Multi-Aspect Data Lab at University of California Riverside.

Welcome to the website of the Multi-Aspect Data Lab at University of California Riverside. 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.

We are always looking for motivated and hard-working undergraduate and graduate students who want to do research in multi-aspect data mining and machine learning. If you are interested in joining our lab and you are already at UCR, send an e-mail with your CV and your particular research interest. If you are not at UCR yet, please apply to the graduate program of the Computer Science and Engineering Department first.

Faculty


Vagelis Papalexakis

Vagelis Papalexakis

Lab Director
Assistant Professor, CSE

PhD Students


Sara Abdali

Sara Abdali

(CSE)

Ekta Gujral

Ekta Gujral

(CSE)

Ravdeep Pasricha

Ravdeep Pasricha

(CSE)

Saba A Syouri

Saba A Syouri

(SUNY Binghamton)

Uday Saini Singh

Uday Saini Singh

(CSE)

Yorgos Tsitsikas

Yorgos Tsitsikas

(CSE)

MS Students


Gautham Mani

Gautham Mani

Kamalika Poddar

Kamalika Poddar

Undergraduate


Richard McGee

Richard McGee

Mario Salazar

Mario Salazar

William Shiao

William Shiao

Het Trivedi

Het Trivedi

Harini Venkatesan

Harini Venkatesan

Ted Zhang

Ted Zhang

Alumni/Past Members


Gisel Bastidas Guacho

Gisel Bastidas Guacho (MS)

Lalitha Dwarapudi

Lalitha Dwarapudi (MS)

Amr Elsisy

Amr Elsisy (BS/MS)

Mehdi Motlagh

Mehdi Hosseini (PhD)

Jiahuan Liu

Jiahuan Liu (MS)

Chaoyun Ma

Chaoyun Ma (MS)

Lufei Xie

Lufei Xie (MS)

Liuqing Yang

Liuqing Yang (MS)

Tianxiong Yang

Tianxiong Yang (MS)

Projects

Coming Soon!

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


navsea

Naval Sea Systems Command

Naval Engineering Education Consortium (NEEC)
Award No.: N00174-17-1-0005

nsf

National Science Foundation

EAGER Award No.: 1746031

navsea

Adobe

Data Science Faculty Award 2017

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.

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