Data Explorer + Flutist + Knowledge Hunter


I'm an applied machine learning researcher and data scientist with specialties in graph analysis and relational learning.
I like to play with data, especially in domains that have been thus far underserved by machine learning.

Currently based in Northern New Mexico, I earned my Masters in computer science from the University of Massachusetts Amherst, and did my undergrad work just down the road at Amherst College. I almost became a professional musician, but opted for computer science and physics instead. In my spare time I enjoy camping, hiking, cooking, playing music, Lindy Hopping, and trying to belly dance.

I prefer my cheeseburgers with green chile.




Curriculum Vitae


My CV is available here.




Where to find my work


2017

Helping Exascale Computers Help Us: Machine Learning for High Performance Computing.
To Appear at NIPS: WiML Workshop 2017.

Haque, Abida, Alexandra DeLucia, and Elisabeth Baseman.
Markov Chain Modeling for Anomaly Detection in High Performance Computing System Logs.
To Appear at SC: HUST Workshop 2017.

"Interpretable Anomaly Detection for High Performance Computing Centers: Monitoring System Logs."
Chesapeake Large-Scale Analytics Conference 2017 (Invited Talk).

Siddiqua, Taniya, Vilas Sridharan, Steven Raasch, Nathan DeBardeleben, Kurt Ferreira, Scott Levy, Elisabeth Baseman, and Qiang Guan.
Lifetime Memory Reliability Data From The Field.
DFT 2017. (Best Paper Nominee)

Baseman, Elisabeth, Nathan DeBardeleben, Kurt Ferreira, Vilas Sridharan, Taniya Siddiqua, and Olena Tkachenko.
Automating DRAM Fault Mitigation By Learning From Experience.
DSN 2017 (Industrial Track).

"Machine Learning for High Performance Computing."
Southern Data Science Conference 2017 (Invited Talk).

2016

Morrow, Adam, Elisabeth Baseman, and Sean Blanchard.
Ranking Anomalous High Performance Computing Sensor Data Using Unsupervised Clustering.
CSCI 2016: Symposium on Parallel and Distributed Computing and Computational Science.

Baseman, Elisabeth, Sean Blanchard, Zongze Li, and Song Fu.
Relational Synthesis of Text and Numeric Data for Anomaly Detection on Computing System Logs.
ICMLA 2016.

Baseman, Elisabeth, Sean Blanchard, Nathan DeBardeleben, Amanda Bonnie, and Adam Morrow.
Interpretable Anomaly Detection for Monitoring of High Performance Computing Systems.
KDD 2016: Outlier Definition, Detection, and Description on Demand Workshop.

Guan, Chung, Nathan DeBardeleben, Panruo Wu, Stephan Eidenbenz, Sean Blanchard, Laura Monroe, Elisabeth Baseman, and Li Tan.
Design, Use, and Evaluation of P-FSEFI: A Parallel Soft Error Fault Injection Framework for Emulating Soft Errors in Parallel Applications.
SIMUTOOLS 2016.

Baseman, Elisabeth, Nathan DeBardeleben, Kurt Ferreira, Scott Levy, Steven Raasch, Vilas Sridharan, Taniya Siddiqua, and Qiang Guan.
Improving DRAM Fault Characterization Through Machine Learning.
DSN 2016 (Industrial Track).

Baseman, Elisabeth, Nathan DeBardeleben, Kurt Ferreira, Scott Levy, Steven Raasch, Vilas Sridharan, Taniya Siddiqua, and Qiang Guan.
"Machine Learning for Automatic Memory Fault Mode Characterization".
Silicon Errors in Logic: System Effects 2016 short talk.

Siddiqua, Taniya, Vilas Sridharan, Nathan DeBardeleben, Elisabeth Baseman, Qiang Guan, Devesh Tiwari, Christian Engelmann, and Saurabh Gupta.
Memory Error Analysis and Lessons Learned from Large-scale Field Data.
2016, LANL/ORNL/AMD internal document.

Baseman, Elisabeth, Nathan DeBardeleben, Kurt Ferreira, Scott Levy, Steven Raasch, Vilas Sridharan, Taniya Siddiqua, and Qiang Guan.
A Machine Learning Approach for Automatic Characterization of Memory Faults.
DOE Conference on Data Analysis 2016 poster.

2015

Baseman, Elisabeth, and David Jensen.
Collaborative Behavior in Social Networks: A Relational Statistical Approach.
Networks in the Social and Information Sciences. NIPS 2015 workshop paper.

Baseman, Elisabeth, and David Jensen.
Exploring Collective Behavior in Social Computation Through Relational Statistical Models.
Women in Machine Learning 2015 workshop poster.

Baseman, Elisabeth, and David Jensen.
Exploring Collective Behavior in Social Computation Through Relational Statistical Models.
Computational Social Science Society of the Americas 2015.

Exploring Collective Behavior in Social Computation Through Relational Statstical Models.
Masters thesis in Computer Science, University of Massachusetts Amherst, May 2015.

2014

Campbell, William, Elisabeth Baseman, and Kara Greenfield.
Content + Context = Classification: Examining the Roles of Social Interactions and Linguist Content in Twitter User Classification.
Social NLP. COLING 2014 workshop paper.

Baseman, Elisabeth, Michael Kearns, Stephen Judd, and David Jensen.
Dynamic Statistical Models of Collective Social Network Behavior.
New England Machine Learning Day 2014 poster.

"Applications of Relational Learning".
Invited talk for MIT Lincoln Laboratory Computing and Analytics group. March 12, 2014.

2013

Campbell, William, Elisabeth Baseman, and Kara Greenfield.
Content + Context Networks for User Classification in Twitter.
Frontiers of Network Analysis. NIPS 2013 workshop paper.

"Applications of Relational Learning for Big Data".
Talk for MIT Lincoln Laboratory Human Language Technology group. Summer 2013.

Baseman, Elisabeth, Michael Kearns, Stephen Judd, and David Jensen.
Statistical Models of Collective Social Network Behavior.
Women in Machine Learning 2013 workshop poster.

2011

Computing with Quantum Physics.
Amherst College honors thesis, 2011.




Machine Learning Tutorials

Returning Summer 2018!




Machine Learning Reading Group

Returning Summer 2018!




Music

I play flute and piccolo, among other instruments.

I also enjoy arranging music for chamber ensembles, and teaching lessons (contact me if you are looking for a teacher!).

I play with the friendliest and craziest band in the world, The Hill Stompers, a street band based in Los Alamos, NM.

You can find more information about upcoming gigs on our Facebook page.




Contact

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