I received B.ASc. degree with distinction in Electrical Engineering from the University of Waterloo in 2017. I am currently a PhD Candidate, advised by Demba Ba, at Harvard University.
I spent summer 2019 at Amazon AI as an Applied Scientist Intern working on neural architectures for multichannel speech enhancement. I will be joining the Applied Sciences Group at Microsoft as a Research Intern for summer 2021.
My research interests are at the intersection of deep learning, statistical signal processing, and computational neuroscience. My PhD research is divided into three tracks:
 modelbased deep learning: I develop networks using probabilistic generative models and dictionary learning, applied to inverse problems such as compressive sensing and denoising.
 deep learning theory: I develop theory for gradient dynamics and implicit acceleration.
 computational neuroscience: I develop algorithms for a variety of problems such as neural encoding in the olfactory system and spike sorting.

Gaussian process convolutional dictionary learning
A. H. Song, B. Tolooshams, and D. Ba
Submitted [paper] 
On the convergence of groupsparse autoencoders
E. Theodosis, B. Tolooshams*, P. Tankala*, A. Tasissa, and D. Ba
Submitted [paper] 
Towards improving discriminative reconstruction via simultaneous dense and sparse coding
A. Tasissa, E. Theodosis*, B. Tolooshams*, and D. Ba
Submitted [paper] [code] 
Unfolding neural networks for compressive multichannel blind deconvolution
B. Tolooshams*, S. Mulleti*, D. Ba, and Y. C. Eldar
IEEE ICASSP [paper] [slides] [poster] 
Unsupervised learning of a dictionary of neural impulse responses from spiking data
B. Tolooshams, H. Wu, P. Masset, V. N. Murthy, and D. Ba
COSYNE [paper] [poster]

Convolutional dictionary learning based autoencoders for natural exponentialfamily distributions
B. Tolooshams*, A. H. Song*, S. Temereanca, and D. Ba
ICML [paper] [code] [slides] 
Convolutional dictionary learning of stimulus from spiking data
A. H. Song*, B. Tolooshams*, S. Temereanca, and D. Ba
COSYNE [paper] [poster] 
Deep residual autoencoders for expectation maximizationinspired dictionary learning
B. Tolooshams, S. Dey, and D. Ba
IEEE Transactions on Neural Networks and Learning Systems [paper] [code] 
Channelattention dense unet for multichannel speech enhancement
B. Tolooshams, R. Giri, A. H. Song, U. Isik, and A. Krishnaswamy
IEEE ICASSP [paper]

Convolutional dictionary learning in hierarchical networks
J. Zazo, B. Tolooshams, and D. Ba
IEEE CAMSAP [paper] 
RandNet: deep learning with compressed measurements of images
T. Chang*, B. Tolooshams*, and D. Ba
IEEE MLSP [paper] [code] [poster]

Scalable convolutional dictionary learning with constrained recurrent sparse autoencoders
B. Tolooshams, S. Dey, and D. Ba
IEEE MLSP [paper] [code]

Robustness of frequency division technique for online myoelectric pattern recognition against contractionlevel variation
B. Tolooshams and N. Jiang
Frontiers in Bioengineering and Biotechnology [paper]
 04/2021: I will be joining the Applied Sciences Group at Microsoft as a Research Intern for summer 2021.
 04/2021: I am preparing a tutorial on “Modelbased deep learning” to be presented at IEEE ICASSP Conference.
 04/2021: I gave a talk and designed workshop tutorials for Neurosur 2021.
 04/2021: I gave a presetation as guest lecturer on "Dictionary learning based autoencoders for inverse problems” at ES201  Decision Theory course at Harvard University.
 01/2021: Our abstract on “Unsupervised learning of a dictionary of neural impulse responses from spiking data” has been accepted to Computational and Systems Neuroscience (Cosyne) 2021 for a poster presentation.
 11/2020: I gave a talk on “On the relationship between dictionary learning and sparse autoencoders” at Computational and Applied Math Seminar at Tufts University.
 09/2020: I am nominated for Google PhD Fellowship.
 03/2020: I will be giving a workshop at 2020 Harvard WECode Conference, the largest studentrun undergraduate women in tech conference. (cancelled)
 03/2020: I presented a poster on “Image denoising and analysis of neural spiking data with recurrent autoencoders for natural exponentialfamily of distributions” at Women in Data Science Cambridge Conference (WiDS) 2020.
 01/2020: I judged at the 8th annual National Collegiate Research Conference organized by Harvard College Undergraduate Research Association.
 07/2019: I received AWS Machine Learning Research Awards (MLRA).
 04/2019: I joined Amazon AI as ML/DSP Research Intern for Summer 2019.
 04/2019: I received QBio Student Fellowship for 20192020 academic year.
 03/2019: I presented a poster on “Deep residual autoencoders for expectation maximizationbased dictionary learning” at Women in Data Science Cambridge Conference (WiDS) 2019.
 10/2018: I am selected to attend the IBROSimons Computational Neuroscience Imbizo 2018.
 09/2018: I am nominated for Microsoft Ada Lovelace Fellowship.
 08/2018: I received QBio Student Award Competition Fellowship, offering $41,200 for 201819 academic year.
 07/2018: I received a travel grant of $500 for MLSP2018.
 09/2017: I started my graduate studies at CRISP Group at Harvard University.
 06/2017: I received my B.ASc. degree with distinction in Electrical Engineering from the University of Waterloo.