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 (2018) and Outstanding Computer Science Masters student at ASU (2018).

scottfreitas.com

safreita@gatech.edu

Curriculum Vitae (PDF)

Github

Google Scholar

Education

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

Honors and Awards

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"

2017CIKM Travel Grant
Funding from NSF and SIGWEB to present at CIKM

2016 — 2017FURI Grant
Undergraduate research grant awarded for work in network connectivity

2016 — 2017Arizona Graduate Scholar Award
Merit scholarship awarded to select number of master students

2010 — 2014Provost's Scholarship
Merit scholarship awarded to select number of incoming undergraduate students

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

Academic Research Experience

Present —Aug. 2018Georgia Institute of Technology, Atlanta, GA
Aug. 2018Graduate Research Assistant, School of Computational Science and Engineering
Advisor: Duen Horng (Polo) Chau
Member of the Polo Club of Data Science where we innovate scalable, interactive, and interpretable tools that amplify human's ability to understand and interact with billion-scale data and machine learning models

May 2018 —Summer 2017Arizona State University, Tempe, AZ
Summer 2017Graduate Research Assistant, School of Computing, Informatics, and Decision Systems Engineering
Advisor: Hanghang Tong
Conducted research in graph based connectivity analysis to improve local graph partitioning. Developed web-based prototype for explainable ranking in complex multi-layered networks.

Summer 2017Arizona State University, Tempe, AZ
Summer Research Assistant, School of Computing, Informatics, and Decision Systems Engineering
Mentor: Ross Maciejewski
Developed interactive augmented reality (AR) graph models in the Microsoft Hololens.

May 2017 —Jan. 2016Arizona State University, Tempe, AZ
Jan. 2016Undergraduate Research Assistant, School of Computing, Informatics, and Decision Systems Engineering
Mentor: Hanghang Tong
Developed fast graph mining algorithms for network connectivity analysis, and award winning web platform for visualization and analysis.

Publications

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

Talks

D²M: Dynamic Defense and Modeling of Adversarial Movement in Networks
Aug. 2019Microsoft Advanced Threat Protection Research Expo

Mining Marked Nodes in Large Graphs
Dec. 2018Microsoft Advanced Threat Protection Group
May 2018ASU Thesis

Local Partition in Rich Graphs
Dec. 2018IEEE International Conference on Big Data

Rapid Analysis of Network Connectivity
Nov. 2017ACM International Conference on Information and Knowledge Management (CIKM)

Network Connectivity Analysis and Visualization in Large Graphs
April 2017Keynote Speaker: ASU Fulton Undergraduate Research Initiative (FURI)
Nov. 2016ASU FURI Research Symposium