Oisin Mac Aodha
My name is Oisin Mac Aodha (how to pronounce my name) and I am a Lecturer (aka Assistant Professor) in Machine Learning in the School of Informatics at the University of Edinburgh. My current research interests are in the areas of computer vision and machine learning, with a specific emphasis on human-in-the-loop methods such as machine teaching.
From 2016-2019 I was fortunate to be a postdoc in Prof. Pietro Perona's Computational Vision Lab at Caltech. Previous to Caltech, I spent three great years (2013-2016) as a postdoc in the Department of Computer Science at the University College of London with Prof. Gabriel Brostow and Prof. Kate Jones. There I worked on interactive machine learning, where our goal was to design algorithms to enable non-programming scientists to semi-automatically explore events of interest in vast quantities of audio and visual data.
I did both my MSc (with Dr. Simon Prince) and PhD (with Prof. Gabriel Brostow) at UCL and have an undergraduate degree in electronic engineering from NUI Galway in Ireland. Before my PhD, I was a research assistant for one year in Prof. Marc Pollefeys' group at ETH Zurich.
Email: oisin (dot) macaodha (at) ed.ac.uk
Twitter: @oisinmacaodha
Office: Informatics Forum 2.24A
From 2016-2019 I was fortunate to be a postdoc in Prof. Pietro Perona's Computational Vision Lab at Caltech. Previous to Caltech, I spent three great years (2013-2016) as a postdoc in the Department of Computer Science at the University College of London with Prof. Gabriel Brostow and Prof. Kate Jones. There I worked on interactive machine learning, where our goal was to design algorithms to enable non-programming scientists to semi-automatically explore events of interest in vast quantities of audio and visual data.
I did both my MSc (with Dr. Simon Prince) and PhD (with Prof. Gabriel Brostow) at UCL and have an undergraduate degree in electronic engineering from NUI Galway in Ireland. Before my PhD, I was a research assistant for one year in Prof. Marc Pollefeys' group at ETH Zurich.
Email: oisin (dot) macaodha (at) ed.ac.uk
Twitter: @oisinmacaodha
Office: Informatics Forum 2.24A
News
Jan 2022: Open postdoc position in AI for biodiversity monitoring at the University of Edinburgh. More info here.Jan 2022: Co-organizing FGVC9 workshop at CVPR 2022.
Dec 2021: Open postdoc in position human-in-the-loop AI at the University of Edinburgh. More info here.
Sep 2021: Selected as a Turing Fellow and ELLIS Scholar.
May 2021: I'll be an Area Chair for BMVC 2021.
Mar 2021: FGVC8 will be held on June 25th 2021. More info here.
Mar 2021: I'll be an Area Chair for NeurIPS 2021.
Click here to read older news.
Dec 2020: Co-organizing FGVC8 workshop at CVPR 2021.
Sep 2020: I'll be an Area Chair for ICLR 2021.
Aug 2020: I'll be a Senior Program Committee Member for AAAI 2021.
Jul 2020: Presenting at the AI for Social Good workshop. Video of the talk is here.
Jul 2020: Giving a talk at the Optimizing Human Learning workshop. Video of the talk is here.
Jun 2020: You can find the videos of the talks from our FGVC7 workshop here.
May 2020: I'll be an Area Chair for NeurIPS 2020.
Mar 2020: Serving as a Program Chair for BMVC 2020. Consider submitting!
Dec 2019: Co-organizing FGVC7 workshop at CVPR 2020.
Nov 2019: Vacancies: Several openings for PhD students - see here for more details.
Oct 2019: Just started as a Lecturer (aka Assistant Professor) in Machine Learning at the University of Edinburgh.
Jul 2019: Serving on the Program Committee for Workshop on Computer Vision for Wildlife Conservation at ICCV 2019. Consider submitting your papers!
Jun 2019: Serving as an Area Chair for WACV 2020.
May 2019: Code released for our Monodepth2 paper.
Apr 2019: FGVC6 competitions now live on Kaggle.
Jan 2019: Co-organizing FGVC6 workshop at CVPR 2019.
Sep 2018: Serving as an Area Chair for ACCV 2018.
Jul 2018: Check out Niantic's occlusion demo using our monocular depth work.
Jun 2018: FGVC5 held at CVPR 2018.
