Timezone: »
Author Information
Frank Wood (University of British Columbia)
Dr. Wood is an associate professor in the Department of Engineering Science at the University of Oxford. Before that he was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London. He holds a PhD from Brown University (â07) and BS from Cornell University (â96), both in computer science. Dr. Wood is the original architect of both the Anglican and Probabilistic-C probabilistic programming systems. He conducts AI-driven research at the boundary of probabilistic programming, Bayesian modeling, and Monte Carlo methods. Dr. Wood holds 6 patents, has authored over 50 papers, received the AISTATS best paper award in 2009, and has been awarded faculty research awards from Xerox, Google and Amazon. Prior to his academic career he was a successful entrepreneur having run and sold the content-based image retrieval company ToFish! to AOL/Time Warner and served as CEO of Interfolio.
Tom Griffiths (Princeton)
More from the Same Authors
-
2021 : A Closer Look at Gradient Estimators with Reinforcement Learning as Inference »
Jonathan Lavington · Michael Teng · Mark Schmidt · Frank Wood -
2018 : Research Panel »
Sinead Williamson · Barbara Engelhardt · Tom Griffiths · Neil Lawrence · Hanna Wallach -
2018 : TBC 1 »
Frank Wood -
2017 : Revealing human inductive biases and metacognitive processes with rational models »
Tom Griffiths -
2017 Workshop: Deep Learning for Physical Sciences »
Atilim Gunes Baydin · Mr. Prabhat · Kyle Cranmer · Frank Wood -
2017 Poster: A graph-theoretic approach to multitasking »
Noga Alon · Daniel Reichman · Igor Shinkar · Tal Wagner · Sebastian Musslick · Jonathan D Cohen · Tom Griffiths · Biswadip dey · Kayhan Ozcimder -
2017 Oral: A graph-theoretic approach to multitasking »
Noga Alon · Daniel Reichman · Igor Shinkar · Tal Wagner · Sebastian Musslick · Jonathan D Cohen · Tom Griffiths · Biswadip dey · Kayhan Ozcimder -
2017 Poster: Learning Disentangled Representations with Semi-Supervised Deep Generative Models »
Siddharth Narayanaswamy · Brooks Paige · Jan-Willem van de Meent · Alban Desmaison · Noah Goodman · Pushmeet Kohli · Frank Wood · Philip Torr -
2016 : Bounded Optimality and Rational Metareasoning in Human Cognition »
Tom Griffiths -
2016 Poster: Bayesian Optimization for Probabilistic Programs »
Thomas Rainforth · Tuan Anh Le · Jan-Willem van de Meent · Michael A Osborne · Frank Wood -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
2015 Workshop: Bounded Optimality and Rational Metareasoning »
Samuel J Gershman · Falk Lieder · Tom Griffiths · Noah Goodman -
2015 Tutorial: Probabilistic Programming »
Frank Wood -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Poster: Algorithm selection by rational metareasoning as a model of human strategy selection »
Falk Lieder · Dillon Plunkett · Jessica B Hamrick · Stuart J Russell · Nicholas Hay · Tom Griffiths -
2014 Poster: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Oral: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2013 Poster: Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies »
Yangqing Jia · Joshua T Abbott · Joseph L Austerweil · Tom Griffiths · Trevor Darrell -
2012 Poster: Human memory search as a random walk in a semantic network »
Joshua T Abbott · Joseph L Austerweil · Tom Griffiths -
2012 Spotlight: Human memory search as a random walk in a semantic network »
Joshua T Abbott · Joseph L Austerweil · Tom Griffiths -
2012 Poster: Burn-in, bias, and the rationality of anchoring »
Falk Lieder · Tom Griffiths · Noah Goodman -
2011 Poster: A rational model of causal inference with continuous causes »
M Pacer · Tom Griffiths -
2011 Poster: An ideal observer model for identifying the reference frame of objects »
Joseph L Austerweil · Abram Friesen · Tom Griffiths -
