About

Hey there! My name is Pravi Devineni. I received my Ph.D in Computer Science from UC Riverside, where I used developed ML models for anomaly detection in large-scale temporal data. I spent several years in academia applying my AI expertise to solve problems in a variety of areas including materials science, urban sciences, nuclear engineering, and currently work in the energy sector. I am broadly interested in the area of AI and cybersecurity and use this blog to showcase my learnings. Find my resume here.

In my free time, I play competitive sports, explore national parks, hike and cook with my husband and our dog. I am extremely passionate about mentoring and love teaching computer science to young kids.

Education

Ph.D., Computer Science, 2018
University of California Riverside, Riverside, CA
Dissertation Title:
GPA: 3.9/4.0

M.S., Computer Science, 2011
Manipal University, India
Dissertation Title: GPA: 8.74/4.0

B.S., Computer Science, 2009
Jawaharlal Nehru Technological University, India
Computer Science and Engineering

Skills and Languages

Programming Languages, Libraries and Tools: Machine Learning Methods:

Work Experience

Duke Energy

Oak Ridge National Laboratory

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Full-time

Media Mentions

• 2021 Computer Science and Mathemetics Mentoring Award

• Instructor for Introduce Your Daughter To AI, 2021

• Instructor, Introduce Your Daughter To AI, 2020

• Volunteer for Introduce Your Daughter To AI, 2019

Talks

Service to Profession

• Workshop organizer
• SMC Data Challenge
• IEEE Big Data BTSD Workshop
• Reviewer, AI Track, GHC
• Panelist, “It takes a village to raise a PhD Student” SIAM SDM 2020

Community Outreach

• Chair, AI Track, GHC 2020 and 2021
• Co-founder, Women in Computing, University of New Mexico, Albuquerque
• Co-founder, Women in Computing, University of California Riverside
• Co-founder, Align
• Instructor and Volunteer, Oak Ridge CS Girls, Oak Ridge, TN
• Volunteer, Annual Hour of Code, Knoxville, TN
• Instructor, Bosque Middle School, Albuquerque

Publications

15. Bill Kay, Hao Lu, Pravallika Devineni, Anika Tabassum, Supriya Chintavali, and Sangkeun Matt Lee. Identification of critical infrastructure via pagerank. IEEE Big Data 2021.

16. Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane, Ketan Maheshwari, Drahomira Herrmannova, Ramakrishnan Kannan et al. Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. Springer Nature 2021.

17. Uday Singh Saini, Pravallika Devineni, and Evangelos E. Papalexakis. Subspace Clustering Based Analysis of Neural Networks. ECML PKDD 2021.

18. Ravdeep S. Pasricha, Pravallika Devineni, Evangelos E. Papalexakis, and Ramakrishnan Kannan. Tensorized feature spaces for feature explosion. IEEE ICPR 2021.

19. Pravallika Devineni, Bill Kay, Hao Lu, Anika Tabassum, Supriya Chintavali, and Sangkeun Matt Lee. Toward quantifying vulnerabilities in critical infrastructure systems. IEEE Big Data 2020.

20. Suzanne Parete-Koon, Peter F. Peterson, Garrett E. Granroth, Wenduo Zhou, Pravallika Devineni, Nouamane Laanait, Junqi Yin et al. Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics. Springer Nature 2020.

21. Steven R. Young, Pravallika Devineni, Maryam Parsa, J. Travis Johnston, Bill Kay, Robert M. Patton, Catherine D. Schuman, Derek C. Rose, and Thomas E. Potok. Evolving energy efficient convolutional neural networks. IEEE Big Data 2019.

22. Nageswara SV Rao, Christopher Greulich, Pradeep Ramuhalli, Sacit M. Cetiner, and Pravallika Devineni. Sensor drift estimation for reactor systems by fusing multiple sensor measurements. IEEE NSS/MIC 2019.

23. Pravallika Devineni, Vagelis Papalexakis, Kalina Michalska, and Michalis Faloutsos. MIMiS: Minimally intrusive mining of smartphone user behaviors. In 2018 IEEE/ACM ASONAM 2018.

24. Pravallika Devineni, Danai Koutra, Michalis Faloutsos, and Christos Faloutsos, Facebook wall posts: a model of user behaviors, Springer SNAM 2016.

25. Saba A. Al-Sayouri, Pravallika Devineni, Sarah S. Lam, Vagelis Papalexakis, and Danai Koutra. “GECS: Graph Embedding Using Connection Subgraphs.” Machine Learning in Graphs Workshop, KDD 2016.

26. Pravallika Devineni, Vagelis Papalexakis, Danai Koutra, A. Seza Doğruöz, and Michalis Faloutsos. “One size does not fit all: Profiling personalized time-evolving user behaviors.” IEEE/ACM ASONAM 2017.

27. Venkata Krishna Pillutla, Zhanpeng Fang, Pravallika Devineni, Christos Faloutsos, Danai Koutra, and Jie Tang. On skewed multi-dimensional distributions: the FusionRP model, algorithms, and discoveries, SIAM SDM 2016.

28. Pravallika Devineni, Danai Koutra, Michalis Faloutsos, and Christos Faloutsos. If walls could talk: Patterns and anomalies in facebook wallposts, IEEE/ACM ASONAM 2015.

29. Yi Wang, Hui Zang, Pravallika Devineni, Michalis Faloutsos, Krishna Janakiraman, and Sara Motahari. Which phone will you get next: observing trends and predicting the choice, IEEE NOMS 2014.

Books

1. Jeffrey Nichols, James Nutaro, Swaroop Pophale, Pravallika Devineni, Theresa Ahearn, and Becky Verastegui, eds. Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation: 21st Smoky Mountains Computational Sciences and Engineering*, Springer Nature, 2022.

Mentoring