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Feb 2024.
3 PhD Calls — If any of my research sounds interesting to you, then this is where to apply to work with me: through the department call (several projects depending on your interests; by 2024-04-01) or the call of the Pioneer Centre for AI (by 2024-04-01) or through this call at the University College Dublin (by 2024-03-22) to work with Leonard Henckel as main and myself as co-supervisor. Other opportunities, such as the DDSA fellowship, are described below. 🎓
Feb 2024.
Check out our new 𝚐𝚊𝚍𝚓𝚒𝚍 for causal structure learning on causal distances between DAGs and CPDAGs and how to compute them efficiently – just pip install gadjid and try it for yourself 🏎️
Oct 2023.
New preprint: spillR: Spillover Compensation in Mass Cytometry Data.
Jun 2023.
Unfair Utilities and First Steps Towards Improving Them – in this new preprint we propose a change of focus and to assess the fairness of the utility while many fairness criteria constrain the policy or choice of predictors.
Apr 2023.
In our recent preprint, we show that “Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models” and describe an easily exploitable pattern, R²-sortability, that, in contrast to var-sortability, remains unchanged under data rescaling or standardization.
Mar 2023.
I have been appointed co-lead at P1 and am thrilled to join Ira Assent and Aasa Feragen as co-lead of the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI (P1) 🤓
Nov 2022.
Danish Data Science 2022 — thanks to all 300+ attendees and everyone on the organising committee for making the first Danish Data Science conference a success! Special thanks to Mihaela van der Schaar, Sara Magliacane, Sune Lehmann, Theofanis Karaletsos, Mine Çetinkaya-Rundel, and Julien Simon for following our invitation and contributing fantastic keynotes!
Nov 2022.
Frederik Hytting Jørgensen is joining us as PhD student at the Copenhagen Causality Lab 🎉 (co-supervised by Jonas Peters)
Oct 2022.
ETH-UCPH-TUM Workshop on Graphical Models – a week full of learning about the latest and coolest in causality, dependent multivariate data, and graphical models.
Sep 2022.
Marco Guazzini presented spillR at the EuroBioC conference — I am fortunate to collaborate with brilliant students and collaborators on this project on correcting for spillover effects in mass cytometry.
Jun 2022.
New causerie: “What is (a helpful mental picture of) a causal model?”.
Mar 2022.
PhD opportunities at the Copenhagen Causality Lab (CoCaLa), apply by April 1, 2022.
July 2021.
After two wonderful years with awesome colleagues and friends both at the University of Copenhagen and in greater Copenhagen, I am happy to announce that I’ll stay, continue as an assistant professor, and keep on cycling to work 🚲
Mar 2021.
Happy to join the UAI 2021 – Organizing Committee as virtual chair together with Christina Heinze-Deml. Send ideas and suggestions on virtually all aspects of facilitating a great virtual conference our way 😉
Mar 2021.
Website overhaul: new backend (pandoc + makefile) and causeries 🤓
Feb 2021.
Looking forward to the summer (school) season ☀️ and the upcoming causality tutorials together with Sorawit Saengkyongam and Jonas Peters.
Oct 2020.
It was great fun to have so many smart students intrigued and interested and asking very smart questions at my lecture on »Causal Models under Variable Transformations – Challenges for Causally Consistent Representation Learning« – great lecture series entirely devoted to causal representation learning at ETH
Sep 2020.
I just got invited to talk at JSM 2021 – I am honoured and looking forward.
Jul 2020.
Looking forward to my talk on causal inference at the Lviv Data Science Summer School Online – participation is free this year! slides / video
Dec 2019.
The recording of our tutorial on Inferring causality from observations together with D Janzing at the CCN 2019 conference is now available here.
Dec 2019.
Our CoCaLa Team won the Causality 4 Climate NeurIPS competition! Among all 190 competitors, with 40 very active, we won the most categories with 18 out of 34, came in second place in all remaining 16 categories, and won the overall competition by achieving an average AUC-ROC score of 0.917 (2nd and 3rd place achieved 0.722 and 0.676, respectively). Congrats and thanks to many great teams and thanks to the organisers for putting a fun competition together. You can check out our slides here, re-watch the NeurIPS session here, read more on the competition results, and check out our brief article and code.
Oct 2019.
Our paper on Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise is out at JMLR! Check out the coroICA project website for audio and visual examples, and instructions on how to get started with the provided Python/R/matlab implementations.
Sep 2019.
Looking forward to our tutorial on “Inferring causality from observations” together with D Janzing at the CCN 2019 conference.
Sep 2019.
I have moved to the lovely bike city Copenhagen and am excited to start my Postdoc with the CoCaLa at the Statistics and Probability Theory Section of the University of Copenhagen.
Apr 2019.
coroICA: confounding-robust ICA for grouped data accepted at JMLR!
Nov 2018.
