juhi chandalia

* Use Deep Learning techniques for computer vision problems in support of Sentient’s retail product. * Use keras and both publicly available pre-trained networks and internally developed networks running on GPUs to produce embedding spaces for customer images. Manage a 300K project to prototype Sentient’s next product. * Tune the compute resources and optimize performance of the Spark clusters used in the project. * Coordinate internal (DevOps, software architects, machine learning specialists) and external resources (subject matter experts) for the project. * Used AWS infrastructure and python, PySpark and SparkSQL to do data munging, feature engineering, and machine learning on a multi-TB dataset.

Led a project to annotate Affectiva’s full video dataset with emotion content. - Used Amazon Elastic MapReduce to process videos using Affectiva’s proprietary emotion analytics technology. - Crowd sourced data labeling using Amazon Mechanical Turk. - Used python to perform inter-rater agreement and clustering on the resulting data to reveal patterns in the U.S. advertising market. Researched current thinking about blink rate as an indicator of viewer engagement and translated findings from controlled lab settings into testable hypotheses for Affectiva’s data. - Used python to process raw data (interpolation, detrending, baselining, sigmoid transformation) to create usable signal trace data and individual and aggregate-level blink metrics to investigate blink synchrony and blink suppression engagement measures. Performed data visualization and created a logistic regression model in python for gaze direction using facial tracking points and head pose data.

Performed ad hoc machine learning, statistical modeling, and clustering analyses. -Prototyped (in R and SQL), tested, productized (with PL/SQL) and validated multiple predictive models for applications such as predicting customer churn in the mobile space. -Analyzed petabyte-sized datasets (using python) from trillions of records per month, derived statistically valid and actionable insights from the data, and presented findings as compelling business cases for areas such as network abuse, analysis of pricing plans based on actual usage, enhancement of advertising offers, churn reduction, and customer care. -Interfaced with customers in order to design effective solutions and presented recommended actions back to customers.

Completed qualifying exams and coursework for doctoral program. Coursework included: Electromagnetic Theory, Atomic and Optical Physics, Nonlinear Optics.