Azim

Ahmadzadeh

Assistant Professor

Department of Computer Science

University of Missouri-St. Louis

E-mail: ahmadzadeh..[at]..umsl..[dot]..edu

Office: 329 ESH 

I am a computer scientist, a data scientist, and an interdisciplinary researcher. My research primarily focuses on the effective use of Artificial Intelligence (Machine Learning and Deep Neural Networks) to provide physicists with a better understanding of the Sun and the physical processes driving its activities. One of the main concerns in this area is to achieve a reliable forecast of the extreme space-weather events of which the Sun is the main creating source, and can have devastating economic and collateral impacts on mankind. The magnitude of such economic impacts have been estimated by the National Research Council in 2008 to be $1-2 trillion for the United States, during the first year of the occurrence alone. The severity of the consequences was recognized in 2016, and reiterated in 2019, by the White House, with executive orders that emphasized preparation for and minimization of the potential impacts.

EDUCATION

Ph.D., Computer Science, 2021, Georgia State University, USA

M.S., Computer Science, 2019, Georgia State University, USA

B.Sc., Computer Science, 2016, Warsaw University of Technology, Poland

A.Sc., Pure Mathematics, 2008, Guilan University, Iran

EXPERIENCE

Assistant Professor at University of Missouri-St. Louis [2023 - Present]

Research Professor at Georgia State University [2022 - 2023]

Postdoctoral Research Associate at Data Mining Lab [2021 - 2022]

AREAS OF INTEREST:

CS: Machine Learning, Deep Learning, Image Processing, Time Series Analysis, Performance Verification, High Dimensional Data, Imbalanced Data, Spatiotemporal Data

Physics: Solar Flare Forecasting, Detection and Segmentation of Solar Events, Cost-Effective Evaluation Metrics for Forecast Models

RESEARCH TOPICS:

• Model Evaluation:

  • Evaluation of Salient-Object Detection with Fine Structures

  • Evaluation of Categorical and Probabilistic Classification

  • Robust Sampling of Rare Events

• Spatiotemporal Analysis:

  • Prediction of Space-Weather Events

  • Detection and Segmentation of Scientific Events

  • Task-Specific Feature Extraction and Optimization

• Multivariate Time Series Data:

  • Statistical Feature Extraction 

  • Time Series Clustering