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Founder | Previous: Bloomberg, BMW (Self Driving), Built AI function @Chime
I have a passion for creating software systems that have an outsized impact on the world. At Chime, my team and I built AI systems that supported all aspects of the company's systems including: * (check and charge back) fraud detection, * ML based credit risk platforms, * real time fraud detection for money movement, * real time fraud detection in payment systems, * NLP/NLU based customer intent models for member services * and built out Chime’s real-time ML Infrastructure and working on our own feature-store! At BMW, my team and I delivered end to end ML solutions including: * AWS based RL model training system, * reinforcement learning based driving policy for autonomous vehicles which beat company's heuristic based system, * fully scalable Machine Learning infrastructure (feature stores/model stores) built from the ground up As an engineering leader I have delivered many large scale software systems including * an Oil Swaps trading platform, * iOS app based health platforms and * the back end of the worlds largest Fixed Income trading system - Bloomberg Bond Trader. SKILLS: Engineering leadership, Machine Learning strategy and execution, Deep Learning, Large scale software design, Ruby on Rails, Python, Engineering management, Web application design, C/C++ , Agile development, FIX, Linux and Fixed Income trading platform creation.
Birla Institute of Technology and Science, Pilani
BE, Electrical and Electronics Engineering
N/A – Present
Birla Institute of Technology and Science, Pilani
MSc, Physics
N/A – Present
Stealth
NewCo.
January 1, 2022 – Present
Grabango
Director of Machine Learning and Computer Vision
January 1, 2020 – January 1, 2021
Chime
Head of Machine Learning & Data Science
January 1, 2018 – January 1, 2020
San Francisco Bay Area
BMW of North America, LLC
Team Lead - Deep Reinforcement Learning & Perception
January 1, 2017 – January 1, 2018
Mountain View, CA
Medidata Solutions
Engineering Manager - Rave Safety Gateway
January 1, 2016 – January 1, 2017
San Francisco Bay Area
Wave-Ai
A Independent Machine Learning Consultant
January 1, 2016 – January 1, 2017
San Francisco Bay Area
RadiumOne
Engineering Manager - Reporting and Analytics, DMP
January 1, 2015 – January 1, 2016
San Francisco Bay Area
FitMountain.com
Entreprenuer - 2 Companies - (CoachAce & FitMountain)
January 1, 2012 – January 1, 2015
San Francisco Bay Area
Bloomberg LP
Manager - Software Development
January 1, 2004 – January 1, 2011
Greater New York City Area
Brookhaven National Laboratory
Software Engineer
January 1, 2002 – January 1, 2004
Upton, NY
Nokia
Software Engineer
January 1, 2000 – January 1, 2002
Long Island, NY
Customer Segmentation of wholesale distributor's clients using Unsupervised Learning (Clustering)
May 1, 2017 – Present
Used K-Means clustering to segment customers of a particular wholeshale distributor in order to better understand customer behaviors and to devise corresponding marketing strategy and operational plan for introducing new services to the market. The project included, data pre-processing, Principal Component Analysis (PCA), independent Component Analysis (ICA), Gaussian Mixture Model clustering (GMM) and K-Means clustering techniques.
Image classification using Deep Learning and CNNs (Convolutional Neural Networks)
May 1, 2017 – Present
In this project I build a Image classifier based on the CIFAR-10 dataset. We: 1. pre process the images to normalize them, 2. one hot encode the labels 3. create a tensorflow convolutional neural network graph 4. split data into a training set and a test set 5. create a model using the training set 6. improve the model by tweaking hyper-parameters, variable initialization values, addition/deletion of convolutional layers and fully connected layers. We then evaluate the training accuracy versus the test accuracy to figure out the number of epochs to run. Finally we evaluate the algorithm on photos found on the internet.
Vehicle Detection & Tracking System (Computer Vision using sliding window technique)
May 1, 2017 – Present
The goal of this project was to create a vehicle detection pipeline. The steps that were taken: 1. Performed a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images 2. Train a classifier Linear Support Vector Machine vehicle classifier using the massive GTA data 3. Apply a color transform and append binned color features, as well as histograms of color, to HOG feature vector to make it more robust 4. Implemented a sliding-window technique and used the trained classifier to search for vehicles in images. 5. Create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles. 6. Estimate a bounding box for vehicles detected in each frame of a video to be able to track the same.
A Traffic Sign Recognition Classifier for Self Driving Cars
April 1, 2017 – Present
Self Driving Cars will have to recognize traffic signs and then obey the same. In this project we build a traffic sign classifier using a Deep Learning. Steps include: 1. normalizing image data 2. balancing the data set 3. building and training a convolutional neural network with a traffic sign dataset 4. tweaking the initializations, hyper parameters, fully connected layer sizes, the CNN filter sizes to increase the accuracy of the system 5. testing the accuracy of the system with images found on the internet
Predicting Boston Housing Prices with Linear Regression
April 1, 2017 – Present
Using historical price data of Boston house prices to create a predictive model. Project included data pre-processing, getting rid of outliers and evaluation of the predictive model.
Find Lane Lines after un-distorting images using camera calibration, perspective transforms and using color and gradient thresholding.
March 1, 2017 – Present
In this project we use many different Computer Vision techniques to come up with a system that can detect lane lines for use in an Autonomous Vehicle : The goals / steps of this project are the following: * Compute the camera calibration matrix and distortion coefficients given a set of chessboard images. * Apply a distortion correction to raw images. * Use color transforms, gradients, etc., to create a thresholded binary image. * Apply a perspective transform to rectify binary image ("birds-eye view"). * Detect lane pixels and fit to find the lane boundary. * Determine the curvature of the lane and vehicle position with respect to center. * Warp the detected lane boundaries back onto the original image. * Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
Finding donors for a non-profit using Support Vector Machines & Random Forests.
March 1, 2017 – Present
In this project I employed several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census. I then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. The goal with this implementation was to construct a model that accurately predicts whether an individual makes more than $50,000 in a bid to create a target market for a non-profit. The dataset for this project originates from the UCI Machine Learning Repository.
Train a Smart Cab to Drive with Reinforcement Learning
March 1, 2017 – Present
In the not-so-distant future, taxicab companies across the United States no longer employ human drivers to operate their fleet of vehicles. Instead, the taxicabs are operated by self-driving agents, known as *smartcabs*. I implemented a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Improved upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.
Use Deep Learning to Clone Driving Behavior
February 1, 2017 – Present
In this project, clone driving behavior using deep neural networks and convolutional neural networks. One way of teaching a Neural network to drive a car is to train it with data collected by watching a human driving a car. In this projects I: 1. drove a car around a simulated race track, 2. recorded images and steering angles from driving around the simulated track, 3. built and trained a convolutional neural network using Keras & tensorflow using the above data, 4. regressed the steering angle using the images as input, 5. pre-processed data, augmented data and tuned the number of layers, size of filters of the neural network to improve performance of the system 6. tested the system by seeing how well it drives around the track on its own.
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from deep learning for autonomous vehicles to customer segmentation and financial fraud detection, demonstrates adaptability and a broad interest in applying ML across various domains. Their experience in both large corporations (BMW, Bloomberg, Nokia) and startups (Grabango, Chime, FitMountain) suggests a versatile cultural fit. The focus on building and leading teams aligns well with roles requiring mentorship and strategic contribution.
Soft Skills & Operational Fit
The candidate's experience as a manager and director, particularly in building teams and functions, suggests strong leadership, communication, and strategic planning skills. Their entrepreneurial background further highlights self-direction and problem-solving abilities. The descriptions of cross-functional collaboration at Chime and BMW indicate good operational fit for complex engineering environments.