Amirali Aghazadeh

Georgia Tech


Coda S1209

Tech Square

Atlanta, GA 30308

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. Current research focus are:

  • ML/AI for Science: generative & language modeling for bioscience
  • Core ML/AI: explainability & robustness in combinatorial spaces
  • Scalable ML/AI: algorithms for fast & efficient training/inference

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


May 11, 2023 I am giving a talk on Fourier Methods for Predicting Protein Function in the MIA series at the Broad institute. Find the link to my talk here: MIA at Broad Institute.
Apr 14, 2023 Our paper on Computing Sparse Fourier Transforms of q-ary Functions is accepted to ISIT.
Mar 27, 2023 Our paper on data-frugal regularization of neural networks is accepted to TMLR.
Mar 1, 2023 I am offering a course on generative and geometric deep learning in Fall 2023.
Feb 16, 2023 I am giving a talk in ITA workshop on explaining deep protein models using Fourier analysis.

selected publications

  1. Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions
    Amirali Aghazadeh, Hunter Nisonoff, Orhan Ocal, David H Brookes, Yijie Huang, and 3 more authors
    Nature Communications 2021
  2. Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells
    Ryan T Leenay, Amirali Aghazadeh, Joseph Hiatt, David Tse, Theodore L Roth, and 6 more authors
    Nature Biotechnology 2019
  3. MISSION: Ultra large-scale feature selection using Count Sketches
    Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, and 1 more author
    In International Conference on Machine Learning (ICML) 2018