Machine Learning engineering at Adobe

TECHNOLOGIES: Python, Hadoop, TensorFlow, Keras, scikit-learn, PyTorch

Developed LSTM-based sequential models, deep clustering models, and active learning models for efficient AI-assisted data labeling suggestions and labeling conflict resolution

Built an LSTM-based RNN model with multi-class logistic regression text classifier to predict which AI conference a paper will get accepted to given the title and the abstract

Served as the AI subject matter expert for an exploratory science project in generative AI techniques by reviewing the literature, advising on techniques and building demonstrations using LSTMs, autoencoders, transformers, BERT, and other state-of-the-art models

Trained chatbots with Watson Assistant, developed classifiers to detect egregious (outstandingly bad) conversations, and built a public demonstration to showcase conversational breakdowns and how chatbots recover from bad conversations

Trained convolutional neural networks for MNIST digit image classification and generated adversarial images to attack the CNNs and built a public demonstration illustrating the concept of CLEVER scores and how they relate to neural network robustness

Mentored and worked with two interns on deep learning projects to generate a dataset of text captions from labeled videos and AI-assisted data labeling conflict resolution respectively

TECHNOLOGIES: Python, TensorFlow, Keras, scikit-learn, PyTorch, JavaScript

Built a speech-based conversational agent for kids with responsive listening features like backchanneling (automatically saying ’uh huh’ and ’hmm’ during conversations)

Worked on MART-based (Multiple Additive Regression Trees) ensemble models for backchannel time and occurrence prediction, kids emotion level prediction and turn-taking time prediction for the conversational agent

Evaluated human-likeness of the agent using conversations with 40 real kids aged 7-11 and obtained satisfactory results from expert human annotation evaluation despite automatic speech recognition challenges with this population

TECHNOLOGIES: Python, Bash, openSMILE, scikit-learn

Teaching assistant for Mobile and IoT Computing Services (08-781/08-766/45-887/45-987) in Spring 2017

Consulting as a web developer on a research project in Social and Decision Sciences department

Built and deployed the full stack for an open, real-time semantic B2B search engine of television ads in Indian TV channels

Implemented and integrated full-text and facet-supported search using Apache Solr, Apache Lucene, and information retrieval algorithms like Learning-to-Rank

TECHNOLOGIES: Python, Bash, NLTK, Django, MySQL, Jinja, HTML/CSS

Worked on maintaining and testing mint.com REST APIs as part of Mint Platform team

TECHNOLOGIES: Java, Bash

Founding and core member of the publications team of PESIT Entrepreneurship Cell and was responsible for the management and reporting events.

Part of research team which analysed voluminous agricultural data and studied patterns of sowing and harvesting to suggest crops to farmers for the next sowing season so as to optimise profits.

Machine Learning
Language and Statistics
Advanced Multimodal Machine Learning
Machine Learning for Text Mining
Deep Learning
Search Engines
Lean Entrepreneurship

Pattern Discovery and Data Mining
Text Retrieval and Search Engines
Cluster Analysis in Data Mining

Algorithms
Data Structures
Databases
Data Mining
Natural Language Processing
Web Technologies
Mobile Systems Engg
Operating Systems
Computer Architecture
Computer Networks
Social Network Analysis