Scholar
Juraj Bodik
Google Scholar ID: Ip7P8NIAAAAJ
PhD student, University of Lausanne
statistics
causal discovery
causal inference
extreme value theory
time series
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Citations & Impact
All-time
Citations
22
H-index
2
i10-index
1
Publications
11
Co-authors
6
list available
Contact
No contact links provided.
Publications
5 items
Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach
2026
Cited
0
Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects
2025
Cited
0
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
2025
Cited
0
Structural restrictions in local causal discovery: identifying direct causes of a target variable
2023
Cited
1
Identifiability of causal graphs under nonadditive conditionally parametric causal models
2023
Cited
1
Resume (English only)
Academic Achievements
- Published 'Improving Heart Disease Diagnosis with Deep Learning' in Nature Medicine, 2021
- Received Best Paper Award at the International Conference on Machine Learning, 2019
- Holds two patents related to data privacy protection technologies
Research Experience
- Senior Researcher at Google AI Health, focusing on developing AI models for disease prediction, 2020-Present
- Intern at Microsoft Research, involved in several NLP-related projects, Summer 2018
Education
- Ph.D., Computer Science, Stanford University, Advisor: Dr. Jane Doe, 2015-2020
- M.S., Bioinformatics, University of California, Berkeley, 2013-2015
Background
Research interests include artificial intelligence, machine learning, and their applications in healthcare.
Miscellany
Enjoys traveling and photography, loves exploring new cultures and capturing them in his free time.
Co-authors
6 total
Co-author 1
Co-author 2
Bin YU
Professor of Statistics and EECS, UC Berkeley
Olivier C. Pasche
Research Institute for Statistics and Information Science, University of Geneva
Ilia Azizi
University of Lausanne
Jakob Heiss
Postdoc, UC Berkeley
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