Asif Syed | ML Scientist

👋 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.

As an active researcher, I explore marine surveillance with AI, disease prediction, and drug discovery. My core expertise includes machine learning, graph neural networks, and natural language processing. Currently, I’m innovating with missing value pattern detection and chemical energy prediction. I’m passionate about advancing AI applications across both industry and scientific domains. As a member of the Advanced Systems Analytics Lab, I’m tackling complex track association challenges.

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 assosciate of the month by the home depot for discovering an opportunity to save around 2M dollars by reducing extra pallet 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 Masters in Industrial Engineering and submitted my theisis 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 Truck for implementing Power Automate and AI to extract invoice data, saving $200k.

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

1. Haque, T., Syed, M. A. B., Das, S., & Ahmed, I. (2024). **Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data.** Journal of Marine Science and Engineering, 12(11), 1913. https://doi.org/10.3390/jmse12111913 2. Syed, M. A. B., & Ahmed, I. (2023). **A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data**. Sensors, 23(14), 6400. https://doi.org/10.3390/s23146400

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.