Ciao! I am a PhD student in the Computer Science Department at Johns Hopkins University, where I am honored to be advised by Jeremias Sulam and to be affiliated with the Mathematical Institute for Data Science. Prior to joining JHU, I obtained my Bachelor’s degree in Biomedical Engineering at Politecnico di Torino, Italy.
My research focuses on:
- Formal machine learning explainability,
- uncertainty quantification,
- weakly supervised learning for medical imaging.
In the past, I have worked with Rhonda Dzakpasu in the Department of Physics at Georgetown University to study spontaneous activity of in vitro, embryonal neural-astrocyte networks.
contact: jtenegg1 at jhu dot edu
|Jun, 2023||I am interning at Profluent as a ML Scientist this summer, working on uncertainty quantification for sequence generation under the supervision of Aadyot Bhatnagar.|
|Apr, 2023||Our paper “How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control” has been accepted for presentation at ICML 2023!|
|Nov, 2022||Our paper “Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT” received a Trainee Research Prize at the RSNA 2022 Annual Meeting. Check out the abstract on Microsoft Research’s project InnerEye website!|
|Jul, 2022||Our paper “Fast Hierarchical Games for Image Explanations” has been accepted for publication in IEEE TPAMI. You can check it out here!|
|Jun, 2022||It was an inspiring week at Princeton ML Theory Summer School! Thanks to Boris Hanin and ORFE for having us; to Nati Srebro, Sebastien Bubeck, Soledad Villar, and Tengyu Ma for their great lectures. Cannot wait to see what these great researchers will achieve!|
- Jul. 14, 2022arxiv
- Jul. 11, 2022ieee tpami
- Aug. 11, 2021front neurosci
- Apr. 15, 2021phys rev e