1. DPhil Computer Science

   University of Oxford, supervised by Prof Yarin Gal, Oxford, UK, Oct 2018 -- Summer 2023_
   Deep active learning and data subset selection using information theory and Bayesian neural networks.

2. Research Engineer (Intern)

   Opal Camera, Remote, Oct 2022 -- Dec 2022
   Validation Pipeline for Gesture Control System.

3. Resident Fellow

   Newspeak House, London, UK, Jan 2018 -- Jul 2018
   AI & Politics event series, science communication.

4. Performance Research Engineer

   DeepMind, London, UK, Oct 2016 -- Aug 2017
   TensorFlow performance improvements (custom CUDA kernels) & profiling (i.a. “Neural Episodic Control”); automated agent regression testing.

5. Software Engineer

   Google, Zürich, CH, Jul 2013 -- Sep 2016
   App & testing infrastructure; latency optimization; front-end development (Dart/GWT).

6. MSc Computer Science

   Technische Universität München, München, DE, Sep 2009 -- Oct 2012
   Thesis “Assisted Object Placement”.

7. BSc Mathematics

   Technische Universität München, München, DE, Sep 2009 -- Mar 2012
   Thesis “Discrete Elastic Rods”.

8. BSc Computer Science

   Technische Universität München, München, DE, Sep 2007 -- Sep 2009
   Thesis “Multi-Tile Terrain Rendering with OGL/Equalizer”.

[1] J. Mukhoti^*, A. Kirsch^*, J. van Amersfoort, P. H. Torr, and Y. Gal, "Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty," CVPR 2023, 2023.

[2] F. Bickford Smith^*, A. Kirsch^*, S. Farquhar, Y. Gal, A. Foster, and T. Rainforth, "Prediction-Oriented Bayesian Active Learning," AISTATS, 2023.

[3] S. Mindermann^*, J. M. Brauner^*, M. T. Razzak^*, A. Kirsch, et al., "Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt," ICML, 2022.

[4] A. Jesson^*, P. Tigas^*, J. van Amersfoort, A. Kirsch, U. Shalit, and Y. Gal, "Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data," NeurIPS, 2021.

[5] A. Kirsch^*, J. van Amersfoort^*, and Y. Gal, "BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning," NeurIPS, 2019.

[6] A. Kirsch, "Black-Box Batch Active Learning for Regression", TMLR, 2023.

[7] A. Kirsch, "Does ‘Deep Learning on a Data Diet’ reproduce? Overall yes, but GraNd at Initialization does not", TMLR, 2023.

[8] A. Kirsch^*, S. Farquhar^*, P. Atighehchian, A. Jesson, F. Branchaud-Charron, Y. Gal, "Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning", TMLR, 2023.

[9] A. Kirsch and Y. Gal, "A Note on "Assessing Generalization of SGD via Disagreement"," TMLR, 2022.

[10] A. Kirsch and Y. Gal, "Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities," TMLR, 2022.

[11] D. Tran, J. Liu, M. W. Dusenberry, et al., "Plex: Towards Reliability using Pretrained Large Model Extensions," Principles of Distribution Shifts & First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, ICML 2022.

[12] A. Kirsch, J. Kossen, and Y. Gal, "Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling," Updatable Machine Learning, ICML 2022, 2022.

[13] A. Kirsch, J. Mukhoti, J. van Amersfoort, P. H. Torr, and Y. Gal, "On Pitfalls in OoD Detection: Entropy Considered Harmful," Uncertainty in Deep Learning, 2021.

[14] A. Kirsch, T. Rainforth, and Y. Gal, "Active Learning under Pool Set Distribution Shift and Noisy Data," SubSetML, 2021.

[15] A. Kirsch^*, S. Farquhar^*, and Y. Gal, "A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions," SubSetML, 2021.

[16] A. Kirsch and Y. Gal, "A Practical & Unified Notation for Information-Theoretic Quantities in ML," SubSetML, 2021.

[17] A. Kirsch, C. Lyle, and Y. Gal, "Scalable Training with Information Bottleneck Objectives," Uncertainty in Deep Learning,

[18] A. Kirsch, C. Lyle, and Y. Gal, "Learning CIFAR-10 with a Simple Entropy Estimator Using Information Bottleneck Objectives," Uncertainty in Deep Learning, 2020.

NeurIPS 2019 (Top Reviewer), AAAI 2020, AAAI 2021, ICLR 2021, NeurIPS 2021 (Outstanding Reviewer), NeurIPS 2022, NeurIPS 2022, TMLR, CVPR 2023.