Scalable. Interactive. Interpretable.
At Georgia Tech, we innovate scalable, interactive, and interpretable tools that amplify human's ability to understand and interact with billion-scale data and machine learning models. Our current research thrusts: human-centered AI (interpretable, fair, safe AI; adversarial ML); large graph visualization and mining; cybersecurity; and social good (health, energy).
Current Polo Club members and recent alum. See all alum.
Fred
CSE PhD
Nilaksh
CSE PhD
Haekyu
CS PhD
Scott
ML PhD
Jay
ML PhD
Austin
ML PhD
Rahul
CS PhD
Jonathan
CS Undergrad
Rob
CS Undergrad
Omar
CS Undergrad
Frank
CS Undergrad
Jon
CS Undergrad
Robert
CS Undergrad
Megan
CS Undergrad
Alex
CS Undergrad
Kevin
CS Undergrad
Polo
Associate Prof
Minsuk
Asst Prof, Oregon State Univ
Shang
Asst Prof, National Taiwan Univ
Anmol
Data Scientist, The Home Depot
Bob
Software Engineer, Google
Alex
HCI PhD, Carnegie Mellon
Will
HCI PhD, Carnegie Mellon
Sasha
CS Undergrad, Fayetteville State University
Dongkyu
Asst Prof, Hanyang Univ

Human-Centered AI

Interpretable, fair, and safe artificial intelligence, through interactive intelligent visualization, with application in adversarial machine learning (how protect AI from harm, and from doing harm).

Communicating with Interactive Articles: Examining interactive article design by synthesizing theory from education, journalism, and visualization
Communicating with Interactive Articles
Examining interactive article design by synthesizing theory from education, journalism, and visualization
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
Bluff: Interactive Interpretation of Adversarial Attacks on Deep Learning
Bluff
Interactive Interpretation of Adversarial Attacks on Deep Learning
Chameleon: Understanding and Visualizing Data Iteration in Machine Learning
Chameleon
Understanding and Visualizing Data Iteration in Machine Learning
Apple
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
REST
Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
CNN 101
Interactive Visual Learning for Convolutional Neural Networks
RECAST: Interactive Auditing of Automatic Toxicity Detection Models
RECAST
Interactive Auditing of Automatic Toxicity Detection Models
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning
Massif
Interactive Interpretation of Adversarial Attacks on Deep Learning
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
Summit
Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
FairVis
Visual Analytics for Discovering Intersectional Bias in Machine Learning
Gamut: Understanding How Data Scientists Understand Machine Learning Models
Gamut
Understanding How Data Scientists Understand Machine Learning Models
Microsoft Research
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
PeopleMap
Visualization Tool for Mapping Out Researchers using Natural Language Processing
Magic Crop: Cropping Headshot Photo with AI
Magic Crop
Cropping Headshot Photo with AI
ActiVis: Visual Exploration of Facebook Deep Neural Network Models
ActiVis
Visual Exploration of Facebook Deep Neural Network Models
Deployed
Facebook
GAN Lab: Playing with Generative Adversarial Networks in Browser
GAN Lab
Playing with Generative Adversarial Networks in Browser
Google
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Visual Analytics in Deep Learning
An Interrogative Survey for the Next Frontiers
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
TeleGam
Combining Visualization and Verbalization for Interpretable Machine Learning
Microsoft Research
NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions
NeuralDivergence
Exploring and Understanding Neural Networks by Comparing Activation Distributions
Discovering Intersectional Bias: Discovery of Intersectional Bias in Machine Learning Using Automatic Subgroup Generation
Discovering Intersectional Bias
Discovery of Intersectional Bias in Machine Learning Using Automatic Subgroup Generation
SHIELD: Fast, practical defense for deep learning
SHIELD
Fast, practical defense for deep learning
Audience Appreciation Award, Runner-up
Intel
ShapeShifter: 1st Targeted Physical Attack on Faster R-CNN Object Detector
ShapeShifter
1st Targeted Physical Attack on Faster R-CNN Object Detector
Intel
ShapeShop: Understanding Deep Learning Representations via Interactive Experimentation
ShapeShop
Understanding Deep Learning Representations via Interactive Experimentation
PNNL
The Beginner's Guide to Dimensionality Reduction: Explore the methods that data scientists use to visualize high-dimensional data
The Beginner's Guide to Dimensionality Reduction
Explore the methods that data scientists use to visualize high-dimensional data
Best Paper, Honorable Mention
Protect AI with JPEG: Simple, powerful defense for deep learning
Protect AI with JPEG
Simple, powerful defense for deep learning
Intel
Interactive Classification: For Deep Learning Interpretation
Interactive Classification
For Deep Learning Interpretation
ADAGIO: Interactive experimentation with adversarial attack and defense for audio
ADAGIO
Interactive experimentation with adversarial attack and defense for audio
Intel
ML Cube: Visual Exploration of Machine Learning Results Using Data Cube Analysis
ML Cube
Visual Exploration of Machine Learning Results Using Data Cube Analysis
Facebook
Mixed Reality: MR for Learning Programming
Mixed Reality
MR for Learning Programming
Best Work-in-Progress, Honorable Mention
Augmenting Coding: AR for Learning Programming
Augmenting Coding
AR for Learning Programming
Best Poster

