Amirali Aghazadeh

I am an Assistant Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. I serve as program faculty for the Machine Learning, Bioinformatics, and Bioengineering Ph.D. programs. I am also affiliated with the Institute for Data Engineering and Science (IDEAS) and the Parker H. Petit Institute for Bioengineering and Bioscience. Prior to Georgia Tech, I was a postdoctoral researcher at Stanford and UC Berkeley, and earned my Ph.D. at Rice University.

My research interests lie at the intersection of Machine Learning, Signal Processing, Deep Learning, and Computational Biology. I aim to develop scalable algorithms that enable machines to learn, predict, and adapt to solve complex problems—especially those arising from the natural sciences, including biology, chemistry, and physics.

Our group draws from and contributes to foundational areas in mathematics, statistics, and computer science, including optimization, harmonic analysis, sketching and streaming algorithms, and high-dimensional statistics.

Our current research focuses include:

• ML/AI for Science: Generative & Geometric Modeling for Sciences
• Core ML/AI: Explainability & Robustness in Combinatorial Spaces
• Scalable ML/AI: Learning and Inference in Massive Scale

I pronounce my first name /æmi:r’æli:/ and last name /ægə’zɑdɛ/.

Watch my talk on Sparsity, Epistasis, and Models of Fitness Functions at Broad Institute: Models, Inference and Algorithm (MIA)