About me

I do machine learning and computer vision research, where I work with geometric models of observed data. The research tend to follow two directions: use geometric constructions to design models, or model constraints on data using geometry. Check the publications for more specific examples, and don't hesitate to get in touch.

News and Updates

September 6, 2017. The European Research Council (ERC) has been generous enough to award me a starting grant. Humbling and awesome :-)


August 30, 2017. Lectured on metric learning at our yearly machine learning summer school. Always fun when people realize that metric learning is "easier" (for a suitable definition of "easy") in infinite dimensions...


July 31, 2017. Georgios, Lars, and I have a new paper at GSI on how to estimate the parameters of the LAND using maximum likelihood. Check it out!


May 5, 2017. I gave a seminar at the statistics department at KU on data augmentation (c.f. the dragon). Super curious audience plus chocolate -- can't be much better than that :-)


April 28, 2017. I was the "opening act" in Jes's seminar series on machine learning at ITU. Great group! and free pizza (yay!)


April 20, 2017. Jeppe Thagaard came in on a 5th place at the Camelyon17 challenge at ISBI. Great stuff on data augmentation!


March 2, 2017. Yesterday was my first day as an associate professor here at CogSys. Yesterday I was also home sick with a delightful fever; I sure hope there's no link between these event...


February 27, 2017. The extension of Grassmann Averages to higher dimensional subspaces has been accepted to CVPR. The submitted version is on arXiv -- I'll link to the camera ready when it's ready!


January 25, 2017. I'm visiting Bodo's group -- so much impressive stuff going on. As always, it was fun to talk about the dragon...


January 23, 2017. Aasa and I attended the yearly celebration of Villum Kann Rasmussen (click for rare pictures of me wearing a tie)


December 21, 2016. The Villum Foundation has awarded me a Young Investigator grant. Absolutely amazing, and very humbling! As a consequence, I have several open positions at both PhD and post doc levels.


December 12, 2016. Returned from NIPS to find a big NVIDIA GPU on my desk. Thanks to the NVIDIA crowd for this delightful gift!


December 6, 2016. Georgios and I presented the LAND paper at NIPS. Huge crowd of fun and interesting researchers!


November 16, 2016. Finally got around to putting the camera-ready version of our NIPS paper on locally adaptive normal distributions online. You can also check out the spotlight video.


August 23, 2016. Had fun lecturing about metric learning at the Advanced Topics in ML summer school. Always fun when the audience almost cries out for a Riemannian approach...


August 12, 2016. Our work on locally adaptive normal distributions has been accepted for NIPS 2016. This is a wonderfully simple way to build well-behaved nonparametric models. I'll link to the camera-ready version when it's finished.


June 26, 2016. Presented our Open Problems paper at COLT. Many interesting discussions followed -- clearly COLT is a very curious community.


June 26, 2016. GIMLI is over. We had an amazing set of speakers, but equally important we also had an amazing set of attendees. Great discussions!


June 16, 2016. It seems I was given an Outstanding Reviewer Award from the ICML 2016 program committee -- thanks!


June 15, 2016. As promised, a preprint is now available for the recent NeuroImage paper. The final version is available from Elsevier.


June 10, 2016. Sofie, myself, and Lars get our work on modeling forward models for EEG source reconstruction accepted at NeuroImage. Sometimes I'm amazed at how far you can get just using PCA... I'll link to the paper as soon as possible.


June 9, 2016. Georgios, Lars and I finally put our work on locally adaptive normal distributions online. This is a really cool example of how Riemannian geometry is useful for nonparametrics!


May 23, 2016. Aasa and I have an "Open Problems" paper at this years COLT posing problems around the probability of seeing a positive definite kernel matrix over geodesic spaces. Quite an intriguing problem; do think about it :-)


May 10, 2016. Politken, the largest Danish newspaper, is running a story about our augmentation paper. Check it out (in Danish).


May 9, 2016. Talked about the augmentation paper at AISTATS. Was great to show of the dragon :-)


May 2, 2016. I finally put the final version of the augmentation paper online. This really is a fun example of how diffeomorphisms can be of great use in machine learning. Hope to see you for the talk at AISTATS!


April 19, 2016. Oren, Michael and I are organizing an ICML workshop on the role of geometry in machine learning. Do submit an abstract!


March 23, 2016. Oren finalized the first draft of the journal version of the diffeomorphisms we published at ICCV. A preprint is now online. Code is also available.


February 5, 2016. I use Sozi (the extension) together with Inkscape for creating slides. Sometimes it can be helpful to have a PDF version of the slides rather than viewing the SVG file in your browser. I've hacked together a naive script for making this conversion -- it may be helpful to you as well.


January 12, 2016. The PAMI version of the Grassmann Average paper is now online. More theory, more experiments, more pictures. Fun stuff!


December 28, 2015. Our paper on learning data augmentation schemes has been accepted at AISTATS. If you just can't wait for the talk, you can enjoy the arXiv version, which essentially match the submitted paper. I'll link to the camera-ready once it's done.


