Dr. Kamyar Azizzadenesheli is a post-doctorate scholar at Caltech.  His research interest is mainly in the area of Machine Learning, from theory to practice, with the main focus in Reinforcement Learning.  He will be joining Purdue University as an Assistant CS Professor in Fall 2020. 

Featured References 

Efficient Exploration through Bayesian Deep Q-Networks 
Kamyar Azizzadenesheli, Animashree Anandkumar 

Surprising Negative Results for Generative Adversarial Tree Search 
Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C Lipton, Animashree Anandkumar 

Maybe a few considerations in Reinforcement Learning Research? 
Kamyar Azizzadenesheli 
 

Additional References 

• Model-Based Reinforcement Learning for Atari  
  Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski 
• Near-optimal Regret Bounds for Reinforcement Learning 
  Thomas Jaksch, Ronald Ortner, Peter Auer 
• Curious Model-Building Control Systems 
  Jürgen Schmidhuber 
• Rainbow: Combining Improvements in Deep Reinforcement Learning  
  Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver 
• Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics 
  Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George 
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm 
  David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis