Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
publications
Higgs self-coupling measurements using deep learning in the (b\bar{b}b\bar{b}) final state
Published in JHEP, 2020
Measuring the Higgs trilinear self-coupling λₕₕₕ at the HL-LHC using di-Higgs (hh → 4b) events. Application of deep learning to suppress multijet background and evaluate boosted jet strategies.
Recommended citation: Amacker, J., Balunas, W., Beresford, L., Bortoletto, D., Frost, J., Issever, C., Liu, J., McKee, J., Micheli, A., Paredes Saenz, S., Spannowsky, M. & Stanislaus, B. (2020). “Higgs self-coupling measurements using deep learning in the \\(b\\bar{b}b\\bar{b}\\) final state.” JHEP, 2020(12), 115.
Download Paper | Download Slides | Download Bibtex
Closed-loop Nash competition for liquidity
Published in Mathematical Finance, 2023
Examines a multiplayer stochastic differential game where agents interact through joint price impact in trading, proving existence of a closed-loop Nash equilibrium and its properties.
Deep Reinforcement Learning for Online Optimal Execution Strategies
Published in arXiv, 2024
Introduces an actor-critic algorithm (DDPG) for learning non-Markovian optimal execution strategies under transient price impact.
Recommended citation: Micheli, A., & Monod, M. (2024). “Deep Reinforcement Learning for Online Optimal Execution Strategies.” arXiv.
Download Paper | Download Slides | Download Bibtex
Diffusion Models for Inverse Problems in the Exponential Family
Published in arXiv, 2025
Extends diffusion models to solve inverse problems under exponential family likelihoods via the ‘evidence trick’, with applications to malaria prevalence inference.
Recommended citation: Micheli, A., Monod, M., & Bhatt, S. (2025). “Diffusion Models for Inverse Problems in the Exponential Family.” arXiv.
Download Paper | Download Slides | Download Bibtex
Fast and slow optimal trading with exogenous information
Published in Finance and Stochastics, 2025
Models the strategic interaction between institutional and high-frequency traders in a multiperiod stochastic Stackelberg game.
Recommended citation: Cont, R., Micheli, A., & Neuman, E. (2025). “Fast and slow optimal trading with exogenous information.” Finance and Stochastics, 29, 553–607.
Download Paper | Download Slides | Download Bibtex
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
Published in arXiv, 2025
Presents NeuralSurv, the first deep survival model integrating Bayesian uncertainty quantification, using a two-stage data-augmentation framework.
Recommended citation: Monod, M., Micheli, A., & Bhatt, S. (2025). “NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification.” arXiv.
Download Paper | Download Slides | Download Bibtex
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.