Profile Summary

Mr. Prashant is an Assistant Professor in the Department of Artificial Intelligence and Data Science at CMR Institute of Technology. He brings a strong blend of academic excellence and industry-aligned expertise in data science, artificial intelligence, and medical analytics. He holds an M.Tech in Data Science from Christ University, Bangalore, graduating with distinction (CGPA 9.6/10), and a B.Tech in Mechanical Engineering.

Between October 2023 and August 2024, he was actively involved with Philips Healthcare, contributing to advanced analytics initiatives within the Diagnostic X-ray (DXR) division. His work focused on developing and deploying data-driven solutions for predictive maintenance and operational efficiency. Key contributions include:

• Implementing fault detection models for imaging subsystems using CAN (Controller Area Network) data.
  
  

• Building a CAN Analyzer Tool to convert raw hex data into interpretable diagnostics for engineering teams.
  
  

• Designing machine learning models to anticipate SkyPlate detector failures.
  
  

• Conducting time-series forecasting to predict usage patterns and failure timelines of CSM brake components in X-ray systems.
  
  

His research in deep learning, specifically on an Edge Attention Module in CNNs, has been accepted for presentation at IJCNN 2025 (IEEE), highlighting his drive for impactful innovation in medical AI.

Mr. Prashant is passionate about mentoring students, fostering academia-industry collaboration, and advancing research in the intersection of machine learning and healthcare.

Research Interests

• Deep Learning & CNNs
  
  

• Medical Image Processing
  
  

• Predictive Maintenance
  
  

• Time Series Forecasting
  
  

• AI in Healthcare
  
  

• Explainable AI (XAI)
  
  

Education

• M.Tech in Data Science, Christ University, Bangalore – CGPA: 9.6/10
  
  

• B.Tech in Mechanical Engineering, Uttarakhand  Technical University
  
  

Publication

• Prashant, et al. “Edge attention module”
  IJCNN 2025 – IEEE International Joint Conference on Neural Networks
  
  

Industry Experience

Data Scientist – Philips Healthcare, Pune
(Oct 2023 – August 2024)

•  Fault Prediction in X-Ray Systems: Designed and trained predictive models to detect early signs of failures in SkyPlate detectors and CSM brake components used in digital radiography.
  
  

•  Forecasting Usage Patterns: Developed time-series forecasting models to predict component wear and replacement cycles using historical usage data of X-ray systems.
  
  

•  CAN Analyzer Tool: Built a tool in Python to decode raw hexadecimal Controller Area Network (CAN) data from medical subassemblies into human-readable diagnostic metrics.
  
  

• Installation Cost Analysis: Conducted in-depth data analysis on X-ray device installation costs, contributing to strategic cost-saving initiatives.
  
  

• Root Cause Insights: Provided actionable insights by correlating error logs with maintenance records to identify high-risk failure patterns and optimize servicing intervals.
  
  

This experience sharpened his skills in data wrangling, feature engineering, modeling, statistical analysis, machine learning and communicating actionable insights in a healthcare environment.

Shape Molding Engineer – Beardsell ltd
(2020 – 2021)

Block Molding Engineer – Beardsell ltd

(2018 – 2019)

Technical Skills

• Languages: Python, SQL, MongoDB
  
  

• Libraries: TensorFlow, Keras, Scikit-learn, OpenCV, Pandas, NumPy
  
  

• Tools: Power BI, Excel, Jupyter, Google Colab, Git
  
  

• Techniques: Machine Learning, Deep Learning, Time-Series Analysis, Feature Selection, Predictive Modeling,  Medical  Image  Processing,  Computer Vision.