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.
August 24, 2018. I talked about data augmentation and CPAB at our machine learning summer school. Enter the dragon...
July 14, 2018. The second installment of GiMLi is now over. What a fantastic show -- thanks to all involved in making this happen!
June 21, 2018. Nicki and I presented the diffeomorphic spatial transformer network at CVPR. Excellent discussions; much fun!
June 14, 2018. New preprint online (it's NIPS formatted, but hasn't been submitted) on why uncertainty quantification is essential for manifold/representation learning. That's at least the case, when you approach the problem from a differential geometric viewpoint, but I expect that the underlying message is generally true.
June 4, 2018. I put up a short note on the non-central Nakagami distribution as I couldn't find information on this distribution elsewhere.
May 14, 2018. It seems I was nominated for teacher-of-the-year award. Thanks to whoever nominated me :-)
March 24, 2018. I have uploaded the camera-ready of our work on diffeomorphic statial transformer nets. If you don't need the flexibility of CPAB, then at least take the matrix exponential of your affine transformation matrix.
February 19, 2018. Nicki's paper on spatial transformer nets appears to be on the magic list of accepted CVPR papers -- yay! I'll link to the PDF once we have finished the camera-ready version.
January 26, 2018. I talked about the geometry of latent variable models (random Riemannian metrics to the rescue!) at the Oberwolfach workshop on Statistics for Data with Geometric Structure (check link for a picture of me and Agnes).
November 10, 2017. Machine Learning & Molecules is over for now -- what an amazing ride! Thanks to all 16 amazing speakers and to the Bard for final disruptions!
November 1, 2017. Some of our latest results on deep generative models are now on arXiv. Lovely to see how geometry vastly improves the usefulness of latent variable models.
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...
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...
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.
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. 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 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.
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 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!
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.
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!
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 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 :-)
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. 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 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.
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 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.