Large Graph Exploration & Visualization

Argo Lite: Interactive Graph Visualization in Browser
Argo Lite
Interactive Graph Visualization in Browser
Atlas: Local Graph Exploration in a Global Context
Atlas
Local Graph Exploration in a Global Context
VIGOR: Interactive Visual Exploration of Graph Query Results
VIGOR
Interactive Visual Exploration of Graph Query Results
Symantec
Graph Playground: Graph Decompositions in 3D
Graph Playground
Graph Decompositions in 3D
Carina: Million-scale Graph Visualization in Web Browsers
Carina
Million-scale Graph Visualization in Web Browsers
FACETS: Adaptive graph exploration
FACETS
Adaptive graph exploration
ETable: Interactive Browsing and Navigation in Relational Databases
ETable
Interactive Browsing and Navigation in Relational Databases
VISAGE: Interactive Visual Graph Querying
VISAGE
Interactive Visual Graph Querying
Scalable Graph Exploration & Visualization: A survey
Scalable Graph Exploration & Visualization
A survey
TourViz: Interactive Visualization of Connection Pathways in Large Graphs
TourViz
Interactive Visualization of Connection Pathways in Large Graphs
Apolo: Explore million-node graphs in real time
Apolo
Explore million-node graphs in real time
GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration
GLO-STIX
Graph-Level Operations for Specifying Techniques and Interactive eXploration
BubbleNet: Multi-resolution Large Graph Exploration
BubbleNet
Multi-resolution Large Graph Exploration
NaturalMotion: Exploring Gesture Controls for Visualizing Time-Evolving Graphs
NaturalMotion
Exploring Gesture Controls for Visualizing Time-Evolving Graphs

Scalable Graph Mining

AttriPart: Local Partition in Rich Graphs
AttriPart
Local Partition in Rich Graphs
Scalable K-Core Decomposition: For Static Graphs Using a Dynamic Graph Data Structure
Scalable K-Core Decomposition
For Static Graphs Using a Dynamic Graph Data Structure
M-Flash: Billion-Scale Graph Computation by Bimodal Block Processing
M-Flash
Billion-Scale Graph Computation by Bimodal Block Processing
M3: billion-scale ML on a PC using virtual memory
M3
billion-scale ML on a PC using virtual memory
MMap: Easy billion-scale graph computation on a PC using virtual memory
MMap
Easy billion-scale graph computation on a PC using virtual memory
Mobile MMap: Scalable Algorithms on the Go
Mobile MMap
Scalable Algorithms on the Go
PEGASUS: Billion-Scale Graph Mining on Hadoop
PEGASUS
Billion-Scale Graph Mining on Hadoop
Open Source Software World Challenge, Silver Award
MAGE: Matching Approximate Patterns in Richly-Attributed Graphs
MAGE
Matching Approximate Patterns in Richly-Attributed Graphs

