Graduate Research Assistant Resume Example

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Jessica Claire
, , 100 Montgomery St. 10th Floor (555) 432-1000,

Aspiring Master student with good academic background and hands-on experience seeking an opportunity in a competitive firm where I can enhance my educational and professional skills.

Education and Training
Expected in Master of Science | Imaging Science Rochester Institute of Technology, Rochester, New York GPA:

Course Work- Advanced Computer vision, Math for Deep learning, Deep Learning for Vision, Image Processing and Computer Vision, Pattern Recognition, Testing of Focal Plane Arrays, Fourier Methods of Imaging, Radiometry, Human Visual System, Design of Digital Systems, Advanced Design of Digital Systems, Engineering Analysis, Random Signals and Noise

Expected in Bachelor of Engineering | Eletronics and Communication Engineering Vellore Institute of Technology, Chennai, Tamil Nadu GPA:

Relevant Course Work- Computer Programming and Problem solving, Data structures and algorithms, Digital Image Processing, Digital Logic Design, VLSI system Design, Digital signal Processing.


Programming Languages: Python(Tensorflow, Pytorch, Keras, OpenCV, Cartopy, etc), C++, MATLAB, Verilog, VHDL

Operating Systems: Mac OSX, Linux, Windows

Software Packages: MATLAB, Jupyter Notebook, Latex, ImageJ, Pisco, Dev C++, Spyder, Cadence, MS Office, Proteus, PSpice, LT Spice, KEIL, Xilinx, JMP, LaTeX

02/2019 to Current Graduate Research Assistant Fred Hutchinson Cancer Research Center | Sunnyside, WA,
  • Developing a climate network on NCEP/NCAR Reanalysis 1 dataset for surface air temperatures.
  • Research is to understand the temporal variability of Monsoon global climate network characteristics.
08/2018 to 12/2018 Lab Teaching Assistant Aquatech | Mooresboro, NC,
  • Worked as a lab teaching assistant to a project based physiology lab in the school of Biomedical Engineering.
  • Job responsibilities include assisting students during lab sessions and grading the students.
05/2018 to 08/2018 Image Processing and Machine Learning Engineer Finisar Corporation | City, STATE,
  • Designed and developed a Computer vision algorithm that measures and qualifies the dimensions of a wafer as per the company's specifications.
  • Developed a system which automatically stores the images  of new wafers and determines if it is qualified based on its distances measured.
  • Measured the required dimensions in the wafer images of 2 technologies. 
  • Two algorithms were developed in python and excel respectively. Both the results are analyzed and compared using JMP software.
12/2014 to 01/2015 Engineering Internship Electronic Corporation Of India Limited | City, STATE,
  • Worked on building and testing communication radios with various frequency ranges that are used in defense. 
  • Learned about the technologies  used in vehicle jammers,  LVDT sensors used in missiles, and PCB Fabrication. 
  • Projects
    01/2018 to Current Mini-Projects Rochester Institute of Technology | Rochester, New York,
    • Image Classification on MNIST: Applied K-Nearest neighbors and Perceptron on MNIST dataset for classification in Python. Acquired 0.96, 0.9717, 0.9693 and 0.88 accuracies for k=1,3,5 and Perceptron respectively. 
    • Prediction of Top-3 categories: CNN trained on ImageNet-1k with pre-trained VGG-16  is used on an image for predicting the top-3 objects of image with their probabilities. 
    • Training a Small CNN: Developed a CNN with 3 hidden convolutional layers with ReLu activation function. Batch Normalization is added between the convolutional layers. Training loss is compared with and without batch normalization as a function of epochs. CIFAR-10 dataset is used. CNN with Batch normalization has got more accuracy.
    • Object Tracking and Video Background Subtraction: MOG algorithm tool is used for background subtraction in a video. Tracked the object in motion for every 30 frames. Obtained an image with object positions at different times through-out the video. Erosion and dilation are used for eliminating noises in the image.
    02/2019 to Current Course- Advanced Computer Vision Project- Classification Analysis on Rotated MNIST | Rochester, New York,
    • Implementing various image classification algorithms on Rotated dataset. MNIST dataset.
    • MNIST dataset is used. Rotation, flipping and shifting of images in dataset are used for analyzing and understanding the classification algorithms.
    • Various classification algorithms like gradient descent, K-Nearest Neighbors, Xception, etc are used and analyzed. 
    • The aim of the project is to find the best algorithm for classifying transformed MNIST dataset.
    09/2018 to 12/2018 Course- Deep Learning for Vision Project-Image Captioning | Rochester, New York,
    • Recurrent Neural Networks(RNNs) and Long-Short Term Memory(LSTM) units are used for captioning images.
    • COCO-dataset is used. The model comprises of encoder and decoder modules. 
    • Encoder is used for object detection and object localization. Decoder consists of RNN and LSTM units for forming sentences.
    02/2018 to 04/2018 Course- Image Processing and Computer Vision Project-Image classification | Rochester, New York,
    • Image classification algorithms like Xception, VGG-19 and SqueezeNet based on convolution neural networks were developed and applied to datasets using Keras.
    • Accuracies were obtained for combinations of CNN models and classifiers.
    • Algorithms were compared based on accuracy and the duration of time.
    01/2018 to 05/2018 Course- Testing of Focal Plane Arrays Project- Testing of a CCD Camera | Rochester, New York,
  • Tested the CCD camera by using Gain calibration, Linearity analysis, Dark current analysis, Quantum efficiency estimation.
  • Pisco Software is used for collecting the images from the CCD camera.
  • ImageJ software and Python programming were used in analyzing the obtained Fitz images.
  • 09/2017 to 12/2017 Course- Radiometry Project- Determine the ’blackbody’ color temperature | Rochester, New York,
    • Images and radiance values were collected using Nikon D50 camera, Tungsten light source, Spectrometer and Helios calibration sphere.
    • Obtained the relationship between the radiance values and the image pixel values.
    • Estimated the blackbody color temperature of the tungsten light source using MATLAB.
    01/2017 to 05/2017 Course- Advanced design of Digital systems Project- Multichannel ADPCM Codec | Rochester, New York,
  • Multi-channel Adaptive Differential Pulse Code Modulation(ADPCM) Codec was developed using single resource architecture for transmitting a signal.
  • Modules were designed using Verilog HDL. They were verified and synthesized at RTL and Gate level.
  • The area, duration, power and test coverage were the metrics observed in the pre-scan and post-scan analysis of the modules.
  • Research Interests

    Image Processing and Computer Vision

    Machine Learning 

    Deep Learning

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    Resume Overview

    School Attended
    • Rochester Institute of Technology
    • Vellore Institute of Technology
    Job Titles Held:
    • Graduate Research Assistant
    • Lab Teaching Assistant
    • Image Processing and Machine Learning Engineer
    • Engineering Internship
    • Master of Science
    • Bachelor of Engineering

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