Amirali Aghazadeh

I am an Assistant Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, where I am a program faculty of Machine Learning, Bioinformatics, and Bioengineering PhD programs. I also have affiliations with the Institute for Data Engineering and Science (IDEAS) and the Parker H. Petit Institute for Bioengineering and Bioscience. Before joining Georgia Tech, I was a postdoc at Stanford and UC Berkeley, and I did my PhD at Rice University.

I am interested in Machine Learning, Signal Processing, Deep Learning, and Computational Biology. My research goal is to advance how machines can learn, predict, and adapt at scale to solve problems in and inspired by emerging technologies and sciences, from biology to chemistry and physics. We draw from and contribute to core areas in math, stat, and computer sciences such as optimization, harmonic analysis, hashing/sketching/streaming, and high-dimensional statistics. Our current research focuses are:

• 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 scales

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)