extra pallets required
- Aug 2024: Published a journal in Journal of Computers in Human Behavior: Artificial Humans "Understanding AI Chatbot Adoption in Education: PLS-SEM Analysis of User Behavior Factors."
- Jan 2024: Started my full-time position as a Data Analyst in supply chain at The Home Depot
- Dec 2023: Presented a paper on "Investigation of Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Approaches" and "A Deep Learning Approach for Satellite and Debris Detection: YOLO in Action" at the 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI).
- Dec 2023: Presented a paper on "Pediatric Bone Age Prediction Using Deep Learning" and "Federated Learning in Manufacturing: A Systematic Review and Pathway to Industry 5.0" at the 2023 26th International Conference on Computer and Information Technology (ICCIT).
- Dec 2023: Completed my Master's in Industrial Engineering and submitted my thesis on "Spatio-Temporal Deep Learning Approaches for Addressing Track Association Problem Using Automatic Identification System (AIS) Data"
- July 2023: Awarded "Idea of the Month" at Volvo Trucks for implementing Power Automate and AI to extract invoice data, saving \$200 K.
- July 2023: Published a journal in MDPI Sensors: "A CNN-LSTM Architecture for Marine Vessel Track Association Using AIS Data."
- May 2023: Finalist in the QCRE Data Challenge for "ML Algorithm Synthesizing Domain Knowledge for Fungal Spore Concentration Prediction."
- April 2023: Submitted a paper to the IISE conference on "Multi-Model LSTM Architecture for Track Association Using AIS Data."
- October 2022: Chaired a session at the INFORMS Annual Meeting on "Advanced Machine Learning."
🛠️ Technical Skills
- 🐍 Programming Languages: Python , R, SQL,
- 🛠️ ML DL Framework: Scikit-learn, Keras, TensorFlow, PyTorch
- 📈Data Analysis: MS Excel, Tableau, Power BI 📈
- 🛠️ MLOps & DevOps: Docker, Kubernetes, Jenkins, Git, GitHub Actions, AWS, Azure ML, MLflow, DVC, Weights & Biases
- 🚀 Model Deployment: FastAPI, Flask, TensorFlow Serving, Model Monitoring, A/B Testing, CI/CD Pipelines
- 📦 Others: Containerization, Infrastructure as Code (IaC), Model Versioning, Experiment Tracking, Model Registry
🔬Research Interest
My research interests span a wide range of areas in data science and artificial intelligence. I’m passionate about machine learning, deep learning, natural language processing (NLP), and large language model (LLM) applications in supply chain management. Additionally, I have a deep interest in causal inference and graph neural network (GNN) applications in digital health and chemical composition analysis. My recent work focuses on deep learning applications in marine surveillance. I’m also keen on exploring missing value imputation techniques and assessing their credibility in various data analysis contexts.
📄 Publications
I have published and presented my work at prestigious conferences and journals, including Journal of Marine Science and Engineering, Sensors, IEEE conferences, and IISE, as well as workshops at various international venues.
Consistency is the key to self-improvement, and my GitHub activity serves as a powerful reminder of my commitment to continuous learning and coding progress.