SP

BIOGRAPHY

Hi👋 I'm Senthil Palanivelu, a builder at heart with a background in Computer Science from University of Massachusetts Boston.

I approach every problem analytically — I love digging into large, complex datasets to find the patterns and trends hiding inside them. Data, to me, is never just numbers; it's a story waiting to be told.

What drives me is uncovering those stories and translating them into clear visualisations and insights that help people make better decisions — and then take better action. I genuinely believe that the right information, presented the right way, can change outcomes.

Over the years that mindset has taken me from clinical research labs in Boston to building end-to-end digital products — always with the same goal: turn raw, complicated data into something useful, clear, and dependable.

Outside of work, I'm curious about how AI is reshaping the way we learn, build, and make decisions — and I try to stay close to those edges.

Senthil Palanivelu

Experience

  • Member of Technical Staff @Amudham Naturals

    Jul 2025 - Present | India
    • Architected and delivered an end-to-end digital platform for a premium food brand, including a high-performance e-commerce site (Next.js, React), an internal profitability analytics tool (Python, Streamlit), and a conversational AI agent built with Chainlit and the Anthropic API that translates natural-language questions into SQL-driven business insights.
    • Architected a responsive e-commerce SPA using Next.js 15 (App Router) and React 19, achieving sub-second page loads via Static Site Generation.
    • Engineered a high-performance Python/Streamlit analytics dashboard to visualize business profitability, processing raw invoice data into actionable insights.
    • Designed and developed TalkToYourData, an intelligent conversational AI agent that transforms natural language questions into SQL queries, executes them against customer order databases, and delivers business insights in plain English.
    • Designed immersive UI/UX with Tailwind CSS 4 and Framer Motion, featuring infinite coordinate-scrolling carousels and smooth checkout transitions.
    • Leveraged SQL to answer business performance questions and built robust ETL pipelines using Pandas and Regex to clean PDF invoice data for precise margin analysis.
    • Implemented secure payment infrastructure using Razorpay API with real-time webhooks, custom GST logic, and automated EmailJS receipts.
    • Developed advanced statistical modules using the Interquartile Range (IQR) method to detect anomalies and visualize profit distributions via interactive Plotly charts.
    • Deployed automated CI/CD pipelines on Netlify and maintained rigorous TypeScript standards to ensure code reliability and rapid feature delivery.

  • Bioinformatician I @Brigham and Women's Hospital

    Sep 2022 - Dec 2024 | United States
    • Led data harmonization projects across multiple research cohorts, standardizing EMG, ECG, and EEG configurations for 1000+ participants from diverse clinical sites
    • Developed and deployed machine learning models for automated sleep staging and brain age prediction using ensemble methods (Random Forest, XGBoost, LightGBM)
    • Created web-based interactive EDF viewer application capturing real-time user inputs and integrating with backend ML models for clinical decision support
    • Built and deployed R Shiny applications on AWS EC2 using ShinyProxy and Docker containers, serving 50+ clinical researchers and sleep medicine practitioners
    • Implemented CI/CD pipelines using GitHub Actions for automated Python package building and PyPI distribution across multiple platforms
    • Conducted comprehensive statistical analysis of SpO2 levels during sleep across 1000 individuals, identifying respiratory health patterns and risk factors
    • Designed reproducible HTML workflows with accompanying scripts, enabling audit trails and replication for regulatory compliance and scientific transparency
    • Optimized computing infrastructure including cloud storage and compute environments, reducing analysis time from days to hours for large sleep datasets
    • Created automated job scheduling scripts for high-throughput analysis of polysomnography data, processing 100+ sleep studies per week

  • Research Associate - Research Math @Nationwide Children's Hospital

    Sep 2021 - Jul 2022 | United States
    • Developed MATLAB programs for processing human sleep EEG data, creating automated analysis pipelines for large-scale sleep studies
    • Implemented Mölle2011 spindle detection algorithm and custom slow oscillation detection methods, enabling high-throughput sleep pattern analysis
    • Applied multi-taper spectral analysis to study neurophysiology of sleep, contributing to understanding of memory consolidation mechanisms
    • Utilized K-means clustering to categorize spatial patterns of slow oscillations (global, local, frontal), revealing novel sleep architecture insights
    • Analyzed sleep brain dynamics in neurodevelopmental populations, supporting clinical research into developmental sleep disorders
    • Implemented Support Vector Machine (SVM) classification for slow oscillations based on source current densities, achieving 87% classification accuracy
    • Conducted topographic analysis of sleep EEG data, identifying biomarkers for healthy brain development and neurodevelopmental challenges

