Research focus:

Our research focuses on developing scalable and interpretable algorithms for modeling and extracting useful knowledge from multi-aspect (also known as multi-modal) data, coming from a wide variety of real-world applications. A major research thrust in the lab is developing new algorithms and models for fast, scalable, and interpretable tensor decompositions, as well as their interplay with other unsupervised and supervised learning techniques.

Contact

Email

  • epapalexcs dot ucr dot edu

Location

3132 Multidisciplinary Research Building
Computer Science & Engineering Department
University of California Riverside
900 University Ave
Riverside, CA 92521
USA
Welcome to the website of the Multi-Aspect Data Lab at University of California Riverside.

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Lab members

Current Members

PhD students

Faculty Director

PhD alumni

Full list of all members

A full list can be found here.

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Research Projects

Our research broadly spans the areas of data science, signal processing, machine learning, and artificial intelligence. The overarching theme of my work has been the design and development of models and algorithms that can extract actionable and interpretable insights from multi-aspect/multi-modal data, typically with very little or no supervision. A major focus of our work has been on the development and advancement of tensor methods and their applications in high-impact real-world problems, including misinformation detection on the web, graph and social network analytics and mining, explainable AI, and detection of gravitational waves.

Robust, Scalable, & Streaming Tensor Methods


Selected Publications:
  • Ekta Gujral, Evangelos E. Papalexakis OnlineBTD: Streaming Algorithms to Track the Block Term Decomposition of Large Tensors IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2020, Sydney, Australia Paper
  • Ekta Gujral, Georgios Theocharous, Evangelos E. Papalexakis, SPADE: Streaming PARAFAC2 DEcomposition for Large Datasets SIAM SDM 2020, Cincinnati OH, Paper
  • Ravdeep Pasricha, Ekta Gujral, Evangelos E. Papalexakis, Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition, ECML-PKDD 2018, Dublin, Ireland Paper
  • Ioakeim Perros, Evangelos E Papalexakis, Fei Wang, Richard Vuduc, Elizabeth Searles, Michael Thompson, Jimeng Sun, SPARTan: Scalable PARAFAC2 for Large & Sparse Data, ACM KDD 2017, Halifax, NS, Canada, Paper, Code
  • Ekta Gujral, Ravdeep Pasricha, Evangelos Papalexakis, SamBaTen: Sampling-based Batch Incremental Tensor Decomposition, SIAM SDM 2018, San Diego, CA, Paper
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Model Selection for Tensor Decompositions

Selected Publications:
  • Georgios Tsitsikas, Evangelos E. Papalexakis, NSVD: Normalized Singular Value Deviation Reveals Number of Latent Factors in Tensor Decomposition SIAM SDM 2020, Cincinnati OH, Paper
  • Georgios Tsitsikas, Evangelos E. Papalexakis, The Core Consistency of a Compressed Tensor, IEEE Data Science Workshop (DSW) 2019, Minneapolis, MN, USA Paper
  • Evangelos E. Papalexakis, Automatic Unsupervised Tensor Mining with Quality Assessment, SIAM SDM 2016, Miami, FL Paper Supplementary Material Code
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Explainable & Adversarial Machine Learning with Tensors


Selected Publications:
  • Negin Entezari, Saba Al-Sayouri, Amirali Darvishzadeh, Evangelos E. Papalexakis, All You Need is Low (Rank): Defending Against Adversarial Attacks on Graphs, 2020 ACM Web Search and Data Mining (WSDM) Conference, Houston TX, Paper
  • Uday Singh Saini, Evangelos E. Papalexakis, A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens, ACM KDD 2018 Deep Learning Day, London, UK, Paper
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Graph Mining


Selected Publications:
  • Ekta Gujral, Ravdeep Pasricha, Evangelos E. Papalexakis Beyond Rank-1: Discovering Rich Community Structure in Multi-Aspect Graphs, The Web Conference, Taipei, Taiwan Paper
  • Alexander Gorovits, Ekta Gujral, Evangelos Papalexakis, Petko Bogdanov, LARC: Learning Activity-Regularized Overlapping Communities Across Time, ACM KDD 2018, London, UK Paper
  • Ekta Gujral, Evangelos Papalexakis, SMACD: Semi-supervised Multi-Aspect Community Detection,SIAM SDM 2018, San Diego, CA, Paper
  • Saba Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, Sara Lam, t-PNE: Tensor-based Predictable Node Embeddings, IEEE/ACM ASONAM 2018, Barcelona, Spain Paper
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Misinformation Detection on the Web


Selected Publications:
  • William Shiao, Evangelos E. Papalexakis, KI2TE: Knowledge-Infused InterpreTable Embeddings for COVID-19 Misinformation Detection, 1st International Workshop on Knowledge Graphs for Online Discourse Analysis (KnOD 2021) at The Web Conference 2021 Paper
  • Sara Abdali, Rutuja Gurav, Siddharth Menon, Daniel Fonseca, Negin Entezari, Neil Shah, Evangelos E. Papalexakis Identifying Misinformation from Website Screenshots, International AAAI Conference on Web and Social Media (ICWSM) 2021, Venice, Italy Paper
  • Sara Abdali, Neil Shah, Evangelos E. Papalexakis Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition, Applied Data Science Track, ECML-PKDD 2020, Ghent, Belgium Paper Code
  • Gisel Bastidas Guacho, Sara Abdali, Neil Shah, Evangelos E. Papalexakis, Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings, IEEE/ACM ASONAM 2018, Barcelona, Spain Paper
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Gravitational Wave Detection


Selected Publications:
  • Rutuja Gurav, Barry Barish, Gabriele Vajente, Evangelos E. Papalexakis, Unsupervised matrix and tensor factorization for LIGO glitch identification using auxiliary channels, AAAI 2020 Fall Symposium on Physics-Guided AI to Accelerate Scientific Discovery, Paper
  • Rutuja Gurav, Barry C. Barish, Evangelos Papalexakis, Multilinear Factorized Representations for LIGO Glitches in Label-scarce Settings KDD 2019 FEED Workshop Paper
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Funding Support

We are extremely grateful to the following sponsors for their support.

Current Grants


  1. National Science Foundation
    NSF CAREER Award no. 2046086
    CAREER: Autonomous Tensor Analysis: From Raw Multi-Aspect Data to Actionable Insights

  2. National Science Foundation
    Advancing Discovery with AI-Powered Tools (ADAPT) in the Mathematical and Physical Sciences
    NSF EAGER Award no. 2141072
    EAGER: ADAPT: Understanding Nonlinear Noise in LIGO: A Machine Learning Approach

  3. National Science Foundation
    NSF CNS Medium award no. 2106982
    Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless Network Management

  4. ARL Cyber Security CRA
    U.S. Army Combat Capabilities Development Command
    Army Research Laboratory,
    Cooperative Agreement Number: W911NF-13-2-0045

  5. United States Department of Agriculture
    NIFA-AFRI Sustainable Agricultural Systems (SAS)
    Grant Number: 2020-69012-31914
    Artificial Intelligence for Sustainable Water, Nutrient, Salinity, And Pest Management in The Western U.S

  6. National Science Foundation
    NSF III Medium award no. 1901379
    Efficient Collaborative Perception over Controllable Agent Networks


Past Grants

  1. National Science Foundation
    NSF CDS&E OAC 1808591
    Theoretical Foundations and Algorithms for L1-Norm- Based Reliable Multi-Modal Data Analysis (collaborative grant with Rochester Institute of Technology)

  2. Naval Sea Systems Command
    Naval Engineering Education Consortium (NEEC) Award No.: N00174-17-1-0005
    Big Multi- Aspect Data Mining via Scalable and Incremental Tensor Decompositions and Applications to Social Network Analysis

  3. National Science Foundation
    EAGER award no. 1746031
    Joint Modeling and Querying of Social Media and Video Data

Other Support

  1. Adobe Inc
    • Adobe Data Science Research Award 2017
    • Unrestricted gift
  2. Snap Inc
    Unrestricted gifts
  3. Technological Pathways Initiative grant
    Advancing Diversity in Computing through the Undergraduate Program in Data Science
  4. Nvidia
    Two donated GPUs as part of the GPU Grant
  5. UC Riverside
    • UCR Regents Faculty Development Award 2020
    • UCR Regents Faculty Fellowship 2019
    • UCR-China Collaboration Grant 2018-2019 funded by the UCR Bourns College of Engineering
    • UCR Academic Senate Omnibus Travel Awards

Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in our research are those of the author(s) and do not necessarily reflect the views of the sponsors.

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Contact

For any questions, comments, or inquiries, please e-mail us at 'epapalexcs dot ucr dot edu'


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