Senior Machine Learning Scientist

Amazon Web Services

Berlin

I am an Applied Machine Learning Scientist at AWS, working on code generation using LLMs. Previously, I worked on recommender systems and learning-to-rank at Amazon Music. Before moving to industry I was a Postdoctoral Research Associate in the Biostatistics Unit of the Medical Research Council at the University of Cambridge, working with Sylvia Richardson. I completed a PhD in applied mathematics at the Center for research in economics and statistics (CREST), Paris supervised by Nicolas Chopin. From October 2017 to April 2018 I visited Pierre E. Jacob at the Department of Statistics at Harvard University. I did my graduate studies in economics, mathematics and statistics under the double degree program of Humboldt University Berlin and ENSAE ParisTech.

My research during PhD and postdoc focused on computational Bayesian methods such as Monte Carlo and approximate inference. At Amazon I work on LLMs for code generation, recommender systems such as learning-to-rank, contextual bandits and off-policy evaluation and learning. I am also interested in causal inference and online experimentation.

- A Buchholz, B London, G Di Benedetto, JM Lichtenberg, Y Stein, T Joachims (2024). Counterfactual ranking evaluation with flexible click models. SIGIR'24.

- JM Lichtenberg, A Buchholz, P Schwoebel (2024). Large Language Models as Recommender Systems: A Study of Popularity Bias GenIR workshop at SIGIR'24.

- JM Lichtenberg, A Buchholz, G Di Benedetto, M Ruffini, B London (2023). Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation. CONSEQUENCES workshop at Recsys23.

- B London, A Buchholz, G Di Benedetto, JM Lichtenberg, Y Stein, T Joachims (2023). Self-normalized off-policy estimators for ranking. CONSEQUENCES workshop at Recsys23.

- G Di Benedetto, A Buchholz, B London, M Jakimov, Y Stein, JM Lichtenberg, V Bellini, M Ruffini (2023). Contextual position bias estimation using a single stochastic logging policy. RecSys 2023 Workshop on Learning and Evaluating Recommendations with Impressions (LERI 2023).

- Buchholz A, Ahfock D, Richardson S (2023). Distributed Computation for Marginal Likelihood based Model Choice. Bayesian Analysis.

- A Buchholz, B London, G Di Benedetto, T Joachims (2022). Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model. CONSEQUENCES workshop at Recsys22.

- Buchholz A, Lichtenberg JM, Di Benedetto G, Bellini V, Stein Y, Ruffini M (2022). Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling. SIGIR'22 Workshop on Reaching Efficiency in Neural Information Retrieval.

- Buchholz A, Bellini V, Di Benedetto G, Stein Y, Ruffini M, Moerchen F (2022). Fair effect attribution in parallel online experiments. The Web Conference 2022 - Industrial track.

- Ruffini M, Bellini V, Buchholz A, Di Benedetto G, Stein Y (2022). Modeling position bias ranking for streaming media services. The Web Conference 2022 - Industrial track.

- Buchholz A, Jacob PE, Chopin N (2021). Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo Bayesian Analysis.

- Buchholz A, Chopin N (2019). Improving approximate Bayesian computation via quasi Monte Carlo. Journal of Computational and Graphical Statistics.

- Buchholz A, (2018). Thesis: High dimensional Bayesian Computation. University Paris Saclay.

- Wenzel* F, Buchholz* A, Mandt S, (2018). Quasi-Monte Carlo Flows. NeurIPS workshop on Bayesian Deep Learning 2018. *(equal contributions)

- Buchholz* A, Wenzel* F, Mandt S, (2018). Quasi-Monte Carlo Variational Inference. ICML 2018. *(equal contributions)

- Ph.D. Applied Mathematics, ENSAE and University Paris Saclay [
**2015-2018**] - M.Sc. Statistics, Humboldt University and Technical University, Berlin [
**2013-2015**] - Diploma Statistics and Economics, ENSAE, Paris [
**2012-2015**] - B.Sc. Economics, Humboldt University, Berlin [
**2010-2013**]

I entirely held the following classes (lecture + tutorial):

- Mathematics for Data Scientice - Hertie School (Berlin, Germany), Spring 2023
- Python for Data Scientists - University of Technology of Troyes (France), Fall 2018 - Course material
- Spark for Data Scientists - University of Technology of Troyes (France), Fall 2018 - Course material

I've also had the pleasure of assisting in teaching several courses (i.e. teaching the tutorials), here are some.

- Mathematical Statistics - ENSAE ParisTech in Fall 2015, 2016 and 2017 with Nicolas Chopin
- Probability Theory - ENSAE ParisTech in Fall 2017 with Cristina Butucea
- Monte Carlo methods - ENSAE ParisTech in Spring 2016 and 2017 with Nicolas Chopin
- Introduction to statistics and econometrics - ENSAE ParisTech in Spring 2017 with Marco Cuturi
- Numerical Analysis - ENSAE ParisTech in Spring 2016 with Jérémie Jakubowicz
- Econometrics I - ENSAE ParisTech in Fall 2015 with Michael Visser
- Econometrics I - Humboldt University Berlin in Spring 2015 with Bernd Droge