Short Bio

Since July 2021, I’m a Lead Data Scientist in the U.S. Omni Data Science & Analytics at Walmart Inc. I help innovate and develop appropriate analytical modeling techniques to drive next-generation Walmart financial services products, with a focus on improving the deeper relationship with the core business of Walmart.

I am very honored to be selected as one of the Google’s WTMAmbassadors. As an Ambassador, I will be an active community leader organizing events, speaking at conferences, creating content and mentoring others. This is a wonderful opportunity to learn & share experiences and leave an impact.

I received my Ph.D. in Computer Science from the University of California Riverside, where I was advised by Prof. Vagelis Papalexakis and supported by the UCR Graduate Fellowship. My thesis focused on modeling and mining multi-aspect graphs with scalable streaming tensor decomposition for real-world applications. During my Ph.D. journey, I interned with Adobe, Snapchat, and LLNL Research teams. Prior to that, I received my B. Tech. in Electronics and Communication Engineering from Punjab Technical University in 2011. I have 5 years of professional experience working as Sr. Network Engineer with Ericsson Global Services India Pvt. Ltd on LTE/LTE-A Technology.

Outside of work, I like to run, hike, dance, gardening and travel. I am improving my dance skill specifically in Bhangra, a Punjabi folk dance.

Research Work

My Ph.D. work spans two synergistic thrusts: first, my work focuses on static multi-aspect graphs, where the goal is to identify coherent communities between nodes by leveraging the tensor structure in the data. Second, as graphs evolve dynamically, my research focuses on handling such streaming updates in the data without having to re-compute the decomposition, but incrementally update the existing results. Under the same hood, my work focuses on the compression of capsule networks via tensor mining for various applications like fake news detection, recommendation systems, etc that can be deployed on embedded systems with limited hardware resources.

Anomaly Detection

E-Commerce

Explainable AI

Tensor Analysis

Professional Experience

Staff Data Scientist (July 2021- Present)
Walmart U.S. Omni Data Science & Analytics
Anomaly Detection and Predictive Modeling

Intern (March 2022- July 2022)
Walmart Intelligent Retail Lab
Computer Vision: Object detection and tracking, Data generation

Sr. Network Engineer (July 2011 - July 2016)
LTE Network Optimization
President Award for outstanding performance

Research Intern
Mentor: Brian Gallagher and Dr. Ming Jang
Tensor Analysis

Research Intern
Mentor: Neil Shah and Leonardo Neves
Explainable AI

Research Intern
Mentor: Georgios Theocharous Anoop Rao
Community Detection

Intern
Understanding Clock Net Routing for Shrinking Technology

Ph. D.

2016-2021
Computer Science and Engineering
University of California Riverside

B.Tech

2007-2011
Electronics and Communication
Punjab Technical University

Publications

Open-Source

P3. Ekta Gujral, Apurva Sinha, Lishi Ji, and Bijayani Sanghamitra Mishra , CPR: Leveraging LLMs for Topic and Phrase Suggestion to Facilitate Comprehensive Product Reviews, arXiv preprint arXiv:2504.13993 Paper New
P2. PAE: Product Attribute Extraction for E-Commerce Fashion Trends Paper
P1. Survey: Anomaly Detection Methods Paper, Code , Paper

