Machine Learning Research Intern
Resume:
ARJUN PRASHANTH
**, **. ******* ******, ******, Massachusetts, 02115 +1-956-***-****
adhtr7@r.postjobfree.com https://www.linkedin.com/in/arjun-p/ https://git.io/arjunp Full-Time Availability: May 2021
EDUCATION
Northeastern University, Boston, MA Sep 2019 - Apr 2021 Khoury College of Computer Sciences CGPA: 3.75
Candidate for a Master of Science in Computer Science Related Courses: Deep Learning, Large Scale Data Processing, Algorithms, Information Retrieval SRM Institute of Science and Technology, Chennai, India Jul 2015 - May 2019 Bachelor of Technology in Software Engineering CGPA: 8.52 / 10.0 Related Courses: Machine Learning, Data Science and Big Data Analytics, Linear Algebra, Probability, Statistics TECHNICAL KNOWLEDGE
Languages: Python, MySQL, Java, C/C++, JavaScript, HTML, CSS, XML, JSON Packages: Numpy, Pandas, Scitkit-Learn, PyTorch, Matplotlib, Seaborn, NLTK, BeautifulSoup Tools/IDEs: PyCharm, Jupyter, ElasticSearch, Google Colab, IntelliJ, MySQL Workbench Operating Systems: Linux, Windows
Other Technologies: AWS (Sagemaker, S3, API Gateway, Lambda, Elastic Beanstalk, RDS, EC2), Git WORK EXPERIENCE
Khoury College of Computer Sciences, Northeastern University, Boston, MA May 2020 - Present Graduate Teaching Assistant
● Assisting Prof. Virgil Pavlu in grading assignments, presenting demos and class logistics for Information Retrieval course.
● Debugging / Troubleshooting the coding assignments of around 25 students every week and answering their questions. DRDO (Defense Research and Development Organization), Bangalore, India Dec 2018 - May 2019 Machine Learning Research Intern
● Implemented a pipeline of 4 different Machine Learning models that achieved an accuracy of 97.3%.
Pipeline included Naive Bayes, XGBoost, KNN, Decision Trees models that (a) identified whether media transfer occurred in a WhatsApp chat and (b) classified WhatsApp messages as delivered, received or seen.
● Researched WhatsApp’s network architecture and discovered patterns in WhatsApp’s traffic flow. PROJECTS
Automatic Speech Recognizer - Achieved loss of 60 over 2000 training samples July 2020
● Implemented an End-to-End Automatic Speech recognition pipeline using Keras.
● Preprocessed raw audio to feature representations like MFCC and Spectrograms.
● Built Acoustic Models to map audio features to the transcribed text.
● Experimented with different Neural Network architectures that include: Deep RNN + TimeDistributed Dense, CNN + RNN + TimeDistributed Dense, Bidirectional RNN + TimeDistributed Dense. Machine Translation - Achieved 98% accuracy over a vocabulary of 230 words June 2020
● Built a Machine Translation model that translates English sentences to French using Keras.
● Developed a comprehensive pipeline to preprocess over 1.8 million English and French words.
● Experimented with different architectures that include: Embedding layer + Bidirectional-GRU, Embedding layer with GRU, Bidirectional-GRU, Vanilla GRU, Encoder-Decoder with LSTM. Sentiment Analysis Web App - Achieved a test accuracy of 84% May 2020
● Developed a Web App that predicts the sentiment of an user input review.
● Performed text cleaning and preprocessing including stemming, stopword removal, tokenization and HTML parsing for over 50,000 reviews and uploaded the transformed data to AWS S3.
● Built an LSTM model with Word Embedding layer using skip-gram architecture to learn sentiments from the data.
● Deployed the model for testing on AWS Sagemaker and achieved a test accuracy of 84%.
● Hosted the model on my Web App using AWS Lambda and AWS API Gateway. Spam/Ham Classifier - Achieved 96% ROC over 75,000 emails April 2020
● Performed text preprocessing by email parsing, stemming and stopword removal using NLTK.
● Indexed the data to Elasticsearch and transformed text data to sparse matrices using CountVectorizer.
● Devised feature extraction using NLP techniques like Skipgrams and TFIDF.
● Developed Decision Trees, Logistic Regression and SVM models to achieve an ROC score of 96%.
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