Shankar Venkitachalam

Shankar Venkitachalam


I am a Staff Machine Learning Engineer at Adobe, where I lead projects on Adobe GenStudio, focusing on the use of Large Language Models (LLMs) for brand-aligned marketing content creation at scale for enterprises. I have developed deep learning models for text and image understanding that power Adobe's Firefly Content Tagging Services.

Previously, I earned my graduate degree in Computer Science from UMass Amherst, where I worked with Andrew McCallum and Pedram Rooshenas on Structured Prediction in NLP.

Before UMass, I worked as a Machine Learning Engineer in the Multimedia Research Group at Snapdeal, Bangalore, and as a software developer at Adobe India. I hold a Bachelor's in Computer Science from the National Institute of Technology Calicut.


Work Experience

Staff Machine Learning Engineer
Adobe GenStudio
June '23 - Present

Lead the development of Adobe GenStudio from ideation to launch, utilizing generative AI models for scalable, on-brand, personalized marketing content creation. Finetuned and aligned LLMs on marketing content from brands for creating brand-adherent content. Optimized models for creativity, lexical diversity, personalization and domain knowledge. Also developed models and pipelines for validation of content adherence to brand style and guidelines.

Senior Machine Learning Engineer
Adobe Firefly Content Tagging Services
Jan '21 - June '23

Created models for content understanding and tagging of text and images, offered as services via APIs, as well as integrated into Adobe products such as AEM. Developed NLP models for Keyphrase extraction, Named Entity recognition and text image classification, and CV models for image classification and color extraction.

Machine Learning Engineer
Adobe Experience Cloud AI
June '19 - Jan '21

Devised a novel conditioning strategy for generation of images conditioned on given semantic attributes using unconditional GANs. Published a paper in AICC workshop at CVPR 2021. Engineered model and pipeline for semi-supervised identification and summarization of marketing segments and critical event detection in online web sessions. Filed two patents and a workshop paper.

Machine Learning Engineer Intern
Adobe Experience Cloud AI
May '18 - Aug '18

Developed anomaly detection and cause analysis models for Creative Cloud marketing and customer journey data, leading to a patent.

Senior Software Engineer, Machine Learning
Snapdeal, Multimedia Research Group
Dec '15 - Mar '17

Built solutions for automatic validation and standardization of new listings. This included text classifiers for category prediction, as well as deep learning models for background extraction and harmful content detection. Also implemented pipelines for feature extraction, clustering and nearest neighbor computation of catalog images, to be used for image-based search and product similarity.

Member of Technical Staff II
Adobe Learning Manager
Aug '13 - Nov '15

Played a key role in building Adobe Learning Manager (formerly Adobe Captivate Prime), from the ground up. Designed and built the backend system and APIs for processing and storage of course content. Integrated third party services for transcoding videos and converting documents to HTML. Also created the administrator role UI.

Software Engineer, R&D
Tejas Networks
Aug '12 - Aug '13

Implemented features in the software layer for enterprise ethernet and optical networking switches and routers.

Education

University of Massachusetts Amherst
Master of Science (MS), Computer Science
Sep '17 - May '19

National Institute of Technology Calicut
Bachelor of Technology (B.Tech), Computer Science and Engineering
Jul '08 - Jul '12

Publications


Directional GAN: A novel conditioning strategy for generative networks
Shradha Agrawal, Shankar Venkitachalam, Dhanya Raghu, Deepak Pai
AICC Workshop, CVPR 2021

CrEOS: Identifying critical events in online sessions
Meghanath Macha, Shankar Venkitachalam, Deepak Pai
Temporal Web Analytics Workshop, WebConf (WWW) 2020

Patents


Generating digital content consistent with context-specific guidelines utilizing prompt augmentation and model tuning
Deepak Pai, Meghanath Macha Yadagiri, Shankar Venkitachalam, Debraj Debashish Basu, Varsha Sankar, Maryam Moosaei

Generative model-assisted content generation and interactive content editing
Varsha Sankar, Shankar Venkitachalam, Meghanath Macha Yadagiri, Deepak Pai, Debraj Debashish Basu

Using Shapley values to evaluate prompt generation parameters
Meghanath Macha Yadagiri, Debraj Debashish Basu, Shankar Venkitachalam, Anish Narang, Deepak Pai

Generating weighted contextual themes to guide unsupervised keyphrase relevance models
Debraj Debashish Basu, Shankar Venkitachalam, Vinh Khuc, Deepak Pai

Quantifying and improving the performance of computation-based classifiers
Debraj Debashish Basu, Ganesh Satish Mallya, Shankar Venkitachalam, Deepak Pai (Filed)

Generating Image Metadata Using A Compact Color Space
Nimish Srivastav, Shankar Venkitachalam, Satya Deep Maheshwari, Mihir Naware, Deepak Pai

Machine learning models applied to interaction data for facilitating modifications to online environments
MY Meghanath, Shankar Venkitachalam, Deepak Pai