Jayant Patil, Ph.D.

Research Scientist | Data Science Expert

I'm a PhD engineer with over ten years of experience working at the intersection of data science, machine learning, infrastructure systems, and software development. My work focuses on making sense of complex, real-world datasets and using them to solve practical problems in electric power networks, civil infrastructure, and reliability engineering.

I've spent years developing models that efficiently assess network reliability, improve system resilience, and guide decision-making. Along the way, I’ve collaborated with interdisciplinary teams, written software, published research, and delivered results that translate well beyond theory.

Curriculum Vitae Research Work Personal Projects

Professional Summary

As a research scientist with a PhD in civil engineering, I transform complex data into actionable insights. I specialize in developing computational methods and machine learning solutions for assessing and improving the reliability of infrastructure, predicting failures, and reducing operational costs for complex infrastructure systems.

Background & Expertise:

Predictive Analytics: Building models that approximate system reliability - the probability of system failures from various causes
Reliability Assessment: Quantifying infrastructure system reliability using network science, dynamic programming, machine learning, graph neural networks, and principled Monte Carlo simulations
Machine Learning Implementations: Developing and deploying ML solutions for real-world problems
Software Development: Writing software using version control and collaboration tools
Cross-functional Collaboration: Translating technical concepts for diverse stakeholders

My expertise brings a unique combination of technical depth and practical problem-solving that can:

Translate research innovations into practical applications
Develop robust predictive maintenance strategies that reduce costs
Create scalable ML solutions for complex infrastructure systems
Optimize operational efficiency through data-driven decision making

I'm actively seeking job opportunities in energy, technology, and finance sectors where I can leverage my expertise to drive innovation and create real-world impact.

Learn More About My Research

Technical Skills

Coding

Python
SQL
MATLAB
Bash
Git

Machine Learning

Scikit-learn
Keras & TensorFlow
Pandas
Computer Vision
XGBoost
CatBoost

Data Visualization

Matplotlib
Seaborn
Tableau
Plotly
D3.js

Cloud Computing

Microsoft Azure
Amazon Web Services (AWS)

Research Summary

Developing computational methods for infrastructure reliability assessment, electric grid modeling, and data analytics

Infrastructure Reliability Assessment

Overview

Pioneered novel computational methods for assessing reliability of critical infrastructure systems, with a focus on electric grid and multi-component systems. My research bridges the gap between theoretical reliability models and practical industry applications.

Key Achievements

Achieved over 1000x improvement in computational time for reliability assessment of radial infrastructure systems through a novel dynamic programming algorithm
Developed an augmented artificial intelligence framework using graph neural networks that predicts the all-terminal network reliability achieving a coefficient of determination (R²) of 0.9934
Created a novel CNN and Convolutional Autoencoder methodology for sensor fault detection that achieved 98.7% accuracy in fault type detection and 99% accuracy in reconstruction
Formulated a closed-form recursive technique for assessing the reliability of multi-state, multi-component wind turbine systems that reduced the computational time by over 1000x

Electric Grid Modeling and Analytics

Overview

Developed parametric models and computer vision solutions for creating synthetic electric grid models. This research enables researchers and industry professionals to better model and understand grid vulnerabilities in data-scarce settings.

Key Achievements

Engineered a scalable methodology for modeling synthetic multi-scale electric grids that enables comprehensive risk, reliability, and resilience studies
Created parametric synthetic power flow models that accurately represent real-world grid behavior
Conducted comprehensive analysis of socioeconomic equity in power outage patterns in a major US County, revealing significant inequity (Gini coefficient of 0.62) and that higher-income areas experience fewer disruptions per capita than middle and low-income areas
Created geospatial visualizations to map power outage metrics against socio-demographic factors at the block group level, enabling identification of vulnerable communities and informing equitable infrastructure improvement strategies

Electric Grid | Synthetic Models | Geospatial Analytics | Equity Analysis

Learn More About My Research

Professional Certifications

Microsoft Certified: Azure Data Scientist Associate

Expertise in designing and implementing data science solutions on Azure, leveraging machine learning techniques for predictive analytics and model deployment.

ML Solution Design Data Exploration Experiment Management Model Training Model Deployment

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Google Data Analytics Certificate

Rigorous training in data analytics tools and techniques, including data preparation, analysis, visualization, and insights communication using R programming.

R Programming Data Preparation Data Cleaning Data Analysis Data Visualization Case Studies

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IBM Applied Software Engineering Fundamentals

Mastery of software engineering principles for data science and AI applications, including Python programming, Linux commands, Git version control, and AI application development.

Python AI Applications Flask Linux Commands Git & GitHub Software Engineering

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Machine Learning Engineering for Production (MLOps)

DeepLearning.AI specialization covering the full MLOps lifecycle, from data management to model deployment and monitoring in production environments.

ML in Production ML Data Lifecycle ML Modeling Pipelines Model Deployment Monitoring Maintenance

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Credentials can be verified by clicking the "Verify Credential" buttons, which link to the official certification platforms.

Personal Projects

Explore my side projects in energy, finance, and software development.

View Projects

Check Out My Blog

Explore my insights on transferable skills from academia to industry, case studies, and more.

Visit Blog

Contact Me

Get in touch with me for collaborations, job opportunities, or just to say hi!

jayantpatil289 AT gmail com

+1 (832) 366-6618

Houston, TX, USA

I am actively seeking new job opportunities. If you believe my skills align with your team's needs, please don't hesitate to reach out. While I am based in Houston, I am also open to remote and relocation options.

GitHub

Google Scholar

ORCID