Data Science Lab

Bern Data Science Day 2021

The Bern Data Science Day brings together data scientists from the University of Bern, the Bern University Hospital, sitem-insel and the Psychiatric University Clinic (UPD) for a unique conference on applied Data Science (DS). It gathers scientists from different domains to network and exchange ideas on emerging trends and research results in data science. The stimulating program will focus on extended poster sessions in lobby mode for networking facilitation, the keynote talks framing the event. Everyone working with or on aspects of data processing is invited to submit abstracts related to the below topics. Abstracts are reviewed by the scientific committee for oral and poster presentation. Vote winning contributions will receive prizes from our sponsors. Posters are reviewed and invited for voluntary upload to the BDSD Zenodo repository with DOI for reference.  

Important dates and deadlines

Abstract submission: Extended till April 15
Registration: April 15
Notification to authors: April 15 and April 21
Poster submission: April 23
Conference day: April 23
Final submission: May 31

Where and How

The event is scheduled online and will only take place on site if official measures allow for it. Online there will be one plenary room and poster session rooms which participants can stroll. The presenters will be present there for exchange and conversations. During the award session an online voting by the scientific committee and all participants will decide on the prize winning contributions.

The location is https://unibe-ch.zoom.us/j/95778680472?pwd=bDN2T0szYkhtNnlHL2piUkhJcUY4UT09 
This year there is no onsite event. 

Agenda April 23

08:45 - 09:00 Welcome
09:00 - 10:00 Plenary with selected oral presentations
10:15 - 12:00 Poster session 1
12:00 - 13:00 Lunch break
13:00 - 14:00 Keynote - Prof. Dr. Tamara Broderick from MIT
14:15 - 16:00 Poster session 2
16:00 - 17:00 Awards and closing session
17:00 - 19:00 Apero (individual)


Registration

Anyone with interest and experience in the topics of Data Science, Machine Learning or Artificial Intelligence through their research can attend. Participation in the event is free of charge, but advance registration is required. To register, please fill out the registration form.

Submission of abstracts

Students, employees and participants in continuing education programs of the University of Bern, the Bern University Hospital, sitem-insel and the Psychiatric University Clinic (UPD) are warmly invited to submit abstracts related to one of the Data Science topics listed below. Abstracts will be reviewed by the scientific committee and authors will be invited to present a poster (electronically on zoom) at the event. A few selected posters will also be offered short oral presentations in the plenary sessions. After the event the contributions can be uploaded to a reviewed Bern Data Science Day repository which provides a digital object identifyer (DOI ) for reference. 

Data Science (DS) call topics:

  • DS algorithms with a view towards Machine Learning and Artifical Intelligence
  • Mathematical and statistical foundations of DS
  • DS applications and challenges in Medicine, Natural Sciences, and Engineering
  • Digital Humanities and Economics 
  • Data representation, formatting, storage, and transfer
  • DS and high-performance computing, e.g. work using the UBELIX cluster 
  • DS programming languages and softwares 
  • Ethical and legal aspects of DS 
  • DS AOB (any other DS business)

Please submit your abstract via this link. You may use this template. If you prefer not to have any submitted material publically exposed, apart from the showing in the event, please also note this in the abstract.

Keynote at 13:00 CET

An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?

by Prof. Dr. Tamara Broderick,  Massachusetts Institute of Technology

Abstract: We propose a method to assess the sensitivity of data analyses to the removal of a small fraction of the data set. Analyzing all possible data subsets of a certain size is computationally prohibitive, so we provide a finite-data metric to approximately compute the number (or fraction) of observations that has the greatest influence on a given result when dropped. We call our resulting metric the Approximate Maximum Influence Perturbation. Our approximation is automatically computable and works for common estimators --- including (but not limited to) OLS, IV, GMM, MLE, and variational Bayes. We provide explicit finite-sample error bounds on our approximation for linear and instrumental variables regressions. At minimal computational cost, our metric provides an exact finite-data lower bound on sensitivity for any estimator, so any non-robustness our metric finds is conclusive. We demonstrate that the Approximate Maximum Influence Perturbation is driven by the signal-to-noise ratio in the inference problem, is not reflected in standard errors, does not disappear asymptotically, and is not a product of misspecification. We focus on econometric analyses in our applications. Several empirical applications show that even 2-parameter linear regression analyses of randomized trials can be highly sensitive. While we find some applications are robust, in others the sign of a treatment effect can be changed by dropping less than 1% of the sample even when standard errors are small.

Flyer

  • Event flyer can be found here.

Organization Committee and Contact

Sigve Haug, Alexander Kashev, Mauricio Reyes, Rahel Luder (email for questions), Raphael Sznitman.

Scientific Committee:

Prof. Dr. Dr. C. Beisbart, Prof. Dr. P. Favaro, Prof. Dr. D. Ginsbourger, PD Dr. S. Haug, Prof. Dr. M. Reyes, Prof. Dr. R. Sznitman, Prof. Dr. C. Tretter, Prof. Dr. A. Tzovara, Prof. Dr. J. Ziegel.

Sponsors

66 registrations and 33 abstract submissions as of April 7:

  • Alessio Ciullo
  • Alexander Kashev
  • Alexander Leichtle
  • Altug Ekici
  • Aminata Gueye
  • André Kurt Bodmer
  • Andrea Gomez
  • Angela De Martiis
  • Anna Broccard
  • Aris Marcolongo
  • Athénaïs Gautier
  • Ben Spycher
  • Benoît Zuber
  • David Haberthür
  • Dominic Ritler
  • Dr. Dario Henri Haux
  • Edén Sorolla
  • Eliane Rohner
  • GARCIA-MIGUEL Alfonso
  • Gedeon Lapnet
  • Gianluca Camparini
  • Gina Cannarozzi
  • Guodong Zeng
  • Guy Schnidrig
  • Hannes Anton Loebner
  • Irene Garcia Ruiz
  • Ivan Diaz
  • Jan Draisma
  • Jie Wu
  • Joel Niklaus
  • Kalongo Hamusonde
  • Kathi Woitas
  • Kosta Shatrov
  • Lionel Rohner
  • Lize Duminy
  • Lucas de Sousa Pacheco
  • Mandes Schönherr
  • Marc Brunner
  • Marianna Rosso
  • Markus Kälin
  • Marlene Kammerer
  • Martina Reichmuth
  • Mauricio Reyyes
  • Michael Single
  • Michael Vock
  • Narayan Schütz
  • Negar Emami
  • Olga Churakova
  • Özhan Özen
  • Pascal Horton
  • Patric Wyss
  • Petra Müller
  • Philippe Meyer
  • Pinar Göktepe
  • Pushkar Kopparla
  • Reto Bürgin
  • Ruben Jose Lopez Dicuru
  • Sabrina Stöckli
  • Sigve Haug
  • Stephan Moreno Gerber
  • Tamara Vaudroz
  • Tim Fischer
  • Valeria Kravtchenko
  • Waldo Valenzuela