Amirali Aghazadeh

Georgia Tech

prof_pic.jpg

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. 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/.

Applicants: If you have a solid foundation in mathemathics and statistics, are proficient in programming, and have a keen interest in AI Foundations for Science, please send your CV to me via email. Include 1-2 sentences explaining your interest in joining our group. We are always looking for new members in our group.

For PhD applicants in Machine Learning: To work with me, please apply to the ML PhD program under the School of ECE. You will be able to work me if you have a different home school (CS, CSE, etc.).

For PhD applicants in Bioengineering: To work with me, please apply to the Bioengineering program under the School of ECE. You will be able to work me if you have a different home school (BME, CHBE, etc.).

For PhD applicants in Bioinformatics: You will be able to work with me regardless of your home school (CSE, BME, etc.).

news

Apr 4, 2024 Huge congrats to Darin Tsui for receiving the NSF GRFP!
Mar 4, 2024 Our new paper on recovering high-order interactions from protein language models is accepted to the ICLR Workshop on Generative and Experimental perspectives in bioMolecular design (GEM).
Jan 5, 2024 We are awarded the Institute for Electronics and Nanotechnology , IEN-1000x seed grant to develop a high resolution acquisition technique for proteins in collaboration with our ChBE colleague Vida Jamali
Nov 8, 2023 We are excited to host the candidates at the EECS Rising Star Workshop 2023 at Georgia Tech where I serve as one of the faculty mentors.
Oct 20, 2023 Happy to receive seed funding grants from the Institute for Data Engineering and Science (IDEAS) to lead the AI for Chemical and Materials Discovery and Microscopy with three other amazing colleagues at GT.

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
    HTML Code
  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
    HTML Code
  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
    HTML Code