Jagdeep C.

Namaste 🙏! I'm a passionate Data Scientist with an academic foundation from Northwestern University, deeply committed to advancing AI and Machine Learning across healthcare, computational biology, and scientific discovery—with particular strength in mathematically rigorous, FDA-aligned model development. Over the past year, I have successfully designed and implemented AI-driven solutions for ECG classification, denoising, medical imaging, and computational chemistry. My expertise blends deep neural architectures with advanced mathematics—Physics-Informed Neural Networks (PINNs), probability theory, stochastic processes, variational methods, and numerical optimization—to solve complex real-world problems with scientific defensibility. 🏥 FDA-Ready Clinical AI Development I have worked closely with FDA regulatory experts to bring models through clinical validation pipelines, ensuring they meet the rigor expected of medical-grade AI. My team is well-equipped to handle FDA documentation requirements (510(k) submissions, Software as a Medical Device (SaMD) artifacts, predicate analysis, validation reports, and risk management per IEC 62304 / ISO 14971) once a model is ready for launch. This means clients don't just get a working model—they get one built with regulatory submission in mind from day one. 🔬 Recent Work & Expertise in AI for Science & Medicine: ✅ ECG Signal Processing & Classification: Built deep learning models (CNNs, LSTMs, Transformers) for denoising, anomaly detection, and classification of ECG, PCG, PPG, and SCG signals—bringing them to gold-standard PhysioNet quality. ✅ Medical Image Processing (DICOM): Developed AI-driven solutions for lumbar spine classification, CT segmentation (TotalSegmentator models), fat tissue segmentation, bone mineral density estimation, and venous insufficiency detection via thermal imaging. ✅ Physics-Informed Neural Networks (PINNs): Applied PINNs to embed governing differential equations directly into the loss function—producing models that respect physical laws, generalize on sparse data, and are inherently more interpretable for scientific and clinical applications. ✅ Drug Discovery & Computational Biology: Used ML techniques to analyze molecular structures and optimize pharmaceutical compound discovery. ✅ Diffusion Models for Vision: Worked on Stable Diffusion and LoRA fine-tuning for text-to-video and image-to-video pipelines without relying on third-party APIs. ✅ AWS AI/ML Pipeline Deployment: Set up cloud-based pipelines on AWS for training, hosting, and deploying scalable ML models. ✅ Mathematical & Optimization Techniques: Leveraged variational calculus, FFT, wavelet transforms, probability theory, and evolutionary algorithms for feature engineering and model enhancement. 🧠 Core Technical Stack: 🚀 Deep Learning Frameworks: TensorFlow, PyTorch, JAX 📐 Mathematical Foundations: PINNs, probability and stochastic processes, variational calculus, numerical optimization, Bayesian inference 🔬 Generative & Diffusion Models: Stable Diffusion, LoRA, GANs, VAEs 📊 Classical ML: Random Forest, SVM, Gradient Boosting, RAG-based retrieval 👁 Computer Vision: CNNs, Vision Transformers, OpenCV, DICOM processing using SwinUNETR (3D U-Net + Swin), segmentation with nnU-Net 📝 NLP: Transformer-based LLMs, Hugging Face, LangChain, LlamaIndex 💡 Signal Processing: FFT, spectrograms, wavelet transforms, optimization methods ☁ Cloud & MLOps: AWS, Azure, GCP, Kubernetes, FastAPI, DevOps pipelines Why Work With Me? 🔹 Specialized in AI for healthcare, computational biology, and scientific discovery 🔹 Strong mathematical and probabilistic foundation—not just black-box deep learning 🔹 Experience working alongside FDA experts on clinical validation, with a team prepared to handle regulatory documentation 🔹 End-to-end delivery: from research and prototyping to deployment and submission-ready artifacts 🔹 Real-world projects that impact both research and industry Let's connect and build scientifically rigorous, regulation-ready AI together!