Feb 2018: Launched iNaturalist 2018 challenge.
Jan 2018: Gave a talk to LA school children about the importance of bats.
Jan 2018: Co-organizing FGVC5 workshop at CVPR 2018.
Aug 2017: Read about the iNaturalist Challenge 2017 that we ran in conjunction with Caltech, iNaturalist, Cornell, Google, and Kaggle.
Jul 2017: Helped organize the Fourth Fine-Grained Visual Categorization workshop at CPVR this summer.
Jul 2017: Read about our monocular depth estimation project at UCL and The Engineer.
Jun 2017: Live deployment of our bat detection system in the former Olympic Park in London. Read about it on the BBC or follow our twitter page to get daily updates.
Sep 2016: Interviewed by SVT for our work on using machine learning to detect bats.
Aug 2016: Read about our handwriting synthesis project at the BBC, UCL, Engadget, Reuters, Gizmodo, and The Irish Times.
Jul 2016: Moved to Caltech to do a postdoc.
Mar 2016: Gave a talk about Bat Detective at UCL's Grant Museum.
Apr 2015: Blog about bat call detection over at Methods in Ecology and Evolution.
Sep 2020: I'll be an Area Chair for ICLR 2021.
Aug 2020: I'll be a Senior Program Committee Member for AAAI 2021.
Jul 2020: Presenting at the AI for Social Good workshop. Video of the talk is here.
Jul 2020: Giving a talk at the Optimizing Human Learning workshop. Video of the talk is here.
Jun 2020: You can find the videos of the talks from our FGVC7 workshop here.
May 2020: I'll be an Area Chair for NeurIPS 2020.
Mar 2020: Serving as a Program Chair for BMVC 2020. Consider submitting!
Dec 2019: Co-organizing FGVC7 workshop at CVPR 2020.
Nov 2019: Vacancies: Several openings for PhD students - see here for more details.
Oct 2019: Just started as a Lecturer (aka Assistant Professor) in Machine Learning at the University of Edinburgh.
Jul 2019: Serving on the Program Committee for Workshop on Computer Vision for Wildlife Conservation at ICCV 2019. Consider submitting your papers!
Jun 2019: Serving as an Area Chair for WACV 2020.
May 2019: Code released for our Monodepth2 paper.
Apr 2019: FGVC6 competitions now live on Kaggle.
Jan 2019: Co-organizing FGVC6 workshop at CVPR 2019.
Sep 2018: Serving as an Area Chair for ACCV 2018.
Jul 2018: Check out Niantic's occlusion demo using our monocular depth work.
Jun 2018: FGVC5 held at CVPR 2018.
Feb 2018: Launched iNaturalist 2018 challenge.
Jan 2018: Gave a talk to LA school children about the importance of bats.
Jan 2018: Co-organizing FGVC5 workshop at CVPR 2018.
Aug 2017: Read about the iNaturalist Challenge 2017 that we ran in conjunction with Caltech, iNaturalist, Cornell, Google, and Kaggle.
Jul 2017: Helped organize the Fourth Fine-Grained Visual Categorization workshop at CPVR this summer.
Jul 2017: Read about our monocular depth estimation project at UCL and The Engineer.
Jun 2017: Live deployment of our bat detection system in the former Olympic Park in London. Read about it on the BBC or follow our twitter page to get daily updates.
Sep 2016: Interviewed by SVT for our work on using machine learning to detect bats.
Aug 2016: Read about our handwriting synthesis project at the BBC, UCL, Engadget, Reuters, Gizmodo, and The Irish Times.
Jul 2016: Moved to Caltech to do a postdoc.
Mar 2016: Gave a talk about Bat Detective at UCL's Grant Museum.
Apr 2015: Blog about bat call detection over at Methods in Ecology and Evolution.
