🌟 Priyanka Gautam | Data Scientist | Researcher

🎓 Ph.D. Candidate | Kansas State University, USA
📍 Kansas, United States

📧 Email: priyankagautam099@gmail.com
📞 Phone: +1 785-317-8301

🌟 About Me

I am Priyanka Gautam, a Ph.D. Candidate at Kansas State University specializing in machine learning, graph theory, and complex networks. I am passionate about solving real-world challenges using data science and AI and love exploring how networks shape our world.

Research Overview

My research focuses on finding the most important and influential points in large, constantly changing networks—like social media, transportation, or power grids. I use mathematical & graph models and data science to understand how these networks behave, helping make them stronger, more efficient, and more resilient to changes or disruptions. 🚀

💡 What I Do

✔ Graph Neural Networks (GNNs) for infrastructure resilience ✔ Influence Maximization in Dynamic Networks to pinpoints important nodes ✔ Causal Inference & Data Science to understand the network dynamics ✔ Network Optimization & Computational Intelligence

🎯 Beyond Research

Outside of academia, I am a strong believer in hard work and continuous learning. I love exploring new experiences and challenges, whether it’s:
✔ Playing sports & recreational activities 🏀🏸
✔ Traveling to new places & cultures ✈️🏔️
✔ Cooking and experimenting with new recipes 🍳🌮
✔ Reading psychology & self-improvement books 📖🧠

I enjoy trying different things and pushing my limits, both in research and life. 🚀

🚀 Technical Skills

Programming: Python, C, C++, MATLAB, SQL, HTML, JavaScript
Machine Learning & AI: PyTorch, TensorFlow, Scikit-Learn, DGL
Data & Cloud Technologies: AWS, GCP, Power BI, Docker
Graph Analytics: NetworkX, Graph Neural Networks (GNNs)

🎓 Education

🎓 Ph.D. Candidate – Electrical & Computer Engineering, Kansas State University (Expected Fall 2025)
🎓 M.Tech. – Computer Science, IIT Gandhinagar (2019)
🎓 B.Tech. – Information Technology, AKTU University (2016)

💼 Work Experience

🔹 Data Science Analyst | Accenture Applied Intelligence

📍 Gurugram, India | 2021 – 2022

Workforce Planning & Optimization Strategy: Worked on an automated tool to optimize the workforce by analyzing job roles, hiring patterns, and market trends. Used Python, NLP, data analytics, machine learning, and Power BI for workflow planning.
New Workforce Strategy: Contributed to research on task categorization and intelligent augmentation, providing AI/ML and Python support.

🔹 Data Science Consultant | Eclerx Service Ltd.

📍 Mumbai, India | 2019 – 2021

NLP Projects: Developed automated tools for loan document extraction and classification modeling. Used regex, K-means, and clustering algorithms for PDF document information extraction.
HR Projects: Created a Flask-based API for Resume Parsing & Ranking, employing a hybrid NLP model (NER model + Spacy).
Image Processing: Designed an API for person image classification into front-face, upper-body, and full-body segments using YOLOv3.
Client Project - Morgan Stanley: Built an ETL pipeline for data extraction and compatibility for pricing predictions from Data lakes and Hadoop clusters.

🧑‍🔬 Research Experience

🔹 Graduate Research Assistant | Kansas State University

📍 Kansas, USA | 2022 - Present

Developing a graph-theoretic framework to study the relationship between interdependent critical infrastructure to pinpoint critical assets and links to incorporate resilience & robustness - GRA NSF Grant.
Collaborating with researchers from PNNL to identify influential nodes in dynamic networks using deep learning and reinforcement learning techniques - PNNL.
Collaboration project on Inferring Network Structure in Models of Opinion Dynamics - AMS MRC 2023.

🔹 Research Assistant | IIT Gandhinagar

📍 Gujarat, India | 2017 - 2019

Thesis: Crowd Counting & Surveillance
AI-Powered Traffic Prediction System
Proposed crowd monitoring solution for Rath Yatra using GPS and sensors.
Developed a web-based system for traffic prediction with JavaScript, HTML, and Google Maps API.
Provided rebar counting solution for TATA INNOVERSE in the steel industry.
Explored state-of-the-art models and developed an innovative approach using depth images for object detection and counting.

