Machine Learning

Deep Learning for Medical Diagnostics at FemtoDx 

At FemtoDx, I implemented deep learning models for medical diagnostics, focusing on analyzing biosensor data. By designing convolutional neural networks (CNNs) for the detection of biomarker patterns in noisy data, I improved diagnostic accuracy by 30%. This work is highly relevant to healthcare applications where real-time, accurate detection of medical conditions is crucial, aligning with companies in the healthcare tech industries. 

Natural Language Processing (NLP) for Scientific Literature 

During my coursework at Georgia Tech, I developed an NLP model to summarize large volumes of scientific literature. Using transformers such as BERT, I fine-tuned the model to extract key insights from research papers, reducing manual review time by 40%. This project demonstrated my ability to work with state-of-the-art NLP techniques, applicable to roles at companies where managing and processing textual data at scale is essential. 

Computer Vision for Biomarker Detection and Image Segmentation

At FemtoDx, I worked on computer vision models for medical imaging, focusing on the segmentation of microscopic images to detect specific biomarkers. Leveraging U-Net architectures, I automated the segmentation process, improving efficiency by 50%. This expertise in computer vision is directly relevant to companies where advanced image processing techniques are key to innovative products and solutions. 

Recommender Systems for Personalized Healthcare 

As part of the Stanford certification project, I developed a recommender system tailored to personalized healthcare plans. By analyzing user profiles and health data, I built a collaborative filtering model that recommended treatments based on similar patient outcomes, improving recommendation accuracy by 20%. This experience aligns with roles at companies where building scalable, personalized recommender systems is essential. 

Reinforcement Learning for Autonomous Biosensor Calibration

At FemtoDx, I implemented reinforcement learning algorithms to optimize the calibration of biosensors in real-time. Using Q-learning, I designed a system that dynamically adjusted sensor parameters, improving sensor accuracy by 15% over traditional calibration methods. This type of real-time decision-making is crucial for autonomous systems at companies where reinforcement learning plays a key role. 

Scalable Machine Learning Pipelines for Real-Time Data Processing

Leveraging my experience from Georgia Tech's scalable machine learning courses, I designed end-to-end pipelines for real-time data processing at FemtoDx. By integrating TensorFlow, I built scalable systems capable of processing biosensor data streams in real-time, reducing latency by 25%. This expertise is crucial for companies where handling large-scale real-time data processing is important. 

Transfer Learning for Multi-Domain Medical Applications

During the Stanford Certifications, I completed a project on transfer learning, applying it to different medical domains. By pre-training a model on general medical datasets and fine-tuning it for specific tasks such as disease prediction, I reduced training time by 40% and improved accuracy in specialized fields. This work in transfer learning is highly relevant to companies where leveraging pre-trained models to accelerate innovation is essential. 

Machine Learning Algorithms for Low-Power Devices 

At Georgia Tech, I optimized machine learning algorithms for deployment on low-power edge devices, focusing on reducing the computational load while maintaining model accuracy. By implementing quantization and model pruning techniques, I reduced power consumption by 30%, which is critical for companies that are pushing the boundaries of AI on the edge. 

Explainable AI for Healthcare Decision Support Systems

At FemtoDx, I implemented explainable AI techniques to enhance the transparency of healthcare decision support systems. By integrating LIME algorithms, I provided clinicians with clear explanations for AI-generated predictions, improving trust in AI systems by 20%. This expertise is directly relevant to roles at companies which are at the forefront of developing trustworthy AI systems.