December 28, 2015. I've added some errata (1, 2, 3, 4) to a few papers. These are merely typos, but it's good to document them when you find them -- let me know if you find more!


November 9, 2015. My PAMI paper on generalizing the classic principal curves to Riemannian manifolds is now online. It's remarkable how well such a simple well-known algorithm works compared to standard Riemannian models.


October 13, 2015. Our paper on learning data augmentation schemes is now available online. This is a great example of why everybody should care about diffeomorphisms!


October 7, 2015. Aasa and I presented the MICCAI paper on probabilistic shortest path tractography. Was great fun to put the random geometries out there!


October 6, 2015. Philipp, Marcel, and I gave a fun tutorial on Gaussian Processes at MICCAI 2015. I had the pleasure of giving people a first taste of probabilistic numerics -- yummy!


September 18, 2015. Our ICCV 2015 paper on simple and efficient representations of diffeomorphisms is now online. I'm very excited that diffeomorphisms can be so simple to work with -- this is really a big change compared to standard representations!


June 28, 2015. Uploaded the SIMBAD abstract, which basically summarizes the CVPR 2015 kernel paper. If you're in Copenhagen in October, do join Aasa's poster :-)


June 17, 2015. I'll be on paternity leave until January 2016, so my response time might be suboptimal in the mean time. If you really want a reply from me, and you don't get one, just keep trying :-)


June 9, 2015. Presented the Geodesic Exponential Kernels paper at CVPR with Aasa and François. Amazing to see such a large audience for a paper with mostly negative results.


May 28, 2015. Seems like I got an Outstanding Reviewer Award from the CVPR organizers -- thanks!


May 26, 2015. The paper presentation video for our MICCAI 2015 paper is now on Youtube -- hope you enjoy my sketchy drawings!


May 20, 2015. Our MICCAI 2015 paper on random Riemannian geometry in tractography is now online. We seem to be able to do more and more with the random geometries -- fun times!


April 21, 2015. Presented some of the tractography work at the Probabilistic Numerics for Differential Equations workshop at Warwick -- very entertaining interactions on the role of uncertainty in computations.


April 13, 2015. Our paper on geodesic exponential kernels has been accepted at CVPR 2015. Check out the paper or the extended abstract.


April 13, 2015. We're currently on our way back from DALI 2015; An extremely exciting small-scale meeting consisting of nothing but awesome talks and discussions.


November 4, 2014. Did you ever feel like designing a Gaussian kernel on a Riemannian manifold using the intrinsic metric? Or perhaps on a more general metric space? If so, you will want to tread lightly as this is generally not possible; check out our recent work on this if you're curious!


October 3, 2014. I just noticed that my Youtube channel has gotten more than one thousand views. Wow -- thanks for watching!


September 24, 2014. Gave a talk about Random Riemannian metrics in John's SLI group at MIT -- I absolutely love it when discussions outweigh presentations (fantastic crowd of people!)


September 23, 2014. Gave a Vision seminar at MIT about Grassmann averages -- a wonderfully sharp and interactive audience (and great food!)


September 17, 2014. Michael and Niklas presented our poster at MICCAI 2014 on using probabilistic numerics in tractography. There was a rather large interest in understanding the hidden uncertainties in the numerical algorithms we tend to take for granted -- wonderful!


September 12, 2014. Gave a talk on Grassmann Averages work at the Dept. of Biomedical Engineering at the City College of New York. Impressive work going on at Lucas's lab.


September 10, 2014. Presented the Grassmann Average work at the Columbia Statistics Department. I really love giving talks in front of such an intelligent and interactive crowd.


August 28, 2014. Lectured the morning session of the Advanced Topics in Machine Learning summer school at DTU -- a rather fun interactive coding session on Geometric Statistics.


August 21, 2014. Attended the Roundtable on Probabilistic Numerics in Tubingen -- it seems the community is beginning to form!


August 7, 2014. The poster teaser video for our MICCAI 2014 paper is now online -- check it out!


August 7, 2014. My talk at CVPR 2014 on Grassmann Averages is now available at techtalks.tv -- enjoy!


July 2, 2014. Posted a blog entry about choices of subspace metrics for the Grassmann Average subspace estimator. Summary: for Gaussian data most metrics gives the first principal component as the subspace average.


June 27, 2014. Presented the paper Grassmann Averages for Scalable PCA at CVPR 2014. I'll link to the talk recording once it goes live.


June 24, 2014. Presented the poster for Model Transport: Towards Scalable Transfer Learning on manifolds with Michael at CVPR 2014. Much fun!


May 30, 2014. The paper Metrics for Probabilistic Geometries has been accepted at UAI 2014. It's all about distributions of manifolds in the form of random Riemannian metrics -- very exciting first steps!


April 14, 2014. The paper Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers has been accepted at MICCAI 2014. This is a cool case-study for probabilistic numerics.