SP

BIOGRAPHY

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

I am a dedicated AI Engineer with a strong passion for researching, designing, implementing, and delivering cutting-edge prediction models for complex biomedical challenges. Leveraging state-of-the-art AI/ML technologies, I aim to advance personalized medicine and transform healthcare solutions.

Senthil Palanivelu

Skills

💡
Python
scikit-learn
CI/CD
MATLAB
Linux
Docker
R
SQL
PyTorch
Pandas
Numpy
Data Engineering
AWS
Jupyter Lab
C++
R Shiny
Data visualization
GitHub actions
matplotlib
Excel
Optimization
Classification
Feature selection/engineering
Neural networks
Clustering
Regression
Statistical modeling
Computer vision
Bayesian statistics
Signal Processing
Predictive modelling
Data analysis
Data science
Shell scripting
GitHub/Version control
HTML & CSS
Containerization
VS code
Software development
Machine Learning
Time series analysis
Algorithms development
GitHub Copilot
AI agents
OpenAI
Prompt Engineering
RAG
Finetuning
LLM

Experience

  • Bioinformatician I @Brigham and Women's Hospital

    Sep 2022 - Dec 2024 | United States
    • Led data harmonization projects to produce an analysis-ready dataset by standardizing EMG, ECG, and EEG channel configurations, units and sample rates across cohorts and delivered a reproducible HTML workflow with scripts, facilitating future audits and replication efforts.
    • Validated, executed algorithms and deployed ML predictive model for automated sleep staging and brain age prediction
    • Experience using Random forest and Gradient boosting framework (LightGBM, XGBoost)
    • Experience with statistical methods such as hypothesis testing, linear regression
    • Developed and deployed a web-based interactive EDF viewer that captures real time user inputs, integrate with backend models and display analytical results.
    • Built web applications using R and deployed R Shiny applications on AWS EC2 using ShinyProxy and Docker containers.
    • Implemented best practices and methods for building efficient docker container images
    • Setup optimized computing infrastructure including compute and storage environments
    • Provided ongoing support to investigate application issues and apply incremental enhancements to existing applications based on user feedback.
    • Manage lab tools website using HTML and CSS
    • Experience in Machine Learning, including predictive modeling, clustering, feature selection methods, and regularization techniques.
    • Created GitHub actions workflow triggers on the creation of new releases and build Python package into distributions before publishing them to PyPI using Twine.
    • Automated the process of building and distributing Python packages across different platforms using GitHub Actions
    • Conducted a detailed statistical analysis of SpO2 levels above 90% during sleep, utilizing data visualization and predictive modeling to assess respiratory health across 1000 individuals.

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

    Sep 2021 - Jul 2022 | United States
    • Developed programs in MATLAB for processing sleep EEG in human brain
    • Developed MALTAB data analysis pipeline for detecting spindles and slow oscillations
    • Studied the neurophysiology of sleep through the lens of multi-taper spectral analysis
    • Used K-clustering algorithm to categorize the spatial patterns of slow oscillations into global, local or frontal
    • Analyzed the topography of sleep brain dynamics in the presence of neurodevelopment challenges
    • Used SVM to classify slow oscillations based on their source current densities
    • Implemented Mölle2011 spindle detection algorithm in MATLAB

  • Research Data Analyst I @Boston University

    Apr 2020 - Aug 2021 | United States
    • Implemented and supported statistical analyses and processing pipelines/scripts in Python for behavioral, EEG and multimodal neuroimaging analysis, including resting ‐ state and task-based fMRI and volumetric data
    • Performed correlation analysis at the brain source level using beamforming
    • Built predictive machine learning models on large, multi-modal data sets
    • Implemented graph theoretical analysis, time-frequency analysis, circular statistics and non-parametric cluster based statistics
    • Provided software support for behavioral / cognitive tasks analysis, including testing and debugging of existing Python MNE based platforms

  • 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 image processing pipelines in Python
    • Trained an encoder-decoder deep learning model for diffusion brain MRI segmentation
    • Deployed deep learning semantic segmentation model production code in AWS EC2
    • Visualized and analyzed fiber track data from diffusion MR tractography
    • Performed technical duties for clinical studies in the field of mental illness
    • Utilize bash scripting and Python for organization of datasets in Neuroimaging data lab
    • Prepare data for analysis across research sites for 200+ clinical, behavioral, and imaging measures
    • Hands on experience in image processing and analysis tools like 3D slicer, Free surfer, FSL and SPM

  • Research Assistant @University of Massachusetts Boston

    Jul 2016 - Aug 2017 | United States
    • Developed Computational pipelines in Python and UNIX Shell Scripting
    • Implemented custom ganglia Python metric modules
    • Optimized code in C for Parallel Computing
    • Maintained Distributed Resource Management - Son of Grid Engine
    • Configured DMTCP Check Point/Restart
    • Configured, Installing and Monitoring Nagios and Ganglia
    • Data Center Operations - Cent OS Installation and configurations
    • Setting up Remote Job Submission with the configure Schrodinger Software Application
    • linux clustering and storage management
    • Creating job Schedule bash scripts for SLURM and Oracle Grid Engine
    • Green High Performance Computing Cluster
    • Install and configure Linux on end-user workstations.
    • Assist users with installation and use of scientific applications.

  • IT Assistant @William Joiner Institute

    Sep 2015 - May 2016 | United States
    • Part of The William Joiner Institute for the Study of War and Social Consequences
    • Managed Website using HTML and Expression engine Tool ( CMS )
    • Designed poster, Created brochures and Flyers using Adobe In Design, Photoshop, Illustrator and Microsoft Publisher.
    • Organized events with fellow staff members

  • IT Specialist @Mphasis

    Oct 2012 - Feb 2014 | India
    • Backup and Restore (BUR): Part of HP Storage Services Management System (SSMS) that does BI reporting and billing for BUR division, Supporting and managing the backup and restore activities for more than 100 clients globally.
    • User and password administration, OS Hardening and working with security related settings Working with ACLs, and other file and directory level security settings.
    • Performance tuning, networking, system security, IO monitoring and analysis.
    • PLSQL, SQL server 2005, SQL Server Reporting Services.
    • Unix Shell scripting, Failure data collection analysis on Linux machines.
    • Upgrading EMC DPA collector agent on linux , Solaris and HP-UX servers.
    • Monitoring the DPA service health on daily basis and ensure the data backup has success.
    • Integrating new servers/clients and the storage pools into Data Protection Advisor and scheduling backups.
    • Identify, diagnose and resolve network and firewall issues with regards to the connectivity issue between the client servers/collectors and Data Protection Advisor server.

Education

  • Master of Science in Computer Science

    Jan 2015 - Jan 2019 | University of Massachusetts Boston
    • Courses: Advanced Algorithms, Algorithms in Bioinformatics, Analysis of Algorithm, Big Data Analytics, Linear Algebra, Calculus, Software Development and Design, Database Management, Computing Data Structure, Mathematical Logic

  • Certifications

    • July 2024 | Mathematics for Machine Learning and Data Science by DeepLearning.AI on Coursera
      • A comprehensive course covering fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
        • View Certificate

    • February 2025 | Google Prompting Essentials by Google on Coursera
      • AI agent design, Multimodal prompting, Prompt chaining, Prompt Design, Prompt evaluation and iteration, Responsible AI.
        • View Certificate