Scott Freitas

I’m a Principal Applied Scientist at Microsoft working at the intersection of applied and theoretical machine learning, with a focus on graph mining and deep learning. My goal is to develop explainable, robust, and efficient next-generation cybersecurity systems.

I completed my Machine Learning PhD at Georgia Tech where I worked with Polo Chau. I co-authored several winning research proposals, including a multi-million dollar DARPA grant; was awarded PhD fellowships from IBM Research, NSF GRFP and Raytheon; and was fortunate to work with amazing researchers at IBM Research, Amazon, Microsoft Advanced Threat Protection, Microsoft Research, Intel and the Naval Air Warfare Center.

Education

Dec. 2021Aug. 2018Ph.D. in Machine Learning
Aug. 2018Georgia Institute of Technology, Atlanta, GA
Advisor: Duen Horng (Polo) Chau
Thesis: Developing Robust Models, Algorithms, Databases and Tools with Applications to Cybersecurity and Healthcare
Committee: Duen Horng (Polo) Chau, Srijan Kumar, Diyi Yang, B. Aditya Prakash, Hanghang Tong
Thesis Thesis Recording (Proposal) Thesis Slides

May 2018 —May 2017M.S. in Computer Science
May 2017Arizona State University, Tempe, AZ
Advisor: Hanghang Tong
Thesis: Mining Marked Nodes in Large Graphs
Committee: Hanghang Tong, Ross Maciejewski, Yezhou Yang
GPA: 4.00/4.00
Thesis

May 2017 —Aug. 2015B.S. in Computer Science
Aug. 2015Arizona State University, Tempe, AZ
Advisor: Ross Maciejewski
Thesis: Guided Augmented Reality Tours using Landmarks and Social Media
GPA: 3.98/4.00
Thesis Thesis Recording

May 2014 —Aug. 2010B.S.E. in Electrical Engineering
Aug. 2010Arizona State University, Tempe, AZ
Advisor: James Aberle
Thesis: Multi-Stage Linear Electromagnetic Accelerator Using Optical Triggering
GPA: 3.64/4.00
Thesis Thesis Recording

Honors and Awards

2021IBM PhD Fellowship
One of sixteen fellows; awarded for my work in developing next-generation explainable defenses

2021Nvidia Data Science Teaching Kit
Helped develop one of five Nvidia teaching kits used by educators around the world

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

2018 — 2021NSF 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

Industry Research Experience

Present —Sep. 2024Microsoft, Redmond, WA
Sep. 2024Principal Applied Scientist (level 65), Microsoft Security Research
• Leading research into LLM-based agents to automatically identify detection and disruption rule gaps.
• Created TITAN, an industry-scale graph mining and reputation propagation system for cyber threat intelligence that models the relationships between millions of entities, alerts, and signals to uncover hidden infrastructure and generate real-time risk scores. TITAN is integrated into Microsoft's Unified Security Operations Platform, where it now protects hundreds of thousands of organizations globally.
Paper Blog Microsoft Ignite Talk
• Developed an adaptive incident prioritization score that assists analysts in prioritizing security incidents for investigation.

Aug. 2024Sep. 2023Microsoft, Redmond, WA
Sep. 2023Senior Applied Scientist (level 64), Microsoft Security Research
• Led an ML research team in architecting and delivering key capabilities for our flagship AI product, Security Copilot, including tailored recommendations for similar incidents, triaging, and remediation. This system is now deployed worldwide, embedded directly into Microsoft Defender XDR and Security Copilot.
Paper Blog Dataset
• Spearheaded the design and development of GraphWeaver, a geo-distributed, graph-based incident correlation platform that powers Microsoft Defender XDR. The system correlates billions of security alerts across hundreds of thousands of enterprises into coherent, high-precision incidents.
Paper Blog

Aug. 2023Jan. 2022Microsoft, Redmond, WA
Jan. 2022Senior Applied Scientist (level 63), Microsoft Security Research
• Developed graph-based algorithms to identify alert correlation gaps, enabling the correlation of millions of alerts into comprehensive incident stories, saving customers millions in investigation time.
• Led the development and execution of a comprehensive research integration plan, successfully help merge two billion-dollar security products, M365D and Sentinel, into Microsoft Defender XDR.
Blog

Dec. 2021 —Sep. 2021IBM Research, Yorktown Heights, NY
Sep. 2021Research Intern, Cyber Security Intelligence (CSI) Team
Mentor: Teryl Taylor, Frederico Araujo, Jiyong Jang
Developed unsupervised graph representation learning techniques to detect suspicious activity in cloud platforms

