I am a graduate student currently pursuing Masters in Applied Mathematics with a concentration in Machine Learning, from Northeastern University. I completed B. Tech (Hons.) from IIT Kharagpur. My primary strengths include Data Structure & Algorithm Implementation, System Designing. Besides this, I hold a strong background in Mathematics, specifically in areas of Calculus, Linear Algebra, Probability & Statistics. I'm looking for looking for fulltime opportunites in the Machine Learning space.

I'm pursuing a master's degree in Applied Mathematics at Northeastern University - College of Science with a concentration in Machine Learning and Statistics.

Derived update rules and implemented Weighted Alternating Least Squares for predicting missing user ratings of MovieLens data. Improved MSE by 62 % compared to baseline (mean predicting) model.

Performed Time Series Analysis of average runs of opening batters in baseball from 1871 – 2015 with a Markov Chain. Calculated autocorrelation between original time series and a simulated time series. Performed GoF test at 5 % significance level to determine valid states of Markov Chain in a two-step transition matrix.

Encoded face image into 128-dimension feature vector (one-shot learning) using FaceNet. Implemented Triplet Loss function to compare Anchor, Positive, and Negative images in training data. Performed face verification and face recognition using the above encodings.

Used 50-dimensional GloVe vectors to represent words. Performed Word Analogy task. Implemented equalization algorithm presented in Boliukbasi et al., 2016 to remove gender bias.

This article contains verified ✅ certification links to all the courses I completed on Coursera.

Senior Software Engineer, September 2020 - September 2020.

Senior Software Engineer, December 2018 - August 2020

Software Engineer II, August 2015 - November 2018

Software Engineer, May 2013 - August 2015

Python, Java, R, C/C++, MATLAB, Mathematica, SQL, PHP, Perl, HTML, CSS, TypeScript, XML, JSON, Visual Basic

Regression, Classification, Ranking, Clustering, Dimensionality Reduction, Bagging, Boosting, Feature Engineering, Neural Networks, Deep Learning, Computer Vision, Natural Language Processing, Optical Character Recognition, Template Matching

PyTorch, TensorFlow, OpenCV, NumPy, pandas, Matplotlib, scikit-learn, SymPy, Spark, Angular, Spring, JUnit, Mockito

Git, Jupyter Notebook, Linux, Docker, Kafka, Hadoop, Hive, Zookeeper, Elasticsearch, PyCharm, IntelliJ IDEA, Oracle BI Publisher