Research

My research now mainly focuses on the following topics

1. Classical and deep Reinforcement learning

2. Computational game theory

3. Multiagent systems 

4. Complex systems

5. Diffusion based generative models

6. Algorithmic foundations of machine and deep learning

7. Quantum machine learning

Some of my publications from recent times (full list in publications)

1. Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
   Daphne Cornelisse, Thomas Rood, Mateusz Malinowski, Yoram Bachrach, Tal Kachman

2. Lyapunov Exponents for Diversity in Differentiable Games
   J Lorraine, P Vicol, J Parker-Holder, T Kachman, L Metz, J Foerster
   arXiv preprint arXiv:2112.14570 (accepted to International Conference on Autonomous
   Agents and Multiagent Systems)

3. Using bifurcations for diversity in differentiable games
   J Lorraine, J Parker-Holder, P Vicol, A Pacchiano, L Metz, T Kachman, L Metz, J Foerster. ICML 2021: The Thirty-eighth International Conference on Machine Learning, 1-11

Some of my patents from recent times

6. Quantum space distance estimation for classifier training using hybrid classical-quantum computing system
   L Horesh, JA Gunnels, T Kachman, CH Crawford
   US Patent 11,164,099

7. Quantum walk for community clique detection
   T Kachman, L Horesh, G Nannicini, MS Squillante, JA Gunnels, …
   US Patent App. 16/573,862

8. Quantum topological classification

T Kachman, KL Clarkson, MS Squillante, L Horesh, IY Akhalwaya

US Patent App. 16/576,046

In a past life I was mainly focused on Physics related things such as

1. Non equilibrium statistical mechanics 

2. Foundations of quantum mechanics

3. Quantum chaos

4. Molecular dynamics and Computational chemistry

5. Nonlinear optics

6. Heat conduction in nanometric systems