Doctoral Research
CHRIST (Deemed to be University), Department of Statistics and Data Science, Bengaluru.
Profile
Textiles, retail leadership, analytics and AI in one professional journey.
I work at the intersection of product knowledge, consumer understanding, sourcing ecosystems and data. Alongside my retail leadership role, I pursue doctoral research at CHRIST University on AI-driven identification of regional Indian saree provenance.
Academic Qualifications
An interdisciplinary foundation across textiles, fashion, computing, management and analytics.
Business Analytics and Intelligence
Executive Programme, Indian Institute of Management Bangalore.
Master of Fashion Management
National Institute of Fashion Technology (NIFT), New Delhi.
Master of Computer Applications
Guru Jambheshwar University, Hisar.
PG Diploma in Personnel Management
Lal Bahadur Shastri Institute of Management Development Studies.
B.Tech. in Textile Technology
Technological Institute of Textiles, Bhiwani.
Professional Journey
Experience across the textile-to-retail value chain.
| Period | Role / Domain | Focus |
|---|---|---|
| 2026 – Present | Buying & Merchandising Head Avantra and Kalanikethan | Buying, merchandising, assortment strategy, vendors, inventory, category performance and team leadership. |
| 2021 – 2026 | Category Management Avantra and Kalanikethan | Private labels, sourcing, portfolio management, merchandise planning and retail analytics. |
| 2015 – 2020 | AJIO / Reliance Ecommerce | Category management and development of the Indie proposition for Indian textile crafts. |
| Earlier career | Manufacturing, teaching, sourcing, ecommerce & entrepreneurship | Textile manufacturing, textile education, Fabindia, Pantaloons, Utsav Fashion and independent ventures. |
Doctoral Research
Artificial intelligence for Indian textile provenance and fine-grained saree recognition.
AI-driven saree origin identification
The research explores how deep visual representations and graph-based relational reasoning can improve classification of visually similar Indian saree traditions while retaining domain relevance and interpretability.
21 clustersRegional / craft-based saree classes
12,436 imagesCurated benchmark dataset
CNN · ViT · GNNVisual and relational modelling
Research interests: Computer VisionDeep LearningVision TransformersGraph Neural NetworksExplainable AITextile Provenance
Certifications
Selected certifications currently visible on my public LinkedIn profile.
| Certification | Issuer | Issued | Credential |
|---|---|---|---|
| Data Analytics Methods for Marketing | Meta | Jan 2023 | 4GFX2SHA3ZB9 |
| Mathematics for Machine Learning: Multivariate Calculus | Imperial College London | Jan 2023 | 2LD2JQ9NP2E2 |
| Computer Vision Deep Dive in Python | Dec 2022 | See LinkedIn | |
| Digital Marketing Analytics in Theory (with Honors) | University of Illinois at Urbana-Champaign / Coursera | Dec 2022 | WSQULYYJW7KD |
| Marketing Attribution and Mix Modeling | Dec 2022 | See LinkedIn | |
| Sample-based Learning Methods | University of Alberta | Jul 2021 | JXC8PLRBSBDH |
| Fundamentals of Reinforcement Learning | University of Alberta | Jul 2021 | 3FST96MWZJEL |
| Simulation Models for Decision Making | University of Minnesota | Jun 2021 | 3BA9SZM8C8Z5 |
| Customer Analytics | Wharton Online | Dec 2020 | 5D7MX69VGAD4 |
| Neural Networks and Deep Learning | DeepLearning.AI | Aug 2020 | 33GSDCATMTVY |
LinkedIn's public view currently exposes only part of the certification list. View the full / current certification profile on LinkedIn →
Books
Books on textile knowledge, fabrics, dyeing and Indian sarees.
Textile Reference
Fabric Structure-Simplified: A Step-by-Step Approach
A beginner-friendly introduction to basic woven structures including plain, twill, satin and their derivatives.
View on Amazon →Textile Reference
Compound Fabric Structure-Simplified: A Step-by-Step Approach
Covers compound structures including damasks, tapestries, brocades, terry pile, velvet, plush, double cloth and backed fabrics.
View on Amazon →Merchandising
Who is Mangalgiri? Essential Fabric Knowledge for Merchandisers
Practical fabric knowledge for professionals from non-textile backgrounds, with emphasis on apparel and Indian ethnic fabrics.
View author page →Dyeing
Why Turquoise Bleeds? Essential Dyeing Knowledge for the Merchandisers
An accessible guide to dye classes, colourfastness, dyeing stages, pigments, natural dyes and practical merchandising implications.
View on Amazon →Indian Sarees
Indian Saris: A Reference for Buyers and Merchandisers
A practical reference to Indian saree traditions, weaving techniques and technical parameters for students, researchers and merchandisers.
View on Amazon →Indian Sarees
Indian Saris-II: A Reference for Buyers and Merchandisers
The second volume in the Indian saree reference series, extending the coverage of regional traditions for buyers and merchandisers.
View on Goodreads →Selected Publications & Scholarly Work
Academic, teaching-case and research outputs.
Champo Carpets: Improving Business-to-Business Sales Using Machine Learning Algorithms
Harvard Business Publishing teaching case, 2021. Co-authored with Puneet Kapoor, Gyanesh Jain, Ashish Sethia and Dinesh Kumar Unni Krishnan.
Deep Learning and Graph-Based Directions for Indian Saree Classification
Research work examining fine-grained textile recognition and graph-based directions for Indian saree provenance classification.
Areas of Expertise
Domain knowledge developed through industry practice, field exposure and research.
Buying & Merchandising
Assortment planning, OTB, pricing, margins, inventory productivity and category strategy.
Indian Sarees & Traditional Textiles
Regional craft identities, construction, motifs, borders, pallus, materials and sourcing ecosystems.
Sourcing & Vendor Development
Supplier development, textile clusters, product specifications, negotiation and commercial partnerships.
Analytics & Artificial Intelligence
Python, machine learning, deep learning, computer vision, graph learning and decision analytics.
External Profiles & Resources
Professional, author and knowledge-sharing profiles.