Karin Knudson's guest appearances

Karin Knudson

Learning Bayesian Statistics

David Spiegelhalter

Andrew Gelman

Michael Lee

Baba Brinkman

Michael Kennedy

Allen Downey

Sean Taylor

Michael Faber

Matthew Kay

Michael Betancourt

Eric Ma

Colin Carroll

Osvaldo Martin

Chris Fonnesbeck

Junpeng Lao

OwlTail

What do neurodegenerative diseases, gerrymandering and ecological inference all have in common? Well, they can all be studied with Bayesian methods — and that’s exactly what Karin Knudson is doing.In this episode, Karin will share with us the vital and essential work she does to understand aspects of neurodegenerative diseases. She’ll also tell us more about computational neuroscience and Dirichlet processes — what they are, what they do, and when you should use them.Karin did her doctorate in mathematics, with a focus on compressive sensing and computational neuroscience at the University of Texas at Austin. Her doctoral work included applying hierarchical Dirichlet processes in the setting of neural data and focused on one-bit compressive sensing and spike-sorting.Formerly the chair of the math and computer science department of Phillips Academy Andover, she started a postdoc at Mass General Hospital and Harvard Medical in Fall 2019. Most importantly, rock climbing and hiking have no secrets for her!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ ! Links from the show, personally curated by Karin Knudson:Karin on Twitter: https://twitter.com/karinknudsonSpike train entropy-rate estimation using hierarchical Dirichlet process priors (Knudson and Pillow): https://pillowlab.princeton.edu/pubs/abs_Knudson_HDPentropy_NIPS13.htmlFighting Gerrymandering with PyMC3, PyCon 2018, Colin Carroll and Karin Knudson: https://www.youtube.com/watch?v=G9I5ZnkWR0AExpository resources on Dirichlet Processes: Chapter 23 of Bayesian Data Analysis (Gelman et al.) and http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdfHierarchical Dirichlet Processes (introduced the HDP and included applications in topic modeling and for working with time-series data and Hidden Markov Models): https://www.stat.berkeley.edu/~aldous/206-Exch/Papers/hierarchical_dirichlet.pdfA Sticky HDP-HMM with applications to speaker diarization (a nice example of how the HDP can be used with HMM, in this case cleverly adapted so that states have more persistence): https://arxiv.org/abs/0905.2592If you want to get deeper into the weeds and also get a sense of the history: Dirichlet Processes with Applications to Bayesian Nonparametric Problems (https://projecteuclid.org/euclid.aos/1176342871) and A Bayesian Analysis of Some Nonparametric Problems (https://projecteuclid.org/euclid.aos/1176342360)This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

What do neurodegenerative diseases, gerrymandering and ecological inference all have in common? Well, they can all be studied with Bayesian methods — and that’s exactly what Karin Knudson is doing.In this episode, Karin will share with us the vital and essential work she does to understand aspects of neurodegenerative diseases. She’ll also tell us more about computational neuroscience and Dirichlet processes — what they are, what they do, and when you should use them.Karin did her doctorate in mathematics, with a focus on compressive sensing and computational neuroscience at the University of Texas at Austin. Her doctoral work included applying hierarchical Dirichlet processes in the setting of neural data and focused on one-bit compressive sensing and spike-sorting.Formerly the chair of the math and computer science department of Phillips Academy Andover, she started a postdoc at Mass General Hospital and Harvard Medical in Fall 2019. Most importantly, rock climbing and hiking have no secrets for her!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> ! Links from the show, personally curated by Karin Knudson:<ul><li>Karin on Twitter: <a href="https://twitter.com/karinknudson" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/karinknudson</a></li><li>Spike train entropy-rate estimation using hierarchical Dirichlet process priors (Knudson and Pillow): <a href="https://pillowlab.princeton.edu/pubs/abs_Knudson_HDPentropy_NIPS13.html" rel="noopener noreferrer nofollow" target="_blank">https://pillowlab.princeton.edu/pubs/abs_Knudson_HDPentropy_NIPS13.html</a></li><li>Fighting Gerrymandering with PyMC3, PyCon 2018, Colin Carroll and Karin Knudson: <a href="https://www.youtube.com/watch?v=G9I5ZnkWR0A" rel="noopener noreferrer nofollow" target="_blank">https://www.youtube.com/watch?v=G9I5ZnkWR0A</a></li><li>Expository resources on Dirichlet Processes: Chapter 23 of Bayesian Data Analysis (Gelman et al.) and <a href="http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf" rel="noopener noreferrer nofollow" target="_blank">http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf</a></li><li>Hierarchical Dirichlet Processes (introduced the HDP and included applications in topic modeling and for working with time-series data and Hidden Markov Models): <a href="https://www.stat.berkeley.edu/~aldous/206-Exch/Papers/hierarchical_dirichlet.pdf" rel="noopener noreferrer nofollow" target="_blank">https://www.stat.berkeley.edu/~aldous/206-Exch/Papers/hierarchical_dirichlet.pdf</a></li><li>A Sticky HDP-HMM with applications to speaker diarization (a nice example of how the HDP can be used with HMM, in this case cleverly adapted so that states have more persistence): <a href="https://arxiv.org/abs/0905.2592" rel="noopener noreferrer nofollow" target="_blank">https://arxiv.org/abs/0905.2592</a></li><li>If you want to get deeper into the weeds and also get a sense of the history: Dirichlet Processes with Applications to Bayesian Nonparametric Problems (<a href="https://projecteuclid.org/euclid.aos/1176342871" rel="noopener noreferrer nofollow" target="_blank">https://projecteuclid.org/euclid.aos/1176342871</a>) and A Bayesian Analysis of Some Nonparametric Problems (<a href="https://projecteuclid.org/euclid.aos/1176342360" rel="noopener noreferrer nofollow" target="_blank">https://projecteuclid.org/euclid.aos/1176342360</a>)</li></ul>This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

#4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson