Multi-Aspect Data Lab @ UC Riverside
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
epapalex
cs
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.
Go to
Members
Research Projects
Funding support
Contact
Lab members
Current Members
Postdocs / Project Scientists
Nicolas Roque dos Santos
Siddharth Soni
(co-mentored with Jon Richardson)
PhD students
Dawon Ahn
Miguel Gutierrez
Yunshu Wu
(co-advised with Greg Ver Steeg)
Taghreed Alanazi
(co-advised with Srikanth Krishnamurthy)
Het Patel
(co-advised with Jia Chen)
Yiran Luo
Sowmya Kadali
Chansong Lim
Kishor Bhaumik
(co-advised with Jia Chen)
Paimon Goulart
Shaan Pakala
Faculty Director
Evangelos (Vagelis) Papalexakis
PhD alumni
William Shiao (now at Atlas)
Uday Singh Saini (now at VISA Research)
Yorgos Tsitsikas
Ravdeep Pasricha
(now at Microsoft)
Ghazal Mazaheri
(co-advised with
Amit Roy-Chowdhury
Sara Abdali
(now at Georgia Tech)
Negin Entezari
(now at Amazon)
Ekta Gujral
(now at Walmart Global Technology)
Pravallika Devineni
(now at Duke Energy Corporation)
Rutuja Gurav
Full list of all members
A full list can be found
here
Go to top
Go to
Members
Research Projects
Funding support
Contact
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.
This section is constantly under construction. For the most up-to-date view of different projects, make sure to check out our
publications
. Below is a sample of past and on-going projects.
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
Go to research projects top
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
Go to research projects top
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
Go to research projects top
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
Go to research projects top
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
Go to research projects top
Go to top
Go to
Members
Research Projects
Funding support
Contact
Funding Support
We are extremely grateful to the following sponsors for their support.
Current Grants
National Science Foundation
NSF CAREER Award no.
2046086
CAREER: Autonomous Tensor Analysis: From Raw Multi-Aspect Data to Actionable Insights
National Science Foundation
NSF CAREER Award no.
2431569
MSI: RPEP: CPS: Trustworthy AI for Transportation Cyber-Physical Systems
National Science Foundation
NSF CREST award no.
2112650
as subaward from UT Rio Grande Valley.
CREST - Center for Multidisciplinary Research Excellence in Cyber-Physical Infrastructure Systems (MECIS)
National Science Foundation
NSF CNS Medium award no.
2106982
Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless Network Management
US Department of Transportation
University Transportation Center under Grant No. 69A3552348340
University Transportation Center for Railway Safety (UTCRS)
in collaboration with The University of Texas Rio Grande Valley
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
National Science Foundation
NSF III Medium award no.
1901379
Efficient Collaborative Perception over Controllable Agent Networks
Past Grants
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
ARL Cyber Security CRA
U.S. Army Combat Capabilities Development Command
Army Research Laboratory,
Cooperative Agreement Number: W911NF-13-2-0045
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)
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
National Science Foundation
EAGER award no.
1746031
Joint Modeling and Querying of Social Media and Video Data
Other Support
Cisco Research
Unrestricted gift
Adobe Inc
Adobe Data Science Research Award 2017
Unrestricted gift
Snap Inc
Unrestricted gifts
Technological Pathways Initiative grant
Advancing Diversity in Computing through the Undergraduate Program in Data Science
Nvidia
Two donated GPUs as part of the
GPU Grant
Lawrence Livermore National Laboratory
Funding to implement and execute the 2022 and
2021 Data Science Challenge
at UCR.
University of California National Labs (UCNL)
Funding to implement and execute the 2023 and 2024 Data Science Challenge
at the University of California Livermore Collaboration Center (UCLCC).
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.
Go to top
Go to
Members
Research Projects
Funding support
Contact
Contact
For any questions, comments, or inquiries, please e-mail us at 'epapalex
cs
dot
ucr
dot
edu'
Go to top
Go to
Members
Research Projects
Funding support
Contact
Template design adapted from
Andreas Viklund