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). 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/.
|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.|
|Feb 1, 2023||We have multiple openings for PhD students!|
|Oct 21, 2022||Gautham receives the MS Bioinformatics GRA award. Congrats!|
- MISSION: Ultra large-scale feature selection using Count SketchesIn International Conference on Machine Learning (ICML) 2018
- Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functionsNature Communications 2021
- Spectral Regularization Allows Data-frugal Learning over Combinatorial SpacesarXiv:2210.02604 2022