We are happy to announce that Nicolas Boumal and Bamdev Mishra (both core developers of manopt) are joining the pymanopt team as maintainers. This will improve integration of new methods as well as maintenance level, and will also help to slowly grow the python userbase transitioning away from non-open non-free matlab.
On another positive note, it appears that FAIR may be using our toolbox...pssst ;-)...which resulted in this pull request by Leon Bottou from Facebook AI Research that could bring PyTorch support to pymanopt in the very near future.
Oct 2018.
Aaron Bahde successfully completed his essay rotation with me on "Different Notions of Causality employed in fMRI Analysis" as part of his master's studies in Neural Information Processing – Congratulations!
Jul 2018.
Information leak in NeurIPS 2018 review process / CMT platform, potentially compromising anonymity of submitters — after I had informed the NeurIPS chairs and CMT responsibles they acted timely and adequately to fix the issue 👍Comments? ⤳ this twitter thread
Jun 2018.
New manuscript and code is out: coroICA: Independent component analysis for grouped data. Check out the project website for an audible example of the "America's Got Talent Duet Problem" as well as a video demonstrating the increased stability of coroICA over pooledICA when applied to EEG data.
Jun 2017.
Our paper Causal Consistency of Structural Equation Models has been accepted for an oral presentation at UAI 2017.
May 2017.
I am happy to confirm the speakers for the causality workshop in July that I am organising. We will have Frederick Eberhardt (Caltech) presenting work on micro and macro causal variables, Caroline Uhler (MIT) on causality in genomics, as well as talks on causality and fairness, group invariance principles for causal inference, and the detection of confounding via typicality principles.
May 2017.
I will be giving a causality tutorial at PRNI 2017. (slides)
Apr 2017.
The recordings of our 2016 OHBM symposium "What Neuroimaging Can Tell Us? From Correlation to Causation and Cognitive Ontologies" are available.
Mar 2017.
I got awarded a CLS exchange fellowship to fund my 6 months research stay at ETH Zurich. I am looking forward to a collaboration with the cardiology section of the University Hospital Zurich as well as TAing for the machine learning lecture at ETH where we organise practical machine learning challenges for ~400 students.
Nov 2016.
I am now associate PhD Fellow of the Max Planck ETH Center for Learning Systems.
Aug 2016.
Our paper MERLiN: Mixture Effect Recovery in Linear Networks got published in the IEEE Journal of Selected Topics in Signal Processing.
Jun 2016.
Attending OHBM 2016 – exciting! Thanks to Russell Poldrack, Martin Lindquist, and Christoph Herrmann for making our symposium "What Neuroimaging Can Tell Us? From Correlation to Causation and Cognitive Ontologies" a success! (The slides for my talk can be found here.)
Mar 2016.
Our symposium "What Neuroimaging Can Tell Us? From Correlation to Causation and Cognitive Ontologies" has been accepted for the OHBM 2016 Annual Meeting. I feel honoured to be organising this together with Moritz and to be part of the speakers line-up together with Russell Poldrack, Martin Lindquist, and Christoph Herrmann.
Mar 2016.
We have released an early version of Pymanopt: A Python Toolbox for Manifold Optimization using Automatic Differentiation. This example demonstrates how to infer the parameters of a Mixture of Gaussian (MoG) model using manifold optimisation instead of expectation maximisation (EM).
Jan 2016.
Moritz's OHBM 2015 educational talk Causal Interpretation Rules for Encoding and Decoding Models in Neuroimaging is online.
Dec 2015.
New manuscript and code is out: MERLiN: Mixture Effect Recovery in Linear Networks.
Aug 2015.
Completed my master's at UCL with a thesis on causal effect recovery from linear mixtures. It's time for holidays in the United States!
Besides an exciting road trip I am also looking forward to interesting intermezzi: I will present our recent work at the Poldrack Lab (Stanford University, September), the LIINC group (Columbia University, October), and will visit Martin Lindquist (Johns Hopkins University, October).
Jun 2015.
A new explainer video (5 min) describing our work is online.
I will present our recent work at this year's UAI Workshop "Advances in Causal Inference" (Amsterdam, July).
May 2015.
Moritz will present our recent work at this year's PRNI workshop (Stanford University, June).
I have been invited for talks at the FMRIB Analysis Group (University of Oxford, July) and the LIINC group (Columbia University, October). Furthermore, I will be visiting Martin Lindquist (Johns Hopkins University, October).
– Looking forward to meeting inspiring people and having interesting discussions!
Nov 2014.
A golden oldie worth a reread: Data Set Selection by Doudou LaLoudouana and Mambobo Bonouliqui Tarare.
Aug 2014.
Let's have a drink, let's have a Kernel!
Feb 2014.
An interesting article: Scientific method: Statistical errors : Nature News & Comment.
Jan 2014.
Videos and slides of MLSS 2013 Tübingen can now be downloaded.
Oct 2013.
An interesting article: Unreliable research: Trouble at the lab | The Economist.
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