Engineering Data

Ashwin Shetty

Los Angeles, California adjnsb@r.postjobfree.com 213-***-**** LinkedIn Github

EDUCATION

University of Southern California, Los Angeles August 2019 - Present Master’s – Electrical and Computer Engineering (GPA- 3.95) Courses: Linear Algebra, Probability, Deep Learning, Mathematical Pattern Recognition, Machine Learning for Data Informatics, Advanced Computer Vision, Analysis of Algorithms

D.J Sanghvi College of Engineering, Mumbai May 2015 – July 2019 Bachelor’s in engineering – Electronics and Telecommunication Engineering (GPA – 3.83) SKILLS

AWS, GCP

Python, C++, Java

PySpark, Pandas, Scikit-Learn

Django, HTML, CSS, JavaScript

Cassandra, MongoDB, SQL

Tensorflow, Pytorch

PROFESSIONAL EXPERIENCE

Stylebot – Los Angeles June 2020 – Present

Machine Learning

Developed and deployed a Rasa chatbot over slack.

Assisted developers with migration from Dialogflow to Rasa open source.

Analyzed and identified issues with deployment over multiple slack workspaces to enhance user experience and determine expedient improvements.

Developed bot vocabulary by tracking user-bot conversations.

Wrote technical documents and manuals for software that was being developed. KAK Infotech Solutions – Mumbai June 2018 – July 2018 Software Development

Developed Machine Learning based solutions for tech companies.

Built a MATLAB program to detect currency denominations and forgeries using neural networks.

Assisted project heads in building and optimizing a chat bot in Python using Tensorflow PROJECTS

Real/Fake Disaster Tweet Detection Github View App

Performed EDA to understand general statistics about fake and real tweets such as length, word frequency, etc.

Preprocessed data by removing stop words, punctuations, numbers and tokenized the cleaned data.

Used term-frequency inverse document frequency transforms to convert text data to numerical features.

Trained a Logistic Regressor, SVM and Multinomial Naive Bayes model and achieved 80.79%, 78.24%, 77.21% val accuracy

Finally used a pretrained BERT (large-uncased) model to obtain 84.31% validation accuracy. Spoken Language Identification using LSTMs Github View App

Crowdsourced audio clips in English, Hindi and Mandarin

Removed periods of silence and extracted 64 dimensional MFC coefficients from audio to serve as features for classification

Implemented a Gated Recurring Unit and trained it to classify languages with an accuracy score of 94% in case English, 61.53% in case of Hindi and 70.59% in case of Mandarin CNNs for Image Colorization Github

Extracted bird image data from the cifar10 dataset and converted them to grayscale to serve as model input

Implemented K means algorithm using 32 clusters to find the main 32 colors components in the dataset

Converted the R,G,B component of images to the nearest main color component

Trained a deep CNN and a trailing dense network with dropout rate of 0.2 in the penultimate layer.

Achieved a final training accuracy rate of 85.56% and validation accuracy rate of 83.2%

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