Meghanath Macha

Meghanath Macha

Head of AI | Behavioral ML | Scalable AI Platforms for Martech

At ZeroToOne.AI, I lead the development of our scalable predictive intelligence platform that transforms billions of consumer signals into actionable insights for personalized martech applications used by Fortune 500 brands. My team builds foundational behavioral models integrating physical and digital traces to predict consumer intent. Previously at Adobe, I led GenStudio development, contributing to Adobe's first GenAI marketing campaign and filing 15+ patents in GenAI, ML, and behavioral modeling.

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About Me

Current Role at ZeroToOne

At ZeroToOne, I lead the strategic development of our AI platform that transforms multi-modal behavioral data into predictive intelligence. I oversee the full technical roadmap—data engineering, model development, and production-scale ML systems that process billions of consumer signals across physical and digital domains daily.

My focus is on advancing transformer-based sequence modeling to integrate spatio-temporal trajectories with digital interaction patterns, creating unified behavioral representations that power our prediction engines. We are building scalable infrastructure for automatic feature engineering, model training, and inference optimization, with robust systems for monitoring, observability, and continuous learning.

Adobe & GenStudio

Previously at Adobe, I helped take GenStudio from concept to production, leading ML initiatives in on-brand content generation, developing brand taxonomies, building semantic knowledge graphs for representing brand identity, and enabling multilingual content generation. My work contributed to Adobe's first AI-driven marketing campaign and resulted in 15+ patents in GenAI, ML, and behavioral modeling.

Academic Foundation

During my Ph.D. at Carnegie Mellon University under Prof. Beibei Li, I focused on behavioral modeling, developing computational methods for privacy-preserving prediction of consumer behavior from large-scale, heterogeneous data. This research—published in leading venues such as KDD, ISR, and Management Science—is now actively applied in ZeroToOne's predictive intelligence systems.

I joined ZeroToOne in its earliest days as a Founding Engineer during my Ph.D., helping to build our ML systems from scratch. As the company grew, I scaled and led the AI team, developing the platform that today processes 1B+ behavioral signals daily, enabling predictive intelligence for Fortune 500 clients across retail, mobility, and finance sectors.

Beyond algorithms and data, my wife and I are based in California, happily residing in what we suspect is actually our cat Binxy's castle.

Education

Carnegie Mellon University

Ph.D. in Information Systems

2016 - 2021

Indian Institute of Technology Kharagpur

M.Sc. and B.Sc. in Mathematics and Computing

2009 - 2014

Experience

ZeroToOne.AI

Head of Artificial Intelligence

2024 - Present

Adobe

Senior Machine Learning Engineer

2021 - 2024

ZeroToOne.AI

Founding ML Engineer

2017 - 2021

Adobe Research India

Research Scientist

2014 - 2016

Core Competencies

Technical Expertise

  • Behavioral Modeling

    • Multi-modal, multi-task foundational models
    • Spatio-temporal trajectory modeling
    • Unified behavioral representations
    • Intent prediction

  • LLMs & GenAI

    • Knowledge graph-augmented generation
    • Parameter-efficient fine-tuning
    • Fact verification systems
    • Prompt optimization

  • Data Engineering & MLOps

    • Distributed data processing
    • Automatic feature engineering
    • Model monitoring at scale
    • Continuous learning systems

Leadership Strengths

  • AI Strategy & Vision

    • Behavioral data platform architecture
    • Predictive intelligence roadmap
    • Enterprise AI transformation

  • Technical Leadership

    • Scaling ML from prototype to production
    • Building multi-disciplinary AI teams
    • Fostering engineering excellence

  • Business Impact

    • Translating data into measurable ROI
    • Data-driven marketing solutions
    • Cross-functional collaboration

AI Impact

Key areas where my research and industry work have delivered measurable business value and advanced the field.

Behavioral Intelligence Platform

I architected ZeroToOne's transformer-based behavioral prediction models, integrating billions of multi-modal signals. I also developed unified behavioral embeddings that power intent prediction across multiple time horizons while maintaining privacy compliance.

Impact: Achieved 85% prediction accuracy for consumer intent, reducing media spend waste for Fortune 500 brands. The platform processes 1B+ daily signals, enabling precise, personalized marketing at scale.

Enterprise-Scale GenAI

I led key initiatives in the development of Adobe's GenStudio Create platform, establishing systems for contextually-aware, cross-language, and multimodal content generation. Additionally, I created ontology-based validation systems to ensure factual accuracy and guideline compliance.

Impact: Delivered a 60% reduction in content creation time while ensuring brand safety, resulting in Adobe's first fully AI-generated marketing campaign.

Privacy-Preserving ML

I pioneered personalized privacy preservation techniques for mobile trajectory data. My work balanced individual privacy with analytical utility for mobility analysis and public health monitoring.

Impact: Enabled privacy-conscious mobility insights during COVID-19 while prioritizing ethical data use for public good.

Explainable Anomaly Detection

I developed algorithms that characterize anomalies in group behaviors using interpretable subspace rules. This enables data scientists to understand complex patterns in high-dimensional datasets.

