Rebecca Willett

Associate Professor


Profile Summary

My research interests include signal processing, machine learning, and large-scale data science. Specific foci include inference from point process data, methods robust to missing data, high-dimensional data coupled with sparse and low-rank models, and streaming data.

One central theme of my research is data-starved inference for point processes — the development of statistically robust methods for analyzing discrete events, where the discrete events can range from photons hitting a detector in an imaging system to groups of people meeting in a social network. When the number of observed events is very small, accurately extracting knowledge from this data is a challenging task requiring the development of both new computational methods and novel theoretical analysis frameworks. This body of research has led to important insights into the performance of compressed sensing in optical systems, tools for tracking dynamic meeting patterns in social network, and novel sparse Poisson intensity reconstruction algorithms for night vision and medical imaging.

https://machinelearning.wisc.edu/

http://cpcp.wisc.edu/

https://lucid.wisc.edu/

Education

Rice University, Houston, TX; M.S./Ph.D. program in the Electrical and
Computer Engineering Department; GPA: 4.03/4.00; Advisor Prof. Robert
Nowak; M.S. in May 2002, Ph.D. in May 2005.

Duke University, Durham, NC; B.S.E. Double major in Electrical
Engineering and Computer Sci- ence; GPA: 3.95/4.00; Ranked 3 in a
class of 230; Graduated with Distinction and Summa cum laude; May
2000.

Research Interests

Signal Processing, Image Processing, Machine Learning, Optimization, Point Processes, High-dimensional Statistics

Awards, Honors and Societies

EEE Senior Member, 2011
AFOSR Young Investigator Program Award Recipient, 2010
DARPA Computer Science Study Group, 2007-present
NSF CAREER Award Recipient, 2007

Publications

http://willett.ece.wisc.edu/publications.html

Links

Courses

Spring 2015-2016

  • ECE 203 - Signals, Information, and Computation

  • ECE 699 - Advanced Independent Study
  • ECE 890 - Pre-Dissertator\'s Research
  • ECE 399 - Independent Study
  • ECE 890 - Pre-Dissertator\'s Research
  • ECE 890 - Pre-Dissertator\'s Research
  • ECE 830 - Estimation and Decision Theory
  • ECE 790 - Master\'s Research or Thesis
  • ECE 699 - Advanced Independent Study
  • ECE 399 - Independent Study
  • COMPSCI 761 - Advanced Machine Learning
  • Secondary Contact

    Alt Ph: (608) 262-2473

    Profile Summary

    My research interests include signal processing, machine learning, and large-scale data science. Specific foci include inference from point process data, methods robust to missing data, high-dimensional data coupled with sparse and low-rank models, and streaming data.

    One central theme of my research is data-starved inference for point processes — the development of statistically robust methods for analyzing discrete events, where the discrete events can range from photons hitting a detector in an imaging system to groups of people meeting in a social network. When the number of observed events is very small, accurately extracting knowledge from this data is a challenging task requiring the development of both new computational methods and novel theoretical analysis frameworks. This body of research has led to important insights into the performance of compressed sensing in optical systems, tools for tracking dynamic meeting patterns in social network, and novel sparse Poisson intensity reconstruction algorithms for night vision and medical imaging.

    https://machinelearning.wisc.edu/

    http://cpcp.wisc.edu/

    https://lucid.wisc.edu/


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