Salar Safarkhani

Cell: 480-***-**** Email: adcdgv@r.postjobfree.com Website: https://github.com/salarsk1 Mailing Address: 518 N. River Rd. Apt. 10, West Lafayette, IN 47906 EDUCATION

Purdue University, Ph.D. Candidate in Mechanical Engineering Concentrated on Computational Science and Engineering (Aug 2015 - Aug 2020)

Arizona State University, MSc in Mechanical Engineering Concentrated on Compu- tational Mechanics (Jan 2013 - Aug 2015)

Amirkabir University of Technology, BSc in Mechanical Engineering (Sep 2007 - May 2012)

SKILLS

Python, C++11, PyTorch, TensorFlow, PyMC3, Probabilistic Programming, Unix/Linux

Machine Learning, Deep Reinforcement Learning, Deep Learning (CNN, RNN), Bayesian Statistics

Uncertainty Quanti cation in Physics, Decision Theory, Game Theory & Mechanism Design, Mathematics of Finance

Pandas, SQL, NoSQL (MongoDB)

PROJECTS

Developed Multi-agent Deep Reinforcement Learning Model (A2C) to Simulate the Bidding, Winning, and Budgeting Strategies in Acquisition Procedures

{ Click here for the code and results

Developed an Automated Portfolio Management Strategy Using Deep Q-Learning

Studying the Convergence of Bayesian Global Optimization (BGO) to Model Sequen- tial Decision Making Process

{ Click here for the code and results

A Probabilistic and Bayesian Approach to Solve Non-Convex Bi-level Optimization Problems in Machine Learning, Game Theory, and Mechanism Design

{ Click here for the code and results

The C++ Implementation of Re ned Level Set Grid Method

{ Developed Thousands of Lines of C++ Software to Solve the Level Set Partial Di erential Equations to Track the Interface in Multiphase Flow

{ Click here for the code and results

Using Variational Inference and Probabilistic Numeric to Quantify the Uncertainty in High Dimensional Stochastic Partial Di erential Equations PROFESSIONAL EXPERIENCE

Graduate Research Assistant at Predictive Science Lab at Purdue University (2016- 2020)

Graduate Teaching Assistant at Purdue University and Arizona State University PUBLICATIONS

Safarkhani, S.; Bilionis, I.; Panchal, J. Towards a Theory of Systems Engineering Processes: A Principal-agent Model of a One-shot, Shallow Process. IEEE Systems Journal 2020.

Safarkhani, S.; Bilionis, I.; Panchal, J. Understanding the E ect of Task Di culty and Problem- Solving Skills on the Design Performance of Agents in Systems Engineering. Journal of Mechanical Design 2019 (under review).

Safarkhani, S.; Kattakuri, V.R.; Bilionis, I.; Panchal, J. A principal-agent model of systems engi- neering processes with application to satellite design. CESUN 2018. https://arxiv.org/abs/1903.06979

Wang, Z.; Safarkhani, S.; Lin, G.; Ruan, X. \Uncertainty Quanti cation of Thermal Conductiv- ities from Equilibrium Molecular Dynamics Simulations". International Journal of Heat and Mass Transfer, 2017

AWARDS

Magoon Award for Excellence in Teaching Assistance, Purdue University, 2016

Completion of Applied Management Principles Program, Krannert School of Management, Purdue University, 2018

Paper of Distinction at ASME/ IDETC Conference, S. Safarkhani, I. Bilionis, J. Panchal, \Un- derstanding the E ect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents In Systems Engineering", 2018

Tau Beta Pi Engineering Honor

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