Bahareh Tolooshams
Bahareh Tolooshams

PhD Candidate, Electrical Engineering
CRISP Group, Harvard University


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.

Research

My research interests are at the intersection of deep learning, statistical signal processing, and computational neuroscience. My PhD research is divided into three tracks:

  • model-based 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.

Publications
2021
  1. Gaussian process convolutional dictionary learning
    A. H. Song, B. Tolooshams, and D. Ba
    Submitted [paper]
  2. On the convergence of group-sparse autoencoders
    E. Theodosis, B. Tolooshams*, P. Tankala*, A. Tasissa, and D. Ba
    Submitted [paper]
  3. Towards improving discriminative reconstruction via simultaneous dense and sparse coding
    A. Tasissa, E. Theodosis*, B. Tolooshams*, and D. Ba
    Submitted [paper] [code]
  4. Unfolding neural networks for compressive multichannel blind deconvolution
    B. Tolooshams*, S. Mulleti*, D. Ba, and Y. C. Eldar
    IEEE ICASSP [paper] [slides] [poster]
  5. 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]
2020
  1. Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
    B. Tolooshams*, A. H. Song*, S. Temereanca, and D. Ba
    ICML [paper] [code] [slides]
  2. Convolutional dictionary learning of stimulus from spiking data
    A. H. Song*, B. Tolooshams*, S. Temereanca, and D. Ba
    COSYNE [paper] [poster]
  3. Deep residual autoencoders for expectation maximization-inspired dictionary learning
    B. Tolooshams, S. Dey, and D. Ba
    IEEE Transactions on Neural Networks and Learning Systems [paper] [code]
  4. Channel-attention dense u-net for multichannel speech enhancement
    B. Tolooshams, R. Giri, A. H. Song, U. Isik, and A. Krishnaswamy
    IEEE ICASSP [paper]
2019
  1. Convolutional dictionary learning in hierarchical networks
    J. Zazo, B. Tolooshams, and D. Ba
    IEEE CAMSAP [paper]
  2. RandNet: deep learning with compressed measurements of images
    T. Chang*, B. Tolooshams*, and D. Ba
    IEEE MLSP [paper] [code] [poster]
2018
  1. Scalable convolutional dictionary learning with constrained recurrent sparse auto-encoders
    B. Tolooshams, S. Dey, and D. Ba
    IEEE MLSP [paper] [code]
2017
  1. Robustness of frequency division technique for online myoelectric pattern recognition against contraction-level variation
    B. Tolooshams and N. Jiang
    Frontiers in Bioengineering and Biotechnology [paper]
Latest News
  • 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 “Model-based 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 student-run 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 exponential-family 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 2019-2020 academic year.
  • 03/2019: I presented a poster on “Deep residual autoencoders for expectation maximization-based dictionary learning” at Women in Data Science Cambridge Conference (WiDS) 2019.
  • 10/2018: I am selected to attend the IBRO-Simons 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 2018-19 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.