Cyber Security & Fraud

D<sup>2</sup>M: Dynamic Defense and Modeling of Adversarial Movement in Networks
D2M
Dynamic Defense and Modeling of Adversarial Movement in Networks
Microsoft Advanced Threat Protection
Cyber MoneyBall: Predicting Cyber Threats with Virtual Security Products
Cyber MoneyBall
Predicting Cyber Threats with Virtual Security Products
Symantec
FairPlay: Fraud and Malware Detection in Google Play
FairPlay
Fraud and Malware Detection in Google Play
MARCO: Fake Review Detection
MARCO
Fake Review Detection
SDM'14 Best Student Paper
AESOP: 99%-accurate Malware Detection
AESOP
99%-accurate Malware Detection
Patented
Deployed
Symantec
Polonium: Malware Detection over 37 Billion File Relationships
Polonium
Malware Detection over 37 Billion File Relationships
Patented
Deployed
Symantec
Latent Gesture: Mobile Protection with Touch Signatures
Latent Gesture
Mobile Protection with Touch Signatures
NetProbe: Auction Fraud Detection
NetProbe
Auction Fraud Detection
Quasi Clique: Uncovering Suspicious Links
Quasi Clique
Uncovering Suspicious Links
Insider Trading Pattern Discovery:
Insider Trading Pattern Discovery
Securities and Exchange Commission

Social Good & Health

DeepPop: Deep Learning on Satellite Imagery for Population Estimation
DeepPop
Deep Learning on Satellite Imagery for Population Estimation
Microsoft AI for Earth
Chronodes: Multi-focus mHealth Visual Data Exploration
Chronodes
Multi-focus mHealth Visual Data Exploration
ACM TiiS 2018 Best Paper, Honorable Mention
mHealth Visual Discovery Dashboard: Making Sense of Mobile Health Data
mHealth Visual Discovery Dashboard
Making Sense of Mobile Health Data
Firebird: Predicting Fire Risk in Atlanta
Firebird
Predicting Fire Risk in Atlanta
KDD'16 Best Student Paper, runner-up
Deployed
Atlanta Fire Rescue Department
ElectroLens: Understanding Atomistic Simulations
ElectroLens
Understanding Atomistic Simulations
PASSAGE
A Travel Safety Assistant with Safe Path Recommendations for Pedestrians
Scalable Architecture in Power Generating Assets: For Anomaly Detection and Visualization
Scalable Architecture in Power Generating Assets
For Anomaly Detection and Visualization
TimeStitch: Interactive Multi-focus Cohort Discovery and Comparison
TimeStitch
Interactive Multi-focus Cohort Discovery and Comparison
Characterizing Smoking and Drinking Abstinence: Using Social Media Communities
Characterizing Smoking and Drinking Abstinence
Using Social Media Communities
Understanding Pediatric Asthma Care: Using Visual Analytics
Understanding Pediatric Asthma Care
Using Visual Analytics
Children's Healthcare of Atlanta

Club Members

Fred Hohman
CSE PhD
HCI+ML. Drummer and frisbee thrower.
Nilaksh Das
CSE PhD
Haekyu Park
CS PhD
Scott Freitas
ML PhD
Zijie (Jay) Wang
ML PhD
Pokémon trainer.
Austin Wright
ML PhD
football fan and film nut
Rahul Duggal
CS PhD
Jonathan Leo
CS Undergrad
Robert Firstman
CS Undergrad
Guitar player. Music and NBA enthusiast.
Omar Shaikh
CS Undergrad
Zhiyan "Frank" Zhou
CS Undergrad
Jon Saad-Falcon
CS Undergrad
Robert Turko
CS Undergrad
Megan Dass
CS Undergrad
Haoyang "Alex" Yang
CS Undergrad
Kevin Li
CS Undergrad
Duen Horng (Polo) Chau
Associate Prof
Covert designer, photographer. Rusty cellist & pianist.

