Meghanath Macha

Meghanath Macha

Senior ML Engineer at Adobe | Ph.D., Carnegie Mellon University

As a Senior Machine Learning Engineer at Adobe, I lead projects utilizing large language models for scalable, enterprise-safe marketing content creation. My initiatives have been instrumental in the development and launch of Adobe GenStudio Create and the company's inaugural AI-driven marketing campaign. I have developed and scaled deep learning models that have been integrated into Adobe's Firefly Content Tagging Services.

I have a proven track record in intellectual property creation, having contributed to over 15 patents. My research in machine learning and management has also been recognized through 10+ publications in prominent conferences and journals such as KDD, WebConf, DMKD, and ISR.

Previously, as a Founding Engineer at a stealth startup during my Ph.D. at Carnegie Mellon University, I led the development of machine learning systems processing 300M+ location data points daily. This work secured multiple Fortune 500 clients and drove significant revenue increases. My doctoral research under Prof. Beibei Li explored the trade-offs of consumer behavior from location data.

My early career at Adobe Research in India and internships in San Jose were instrumental in developing methods for marketing attribution and fraud detection. A summer internship at Amazon enhanced my skills in active learning methods for record linkage. I hold dual degrees in Mathematics and Computing from I.I.T. Kharagpur, where I graduated at the top of my class in 2014.

Education

Carnegie Mellon University

Ph.D. in Information Systems Aug. 2016 - March 2021

Indian Institute of Technology Kharagpur

M.Sc. and B.Sc. in Mathematics and Computing2009 - 2014

Work Experience

Adobe GenStudio

Senior Machine Learning Engineer Jan. 2022 - Present

Adobe, Experience Cloud

Machine Learning Engineer Mar. 2021 - Jan. 2022

Stealth Startup, Carnegie Mellon University

Founding Engineer Mar. 2020 - Mar. 2021

Adobe, Data Science Lab

Machine Learning Intern Summer 2017 & 2019

Adobe, Digital Marketing Research

Research Associate June. 2014 - July. 2016

Awards

Best Paper Award

WITS 2019, Munich, Germany December 2019

Suresh Konda Award

Best First Paper at Heinz, CMU May 2018

CMU - PwC Presidential Fellowship

Outstanding academic excellence 2017 - 2018

Institute Silver Medal

Highest GPA in undergraduate July 2014

Prof. K.L. Chopra Award

Best Masters thesis in Science July 2014

Publications

[ For updated list please use: Google Scholar | DBLP ]

[R13]
Privacy Choice during Crisis

Anindya Ghose, Beibei Li, Meghanath Macha, Chenshuo Sun, Natasha Zhang Foutz and Josh Anton
Management Science forthcoming
Shorter version at ICIS 2020 ] [ working paper ]

[R12]
Perils of Location Tracking? Personalized and Interpretable Privacy Preservation in Consumer Trajectories

Meghanath Macha, Beibei Li, Natasha Zhang Foutz and Anindya Ghose
WITS 2019 Best Paper Award
Information Systems Research
Presented at NYU/ABA NextGen Antitrust Conference, 2020
[ working paper | video]

[R11]
Multiple Attribute Fairness: Application to Fraud Detection

Meghanath Macha, Sriram Ravindran, Deepak Pai, Anish Narang and Vijay Srivastava
Ethical Artificial Intelligence: Methods and Applications, KDD 2022 [ paper ]

[R10]
Revisiting How to Focus: Triplet Attention for Joint Entity and Relation Extraction

Debraj Basu Meghanath Macha, and Deepak Pai
Document Intelligence Workshop, KDD 2022 [ paper ]

[R9]
Value and Tradeoffs in Learning from Consumer Location Data

Meghanath Macha
Thesis [ link ]

[R8]
Trading Privacy for the Greater Social Good: How Did America React During COVID-19?

