About me? 🧐
I am CS grad student at University of Southern California. I tinker with and am passionate about Machine Learning, Databases, Artificial Intelligence, Data Analytics, Theory of Computation and Robotics.
When I am not doing CS, I read about all things Aerospace. I dream of going to Space one day.
I love playing the Devil’s Advocate, thriving on the process of shredding arguments and beliefs and letting the ribbons drift in the wind for all to see. Why? For the simple reason that it’s fun.
Also, I am curious about all things under the Sun (and above).
Schools that I went to
and
Things that I know
Education
M.S. Computer Science
University of Southern California
May 2020
B.E. Computer Engineering
Gujarat Technological University
May 2018
Skills
(Some languages, databases & frameworks that I’ve been working with lately)
Python
MongoDB
TensorFlow
Keras
Pandas
OpenCV
PySpark
PyTorch
Scala
A few sidequests that turned out to be successful 🤷
Credit card Fraud Detection
Implemented Decision Tree, Random Forest and Deep Neural Network (5 layers) on a real anonymized dataset. Used undersampling and oversampling (SMOTE) with Deep Neural Network to balance the highly imbalanced dataset (very less fraudulent transactions as compared to non-fraudulent ones). Achieved 99.6% test accuracy using SMOTE with NN along with 0 Type-II errors (false negatives).
Classification of handwritten digits from MNIST.
Built a linear neural network from scratch to classify handwritten digits (0-9) taken from MNIST datasubset in json format. Implemented Mini Batch Gradient Descent, ReLU activation function, softmax for classification and dropout to handle overfitting. Achieved test accuracy of 92.6%.
Home Security System using Face Recognition
Built system to recognize faces and give access to home, based on access profile (family, friend, intruder) in database. Possesses multi-level security. (PIR motion sensor inside for motion detection and webcam outside home for surveillance and face recognition) Owner/s can access system remotely and can also see live video surveillance on mobile phones/PC at any time. Used Raspberry Pi + Camera, Arduino. Leveraged Pushover API and Blynk API. RTSP for video surveillance.
(more projects in the resume)Hit me up!