Conference

Ekta Gujral and Sinha, A.,From Tokens to Tribes: Understanding LLMs for Social Graph Community Detection, submitted in 4th 2025 IEEE World Conference on Applied Intelligence and Computing (AIC 2025)New
Ekta Gujral and Sinha, A. ,LLMs Between the Nodes: Community Discovery Beyond Vectors, accepted in 11th International Conference on Artificial Intelligence and Soft Computing (AIS 2025) Paper New
Sinha, A. and Ekta Gujral ., 2024 Attribute Extraction for E-Commerce Fashion Data, (BIDA 2025)
Ekta Gujral and Evangelos E. Papalexakis, “Aptera: Automatic PARAFAC2 Tensor Analysis”, ASONAM 2022 .
Ekta Gujral, Neil Shaw, Leonardo Neves, and Evangelos E. Papalexakis, “NED: Niche Detection in User Content Consumption Data", (CIKM 2021)
Alexander Gorovits, Lin Zhang, Ekta Gujral, Evangelos E. Papalexakis, and Petko Bogdannov, "MYRON: Mining burstY gRoups frOm iNteractions", (CIKM 2021).
Ekta Gujral, Georgios Theocharous, and Evangelos E. Papalexakis, “C3APTION: Constraint Coupled CP and PARAFAC2 Tensor Decomposition”, (ASONAM 2020).
Ravdeep Pasricha, Ekta Gujral, and Evangelos E. Papalexakis, "Adaptive Granularity in Tensors: A Quest for Interpretable Structure”, (MLG 2020).
Ekta Gujral, and Evangelos E. Papalexakis, “OnlineBTD: Streaming Algorithms to Track the Block Term Decomposition of Large Tensors”, (DSAA 2020)
Ekta Gujral, Georgios Theocharous, and Evangelos E. Papalexakis, "POPLAR: Parafac2 decomposition using auxiliary information", (IEEE SAM 2020)
Ekta Gujral, Ravdeep Pasricha, and Evangelos E. Papalexakis, "Beyond Rank-1: Discovering Rich Community Structure in Multi-Aspect Graphs", (The Web Conference 2020)
Ekta Gujral, Georgios Theocharous, and Evangelos E. Papalexakis, "SPADE: Streaming PARAFAC2 Decomposition for Sparse Datasets ", (SDM 2020)
Ekta Gujral, Ravdeep Pasricha, Tianxiong Yang, and Evangelos E. Papalexakis, "OCTen: Online Compression-based Tensor Decomposition", (CAMSAP 2019)
Ekta Gujral, Georgios Theocharous, Anup Rao, and Evangelos E. Papalexakis, "HACD: Hierarchical Agglomerative Community Detection in Social Networks", (MLSP 2019)
Saba Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, and Sarah Lam, "t-PINE: Tensor-based Predictable and Interpretable Node Embeddings", (ASONAM 2018)
Ravdeep Pasricha, Ekta Gujral, and Evangelos E. Papalexakis, "Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition", (ECML- PKDD 2018)
Alexander Gorovits, Ekta Gujral, Evangelos E. Papalexakis, and Petko Bogdannov, LARC: Learning Activity-Regularized overlapping Community across Time", (KDD 2018)
Ekta Gujral, and Evangelos E. Papalexakis. "SMACD: Semi-supervised Multi-Aspect Community Detection” (SDM 2018)
Ekta Gujral, Ravdeep Pasricha, and Evangelos E. Papalexakis. "SamBaTen: Sampling-based Batch Incremental Tensor Decomposition.” (SDM 2018)

Journals Publications

Ravdeep Pasricha, Ekta Gujral, and Evangelos E. Papalexakis, "Adaptive Granularity in Tensors: A Quest for Interpretable Structure”, (Big Data 2022)
Saba Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, and Sarah Lam, "t-PINE: Tensor-based Predictable and Interpretable Node Embeddings", Accepted in Social Network Analysis and Mining 2019

Workshop-Poster

Saba Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, and Sarah Lam, "t-PINE: Tensor-based Predictable and Interpretable Node Embeddings", MLG-KDD 2018.
Gujral, Ekta. "fMVR:Shall I Get My Video Back? Feature Matching based Video Reconstruction". (accepted poster in WiCV 2018 (CVPR), IntelliSys2018 ), Paper-IS, Paper-WiCV, BibTex
Gujral, Ekta, and Evangelos E. Papalexakis. "Semi-supervised Multi-Aspect Community Detection” (WSDM HeteroNAM 2018, Socal Social Workshop),Paper, BibTex