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Software Systems Engineer at TMSI LLC
I am currently working as a Software Systems Engineer in TMSI LLC. I have a Masters’ degree in Electrical Engineering and have previous experience working as a Machine Learning Engineer in the R&D division of Mesnac Americas Co.,Ltd. I enjoy working in a research environment and have a good aptitude to learn and a desire to contribute to the development of new technologies. Professional experience coupled with numerous projects which involved coding in multiple programming languages has contributed to my growth as a programmer and an algorithm developer. I never shy away from hard work and always get the job done. Hard work, sincerity and my ability to grasp things quickly is what has helped me in maintaining a strong academic record of which I am proud. Skills include: * Knowledge of supervised and unsupervised learning algorithms, reinforcement learning, path planning. * Proficiency in MATLAB, Python, C++. * Excellent algorithm development skills. * Experience in device interfacing and implementation of communication protocols. * Experience in flash programming of Atmega, ARM microcontrollers. * Experience with tools like Eclipse, Visual Studio, MATLAB, AVR studio, IAR Embedded Workbench. * Strong problem solving and analytical skills. I would love to tell you more about myself and what I can bring to the table. Please feel free to call me on 405-837-6114. You can also email me at savyasachi.g@gmail.com.
Oklahoma State University
Master of Science (M.S.), Electrical Engineering
January 1, 2013 – January 1, 2015
Nagpur University
Bachelor of Engineering (B.E.), Electronics Engineering
January 1, 2007 – January 1, 2011
TMSI LLC
Software Systems Engineer
July 1, 2018 – Present
Canton, Ohio Area
Mesnac Co., Ltd
Machine Learning Engineer
August 1, 2015 – July 1, 2018
Akron, Ohio
Heta Datain
Embedded Systems Engineering Intern
June 1, 2014 – August 1, 2014
Nagpur, India
Advance Sensing Computation & Control Lab, OSU
Research Associate
January 1, 2014 – May 1, 2014
Stillwater, Oklahoma
Infosys
Systems Engineer
February 1, 2012 – October 1, 2012
Pune Area, India
Computer Vision-Face recognition
April 1, 2015 – Present
* Performed face recognition on 12 persons using Principal Component Analysis (PCA) with 10 images per person. * Implemented the PCA algorithm using the Eigen face method for face recognition in Matlab. * Utilized 5 images for training and the other 5 for testing the accuracy of the PCA algorithm.
Computer Vision-Object detection
April 1, 2015 – Present
* Performed object detection on circular objects using Markov Chain Monte Carlo (MCMC) sampling method. * Implemented MCMC sampling which involves Gibbs and Metropolis sampling in Matlab. * Detected overlapping objects also to give the total number of objects in the image.
Computer Vision-Image segmentation
March 1, 2015 – Present
* Performed image segmentation using clustering algorithms on grayscale and RGB image based on texture and color. * Implemented the two clustering algorithms, K-means and Expectation-Maximization, in Matlab. * Generated a video file to show the different stages of image segmentation.
Computer Vision-Texture analysis and Classification
February 1, 2015 – Present
* Performed texture analysis on 59 images using two algorithms. * Created a texture library of all the 59 images by computing certain statistics of each of the images. * Implemented Gabor filter bank and Laplacian Pyramid method in Matlab to perform the texture analysis. * Evaluated the two algorithms on the basis of the percentage of correct classification (PCC) for each image and the average PCC over the 59 images. * Comprehensively tested both the algorithms by using a combination of statistics and also different filter parameters.
Computer Vision- Retinal blood vessel extraction
January 1, 2015 – Present
* Performed edge detection by extraction of blood vessels in retinal images using three different algorithms. * Implemented Laplacian-of-Gaussian, Matched Filter and Canny filter in Matlab to perform edge detection. * Tested the three algorithms on variety of images of different resolutions and made a comparison of the results.
Optimization application in CMOS analog circuit design
December 1, 2014 – Present
* Applied optimization technique to design a simple common-source amplifier circuit. * Determined the area of transistor using optimization technique to minimize the power consumption and meet the constraints. * Derived the objective function and implemented it in code.
2D Space simulator in Unity
December 1, 2014 – March 1, 2015
* As the Automation Contest Chair, developed a 2D space simulator using Fuzzy Logic for Automation Day 2015 at OSU. * Simulator developed in Unity using C# for coding. Utilized the physics engine of Unity to simulate real situations. * Consisted of multiple stages with varying difficulty levels which tested the problem solving and game play skills of the players.