Aug. 2021 —May 2021Amazon, Seattle, WA
May 2021Applied Scientist Intern, Fraud Detection and Risk Transaction (CTPS)
Mentor: Hao Zheng, Yanni Lai
Created unsupervised and semi-supervised approaches to prevent fraudulent transactions across the Amazon marketplace

May 2020 —Aug. 2020Microsoft, Redmond, WA
Aug. 2020Research Intern, Microsoft ATP + Microsoft Research
Mentor: Karishma Sanghvi, Yuxiao Dong
Designed semi-supervised graph neural network approach to detect malicious software

Aug. 2019 —May 2019Microsoft, Redmond, WA
May 2019Research Intern, Microsoft Advanced Threat Protection (ATP)
Mentor: Andrew Wicker, Joshua Neil
• Created first framework to model lateral attacks on enterprise networks, enabling IT admins to quantify and mitigate network vulnerability to lateral attacks
Paper

March 2015 —Dec. 2014General Dynamics, Scottsdale, AZ
Dec. 2014Systems Engineer, Mission Systems
Worked on the Integrated Threat Force team to develop and refine the communication technology systems.

Aug. 2013 —May 2013Naval Air Warfare Center, Point Mugu, CA
May 2013Research 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
Mentor: 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
Mentor: 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.

Aug. 2017 —May 2017Arizona State University, Tempe, AZ
May 2017Summer 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

Web Scale Graph Mining for Cyber Threat Intelligence
Scott Freitas, Amir Gharib
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Toronto, Canada, 2025.
Project PDF Blog Video BibTeX Deployed in Microsoft Unified Security Operations Platform Presented at Microsoft Ignite 2024

			
@article{freitas2025web,
  title={Web Scale Graph Mining for Cyber Threat Intelligence},
  author={Freitas, Scott and Gharib, Amir},
  journal={KDD},
  year={2025},
  publisher={ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining}
}
		

AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Security
Scott Freitas, Jovan Kalajdjieski, Amir Gharib, Rob McCann
The ACM Web Conference (WWW). Sydney, Australia, 2025.
Project PDF Blog Dataset BibTeX Deployed in Microsoft Copilot for Security product

			
@article{freitas2025ai,
  title={AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Security},
  author={Freitas, Scott and Gharib, Amir},
  journal={WWW},
  year={2025},
  publisher={The ACM Web Conference}
}
		

GraphWeaver: Billion-Scale Cybersecurity Incident Correlation
Scott Freitas, Amir Gharib
ACM International Conference on Information and Knowledge Management (CIKM). Boise, Idaho, 2024.
Project PDF Blog BibTeX Deployed in Microsoft Defender XDR product Keynote talk at CIKM Industry Day

			
@article{freitas2024graphweaver,
  title={GraphWeaver: Billion-Scale Cybersecurity Incident Correlation},
  author={Freitas, Scott and Gharib, Amir},
  journal={CIKM},
  year={2024},
  publisher={ACM International Conference on Information and Knowledge Management}
}
		

Graph Vulnerability and Robustness: A Survey
Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, Duen Horng (Polo) Chau
IEEE Transactions on Knowledge and Data Engineering (TKDE). 2022.
PDF BibTeX

			
@article{freitas2022graph,
  title={Graph Vulnerability and Robustness: A Survey},
  author={Freitas, Scott, Yang, Diyi and Kumar, Srijan and Tong, Hanghang and Chau, Duen Horng},
  journal={TKDE},
  year={2022},
  publisher={IEEE Transactions on Knowledge and Data Engineering}
}
		

MalNet: A Large-Scale Image Database of Malicious Software
Scott Freitas, Rahul Duggal, Duen Horng (Polo) Chau
ACM International Conference on Information and Knowledge Management (CIKM). Atlanta, GA, 2022.
Demo PDF Dataset Code BibTeX

			
@article{freitas2021malnet,
  title={MalNet: A Large-Scale Image Database of Malicious Software},
  author={Freitas, Scott and Duggal, Rahul and Chau, Duen Horng},
  journal={CIKM},
  year={2022},
  publisher={ACM International Conference on Information and Knowledge Management}
}
		

A Large-Scale Database for Graph Representation Learning
Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng (Polo) Chau
Neural Information Processing Systems Datasets and Benchmarks (NeurIPS). Virtual, 2021.
Project Demo PDF Blog Dataset Code BibTeX

