Engineer Data

JATIN CHINCHKAR

adjdqa@r.postjobfree.com 919-***-**** linkedin.com/in/jatinchinchkar github.com/jchinch SUMMARY

I am a committed and a motivated engineer with 22 months of experience and a knack of problem solving. I am looking forward to use my knowledge and experience to solve real world problems and learn from your organization. EDUCATION

Master of Science, Electrical Engineering Aug 2019 - Present North Carolina State University GPA: 3.9

Bachelor of Engineering, Electronics and Telecommunications Jun 2013 - May 2017 Pune Institute of Computer Technology GPA: 3.46

EXPERIENCE

Software Engineer, Atos India Pvt. Ltd Jun 2017 - Apr 2019

• Developed and optimized an application for calculating required number of spare parts for a manufacturing company.

• Implementation of an algorithm to handle testing of machine components.

• Responsible for handling an application for storing data of employees traveling from one country to another.

• Implemented SQL queries for efficient data retrieval from company employee database by HR team. PROJECTS

Action recognition in videos

• Implemented Neural Network model using BN-Inception backbone and a GSM model in PyTorch to recognize various types of actions in a video from UCF101 data set. Achieved an accuracy of 74% on the test dataset. Implementation of Adaboost algorithm for face, non-face classification

• Built a Haar like feature extractor from face, non-face image datasets.

• Combined top ten haar features as weak classifiers to implement a single strong classifier using Adaboost algorithm.

• Model was trained on large positive and negative images. Accuracy of 90% was achieved on face and non-face test images.

Water-stress detector in soybean plants using Neural Networks

• Handled imbalanced Soyabean leaf dataset with classes designated based on extent of wilting, using Synthetic Minority Oversampling Technique (SMOTE).

• Applied the concepts of transfer learning using VGG16 network, to extract high level features and added CNN network to extract low level features and performed classification using Tensorflow.

• Incorporated weather data to the image data by fusing the two CNN models and improved overall accuracy of the model, as a part of Phase-II of the project. Achieved top 5 performance for the model in the entire class. Facial Image Classification using statistical model for Computer Vision

• Generated train and test dataset from CMU-MIT dataset using Intersection over Union (IoU) criteria.

• Developed single gaussian, mixture of gaussian, t distribution, mixture of t distribution and factor analyzer models using Expectation Maximization algorithm.

• Analyzed model by ROC curve and misclassification rate by testing on a test dataset. Implementation of Time Series Forecasting model

• Designed and developed simulation model for Time series Forecasting for temperature data from weather station.

• Performed data processing to remove trends, seasonality in the data for better model performance.

• Implemented Simple Moving Average, Exponential Moving Average, AutoRegression models to predict temperature with low error.

Text classification by Transfer Learning

• Implemented a spam, non spam text classification using Deep transformers.

• Extracted features using BERT model and implemented classification model using a Neural Network. Achieved 78% accuracy on the toy dataset

SIFT Keypoints Detection using Laplacian Blob Detector

• Created Laplacian scale space by convoluting image with Laplacian of Gaussian (LoG) filter and then normalizing image by varying the scale in order to detect all the possible keypoints.

• Performed 2D and 3D non-maxima suppression to detect unique keypoints and highlighted them using circles of respec- tive radii based on their location in scale space. SKILLS

Coursework: Neural Networks, Pattern Recognition, Computer Vision and Deep Learning, Design and Analysis of Algorithms, Digital Imaging Systems, Digital Signal Processing, Computer Networks Programing Language: Python, MATLAB, JAVA7, SQL, C++ Platforms and Libraries: OpenCV, PyTorch, Tensorflow, Numpy, PIL, SciPy, Eclipse

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