Hi there! I’m a MD PhD who is excited about machine learning in healthcare. Currently, I’m doing a postdoc at Stanford University in the Computer Science department with Prof. Jure Leskovec. Before, I completed my PhD at ETH Zurich in the Machine Learning and Computational Biology Lab with Prof. Karsten Borgwardt. My research focuses on developing large-scale biomedical foundation models, with the ultimate vision to unlock generalist models for biomedical AI. Currently, I am on the academic job market, thus any pointers are more than welcome!
News
- 2023-10-05:
Delighted to announce that our paper Zero-shot causal learning got accepted to NeurIPS 2023 as a Spotlight! We developed a framework for meta-learning treatment effects across thousands of drugs and millions of patients to unlock a new capability: predicting personalized treatment effects zero-shot, i.e. without ever having observed the intervention before (e.g., a novel drug). Check out the thread.
- 2023-07-27:
Excited to announce Med-Flamingo, a multimodal medical few-shot learner! Stoked to see our thread to reach >200k impressions! Furthermore, the paper got covered by Huggingface Daily Papers and the medical AI newsletter Dr. Penguin!
- 2023-07-17:
Thrilled to give a talk at IBM Research Zurich today! I will talk about our recent Nature paper and some exciting ongoing medical foundation model work.
- 2023-07-11:
Honored to join Stanford HAI today to give an executive education course on multimodality in foundation models!
- 2023-06-22:
Thrilled to talk today about Generalist medical AI at Stanford’s MedAI Group exchange. I will present our recent Nature paper and some cool ongoing work.
- 2023-05-25:
Excited to give a guest lecture at Stanford in Prof. Emma Brunskill’s CS 31 N course ‘Counterfactuals: The Science of What Ifs?’. Hamed and I present and discuss our recent paper Zero-shot causal learning.
- 2023-04-12:
A dream come true. Thrilled to announce our paper Foundation models for generalist medical AI was published today in Nature! Check it out here.
- 2023-04-10:
Excited to give a talk at Prof. Sean Mackey’s lab at Stanford Medicine titled Medical foundation models: a primer on pre-trained, multimodal medical AI.
- 2023-02-05:
New preprint: Zero-shot causal learning. First paper of my postdoc! Check it out here
- 2022-11-05:
Humbled to be invited to present my PhD thesis at the ETH Silver Medal Presentations Seminar at ETH Zurich!
- 2022-10-05:
I just had the honor to give a talk at the Swiss MD/PhD retreat, presenting a Hitchhiker’s guide to medical AI!
- 2022-08-29:
Excited to be invited to talk about medical AI at the Stanford Center for Biomedical Informatics Research!
- 2022-02-07:
Thrilled to announce that I will join Stanford University as a postdoctoral scholar!
- 2022-01-24:
Happy to announce that our paper Topological Graph Neural Networks was accepted to ICLR 2022!
- 2021-12-14:
I successfully defended my PhD Thesis! Check out the wonderful doctoral hat I received here!
- 2021-08-01:
Excited to release the preprint of our multi-centre study for predicting sepsis!
- 2021-05-29:
Check out our systematic review on machine learning-based sepsis prediction that was just published in Frontiers in Medicine! And here a summarizing thread (followed by an engaged discussion).
- 2021-04-13:
Our survey on topological machine learning has been accepted to Frontiers in Artificial Intelligence. Check it out here!
- 2021-01-08:
Our work Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis was accepted for publication in Applied Sciences. Congratulations and thanks to all collaborators!
- 2020-12-02:
Our paper Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score has won a best paper award at ICHI 2020, the 8th IEEE International Conference on Healthcare Informatics! The price is honored with 500 USD, congrats to the entire team!
- 2020-11-12:
Excited to talk about Machine learning for personalized medicine together with Bastian Rieck at the Young Scientists Lunch Webinar, Swiss Society for Biomaterials and Regenerative Medicine.
- 2020-10-31:
Our work Challenging Euclidean Topological Autoencoders was accepted for presentation at the Neurips 2020 Workshop on TDA and Beyond.
- 2020-09-03:
Thrilled to be invited to talk about topological representation learning at the DataSig Seminar of the Mathematical Institute, University of Oxford.
- 2020-09-02:
Check out our new systematic review (medRxiv preprint) on the early prediction of sepsis using machine learning.
- 2020-07-03:
Happy to hear that our work Enhancing statistical power in temporal biomarker discovery through representative shapelet mining was accepted for presentation at ECCB 2020.
- 2020-07-02:
Our work Path Imputation Strategies for Signature Models was accepted for presentation at the Workshop on the Art of Learning with Missing Values (ARTEMISS) at ICML 2020.
- 2020-06-01:
Super excited to have 2 papers accepted at ICML 2020: Topological Autoencoders and Set Functions for Time Series.