Saumil Shah
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Location: College Park, MD, USA
School: University of Maryland College Park
Field of Study: robotics

Saumil Shah

Saumil Shah Email : ------------
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University of Maryland College Park, MD
Masters of Engineering in Robotics : GPA: 3.9 / 4.0 Aug 2019 - present
Courses: Software Development for Robotics,Classical and deep learning approaches to geometrical computer vision, Perception for
autonomous robots, Planning for autonomous robots, Introduction to Robot Modeling, Control of Robotic Systems.

Sardar Vallabhbhai National Institute of Technology Surat, INDIA
Bachelor of Technology in Mechanical Engineering; CGPA: 8.22/10.0 July 2013 - May 2017
Skills Summary
• Languages: Python(Numpy - Pandas - MultiThreading and MultiProcessing - Matplotlib - TensorFlow - PyTorch - PyQt5),
C++ (Qt - OpenCV), C
• Softwares: Solidworks, Creo 2.0, MATLAB, Simulink, Minitab, Scilab, Kdenlive, GIMP, ImageJ
• Tools: ROS, Gazebo, Git, GTest and GMock, CMake, Arduino, Raspberry Pi

Sahajanand Medical Technologies Pvt. Ltd. Surat, INDIA
Engineer - Research and Developemnt July 2017 - July 2019
◦ Crack Detection on Stent Surface Using Deep Learning: Aimed to detect defects on stent surface without any human
interaction. Use of convolutional neural network using PyTorch for detection of defects using microscope images.
◦ Deliverability Testing Machine for Catheter : An experimental assessment of catheter trackability force measurement
for in-vitro testing. GUI based software using PyQt-Python, stepper control, sensor, data acquisition. Design of machine
using SolidWorks.
◦ Inflation Device-New Product Development: Designed and developed for inflation of angioplasty balloons and stent
implantation. Designed using SolidWorks, Quality Function Deployment for user requirements, testing, FMEA and design file
generation for regulatory needs.
◦ Development of Microfluidic Coating Apparatus: Precision drug coating on stent (cardiovascular medical device).
Touchscreen based, software controlled made with raspberry pi. Designed using Solidworks.
• Vision-based Object Inspection Robotic System: Development of UR5 robot based inspection system in Gazebo. Vision
based measurement developed with ROS and OpenCV. Developed with pair programming and agile development process. Travis
continuous integration and unit testing with google test suit and ROS level two testing. [GitHub] (Dec 2019)
• Observer based optimal control system for dual pendulum on cart: Development and simulation of optimum control.
Kalman filter based observer design and programmed and simulated using Python. [GitHub] (Dec 2019)
• CDPR - Cable Driven Parallel Robot: 6 degrees of freedom cable-based parallel robot for pick and place application in the
warehouse. Kinematics and grasping simulated in Gazebo. [GitHub] (Dec 2019)
• Human Obstacle Detection: HOG feature based SVM classifier with OpenCV C++. Developed with AIP (Agile iterative
process) and pair programming. Travis continuous integration and unit testing with google test suit. [GitHub] (Oct 2019)
• ABU Robocon India: Two Robots capable of playing a doubles badminton match. (Aug 2014 - Mar 2015)
• 3D Simulation model of 6-DOF stewart platform: 3D model of stewart platform was developed in Creo and 3D simulation
was developed in Simulink with Simmechanics link. (May 2016 - June 2016)
• Forward Kinematics of Stewart Platform: Forward kinematics of a 6-6 Stewart-Gough platform using a deep neural
network. A working model was made using stepper motor and arduino as a proof of concept. (Aug 2016 - May 2017)
• Deep learning with python and PyTorch - IBM: Learned how to use popular Python libraries such as NumPy, Pandas and
the PyTorch Deep Learning library to build Neural Networks and Deep Learning models.
• Statistics and R - Harward: Use of R programming language for statistical analysis
• Creo Parametric 2.0 - PTC: Learned how to use Creo 2.0 for computer aided design.
• 7 QC Tools for manufacturing - MINITAB : Learned how to use 7 QC (Quality Control) tools with Minitab for sustainable

robotics, ROS, Gazebo, C++, Python, Solidworks, Camera, deep learning, computer vision, Matlab, pytorc