Arash Nasr-Esfahany
Graduate Student
Room
32-G968
Arash is a fourth-year PhD student at MIT CSAIL, advised by Mohammad Alizadeh. He is interested in modeling large-scale computer systems with causality and machine learning.
In his Master's thesis, he designed a causal machine learning method for unbiased trace-driven simulation (CausalSim) which received the 🏆best paper award🏆 at NSDI'23.
Before MIT, he did his undergrad in the Electrical Engineering department, Sharif University of Technology.
He spent the summer of 2022 as a research intern at Causality and Machine Learning group, Microsoft Research Redmond, working with Emre Kiciman.
Last updated May 01 '23
Research Areas
Impact Areas
Publications
Abdullah Alomar*, Pouya Hamadanian*, Arash Nasr-Esfahany*, Anish Agarwal, Mohammad Alizadeh, Devavrat Shah
CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation
20th USENIX Symposium on Networked Systems Design and Implementation (NSDI '23) 🏆Best Paper Award🏆
Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah
Counterfactual Identifiability of Bijective Causal Models
Fortieth International Conference on Machine Learning (ICML '23)
Mehrdad Khani, Pouya Hamadanian, Arash Nasr-Esfahany, Mohammad Alizadeh
Real-Time Video Inference on Edge Devices via Adaptive Model Streaming
IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4572-4582
Pouya Hamadanian, Arash Nasr-Esfahany, Siddartha Sen, Malte Schwarzkopf, Mohammad Alizadeh
Locally Constrained Policy Optimization for Online Reinforcement Learning in Non-Stationary Input-Driven Environments