			
@article{freitas2021large,
  title={A Large-Scale Database for Graph Representation Learning},
  author={Freitas, Scott and Chen and Dong, Yuxiao and Neil, Joshua and Chau, Duen Horng},
  journal={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks},
  year={2021},
  publisher={NeurIPS}
}
		

Evaluating Graph Vulnerability and Robustness using TIGER
Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, Duen Horng (Polo) Chau
ACM International Conference on Information and Knowledge Management (CIKM). Virtual, 2021.
PDF Blog Video Code BibTeX Featured in Nvidia Data Science Toolkit

			
@article{freitas2020evaluating,
  title={Evaluating Graph Vulnerability and Robustness using TIGER},
  author={Freitas, Scott and Yang, Diyi and Srijan, Kumar and Tong, Hanghang and Chau, Duen Horng},
  journal={CIKM},
  year={2021},
  publisher={ACM International Conference on Information and Knowledge Management}
}
		

EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
Omar Shaikh, Jon Saad-Falcon, Austin P Wright, Nilaksh Das, Scott Freitas, Omar Asensio, Duen Horng Chau
ACM Conference on Human Factors in Computing Systems (CHI). Virtual, 2021.
Demo PDF Video Code BibTeX

			
@article{shaikh2021energyvis,
  title={EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models},
  author={Shaikh, Omar and Saad-Falcon, Jon and Wright, Austin P and Das, Nilaksh and Freitas, Scott and Asensio, Omar Isaac and Chau, Duen Horng},
  journal={ACM Conference on Human Factors in Computing Systems (CHI)},
  year={2021}
}
		

UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
Scott Freitas, Shang-Tse Chen, Zijie J. Wang, Duen Horng (Polo) Chau
IEEE International Conference on Big Data (Big Data). Atlanta, GA, 2020.
Project PDF Blog Video Code BibTeX

			
@article{freitas2020detecting,
  title={UnMask: Adversarial Detection and Defense Through Robust Feature Alignment},
  author={Freitas, Scott and Chen, Shang-Tse and Wang, Zijie J. and Chau, Duen Horng},
  journal={IEEE Big Data},
  year={2020},
  publisher={IEEE}
}
		

HAR: Hardness Aware Reweighting for Imbalanced Datasets
Rahul Duggal, Scott Freitas, Sunny Dhamnani, Duen Horng (Polo) Chau, Jimeng Sun
IEEE Conference on Big Data (Big Data). Orlando, USA, 2021.
PDF Video BibTeX

			
@article{duggal2021har,
  title={HAR: Hardness Aware Reweighting for Imbalanced Datasets},
  author={Duggal, Rahul and Freitas, Scott and Dhamnani, Sunny and Chau, Duen Horng and Sun, Jimeng},
  journal={IEEE Conference on Big Data},
  year={2021},
  publisher={IEEE}
}
		

Argo Lite: Open-Source Interactive Graph Exploration and Visualization in Browsers
Siwei Li, Zhiyan Zhou, Anish Upadhayay, Omar Shaikh, Scott Freitas, Haekyu Park, Zijie J. Wang, Susanta Routray, Matthew Hull, Duen Horng (Polo) Chau
ACM International Conference on Information and Knowledge Management (CIKM). Virtual, 2020.
Demo PDF Code BibTeX

			
@inproceedingst{li2020argo,
  title={Argo Lite: Open-Source Interactive Graph Exploration and Visualization in Browsers},
  author={Li, Siwei and Zhou, Zhiyan and Upadhayay, Anish and Shaikh, Omar and Freitas, Scott and Haekyu, Park and Zijie, Wang and Routray, Susanta and Hull, Matthew and Chau, Duen Horng},
  journal={CIKM},
  year={2020},
  publisher={ACM International Conference on Information and Knowledge Management}
}
		

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 Blog Video Code BibTeX * Authors contributed equally

			
@inproceedings{duggal2020rest,
  title={REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild},
  author={Duggal, Rahul and Freitas, Scott and Xiao, Cao and Chau, Duen Horng and Sun, Jimeng},
  journal={WWW},
  year={2020},
  publisher={The Web Conference}
}
		

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 Blog BibTeX

			
@inproceedings{freitas2020d2m,
  title={D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks},
  author={Freitas, Scott and Wicker, Andrew and Chau, Duen Horng and Neil, Joshua},
  journal={SIAM International Conference on Data Mining},
  year={2020},
  publisher={SIAM}
}
		

