Rishab Pal is a machine learning engineer with experience developing self-learning robotic process automation software using machine learning techniques such as imitation learning and reinforcement learning. He has worked on projects involving image processing, distributed deep learning, gesture recognition achieving 95.76% accuracy, one-shot face recognition achieving 97.23% accuracy, and predictive analytics solutions achieving 83.67% accuracy. Rishab has a B.Tech in computer science and engineering from DIT University with an 8.74 CGPA.

1. 57A MDC Sector 4
Panchkula Haryana
India 134114
TECHNICAL EXPERIENCE
RISHAB PAL
rishabpal.com
(+91) 7409577665
(+91) 7017919175
palrishab569@gmail.com
GitHub: https://goo.gl/TY3UNH
LinkedIn: https://goo.gl/4WiDiC
Machine Learning Engineer Trantor Software Pvt. Ltd. Aug 2019 – Present
FortressIQ
• Developing a self-learning Robotic Process Automation software using Machine Learning techniques like
Imitation Learning, Few Shot Learning, Multiple Instance Learning and Reinforcement Learning using user
generated data.
Software Engineer Yamaha Motor Solutions India Pvt. Ltd. Jul 2018 – Jul 2019
Gear Balancer (May 2019 – Jul 2019)
• Identifying faulty gears using image processing and deep learning.
Face Recognition (Mar 2019 – Apr 2019)
• Improved model accuracy and inference latency using PCA and OpenVino inference engine.
• Optimized the feature extractor by changing it to Light-CNN for deploying on low compute devices.
Distributed Deep Learning (Feb 2019 – Mar 2019)
• Distributed Model training architecture setup on Microsoft Azure for large datasets to reduce training time.
• Implemented with Horovod and OpenMPI.
Public Personal Mobility (Jul 2018 – Feb 2019)
Gesture Detection
• Build a Convolutional Neural Network for Hand Gesture Recognition using TensorFlow. Used Bayesian
Optimization techniques for hyperparameter tuning in Neural Networks. Accuracy: 95.76%
Face Recognition
• Developed a One-Shot Face Recognition using SSD Mobilenet for Face Detection and Inception Resnet v1 for
feature extraction. Accuracy: 97.23%
• Also used Two Pathway Generative Adversarial Network for photorealistic and identity preserving frontal view
synthesis from face image under any poses.
Software Engineer Trainee Yamaha Motor Solutions Ltd. India Jan 2018 – Jun 2018
• Predictive Analytics Solutions for retail sales forecasting using ARIMA, Exponential Smoothing and Holt’s Winter
for time series analysis. Accuracy: 83.67%
• Performed transfer learning a YOLO-v3 object detection model to count the number and type of vehicles on
drone videos. Accuracy: 93.56%
• Implemented Deep Convolutional Generative Adversarial Network to synthesize new vehicle designs.
• Implemented IOT sensors using Arduino and RaspberryPi.
Machine Learning Intern L&T Technology Services Jun 2017 - Jul 2017
• Data driven prediction models of energy use in a house/building.
• Used PCA, Support Vector Machines and ensemble learning methods like Random Forest and XGBoost for energy
usage prediction. Accuracy: 85.34%
Technical Volunteer PieLinks.com – DIT Chapter Oct 2014 - Jan 2016
• Checked for potential bugs on website.
• Helped to manage a team of undergrads to provide inputs for development of the website.
EDUCATION
• B. Tech (2014 - 2018): Computer Science and Engineering, DIT University, Dehradun, UK, CGPA: 8.74.
• 12th
(C.B.S.E., 2014): Science, Guru Teg Bahadur Public School, Durgapur, W.B., 85.4%
• 10th
(I.C.S.E., 2012): St. Xavier’s School, Durgapur, W.B., 84.5%
PROGRAMMING LANGUAGES AND FRAMEWORKS
• Python, Java, C++, SQL.
• Tensorflow, Keras, PyTorch, OpenCV, NLTK, Git.