🏗️ Projects

🔹 GNN-Based Criticality Analysis in Infrastructure Networks

📜 Publication
✔ Identified critical nodes & links in urban infrastructure
✔ Achieved 97%+ accuracy using GNNs
Developed a scalable, adaptable framework using Graph Neural Networks (GNNs) to identify critical nodes/links in interconnected infrastructure networks. Incorporated performance-based feature metrics alongside traditional network-based metrics (like degree and eigenvector centrality) for vulnerability assessment. Achieved high accuracy in node (92.34% to 97.24%) and link (98.64% to 99.01%) classification using the CLARC dataset, demonstrating the efficacy of GNNs in pinpointing critical nodes and links.

🔹 Transductive GNN Learning for Power Grid Resilience

📜 Publication
✔ Developed scalable GNN models for grid failure prediction
✔ Improved prediction speed 2× over traditional methods
Developed a novel approach using transductive Graph Neural Network (GNN) learning to enhance power grid resilience by identifying critical nodes and links. The GNN-based method leverages the grid’s graph structure and operational data to learn resilience metrics, outperforming traditional simulation-based methods. Demonstrated the approach’s efficacy through case studies on node criticality and cascading outages, highlighting its scalability and accuracy.

🔹 ARISE Kansas NSF EPSCoR

🌐 Project Website
✔ Designed adaptive ML models for resilience analysis
✔ Integrated social equity metrics for better infrastructure planning

📚 Publications

P. Gautam, A. Sreejith, B. Natarajan, “Transductive Graph Neural Network Learning for Grid Resilience Analysis,” IEEE SmartGridComm, 2023. 🔗 DOI
P. Gautam, B. Natarajan, “GNN-based Criticality Analysis in Interconnected Infrastructure Networks,” IEEE GreenTech, 2024. 🔗 DOI
P. Gautam et al., “GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs,” AAAI Workshop 2025 (Accepted).

🎤 Talks & Presentations

✔ AMS MRC on Complex Social Systems (2023) – Opinion Dynamics in Graphs
✔ IEEE GreenTech Conference (2024) – GNN-based Infrastructure Analysis
✔ ARISE Annual Symposium (2023, 2024) – ML Frameworks for Resilience Computation
✔ 3MT Talk at Kansas State University (2024) – System Hardening for Infrastructure Networks

🎓 Workshops & Certifications

AMS MRC Week on Complex Systems (2023)
- Focused on Opinion Modeling in Graphs and Network Reconstruction.
Summer School on Computer Vision & Basics of Modern AI (2022)
- IIIT Hyderabad
- Covered advanced topics, including CNNs, RNNs, Vision and Language, Multi-View Geometry, and Biometric Systems.
Summer School on Machine Learning and Advanced AI (2022)
- IIIT Hyderabad
- Covered GANs, VAEs, RL, NLP, and Deep Learning, followed by lab sessions on implementation.
14th Annual ADMA Conference and Graph Theory Day (2022)
- DAIICT and IIT Gandhinagar
FLY: Finding the Leader in You (16-hour Short Course) (2022)
- IIT Gandhinagar
- Focused on developing a competitive mindset through active learning methodologies.
Certified Marketing Analytics Practitioner (2022)
- Henry Harvin
- Completed a 4-day/32-hour training program focused on analytics tools such as R and Advanced Excel.

🏛 Research Lab

This research is conducted as part of the Cyber-Physical Systems and Wireless Networking (CPSWIN) Group at Kansas State University.

🙏 Acknowledgments

This work is supported by:
✔ National Science Foundation (NSF) Award No. OIA-2148878
✔ State of Kansas - Kansas Board of Regents
✔ U.S. Department of Energy (DOE) Exascale Computing Project (ExaGraph) at PNNL

✨ Let’s Connect!

📩 Email: priyankagautam099@gmail.com
💼 LinkedIn
🔗 Portfolio
📜 Google Scholar