Amir Ali Nasrollahzadeh

ac9szb@r.postjobfree.com Profile: LinkedIn, GitHub, Webpage

SUMMARY

Data scientist using mathematical modeling, optimization techniques, machine learning, and simulation to derive solutions and managerial insights for complex systems using data

PROFESSIONAL EXPERIENCE

Clemson University, Clemson, South Carolina, USA Aug 2013 - May 2019

Research Assistant

Developing dynamic and adaptive models (Stochastic Modeling, MDP, Time-series, Regression, Operation Research techniques)

Adapting data-driven solutions algorithms (Reinforcement learning, Knowledge gradient, Bayesian Inference, Machine Learning)

Experienced in agent-based and discrete event simulation (R, C++, VB.NET, Arena)

Designing statistical analysis and optimization solution algorithms (ARIMA, ANOVA, R, Python, CPLEX, Gurobi)

Writing multiple scientific papers and international presentations (M&SOM, JOC, NATURE, INFORMS, IISE, POMS)

1.Real-time ambulance repositioning: Achieving 18% reduction in response time considering the proposed policies.

oFormulating a new design for emergency vehicle management considering unconventional decisions such as delaying low priority calls, redeploying vehicles to different bases, or relocating idle vehicles to cover for busy vehicles.

oThe new design is based on a stochastic process, predicts future state of the system and reacts to future demand. It is calibrated and validated by real historical data and runs in real-time.

oThe process is formulated in dynamic programming framework. The real-time performance is made possible by developing a multiple linear regression model to approximate the solution. The simulation is coded in VB.NET and uses CPLEX solver to estimate lower bounds by finding the solution to an integer program.

2.Optimal stopping of an online learning algorithm: New robust stopping rules ensuring a minimum level of accuracy.

oProposed a new framework in sampling vs. stopping with respect to Net Present Value (NPV). Unlike current learning methods that only produce accurate rules when the unknown function is not flat, my proposal achieves a high probability of correct decision for any function.

oThe new design uses a reinforcement learning algorithm and a diffusion approximation to evaluate stopping decision. The algorithms are coded in R and parallelized to reduce computation.

3.Optimal learning of unknown functions: Proposing a new and efficient learning technique for dose-response estimation.

oThe new design reduced the complexity and significantly improved the computation effort required to derive new estimate of the unknown function using observational data.

oCurrently, I am working on extending this design to allow for unknown correlation structures.

4.Cost-effectiveness analysis of diabetes interventions: Demonstrating that lifestyle interventions in patients with pre-diabetes symptoms reduces the risk of progression to diabetes, increases survival rate, and has financial benefits for the population.

oSimulating the progression of diabetes in U.S. using a coupled agent-based and population-based simulation model validated by real data. The coupled simulation model was a novel model coded in C++ and used regression, Cox hazard models and real data to estimate the parameters. The result confirmed that some lifestyle intervention in pre-diabetes stage not only can increase survival rates but may also reduce health care costs in the long run.

5.Predicting the outcome of state elections 2016: Developing a machine learning algorithm using Bayesian random walks and logit regression to predict the outcome of 2016 U.S. presidential election in each state.

6.Health care operations management: Designing syllabus and teaching a class

oTopics: Optimal facility location, blood inventory management, hospital job and personnel scheduling and queueing theories.

oI also taught the class how to code optimization problems utilizing Gurobi (gurobipy) in Python.

7.Published a paper in M&SOM, submitted papers, JOC (under 2nd revision), Service Sciences, Nature (Scientific Reports: under 2nd revision), and presented in multiple international conferences such as INFORMS, IISE, and POMS

Sharif University of Technology, Tehran, Iran Nov 2012 - Jun 2013

BSc Engineer

Developing qualitative models (Linear and Discrete programming, Analytical Hierarchical Process)

Simulation and Analysis (Coding human resource management and inventory management policies in VB.NET)

1.Human resource allocation in a university campus: Developing a linear programming method coupled with analytical hierarchical process to match each position with the best candidate positions based on a numerical scoring system designed to reflect experience, knowledge, leadership qualities and colleagues’ feedback.

BASAco International, Tehran, Iran May 2012 - Aug 2012

Internship

Data Analytics for an ERP systems consulting firm (SQL and Excel)

SKILLS

Modeling: Dynamic Modeling, Stochastic Optimization, Optimal Learning, Bayesian Inference, Machine Learning, Simulation, Predictive Analysis, Forecasting, Graph Theory and Network Modeling

Programming: C++, R, Python, VB.NET, MATLAB, MySQL, Hadoop

Optimization: CPLEX, Gurobi, AMPL

EDUCATION

Clemson University, Clemson, South Carolina, USA Aug 2013 – May 2019

PhD. Industrial Engineering

Sharif University of Technology, Tehran, Iran Sep 2008 – June 2013

B.S. Industrial Engineering

PUBLICATIONS

Nasrollahzadeh, A., Khademi, A., Mayorga, M., 2018, “Real-Time Ambulance Dispatching and Relocation," Manufacturing & Service Operations Management, 20(3) 467-480.

Nasrollahzadeh, A., Khademi, A., “Optimal Stopping of Adaptive Dose-Finding Trials," Submitted to Service Sciences

Khademi A., Shi, L., Nasrollahzadeh, A., Narayanan, H., Appel, L.J., Chen, L., “Compare Pre-diabetes Life-Style Interventions: An Integrated Microsimulation and Population Simulation Model," Submitted to Scientific Reports, Nature

Nasrollahzadeh, A., Khademi, A., “Dynamic Programming to Response-Adaptive Dose-Finding Clinical Trials," submitted to INFORMS Journal on Computing

HONORS

Best Graduate Student Paper Award 2018, Industrial Engineering Department, Clemson University

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