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.
PDF BibTeX

			
@article{freitas2019extracting,
  title={Extracting Knowledge For Adversarial Detection and Defense in Deep Learning},
  author={Freitas, Scott and Chen, Shang-Tse and Chau, Duen Horng},
  journal={KDD LEMINCS Workshop},
  year={2019},
  publisher={ACM}
}
		

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 PDF BibTeX

			
@inproceedings{freitas2018local,
  title={Local Partition in Rich Graphs}, 
  author={Freitas, Scott and Cao, Nan and Xia, Yinglong and Duen Horng, Chau and Tong, Hanghang}, 
  booktitle={IEEE International Conference on Big Data}, 
  year={2018}, 
  publisher={IEEE}
}
		

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 PDF BibTeX * Authors contributed equally

			
@inproceedings{kang2018xrank,
  author = {Kang, Jian and Freitas, Scott and Yu, Haichao and Xia, Yinglong and Cao, Nan and Tong, Hanghang},
  title = {X-Rank: Explainable Ranking in Complex Multi-Layered Networks},
  booktitle = {Proceedings of the 27th ACM International Conference on Information and Knowledge Management},
  year = {2018},
  location = {Torino, Italy},
  publisher = {ACM}
} 
		

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 PDF Video Code BibTeX Best Demo Paper, Runner up

			
@inproceedings{Freitas:2017:RAN:3132847.3133170,
  author = {Freitas, Scott and Tong, Hanghang and Cao, Nan and Xia, Yinglong},
  title = {Rapid Analysis of Network Connectivity},
  booktitle = {Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
  year = {2017},
  location = {Singapore, Singapore},
  publisher = {ACM},
} 
		

Datasets and Tools

GUIDE: Largest public collection of real-world cybersecurity incidents
2024 Scott Freitas, Jovan Kalajdjieski, Amir Gharib, Rob McCann
Dataset

MalNet-Image: Largest dataset for image-based malware classification
2022 Scott Freitas, Rahul Duggal, Duen Horng (Polo) Chau
Dataset

MalNet-Graph: Largest dataset for graph representation learning and classification
2021 Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng (Polo) Chau
Dataset

TIGER: Comprehensive Python toolbox to evaluate graph vulnerability and robustness
2021 Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, Duen Horng (Polo) Chau
Code

Patents

Threat Actor Infrastructure Profiling Using a Graph and Reputation Propagation (Filed)
2024 Scott Freitas, Amir Gharib
Microsoft

Adaptive Incident Prioritization Engine in a Security Management System (Filed)
2024 Scott Freitas, Amir Gharib
Microsoft

Geographically Diversified Embedding-Based Guided Response to a Security Alert (Filed)
2024 Scott Freitas, Jovan Kalajdjieski, Amir Gharib, Rob McCann
Microsoft

Cybersecurity Incident Correlation (Filed)
2024 Scott Freitas, Amir Gharib
Microsoft

Hierarchical Representation Models (Filed)
2023 Jovan Kalajdjieski, Scott Freitas, Amir Gharib, Rob McCann
Microsoft

Talks

Simplify your SOC with Rob Lefferts and Allie Mellen
Nov. 2024Microsoft Ignite

GraphWeaver: Billion-Scale Cybersecurity Incident Correlation
Oct. 2024Research Paper Invited for Keynote Talk at CIKM Industry Day

Clustering Process Activity in Cloud Environments using Graph Representation Learning
Dec. 2021IBM Research
Dec. 2021DARPA CHASE: Cyber-Hunting at Scale

Detecting Financial Fraud in Online Marketplaces
August 2021Amazon

Developing Robust Models, Algorithms, Databases and Tools with Applications to Cybersecurity and Healthcare
October 2021GE Research
Dec. 2021Georgia Institute of Technology
May 2021Georgia Institute of Technology

Exploring Graph Neural Networks for Malware Detection
July 2020Microsoft Advanced Threat Protection

On the Robustness and Vulnerability of Graphs
April 2020Georgia Institute of Technology

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 2018Arizona State University

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

Press

June 2025"Introducing TITAN-Powered Recommendations in Security Copilot Guided Response",

December 2024"Hyperscale ML threat intelligence for early detection & disruption",

Nov. 2024"Ignite news: What's new in Microsoft Defender XDR?",

Sept. 2024"AI-Driven Guided Response for SOCs with Microsoft Copilot for Security",

August 2024"Cybersecurity incident correlation in the unified security operations platform",

April 2024"Triage and investigate incidents with guided responses from Microsoft Copilot in Microsoft Defender",

Dec. 2021"Congratulations to the Newest PhDs from Georgia Tech",

June 2021"New NVIDIA Partnership Bridges Education Gap for Data Science and Machine Learning",