  • Research Data Analyst I @Boston University

    Apr 2020 - Aug 2021 | United States
    • Implemented statistical analysis pipelines in Python for multimodal neuroimaging data fusion (EEG, fMRI, behavioral), supporting translational neuroscience research
    • Performed brain source analysis using beamforming techniques and advanced signal processing methods for EEG/MEG data interpretation
    • Built predictive machine learning models on large, high-dimensional neuroscience datasets to identify biomarkers for cognitive and clinical outcomes
    • Developed graph theoretical analysis frameworks for brain network connectivity, revealing novel insights into neural communication patterns
    • Implemented time-frequency analysis, circular statistics, and non-parametric cluster-based statistics for complex neurophysiological data
    • Provided software development support for MNE-Python based platforms, including testing, debugging, and feature enhancement
    • Managed and organized multi-modal datasets from translational studies encompassing physiological, neuroimaging, clinical, and behavioral measurements

  • Clinical Research Coordinator II @Massachusetts General Hospital

    Jan 2019 - Mar 2020 | United States
    • Part of Center for Computational Imaging Anatomy at Massachusetts General Hospital and Psychiatry Neuroimaging Laboratory at Brigham and Women's Hospital
    • Developed and trained encoder-decoder deep learning models for diffusion brain MRI segmentation, achieving 95% accuracy on clinical datasets
    • Deployed production machine learning models on AWS EC2 infrastructure, serving real-time predictions for clinical research applications
    • Analyzed fiber track data from diffusion MR tractography to map brain connectivity patterns in mental health research studies
    • Coordinated technical aspects of multi-site clinical studies involving 200+ participants across neuroimaging, clinical, and behavioral data streams
    • Built Python-based image processing pipelines for multimodal neuroimaging analysis including resting-state and task-based fMRI
    • Utilized professional neuroimaging tools (3D Slicer, FreeSurfer, FSL, SPM) for volumetric brain analysis and preprocessing workflows
    • Organized and standardized large datasets from diverse research sites, ensuring data quality and regulatory compliance

  • Research Assistant @University of Massachusetts Boston

    Jul 2016 - Aug 2017 | United States
    • Developed computational pipelines in Python and UNIX shell scripting, automating research workflows for 200+ faculty across multiple departments
    • Optimized C code for parallel computing environments, achieving 40% performance improvements on high-performance computing clusters
    • Maintained distributed resource management systems (Son of Grid Engine, SLURM) supporting petabyte-scale storage and thousands of compute cores
    • Implemented custom Ganglia Python metric modules and configured Nagios monitoring systems, reducing system downtime by 85%
    • Configured DMTCP checkpoint/restart functionality, preventing loss of weeks-long computational jobs during system maintenance
    • Managed data center operations including CentOS installation, Linux clustering, and enterprise storage systems
    • Provided technical support for scientific applications including Schrödinger software configuration and remote job submission setup

  • IT Assistant @William Joiner Institute

    Sep 2015 - May 2016 | United States
    • Managed institute website using HTML and Expression Engine CMS, ensuring content accuracy and user accessibility for academic research dissemination
    • Designed marketing materials including posters, brochures, and flyers using Adobe Creative Suite (InDesign, Photoshop, Illustrator) and Microsoft Publisher
    • Coordinated and organized institutional events with cross-functional staff teams, supporting academic conferences and community outreach initiatives
    • Maintained web content management workflows and updated digital resources to support research publication and public engagement efforts

  • IT Specialist @Mphasis

    Oct 2012 - Feb 2014 | India
    • Managed backup and restore operations for 100+ global clients using HP Storage Services Management System (SSMS), ensuring 99.9% data availability and business continuity
    • Administered cross-platform Unix systems (Linux, Solaris, HP-UX) including OS hardening, performance tuning, and security configuration management
    • Developed SQL Server 2005 and PL/SQL database solutions for business intelligence reporting and automated billing processes for backup services division
    • Implemented EMC Data Protection Advisor across enterprise environments, reducing backup failure detection time by 75% through proactive monitoring
    • Diagnosed and resolved complex network connectivity and firewall issues between client servers and data protection infrastructure, maintaining 24/7 service availability
    • Created Unix shell scripts for automated failure data collection and analysis, improving incident response time by 60%
    • Configured Access Control Lists (ACLs) and file-level security settings across multiple platforms, ensuring compliance with enterprise security standards