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. 

Professional Experience

Publications

 Conference

• P1. Survey: Anomaly Detection Methods Paper,   Code , Paper New

• 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