Alumni

Dr. Acar Tamersoy
CS PhD
Research Scientist, Symantec Research Labs
Dr. Robert Pienta
CSE PhD
Data Scientist, Mailchimp
Dr. Chad Stolper
CS PhD
Software Engineer, Google
Dr. Minsuk Kahng
CS PhD
Asst Prof, Oregon State Univ
Human-Centered AI, Data Visualization
Dr. Shang-Tse Chen
CS PhD
Asst Prof, National Taiwan Univ
AI-infused Security: theory + applications for deep learning attack and defense.
Jaegul Choo
CSE PhD
Associate Professor, KAIST
Zhiyuan "Jerry" Lin
CS Undergrad
CS PhD, Stanford
Ramen lover and hackathon hacker.
Elias Khalil
CSE Master
Asst Prof. Univ of Toronto
Paras Jain
CS Undergrad
CS PhD, UC Berkeley
Peter Polack
MS Digital Media
PhD Information Studies, UCLA
Samuel Clarke
CS Undergrad
CS Master, Carnegie Mellon
Srishti Gupta
CS Master
Data Engineer, Hike Messenger
Dezhi “Andy” Fang
CS Undergrad
Software Engineer, Citadel
Anmol Chhabria
MS CSE
Data Scientist, The Home Depot
Siwei "Bob" Li
CS Undergrad
Software Engineer, Google
Kristina Marotta
CS PhD, Univ of North Carolina
Matthew Keezer
CS Master
Software Engineer, IBM
Madhuri Shanbhogue
CS Master
Software Engineer ML, Google
Varun Bezzam
CS Master
Software Engineer, Heroku
Prasenjeet Biswal
CS Master
Software Engineer, Yahoo
Nathan Dass
CS Undergrad
Software Engineer, Google
Sam Ford
CS Undergrad
Software Engineer, Square
Joon Kim
CS Undergrad
Site Reliability Engineer, Mailchimp
Ángel (Alex) Cabrera
CS Undergrad
HCI PhD, Carnegie Mellon
Will Epperson
CS Undergrad
HCI PhD, Carnegie Mellon
Sasha Richardson
CS Undergrad
CS Undergrad, Fayetteville State University
Dong-Kyu Chae
Postdoc
Asst Prof, Hanyang Univ
Meera Kamath
CS Master
Software Engineer, Microsoft
Florian Foerster
HCI Master
Research Manager, Facebook
Yiqi (Victor) Chen
CS Undergrad
Software Engineer, Facebook
Aakash Goel
CS Master
Google
Shan Li
HCI Master
User Experience Designer, Fortinet
Jake Williams
CS Undergrad
Machine Learning Engineer, Square
Kshitij Kulkarni
ECE Undergrad
EECS PhD, UC Berkeley
Hugo Armando Gualdron Colmenares
CS Master, University of São Paulo
Changhyun Lee
CS Master
Software Enginner, Google
Kaeser Sabrin
CS PhD
Software Engineer, LinkedIn
Sudeep Agarwal
CS Undergrad
Software Engineer, Apple

Data and Visual Analytics

Data and Visual Analytics (DVA) is data science course at Georgia Tech, for both graduate (as CSE6242) and undergraduate students (as CX4242). CSE 6242 is a required core course of the Master of Science in Analytics (MSA). In Spring 2018, the campus version of the course was "mirrored" and offered as a core course of the Online Master of Science in Analytics (OMS Analytics) program. In Fall 2018, the online offering was further expanded to the Online Master of Science in Computer Science (OMS CS) program. Instructors: Dr. Mahdi Roozbahani (since Fall 2019) and Prof. Polo Chau (since Spring 2013).

KDD IDEA Workshop

Interactive Data Exploration and Analytics. Great IDEAs, 6 years in a row!

Sponsors

Our research has been made possible by support and funding from: NSF (CNS1704701, IIS1563816, IIS1551614, TWC1526254, IIS1217559), NIH (1-U54EB020404-01), DARPA, NASA, Children's Healthcare of Atlanta, Google, Symantec, Yahoo, Intel, Microsoft, eBay, Amazon, LogicBlox, LexisNexis Dean's Award, James C. Edenfield Faculty Fellowship, Raytheon Faculty Fellowship.