ML Minor and ML Concentration - Senior Projects
Sample of available research projects (updated September 2019). Contact your favorite professor for updates.
If you are working with a faculty member who is not Machine Learning Core Faculty, you need to be co-advised by a Core faculty member. Non-Core faculty members in the chart below are marked with an asterisk (*).
|Leman Akoglu *||
Interactive visualization and summarization of social circles in online social networks
Profiling users by inferring missing attributes from their friends'
Privacy Preserving Machine Learning
Life Long Learning
|Kai-Min Chang *||
Exploiting Longitudinal EEG Input in a Reading Tutor
|Artur Dubrawski *||Learning to support decisions with complex data with applications including healthcare, medical imaging, equipment maintenance, nuclear safety and food safety (multiple projects available)|
|Bill Eddy *||Verification of on-line forms for Census Bureau using record-matching and geographical data|
|Scott Fahlman *||
Symbolic learning from single or a few examples
Using symbolic knowledge to guide statistical learning (and vice versa)
Learning by reading
|Christos Faloutsos||Visualization and interaction with large graph|
|Matt Gormley||Machine Learning for Natural Language Processing|
|Eunsu Kang *||Exploring the intersection and Art and Machine Learning. Looking into the possibility of Creative AI.|
|Ken Koedinger *||Machine learning to model human learning and automate assessment and tutoring of math, science, and computer science in online courses|
Jian Ma *
|Decoding what's encoded in the human genome using machine learning.|
|Using ML + Crowd to quality check data analysis procedures|
|Jack Mostow *||Fit and tune student models using performance data from an individual student or small population of students. The context is the XPRIZE Global Learning Challenge to develop an open source Android app for African children to learn basic reading, writing, and numeracy without human teachers.|
|Robert Murphy *||
We work on learning statistical generative models for the structure and distribution of organelles within cells directly from microscope images. There is a review article available at http://murphylab.cbd.cmu.edu/publications/204-murphy2016.pdf that summarizes our past work on the CellOrganizer project. The fundamental machine learning question is to be able to decide which of various modeling methods provides a better representation of the information in images of populations of cells.
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|Jeff Schneider||Recognizing bird species from audio of their singing|
Online and active optimization for material discovery
Personalized prediction of cardiovascular risk from brain scans
|Michael Tarr *||Predicting neural vision data using deep networks|
|Jeremy Weiss *||Machine learning for health care: forecasting disease complications|
|Kun Zhang *||Causal analysis of air pollution|