Alyan Khan

Machine Learning Engineer | Quantitative Research | Signal Processing Enthusiast I am a passionate Machine Learning Engineer with a strong foundation in deep learning, computer vision, and statistical modeling. Currently pursuing an M.S. in Electrical and Computer Engineering at Georgia Tech (GPA: 3.8/4.0), I specialize in LLM optimization, time-series forecasting, and real-time algorithm development. My experience spans both academic research and industry applications, with a focus on AI-driven decision-making and real-time systems. Key Interests & Expertise: 1) Deep Learning & Model Optimization: Developed quantized transformer-based LLMs, achieving a 3x reduction in model size and a 40% increase in inference speed. 2) Computer Vision & Signal Processing: Designed a real-time facial blood perfusion monitoring system using custom U-Net architectures. 3) Quantitative Analysis & Financial AI: Built sentiment-driven stock prediction models leveraging NLP, time-series forecasting, and risk assessment. 4) Edge AI & IoT: Developed a fall detection system using mmWave radars, Kalman filters, and PointNet++ to track human motion in real-time. What Drives Me: I thrive at the intersection of mathematical modeling, AI optimization, and real-world problem-solving. Whether it’s improving LLM efficiency, enhancing health monitoring systems, or predicting stock trends, I am driven by the challenge of transforming raw data into actionable insights. Looking to Connect: I am always open to discussions on AI research, quantitative finance, and real-time ML applications. If you’re working on cutting-edge AI, algo trading, or signal processing, let’s connect! #MachineLearning #QuantFinance #DeepLearning #AIOptimization #SignalProcessing #ComputerVision #LLMs