Two Stage OTA design
November 1, 2014 – December 1, 2014
* Designed a two stage OTA to operate in subthreshold in Cadence. * Simulated the circuit to verify that it met the specifications. * Designed the circuit with two different topologies - Telescopic and folded cascode. * Designed a PTAT bias generator circuit to generate bias for the OTA.
Neural Networks-Extreme Learning Machine
April 1, 2014 – May 1, 2014
* Implemented the concepts of the paper ‘Extreme Learning Machine’ (ELM) in Matlab. * Evaluated the ELM algorithm in terms of training time and RMS error by doing function approximation on Sine and Sinc functions. * Devised tests to compare the ELM algorithm to Steepest Descent Back-Propagation algorithm (SDBP). * ELM is an extremely fast neural network algorithm applicable to single hidden layer feedforward networks with infinitely differentiable transfer functions. * ELM algorithm was much faster than SDBP with the same amount of accuracy.
Neural Networks-Function approximation using a three layer neural network
March 1, 2014 – Present
* Performed function approximation on a sine function using a three layer neural network in Matlab. * Provision for user to enter the number of neurons in the hidden layer and the learning rate. * Tested the neural network for 2 and 10 neurons in the hidden layer with 3 different learning rates. * Steady state of sum squared error used as the testing criteria to stop the training. * Observed that the maximum stable learning rate is inversely proportional to the number of neurons in the hidden layer.
Neural Networks-Single layer perceptron design for classification
February 1, 2014 – Present
* Designed a single layer perceptron in Matlab to classify two sets of data. * Created training and testing data set by adding random noise to simulate a real world scenario. * Network was trained with random weights and bias to analyze the classification results. * Plotted the simulation results showing evolved decision boundary after the network training.
Android application for smart home
February 1, 2014 – May 1, 2014
* Developed an android user interface application for smart home system. * Collected data from the server and processed it to show the person's trajectory in real time on a mobile device. * The application helps the user to track the position of the person in real time in home environment.
Application tool for predicting Wind Power Generation using Stochastic methods
December 1, 2013 – Present
* Used stochastic model to determine wind power generation with wind velocity as random input variable. * Utilized LabVIEW to compare different probability distribution functions for wind velocity. * Designed virtual application tool that predicted the wind power generation graphically using virtual instruments simulation model. * Studied the concepts related to Stochastic Systems, Weibull distribution, wind power generation parameters, etc.
iRobot control using Beagle Board
November 1, 2013 – Present
* Configured iRobot using Beagle Board to track a moving object in real time. * Performed real time image processing on video captured from webcam using C++ programming and OpenCV libraries. * The RGB image converted to binary format using HSV values to distinguish the color and track the object.
Dexterous Robotic Arm
August 1, 2010 – May 1, 2011
* Developed a three fingered robotic arm which copied human arm movement. * Sensors mounted on human hand to detect movement. * Used Atmega32 microcontroller for processing sensor data and generating the necessary control signals.
Ambient temperature display on LCD
January 1, 2010 – April 1, 2010
* Developed an ambient temperature display module using Atmega 8 microcontroller. * Programmed the controller in Embedded C and used AVR studio to dump it in the microcontroller. * Designed and fabricated PCBs for the temperature display module.
C++ Nanodegree
Udacity
June 24, 2026 – Present
Applied Machine Learning in Python
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a strong inclination towards research and development, particularly in areas like Computer Vision, Neural Networks, and Robotics. This aligns well with roles that require innovation and a deep dive into complex technical problems. The variety of projects, from academic research to industry applications, suggests a versatile individual who can adapt to different work environments. The certifications in Machine Learning further demonstrate a commitment to continuous learning and staying updated with relevant technologies. The target role of 'Data Analyst' aligns with the candidate's strong background in data processing, algorithm implementation, and analytical thinking, although their experience leans more towards ML Engineering than pure data analysis.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude, evidenced by devising multiple approaches for collision avoidance and comparing algorithms. Their experience in project management suggests good organizational and planning skills. The diversity of projects, from embedded systems to machine learning and web applications, points to adaptability and a broad interest in technical challenges. However, without direct assessment data on communication or teamwork, these are inferred from the quality of project descriptions and professional experience.