Fine-Grained Image Analysis with Deep Learning: A Survey
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, and Serge Belongie
PAMI 2021
[paper]
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, and Serge BelongiePAMI 2021
[paper]
Shazam for Bats: Internet of Things for Continuous Real-Time Biodiversity Monitoring
Sarah Gallacher, Duncan Wilson, Alison Fairbrass, Daniyar Turmukhambetov, Michael Firman, Stefan Kreitmayer, Oisin Mac Aodha, Gabriel Brostow, and Kate Jones
IET Smart Cities 2021
[paper]
Sarah Gallacher, Duncan Wilson, Alison Fairbrass, Daniyar Turmukhambetov, Michael Firman, Stefan Kreitmayer, Oisin Mac Aodha, Gabriel Brostow, and Kate JonesIET Smart Cities 2021
[paper]
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation
Octave Mariotti, Oisin Mac Aodha, and Hakan Bilen
ICCV 2021
[paper]
Octave Mariotti, Oisin Mac Aodha, and Hakan BilenICCV 2021
[paper]
When Does Contrastive Visual Representation Learning Work?
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, and Serge Belongie
Arviv 2021
[paper]
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, and Serge BelongieArviv 2021
[paper]
Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, and Oisin Mac Aodha
CVPR 2021 (Oral)
[paper] [code/data] [video]
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, and Oisin Mac AodhaCVPR 2021 (Oral)
[paper] [code/data] [video]
Teaching Multiple Concepts to a Forgetful Learner
Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, and Adish Singla
NeurIPS 2019
[paper]
Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, and Adish SinglaNeurIPS 2019
[paper]
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, and Yisong Yue
NeurIPS 2018
[paper]
Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, and Yisong YueNeurIPS 2018
[paper]
Context Embedding Networks
Kun ho Kim, Oisin Mac Aodha, and Pietro Perona
CVPR 2018 (Spotlight)
[paper]
Kun ho Kim, Oisin Mac Aodha, and Pietro PeronaCVPR 2018 (Spotlight)
[paper]
Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection
Oisin Mac Aodha, Rory Gibb, Kate Barlow, Ella Browning, Michael Firman, Robin Freeman, Briana Harder, Libby Kinsey, Gary Mead, Stuart Newson, Ivan Pandourski, Stuart Parsons, Jon Russ, Abigel Szodoray-Paradi, Farkas Szodoray-Paradi, Elena Tilova, Mark Girolami, Gabriel J. Brostow, and Kate E. Jones
PLOS Computational Biology 2018 [paper] [webpage]
Oisin Mac Aodha, Rory Gibb, Kate Barlow, Ella Browning, Michael Firman, Robin Freeman, Briana Harder, Libby Kinsey, Gary Mead, Stuart Newson, Ivan Pandourski, Stuart Parsons, Jon Russ, Abigel Szodoray-Paradi, Farkas Szodoray-Paradi, Elena Tilova, Mark Girolami, Gabriel J. Brostow, and Kate E. JonesPLOS Computational Biology 2018 [paper] [webpage]
Near-Optimal Machine Teaching via Explanatory Teaching Sets
Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, and Yisong Yue
AISTATS 2018
[paper]
Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, and Yisong YueAISTATS 2018
[paper]
Interpretable Machine Teaching via Feature Feedback
Shihan Su, Yuxin Chen, Oisin Mac Aodha, Pietro Perona, and Yisong Yue
NeurIPS Workshop Teaching Machines, Robots, and Humans 2017
[paper]
Shihan Su, Yuxin Chen, Oisin Mac Aodha, Pietro Perona, and Yisong YueNeurIPS Workshop Teaching Machines, Robots, and Humans 2017
[paper]
Putting the Scientist in the Loop - Accelerating Scientific Progress with Interactive Machine Learning
Oisin Mac Aodha, Vassilios Stathopoulos, Michael Terry, Kate E. Jones, Gabriel J. Brostow, and Mark Girolami
ICPR 2014
[paper]
Oisin Mac Aodha, Vassilios Stathopoulos, Michael Terry, Kate E. Jones, Gabriel J. Brostow, and Mark GirolamiICPR 2014
[paper]
Evolving Plastic Responses in Artificial Cell Models
John Maher, Fearghal Morgan, and Oisin Mac Aodha
Congress on Evolutionary Computation 2009
[paper]
John Maher, Fearghal Morgan, and Oisin Mac AodhaCongress on Evolutionary Computation 2009
[paper]
Team
Mehmet Aygün - Started PhD in 2021 at UoESantiago Martínez Balvanera - Started PhD in 2021 at UCL with Kate Jones
Omiros Pantazis - Started PhD in 2020 at UCL with Kate Jones
Teaching
Sep 2020 & 2021 UoE: Introductory Applied Machine LearningDec 2015 UCL: Introduction to Machine Learning for Ecologists