Scott Freitas

Currently a Ph.D. student at Georgia Tech, studying machine learning in the department of Computational Science and Engineering advised by Polo Chau.

Research interests include machine learning and large scale graph mining, with application to cybersecurity, healthcare and finance.

Recent awards include NSF Graduate Research Fellowship and Outstanding Computer Science Masters student at ASU.


2020Our paper "REST:Robust and Efficient Neural Networks for Sleep Monitoring in the Wild" was accepted to WWW'20

2020Our paper "D2M:Dynamic Defense and Modeling of Adversarial Movement in Networks" accepted to SDM'20

2019Had a great summer interning with the Advanced Threat Protection (ATP) team @ Microsoft

Research Highlights

Developing noise robust and efficient neural nets for home sleep monitoring
WWW 2020
Modeling and quantifying network vulnerability to lateral movement in enterprise networks
SDM 2020
Identifying local partitions using network topology + attribute information
Big Data 2018

Present —Aug. 2018Ph.D. in Machine Learning
Aug. 2018Georgia Institute of Technology, Atlanta, GA
Advisor: Duen Horng (Polo) Chau
Anticipated Graduation: Spring 2022

May 2018Summer 2017M.S. in Computer Science
Summer 2017Arizona State University, Tempe, AZ
Advisor: Hanghang Tong, Thesis: "Mining Marked Nodes in Large Graphs"
Overall GPA: 4.00/4.00

May 2017 —Aug. 2015B.S. in Computer Science
Aug. 2015Arizona State University, Tempe, AZ
Overall GPA: 3.98/4.00

May 2014 —Aug. 2010B.S.E. in Electrical Engineering
Aug. 2010Arizona State University, Tempe, AZ
Overall GPA: 3.64/4.00

Awards (selected)

2019Raytheon Research Fellowship
Awarded for my PhD work in adversarial machine learning

2018 — 2023NSF Graduate Research Fellowship
National Science Foundation recognizes and supports outstanding graduate students in STEM fields

2018Outstanding Computer Science Masters Student (ASU)
Awarded to single master student demonstrating exemplary performance

2017Best Demo Award, Runner Up at CIKM '17
For "Rapid Analysis of Network Connectivity"

Industry Research Experience (selected)

Summer 2019Microsoft, Seattle, WA
Research Intern, Windows Defender: Advanced Threat Protection
Mentor: Andrew Wicker, Joshua Neil
Developed graph based approach to quantifying and mitigating vulnerability to adversarial attacks on networks

Summer 2013Naval Air Warfare Center, Point Mugu, California
Research Intern, Naval Research Entperprise Internship Program (NREIP)
Mentor: Balaji Iyer
Explored methods of preventing electromagentic interference from coupling into superconducting receivers


REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
Rahul Duggal*, Scott Freitas*, Cao Xiao, Duen Horng (Polo) Chau, Jimeng Sun
The Web Conference (WWW). Taipei, Taiwan, 2020.
Project PDF BibTeX * Authors contributed equally

D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks
Scott Freitas, Andrew Wicker, Duen Horng (Polo) Chau, Joshua Neil
SIAM International Conference on Data Mining (SDM). Cincinnati, Ohio, 2020.
Project PDF BibTeX

Extracting Knowledge For Adversarial Detection and Defense in Deep Learning
Scott Freitas, Shang-Tse Chen, Duen Horng (Polo) Chau
KDD Workshop: Learning and Mining for Cybersecurity (LEMINCS). Anchorage, Alaska, 2019.
Project PDF BibTeX

Local Partition in Rich Graphs
Scott Freitas, Nan Cao, Yinglong Xia, Duen Horng (Polo) Chau, Hanghang Tong
IEEE International Conference on Big Data (Big Data). Seattle, Washington, 2018.
Project Demo PDF BibTeX

X-Rank: Explainable Ranking in Complex Multi-Layered Networks
Jian Kang*, Scott Freitas*, Haichao Yu, Yinglong Xia, Hanghang Tong
ACM International Conference on Information and Knowledge Management (CIKM). Turin, Italy, 2018.
Project Demo PDF BibTeX * Authors contributed equally

Rapid Analysis of Network Connectivity
Scott Freitas, Hanghang Tong, Nan Cao, Yinglong Xia
ACM International Conference on Information and Knowledge Management (CIKM). Singapore, 2017.
Project Demo PDF Video Code BibTeX Best Demo Paper, Runner up