Olds.
- 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.