Impact: Enhanced fraud detection accuracy while providing human-readable explanations for reviewers and compliance teams.

Research & Patents

Key Publications

My research spans GenAI, traditional ML, privacy-preserving techniques, explainable models, marketing analytics, and social impact applications, with Tier 1 publications. My recent work focuses on efficient brand adaptation in foundation models, aligning with my patent portfolio.

GenAI & Foundation Models Click to view papers

BrandAdaptedLM: Enhancing Instruction Following Models with Efficient Brand Transfer

Pritika Ramu, Apoorv Saxena, Meghanath Macha, Varsha Sankar

ACL ARR 2025 (February Submission), 15 Feb 2025

Fine-tuning Brand Adaptation In Submission Patent-Aligned

Explainable AI & Anomaly Detection Click to view papers

Explaining anomalies in groups with characterizing subspace rules

Meghanath Macha, Leman Akoglu

Data Mining and Knowledge Discovery (DMKD), 2018

Anomaly Detection Interpretable ML 60 citations Most Cited

ConOut: Contextual Outlier Detection with Multiple Contexts

Meghanath Macha, Deepak Pai, Leman Akoglu

ECML PKDD, 2018

Outlier Detection Multi-context Learning 10 citations

Privacy & Data Ethics Click to view papers

Personalized privacy preservation in consumer mobile trajectories

Meghanath Macha, Natasha Zhang Foutz, Beibei Li, Anindya Ghose

Information Systems Research (ISR), 2023

Location Privacy Trajectory Analysis 36 citations Top Journal

Marketing Analytics & User Behavior Click to view papers

A non-parametric approach to the multi-channel attribution problem

Meghanath Macha, Shiv Kumar Saini, Ritwik Sinha

Web Information Systems Engineering (WISE), 2015

Marketing Analytics Non-parametric Methods 26 citations

CrEOS: Identifying Critical Events in Online Sessions

Meghanath Macha, Shankar Venkitachalam, Deepak Pai

Companion Proceedings of The Web Conference, 2020

User Behavior Sequence Modeling 2 citations Patent-Aligned

AI for Social Impact Click to view papers

Privacy Choice during Crisis: How did America react during COVID-19?

Anindya Ghose, Beibei Li, Meghanath Macha, Chenshuo Sun, Natasha Zhang Foutz

Management Science, 2024 (Previously arXiv preprint, 2020)

Privacy Economics COVID-19 27 citations Top Journal

Social determinants of health: Insights from location big data

Meghanath Macha, Beibei Li, Natasha Zhang Foutz

SSRN, 2021

Healthcare Geospatial Analysis 5 citations

For a complete list of publications, please visit: Google Scholar DBLP

Research Impact: 250+ citations · h-index: 9 · i10-index: 9

Key Patents

My patent portfolio spans multiple domains in AI, showcasing innovations in GenAI, bias mitigation, marketing analytics, and data privacy systems. My recent focus has been on GenAI technologies, with patents covering content generation, prompt engineering, and retrieval systems.

GenAI & Content Creation Click to view patents

Retriever Machine-Learning Model Training Data Generation and Implementation

Systems for training and implementing retriever models that efficiently locate relevant content for GenAI systems.

RAG Data Generation Filed

Generating Digital Content Consistent with Context-Specific Guidelines Utilizing Prompt Augmentation

Novel approach for ensuring AI-generated content adheres to specific contextual guidelines through advanced prompt engineering techniques.

Prompt Engineering Content Guidelines Filed

Using Shapley Values to Evaluate Prompt Generation Parameters

Game-theoretic approach applying cooperative game theory principles to quantify the contribution of prompt components, enabling systematic prompt engineering optimization.

Game Theory Prompt Engineering Filed

Machine Learning Systems and Techniques for Audience-Targeted Content Generation

AI systems that generate content specifically tailored to target audience preferences and characteristics.

Personalization Content Generation Filed

Generative Model-Assisted Content Generation and Interactive Content Editing

Systems for AI-assisted content generation with interactive editing capabilities, enhancing the creative workflow between humans and AI.

Human-AI Collaboration Interactive Design Filed

Custom Content Generation

Systems for document processing, generating output images depicting products and themes using AI models trained to generate images consistent with a brand.

Image Generation Brand Consistency Published

Data Systems & User Experience Click to view patents

Machine learning models for online environments

Novel techniques for analyzing interaction data to facilitate modifications to online environments and improve user experience.

User Behavior UX Optimization Granted

Value function-based estimation of multi-channel attributions

Techniques for analyzing marketing channels using non-parametric estimation to generate value functions at a user-level.

Marketing Analytics Non-parametric Methods Granted

Responsible AI & Fairness Click to view patents

Machine Learning Models Utilizing a Fairness Deviation Constraint and Decision Matrix

Framework introducing a fairness deviation constraint during model training with a decision matrix balancing accuracy and fairness metrics for bias mitigation across demographic groups.

Bias Mitigation Algorithmic Fairness Patent Pending

For a complete list of patents, please visit: Justia Patents

Patent Portfolio: 15+ patents spanning AI, privacy, marketing, and data science

Get In Touch

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