2011 Poster: Hierarchically Supervised Latent Dirichlet Allocation »
Adler J Perotte · Frank Wood · Noemie Elhadad · Nicholas Bartlett -
2011 Poster: Testing a Bayesian Measure of Representativeness Using a Large Image Database »
Joshua T Abbott · Katherine Heller · Zoubin Ghahramani · Tom Griffiths -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Spotlight: Probabilistic Deterministic Infinite Automata »
David Pfau · Nicholas Bartlett · Frank Wood -
2010 Spotlight: Learning invariant features using the Transformed Indian Buffet Process »
Joseph L Austerweil · Tom Griffiths -
2010 Poster: Learning invariant features using the Transformed Indian Buffet Process »
Joseph L Austerweil · Tom Griffiths -
2010 Poster: Probabilistic Deterministic Infinite Automata »
David Pfau · Nicholas Bartlett · Frank Wood -
2009 Workshop: Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain »
Noah Goodman · Edward Vul · Tom Griffiths · Josh Tenenbaum -
2009 Poster: Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling »
Lei ShiUpdateMe · Tom Griffiths -
2009 Spotlight: Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling »
Lei ShiUpdateMe · Tom Griffiths -
2009 Poster: Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning »
Anne Hsu · Tom Griffiths -
2009 Oral: Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning »
Anne Hsu · Tom Griffiths -
2009 Poster: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Spotlight: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2008 Workshop: Machine learning meets human learning »
Nathaniel D Daw · Tom Griffiths · Josh Tenenbaum · Jerry Zhu -
2008 Poster: Characterizing neural dependencies with Poisson copula models »
Pietro Berkes · Frank Wood · Jonathan W Pillow -
2008 Poster: Modeling the effects of memory on human online sentence processing with particle filters »
Roger Levy · Florencia Reali · Tom Griffiths -
2008 Oral: Modeling the effects of memory on human online sentence processing with particle filters »
Roger Levy · Florencia Reali · Tom Griffiths -
2008 Spotlight: Characterizing neural dependencies with Poisson copula models »
Pietro Berkes · Frank Wood · Jonathan W Pillow -
2008 Poster: How memory biases affect information transmission: A rational analysis of serial reproduction »
Jing Xu · Tom Griffiths -
2008 Poster: Analyzing human feature learning as nonparametric Bayesian inference »
Joseph L Austerweil · Tom Griffiths -
2008 Poster: A rational model of preference learning and choice prediction by children »
Chris Lucas · Tom Griffiths · Fei Xu · Christine Fawcett -
2008 Poster: Dependent Dirichlet Process Spike Sorting »
Jan Gasthaus · Frank Wood · Dilan Gorur · Yee Whye Teh -
2008 Spotlight: Analyzing human feature learning as nonparametric Bayesian inference »
Joseph L Austerweil · Tom Griffiths -
2008 Spotlight: A rational model of preference learning and choice prediction by children »
Chris Lucas · Tom Griffiths · Fei Xu · Christine Fawcett -
2008 Spotlight: How memory biases affect information transmission: A rational analysis of serial reproduction »
Jing Xu · Tom Griffiths -
2008 Poster: Modeling human function learning with Gaussian processes »
Tom Griffiths · Chris Lucas · Joseph Jay Williams · Michael Kalish -
2007 Oral: Markov Chain Monte Carlo with People »
Adam Sanborn · Tom Griffiths -
2007 Poster: Markov Chain Monte Carlo with People »
Adam Sanborn · Tom Griffiths -
2007 Poster: A Probabilistic Approach to Language Change »
Alexandre Bouchard-Côté · Percy Liang · Tom Griffiths · Dan Klein -
2006 Poster: Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Mod »
Mark Johnson · Tom Griffiths · Sharon Goldwater -
2006 Poster: A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments »
Daniel Navarro · Tom Griffiths