Teaching

I am an adjunct faculty at Santa Clara University. I teach a course on data science and machine learning. I have also given several guest lectures at Stanford University.

• Data Science / ML for Cybersecurity
  • COEN 396A @SCU, Sping 2023 (course description)
  • Internal course at Opentext, Fall 2023
• COEN 281 @SCU: Pattern Recognition and Data Mining (Course description)
  • Fall 2022
  • Fall 2018 (35 students)
  • Fall 2016 (36 students)
  • Fall 2015 (26 students)
• Guest lectures: Computational Sustainability @ Stanford
  • Smart Buildings (slides)
  • Sustainable Data Centers (slides)
• Guest lecture: Intelligent Energy Systems @ Stanford
  • Data Mining Techniques to Enhance Sustainability of Data Centers
• Guest lecture: Intelligent Energy Systems: Energy and Big Data @ Stanford
  • Data Mining for Sustainable Data Centers

Mentoring

I have been fortunate to mentor the following students, either during their internships with me or through research collaborations with their universities. (Project titles are listed in parentheses below.)

• Deb Patnaik, Virginia Tech, 2008-2010 (also on Ph.D. thesis committee)
    (Temporal motif mining for data center chillers)
• Hyungsul Kim, UIUC, Summer 2010
    (Energy disaggregation)
• Naren Sundaravaradan, Virginia Tech, 2010-2011
    (Automating life cycle assessment)
• M. Shahriar Hossain, Virginia Tech, 2010-2012
    (Automating life cycle assessment)
• Gowtham Bellala, University of Michigan, 2011
    (Building energy analytics)
• Prithwish Chakraborty, Virginia Tech, 2011-2012
    (PV power prediction)
• Huijuan Shao, Virginia Tech, 2011- 2016 (also on Ph.D. thesis committee)
    (Energy disaggregation)
• Aniket Chakraborty, OSU, Summer 2014
    (Sensor anomaly detection using graphical models)
• Hao Peng, Purdue University, Summer 2014
    (Distributed R)
• Vishrut Gupta, CMU, Summer 2014
    (Distributed R)
• Luyan Wu, Stanford, Summer 2014
    (Distributed R)
• Yanping Chen, UCI, Summer 2014
    (Scalable hierarchical clustering)
• Roshan Dathathri, UT Austin, Summer 2015
    (High performance distributed belief propagation for large scale graphical models)
• I-Ta Lee, Purdue University, Summer 2017
    (Feature Learning for Security)
• Cosmo Zhang, Purdue University, Summer 2017
    (Model Interpretability for Security)
• Simon Koeder, Baden-Wuerttemberg State University, Summer 2017
    (Anomaly Detection in netflow data)
• Nikhil Muralidhar, Virginia Tech, 2018
    (Incorporation of domain knowledge into ML training)
• Raihan Islam, Virginia Tech, 2018
    (Incorporation of domain knowledge into ML training)
• Shengzhe Xu, Virginia Tech, 2019 - 2025 (also on Ph.D. thesis committee)
    (Neural generative models for synthetic generation of security data)
• Ferdinand Koenig, 2021
    (Anomaly detection in network traffic data)
• Yansong Li, University of Ottawa, 2023 - present
    (Software vulnerability detection and repair)
• A few students at Dalhousie University, 2023 - present
    (threat detection in security data)
• Alex De Furia, University of Ottawa, 2025 - present
    (Software vulnerability detection and repair)