Olds.
- 2024-Apr.
-
Two papers accepted: 1) spillR:
Spillover Compensation in Mass Cytometry Data accepted at
Bioinformatics; 2) our new 𝚐𝚊𝚍𝚓𝚒𝚍 for causal structure
learning – just
pip install gadjid and give it a try
🏎️ – accepted at UAI
2024.
- 2024-Feb.
-
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 🏎️
- 2024-Feb.
-
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. 🎓
- 2023-Oct.
-
In our NeurIPS 2023 paper, we
introduce “A Scale-Invariant Sorting Criterion to Find a Causal Order in
Additive Noise Models” and show that R²-sortability, in contrast to var-sortability, remains
unchanged under data rescaling or standardization.
- 2023-Oct.
-
New preprint: spillR: Spillover
Compensation in Mass Cytometry Data.
- 2023-Jul.
-
We released CausalDisco
🪩 –
pip install CausalDisco to use simple SortnRegress
baseline causal discovery algorithms and evaluate the R²- and
Var-sortability of structure learning tasks. Check out our recent NeurIPS paper for more
🤓
- 2023-Jun.
-
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.
- 2023-Apr.
-
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.
- 2023-Mar.
-
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) 🤓
- 2022-Nov.
-
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!
- 2022-Nov.
-
Frederik Hytting Jørgensen is joining us as PhD student at the Copenhagen
Causality Lab 🎉 (co-supervised by Jonas Peters)
- 2022-Oct.
-
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.
- 2022-Sep.
-
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.
- 2022-Jun.
-
New causerie: “What is (a
helpful mental picture of) a causal model?”.
- 2022-Mar.
-
PhD
opportunities at the Copenhagen Causality Lab (CoCaLa), apply by
April 1, 2022.
- 2021-Jul.
-
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 🚲
- 2021-Mar.
-
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 😉
- 2021-Mar.
-
Website overhaul: new backend (pandoc + makefile) and causeries 🤓
- 2021-Feb.
-
Looking forward to the summer (school) season ☀️ and the upcoming causality tutorials together with Sorawit Saengkyongam and Jonas Peters.
- 2020-Oct.
-
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
- 2020-Sep.
-
I just got invited to talk at JSM 2021 – I am
honoured and looking forward.
- 2020-Jul.
-
Looking forward to my talk on causal inference at the Lviv
Data Science Summer School Online – participation is free this year!
slides / video
- 2019-Dec.
-
The recording of our tutorial on Inferring causality
from observations together with D Janzing at the CCN 2019 conference is
now available
here.
- 2019-Dec.
-
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.
- 2019-Oct.
-
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.
- 2019-Sep.
-
Looking forward to our tutorial on “Inferring causality from
observations” together with D Janzing at the CCN 2019 conference.
- 2019-Sep.
-
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.
- 2019-Apr.
-
coroICA: confounding-robust ICA for grouped
data accepted at JMLR!
- 2018-Nov.
-
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.
- 2018-Oct.
-
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!
- 2018-Jul.
-
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
- 2018-Jun.
-
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.
- 2017-Jun.
-
Our paper Causal Consistency of Structural Equation
Models has been accepted for an oral presentation at UAI 2017.
- 2017-May.
-
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.
- 2017-May.
-
I will be giving a causality
tutorial at PRNI 2017. (slides)
- 2017-Apr.
-
The recordings
of our 2016 OHBM symposium "What
Neuroimaging Can Tell Us? From Correlation to Causation and Cognitive
Ontologies" are available.
- 2017-Mar.
-
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.
- 2016-Nov.
-
I am now associate PhD Fellow of the Max Planck ETH Center for Learning
Systems.
- 2016-Aug.
-
Our paper MERLiN: Mixture Effect Recovery in
Linear Networks got published in the IEEE Journal of Selected Topics
in Signal Processing.
- 2016-Jun.
-
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.)
- 2016-Mar.
-
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.
- 2016-Mar.
-
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).
- 2016-Jan.
-
Moritz's OHBM 2015 educational talk Causal Interpretation Rules for Encoding and Decoding
Models in Neuroimaging is online.
- 2015-Dec.
-
New manuscript and code is out: MERLiN: Mixture
Effect Recovery in Linear Networks.
- 2015-Aug.
-
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).
- 2015-Jun.
-
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).
- 2015-May.
-
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!
- 2014-Nov.
-
A golden oldie worth a reread: Data
Set Selection by Doudou LaLoudouana and Mambobo Bonouliqui Tarare.
- 2014-Aug.
-
Let's have a drink, let's have a Kernel!
- 2014-Feb.
-
An interesting article: Scientific
method: Statistical errors : Nature News & Comment.
- 2014-Jan.
-
Videos and slides of MLSS
2013 Tübingen can now be downloaded.
- 2013-Oct.
-
An interesting article: Unreliable
research: Trouble at the lab | The Economist.