Education

Ph.D in Computer Science (Machine Learning) ETH Zürich and Max Planck Institute, 2021 Dissertation: Dynamics of Learning and Learning of Dynamics (Download)
M.Sc. in Electrical Engineering, University of Tehran, 2015 Thesis: Natural Image Representation by Redundancy Reduction Inspired by Neuroscience
B.Sc. in Electrical Engineering, University of Tehran, 2012

Work experience

Spring 2021: Research Scientitst Intern
- DeepMind
- Research: Multi Agent RL / distributional RL

Winter 2021: Research Scientitst Intern
- Google
- Research: Dynamic pricing and stochastic dynamical systems

Summer 2018: Intern
- Microsoft Research
- Research: Graph Neural Networks, Reinforcement Learning

Fall 2019: Visiting Researcher
- ETH Zürich
- Research: Invariances in nonlinear systems
- Supervisor: Prof. John Lygeros
Winter 2019: Visiting Researcher
- ETH Zürich
- Research: Time series analysis
- Supervisor: Prof. Gunnar Rätsch

Organizing GeneDisco challenge for drug target identification (Winter 2022)
Organizing Machine Learning for Drug Discovery workshop in ICLR2022 (Spring 2022)
Organizing Gamification and Multiagent Solutions workshop in ICLR2022 (Spring 2022)
Organizing ELLIS Against Covid-19 workshop (Spring 2020)
Organizing Disentanglement Challenge, Vancouver, Canada (NeurIPS 2019)
Organizing Machine Learning Summer School, Tübingen, Germany (MPI-IS 2017)
Reviewing for NeurIPS, ICML, ICLR, UAI, CDC, J of Neural Computation.

Machine Learning
- Learning theory, Deep neural networks
- Reinforcement Learning, Generative Models
Dynamical systems
- Nonlinear systems (identification and control), data-driven control
- stochastic systems, Lyapunov methods
Biology
- Functional genomics, Single-cell data analysis
- Gene regularoty networks, Causal cell dynamics
Programming
- Python, C++, Bash
- Tensorflow, PyTorch
- Probablisitc Programming (PyMC, Edward)