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I’m Shruthi Gokul, an Electronics and Communication Engineering graduate (Anna University) who now works at the intersection of AI and medical imaging, especially for analysis and early detection in cancer and sepsis. In my research work at IIT Madras, I’ve explored graph-theoretic characterization of nuclear spatial organization, fractal-dimension-based analysis of cellular nuclei, and machine-learning pipelines for non-invasive sepsis monitoring and subtype identification. I’m also passionate about deploying efficient models and building practical systems—whether that’s compressing LLMs and enabling edge inference on Raspberry Pi for an educational chatbot project, or experimenting with TinyML-style deployment concepts for real-time inference. I enjoy collaborating across teams, following standards and compliance when designing biomedical engineering solutions, and communicating my work through posters, accepted conference submissions, and presentations.…I’m Shruthi Gokul, an Electronics and Communication Engineering graduate (Anna University) who now works at the intersection of AI and medical imaging, especially for analysis and early detection in cancer and sepsis. In my research work at IIT Madras, I’ve explored graph-theoretic characterization of nuclear spatial organization, fractal-dimension-based analysis of cellular nuclei, and machine-learning pipelines for non-invasive sepsis monitoring and subtype identification. I’m also passionate about deploying efficient models and building practical systems—whether that’s compressing LLMs and enabling edge inference on Raspberry Pi for an educational chatbot project, or experimenting with TinyML-style deployment concepts for real-time inference. I enjoy collaborating across teams, following standards and compliance when designing biomedical engineering solutions, and communicating my work through posters, accepted conference submissions, and presentations.

Shruthi Gokul

Data Scientist, AI Engineer, Data Engineer, +2

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I’m Shruthi Gokul, an Electronics and Communication Engineering graduate (Anna University) who now works at the intersection of AI and medical imaging, especially for analysis and early detection in cancer and sepsis. In my research work at IIT Madras, I’ve explored graph-theoretic characterization of nuclear spatial organization, fractal-dimension-based analysis of cellular nuclei, and machine-learning pipelines for non-invasive sepsis monitoring and subtype identification. I’m also passionate about deploying efficient models and building practical systems—whether that’s compressing LLMs and enabling edge inference on Raspberry Pi for an educational chatbot project, or experimenting with TinyML-style deployment concepts for real-time inference. I enjoy collaborating across teams, following standards and compliance when designing biomedical engineering solutions, and communicating my work through posters, accepted conference submissions, and presentations.…I’m Shruthi Gokul, an Electronics and Communication Engineering graduate (Anna University) who now works at the intersection of AI and medical imaging, especially for analysis and early detection in cancer and sepsis. In my research work at IIT Madras, I’ve explored graph-theoretic characterization of nuclear spatial organization, fractal-dimension-based analysis of cellular nuclei, and machine-learning pipelines for non-invasive sepsis monitoring and subtype identification. I’m also passionate about deploying efficient models and building practical systems—whether that’s compressing LLMs and enabling edge inference on Raspberry Pi for an educational chatbot project, or experimenting with TinyML-style deployment concepts for real-time inference. I enjoy collaborating across teams, following standards and compliance when designing biomedical engineering solutions, and communicating my work through posters, accepted conference submissions, and presentations.

Available to hire

I’m Shruthi Gokul, an Electronics and Communication Engineering graduate (Anna University) who now works at the intersection of AI and medical imaging, especially for analysis and early detection in cancer and sepsis. In my research work at IIT Madras, I’ve explored graph-theoretic characterization of nuclear spatial organization, fractal-dimension-based analysis of cellular nuclei, and machine-learning pipelines for non-invasive sepsis monitoring and subtype identification.

I’m also passionate about deploying efficient models and building practical systems—whether that’s compressing LLMs and enabling edge inference on Raspberry Pi for an educational chatbot project, or experimenting with TinyML-style deployment concepts for real-time inference. I enjoy collaborating across teams, following standards and compliance when designing biomedical engineering solutions, and communicating my work through posters, accepted conference submissions, and presentations.

Skills

AI Data Labelling

Data Visualization

Freelance Gig

Experience Level

AI Data Labelling

Expert

Data Visualization

Expert

Work Experience

Project Associate, Non-Invasive Imaging and Diagnostic Lab (NIID) at Indian Institute of Technology Madras (IIT Madras)

October 1, 2024 - June 30, 2025

Worked on AI and quantitative imaging projects in renal cell carcinoma and sepsis detection. Conducted graph-theoretic characterization of nuclear spatial organization by extracting Betti numbers (β0, β1) and clustering coefficients to quantify nuclear connectivity; optimized segmentation thresholds using the elbow method and validated statistical significance between tumor and normal tissues. Performed mechanics-focused analysis of the cellular nucleus by computing fractal dimensions for classification using a segmentation and analysis pipeline built with Detectron2 and box-counting methods. Contributed to a real-time, non-invasive intelligent system for sepsis detection and monitoring by integrating physiological/omics/non-omics data, building ML models for stage classification and progression prediction, and implementing a hypergraph neural network for identifying subtypes within sepsis stages to support personalized risk stratification. Explored TinyML deployment for energy-effic

Project Assistant at Centre for Sponsored Research and Consultancy, Anna University

March 1, 2022 - February 28, 2023

Developed an AI educational chatbot for NLP and generation, supporting pedagogy in an academic setting. Researched and implemented pruning techniques for HuggingFace BERT and related LLM components to improve efficiency. Achieved model compression by reducing weights from 32-bit to 8-bit with minimal accuracy loss and pruned up to ~45% of the structure while maintaining output fidelity. Designed a frontend optimized for deployment on Raspberry Pi to enable edge inference and presented the work during National Science Day to jury members and research scholars.