I consider myself a generalist as opposed to a specialist. In short, a specialist tends to focus on a relatively narrow domain and become an expert in it. A generalist, however, broadens the domain at the expense of losing depth. While this might hinder a generalist from becoming a true expert in one domain, it allows him/her to connect and join ideas from different disciplines and act as a bridge between specialists. As a generalist, I try to be topic- and method-agnostic, but I have priorities in my research, nonetheless.
Since 2018, my advisors (Prof. Nick Street and Prof. Barrett Thomas) and I started exploring the intersection of Reinforcement Learning (a branch of machine learning that deals with finding the best course of action in sequential decision making settings) and the other two main branches of machine learning: Supervised Learning (that deals with computers learning from labeled data, like finding cats and dogs in an image), and Unsupervised Learning (where machines try to find patterns and similarities in observations). We decided to emphasis on Health Analytics in this endeavor. More specifically, we look at warfarin dosing.
The idea is that we could get better results if we iteratively use RL to learn the optimal policy (best action for any given state/ situation) and use the learned policy and observed trajectories (sequences of states, actions, and outcomes) to segment the input. The result might be easier to implement and use, since the segmentation makes it easier to understand the actions proposed by the model, and actions within a segment will have less variability. In the case of warfarin, for example, our model learns how to adjust the dose of warfarin for patients, but does not put patients into different segments. Then we use the dose-responses to segment patients, and retrain the model. Eventually, the model can put different patients into different segments, and prescribe the dose according to their assigned segment.
My other areas of interest include:
Ashrafi, M., & Anzabi Zadeh, S. (2017). "Lifecycle risk assessment of a technological system using dynamic Bayesian networks." Quality and Reliability Engineering International, 33(8), 2497-2520, DOI: 10.1002/qre.2213.
Anzabi Zadeh, S. (March 2022). Optimizing warfarin dosing using deep reinforcement learning, Tippie Advisory Council, Iowa City, IA.
Anzabi Zadeh, S., Street, W., & Thomas, B. (October 2021). Optimal Dosing Protocol For Warfarin Using Deep Reinforcement Learning, INFORMS Annual Meeting, Anaheim, CA.
Anzabi Zadeh, S., Street, W., & Thomas, B. (November 2020). Application Of Deep Reinforcement Learning In Optimal Warfarin Dosing. INFORMS Annual Meeting, Virtual.
Anzabi Zadeh, S., Street, W., & Thomas, B. (October 2019). Optimal Warfarin Dosing Using Reinforcement Learning. INFORMS Annual Meeting, Seattle, WA.
Namdar, J., Zhao, K., Anzabi Zadeh, S., Blackhurst, J. (October 2019). Modeling and Analysis of Cascading Disruptions in Chain Network: A Cascading Simulation Model, INFORMS Annual Conference, Seattle, WA.
For my dissertation, I developed a Reinforcement Learning package in Python called ReiL. It is not the best, or the fastest, or the most efficient, or even the right way of doing RL, but it is available for use under MIT License. You may install it from PyPI (pip install ReiL), or fork the source code from the repository here: https://research-git.uiowa.edu/sanzabizadeh/Reinforcement-Learning
Most of the code is fully type annotated and documented, and I am dedicated to continue improving and supporting it.
Wootton, S., & Horne, T. (2013) Strategic Thinking: A Step-by-step Approach to Strategy and Leadership (N. Dehghan, A. Moghaddar, S. Anzabi Zadeh, Trans.) Fozhan (Original Work Published 2000).
Anzabi Zadeh, S., Saniee, S., & Yekta Kooshali, A. (2005) “Resources and Their Role in Corporate Strategy”, MASAF quarterly journal (in Farsi).
The Effect of Integration on Supply Chain Performance (Supervisor: Prof. S.H. Zegordi).
Solving Fuzzy Decompositions (SoT and ToS) using Electromagnetic Metaheuristic Method (Supervisor: Prof. M.H. Fazel Zarandi).
Member of MASAF Strategic Management Research Group, Amirkabir University of Technology (2004 - 05).