April 2021"ML Student Earns Prestigious IBM Ph.D. Fellowship Award",

April 2021"IBM PhD Fellowship Awardees Announced",

April 2021"Accelerated Data Science in the Classroom: Teaching Analytics and Machine Learning with RAPIDS",

April 2020"Georgia Tech and Intel Awarded Multimillion-Dollar Program to Defend Against Attacks on AI",

April 2020"DARPA Snags Intel to Lead its Machine Learning Security Tech",

April 2020"Machine Learning Technique Helps Wearable Devices Get Better at Diagnosing Sleep Disorders and Quality",

Feb. 2019"Raytheon Awards Two ML@GT Students Graduate Research Assistantships",

July 2018"NSF Graduate Research Fellow wants to use computer science to solve society’s toughest problems",

Grants and Funding

2021IBM PhD Fellowship
IBM Research PhD Fellowship Awardee
Funded: $95,000

2020Google Cloud Research Grant
Large Scale Malware Analysis
Funded: $5,000 Google cloud credits

2018 — 2022Guaranteeing AI Robustness against Deception (GARD)
DARPA Research Grant
Co-PIs: Jason Martin, Duen Horng (Polo) Chau
Funded: multi-million
Helped formulate adversarial defense techniques

2018Amazon AWS Research Grant
Adversarial Re-Training and Model Vaccination for Robust Deep Learning
Funded: $5,000 AWS cloud credits

2018Nvidia GPU Grant
Defending Adversarial Attacks by Robust, Inference-time Local Linear Approximation
Funded: Nvidia Titan V GPU worth $3,000

2019Raytheon Research Fellowship
Extracting Knowledge For Adversarial Detection and Defense
Funded: $25,000

2018 — 2023NSF Graduate Research Fellowship Program (GRFP)
Multi-level Interdiction and Assistance Modeling for Natural Disasters
Funded: Full tuition + $102,000

2016 — 2017FURI Grant
Network Connectivity Analysis and Visualization in Large Graphs
Funded: $3,000

Teaching

Spring 2021Graduate Teaching Assistant
Georgia Institute of Technology, Atlanta, GA
Data and Visual Analytics, Instructor: Duen Horng (Polo) Chau

Fall 2020Graduate Teaching Assistant
Georgia Institute of Technology, Atlanta, GA
Data and Visual Analytics, Instructor: Duen Horng (Polo) Chau

Fall 2013Undergraduate Teaching Assistant
Arizona State University, Tempe, AZ
Fulton Undergraduate Research Experience (FSE 294), Instructor: Joshua Lyon
Designed and taught introductory lesson plans to new engineering students

Mentoring

Summer 2023Davinder Kaur at Microsoft
Ph.D. in Computer Science, Indiana University–Purdue University Indianapolis

Summer 2023Joshua Feinglass at Microsoft
Ph.D. in Computer Engineering, Arizona State University

Fall 2020Summer 2020Kevin Li
Summer 2020B.S. in Computer Science, Georgia Institute of Technology

Fall 2020Spring 2020Omar Shaikh
Spring 2020B.S. in Computer Science, Georgia Institute of Technology

Fall 2020Spring 2020Jon Saad-Falcon
Spring 2020B.S. in Computer Science, Georgia Institute of Technology

Fall 2020Spring 2020Frank Zhou
Spring 2020B.S. in Computer Science, Georgia Institute of Technology

Service

Hiring Committee

Microsoft Security Research (Microsoft) 2024-2025

Microsoft Security Research Summer Interns (Microsoft) 2022-2025

Program Committee

Association for the Advancement of Artificial Intelligence (AAAI) at AAAI 2021

ACM International Conference on Information and Knowledge Management (CIKM) at ACM CIKM 2020

Reviewer

Practice of Knowledge Discovery in Databases (ECML-PKDD) 2021

International Conference on Computer Vision (ICCV) 2021

Conference on Computer Vision and Pattern Recognition (CVPR) 2021

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019, 2025

International Conference on Machine Learning (ICML) 2019

Technology Skills

OS and Tools: Ubuntu, Unix command line, Windows, PyCharm, Azure, Synapse, Git, Latex, AWS EC2

Programming: Python, PySpark, Kusto, SQL, Matlab, Java, C#, C++, JavaScript, HTML

Research: Machine learning, Data mining, Graph mining, Data science, Artificial intelligence, Generative AI, Large language models (LLMs), Deep learning, Computer vision, Natural language processing (NLP), Anomaly detection, Cybersecurity