🤖 AI and Machine Learning
Developed deep learning models for medical image analysis and NLP tasks.

🚀 Projects
Led development of AI tools for ICU patient assessment, object detection, and diagnostic support for various data types (e.g., text, numeric, images, GIS data).

📚 Research
Co-authored multiple publications on AI applications in healthcare, including patient acuity assessment, lymph node segmentation, and diagnostic tools.

• Developed an algorithm for small object detection in CT images using Dense Atrous Spatial Pyramid Pooling and a Spatial Context Network with Reverse Axial Attention.
• Created an acuity assessment pipeline for ICU patients using explainable AI to identify factors contributing to patient deterioration.
• Built a semi-supervised object detection pipeline for electric pole detection using Fast-RCNN (Resnet50) and YoloV4.
• Developed a semi-supervised CNN model for abnormality detection in dairy products.
• Built the backend algorithm for RadAssist, a web app for detecting and localizing Intracranial Hemorrhage in CT images.

• Co-developed a Flutter-based Android app for electric pole detection from dashboard camera images.
• Created SemRad, a tool for abnormality detection in chest X-rays using ResNet101 and CAM.
• Developed semDDX, an Android app to assist with differential diagnoses for medical students.
• Built Symptom Checker, an Amazon Alexa skill for symptom-based disease detection.

• 🥈 Silver Medal in APTOS 2019 Blindness Detection
• 🥉 Bronze Medal in SIIM-ISIC Melanoma Classification

• Guided an undergraduate on "COVID Infection Analysis via Lung Lobe Segmentation using Deep Learning."
• Supervised two high-school students in hardware security and machine learning under the SSTP program.

OPS STUDENT (February, 2024 - May, 2024)

• Analyzed patient acuity in ICU using wearable sensors.
• Conducted mobility assessments for predictive analysis of ICU patients' acuity.

OPS STUDENT (May, 2023 - February, 2024)

• Developed CNN-based lymph node segmentation for cancer staging using CT images.
• Detected HNSCC type in CT images using radiomic features.

MACHINE LEARNING ENGINEER (Dec 2019- Dec 2020)

• Performed anomaly detection with metric learning loss.
• Built an Android app for object detection.
• Conducted NLP-based sentiment analysis with transformer models.

MACHINE LEARNING RESEARCHER (March 2017- May 2019)

• Developed DL models for disease classification from chest X-rays.
• Built Alexa skills and Android apps for differential diagnosis.

• 💻 Programming Languages: C, C++, MATLAB, JAVA, Python
• 📚 Libraries & Frameworks: OpenCV, BeautifulSoup, Pandas, Pydantic
• 🧠 Machine Learning Frameworks: Scikit-Learn, TensorFlow, Keras, PyTorch, Pytorch-Lightning, Fast.ai
• 🛠️ Integrated Development Environment (IDE): PyCharm, Android Studio, IntelliJ IDEA, Visual Studio, Arduino, ROS
• ☁️ Cloud Platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP)
• 🔌 Hardware: Arduino, Raspberry Pi, AVR Microcontrollers

• 🎓 HERBERT WERTHEIM COLLEGE OF ENGINEERING, ECE Department, UNIVERSITY OF FLORIDA
  Master of Science in Electrical and Computer Engineering, Graduated: Spring 2024
  Major: Signal and Systems
• 🎓 BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY (BUET)
  Bachelor of Science in Electrical and Electronic Engineering, Graduated: February 2017
  Major: Power System | Minor: Electronics

• “The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)” - Jessica Sena, Tahsin Mostafiz, Read on arXiv
• “Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning” - Md Mahfuz AlHasan, Saba Ghazi-moghadam, Read on Springer
• “EVHA: Explainable Vision System for Hardware Testing and Assurance” - Accepted at ACM JETC
• “A Web-based Assistive Tool for Emergency Physicians in Diagnosing Intracranial Hemorrhage Subtypes” - SIIM 2020 Annual Meeting Abstract
• “Pathology Extraction from Chest X-Ray Radiological Reports: A Performance Comparison” - Tahsin Mostafiz, Dr. Khalid Ashraf, Read on arXiv