Anindya Ghose, Beibei Li, Meghanath Macha, Chenshuo Sun, Natasha Zhang Foutz and Josh Anton
International Conference on Information Systems (ICIS) 2020 [ paper ]

[R7]
CrEOS: Identifying Critical Events in Online Sessions

Meghanath Macha, Shankar Venkitachalam and Deepak Pai.
Temporal Web Analytics Workshop , WebConf (WWW) 2020 [ paper ]

[R6]
Geo-Targeting, Privacy, and the Rise of Consumer Location Trajectories

Meghanath Macha, Beibei Li and Natasha Zhang Foutz
International Conference on Information Systems (ICIS) 2019 [ paper ]

[R5]
ConOut:Contextual Outlier Detection with Multiple Contexts

Meghanath Macha, Deepak Pai and Leman Akoglu
ECML PKDD 2018 [ paper | code | website ]

[R4]
X-PACS: eXPlaining Anomalies by Characterizing Subspaces

Meghanath Macha, Leman Akoglu
DMKD Special Issue , ECML PKDD 2018 Journal [ paper | arxiv | code | website ]

[R3]
Anti-Ad Blocking Strategy: Measuring Its True Impact

Atanu R. Sinha, Meghanath Macha, Pranav Maneriker, Sopan Khosla, Avani Samdariya, and Navjot Singh
Advertisement workshop, KDD 2017 [ paper ]

[R2]
A non-parametric approach to the multi-channel attribution problem

Meghanath Macha, Shiv Kumar Saini and Ritwik Sinha
Web Information System Engineering ( WISE ) 2015 [ paper ]

[R1]
Modelling visit similarity using click-stream data: A supervised approach

Deepak Pai, Abhijit Sharang, Meghanath Macha, and Shradha Agrawal
Web Information System Engineering ( WISE ) 2014 [ paper ]

Patents

[ For updated list please use Justia Patents ]

[P13]
Generating Digital Content Consistent with Context specific guidelines utilizing prompt augmentation and model finetuning

Deepak Pai, Meghanath Macha, Debraj Debashish Basu

[P12]
Audience Targeted Content Generation

Shubham Lohiya, Meghanath Macha, Deepak Pai

[P11]
Using Shapley Values to Evaluate Prompt Generation Parameters

Meghanath Macha, Debraj Debashish Basu, Deepak Pai, Anish Narang, Shankar Venkitachalam

[P10]
Custom Content Generation

Shradha Agrawal, Debraj Debashish Basu, Deepak Pai, Nimish Srivastav, Meghanath Macha, Ambareesh Revanur

[P9]
A practical approach to ensure fairness in machine learning

Meghanath Macha, Sriram Ravindran, Anish Narang, Vijay Srivastava, Deepak Pai [ To be filed ]

[P8]
A method to identify stages in the path to online e-commerce

Meghanath Macha, Shankar Venkitachalam and Deepak Pai [ To be filed ]

[P7]
User Segment Generation and Summarization

Meghanath Macha and Deepak Pai [ Filed ]

[P6]
Techniques to quantify efectiveness of site wide actions

Atanu Sinha, Meghanath Macha, Pranav Maneriker, Sopan Khosla, Avani Samdariya and Navjot Singh [ link ]

[P5]
Generating and utilizing a conversational index for marketing campaigns

Meghanath Macha, Moumita Sinha, Kokil Jaidka and Niyati Chhaya [ link ]

[P4]
Simulation-based evaluation of a marketing channel attribution model

Meghanath Macha, Ritwik Sinha and Shiv Kumar Saini [ link ]

[P3]
Buying Stage Determination in a Digital Medium Environment

Meghanath Macha, Ritesh Noothigattu, Shubham Garg, Abhishek Kandoi and Atanu R. Sinha [ link ]

[P2]
Value function-based estimation of multi-channel attributions

Meghanath Macha, Ritwik Sinha and Shiv Kumar Saini [ link ]

[P1]
Visitor session classification based on clickstreams

Deepak Pai, Abhijit Sharang, Meghanath Macha, and Shradha Agrawal [ link ]