Machine Learning Research Assistant

Alyan Khan

346-***-**** ad5w0a@r.postjobfree.com LinkedIn

Education

Georgia Institute of Technology August 2023 – May 2025 Master of Science in Electrical and Computer Engineering (CGPA: 3.75/4) University of Manchester September 2018 – August 2021 Bachelor of Science in Electrical and Electronic Engineering (First Class) Relevant Courses: (Masters) Statistical Machine Learning, Machine Learning, Online Decision Making, Digital Image Processing, Advanced Wireless Networks, Signals and Systems, Advanced Signal Processing Experience

Georgia Institute of Technology Atlanta, US

Research Assistant May 2024 – Present

• Contributing to a collaborative research initiative, focusing on the application of CCTV cameras for real-time blood perfusion detection in facial analysis within medical settings.

• Refined a U-Net architecture to enhance its performance for precise white balancing in facial color analysis. This refined output serves as a crucial input for our robust model, meticulously devised to discern whether a patient’s condition is deteriorating or improving.

Linxon Manchester, UK

Primary Applications Engineer January 2022 – June 2023

• Utilized AutoCAD to meticulously design substation layouts, incorporating client technical specifications and comprehensively analyzing equipment spec sheets.

• Conducted thorough validation and verification assessments on system architecture to ensure alignment with specifications and compliance with client standards. Projects

Human Body Dimension Estimation OpenCV, PyTorch, SMPL, CUDA Present

• Developing a neural anthropometer leveraging SMPL-based 3D human mesh synthesis and PyTorch implementation to measure human body dimensions accurately from front-facing camera.

• Implementing a compact convolutional neural network (CNN) architecture trained on SMPL generated 3D body meshes to output eight human body dimensions in meters, thereby contributing to enhanced accuracy and resource efficiency in anthropometric measurement tasks.

Fall Detection using mmWave radars Linux, scikit-learn, Pandas, PyTorch, FilterPy April 2024

• Engineered a robust fall detection system utilizing mmWave radars, synchronizing two units strategically positioned within the room to enhance point cloud resolution.

• Utilized DBScan clustering to eliminate static noise, coupled with Kalman filter-based object tracking, and subsequently employed PointNet++ for precise fall event determination, ensuring high accuracy in real-world fall detection scenarios.

Human Detection using YOLOv7 OpenCV, YOLOv7, PyTorch, CUDA February 2024

• Developed a real-time human detection and tracking system using YOLOv7 and SORT algorithms, achieving robust detection and tracking performance on video streams.

• Integrated bounding box drawing functionalities to visualize detected humans with options for saving object IDs and dimensions, enhancing analysis capabilities.

Image Denoising OpenCV, PyTorch, Pandas December 2023

• Created and refined a specialized convolutional neural network (CNN) model designed specifically for image denoising. Demonstrated significant enhancements in image quality and preservation, underscoring proficiency in devising signal processing algorithms and leveraging statistical analysis techniques.

• Executed CNN and Kalman filter implementations, achieving a 30% performance boost with CNN and assessing image quality using PSNR, SSIM, CW-SSIM, and SUMMER metrics. Technical Skills

Languages: Python, C/C++, CSharp, SQL

Libraries: Pandas, NumPy, Matplotlib, TensorFlow, PyTorch, Scikit-Learn, Scipy, OpenCV, Keras Developer Tools: Git, Studio, MATLAB,Visual Studio, PyCharm, Eclipse, Jupyter, Tableau

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