Education
- Ph.D. EECS, Massachusetts Institute of Technology, 2011.
- M.S. EECS, Massachusetts Institute of Technology, 2006.
- B.S. Electrical Engineering, University of Maryland, College Park, 2004.
Professional Appointments
- Harvard University, School of Engineering and Applied Sciences, Cambridge, MA
Dean of Undergraduate Studies for Bioengineering (August 2020–Present)
- Harvard University, School of Engineering and Applied Sciences, Cambridge, MA
Associate Professor of Electrical Engineering and Bioengineering (July 2019–Present)
- Harvard University, School of Engineering and Applied Sciences, Cambridge, MA
Assistant Professor of Electrical Engineering and Bioengineering (July 2015–June 2019)
- Manifold AI – Algorithm Design and Development, Oakland, CA
Senior Data Science Consultant (September 2018–Present)
- Neuroscience Statistics Research lab, Cambridge, MA
Massachusetts Institute of Technology
Research Assistant/Post-doc fellow (Fall 2007–Summer 2014)
- Google – Anomaly Detection -and Trend Estimation, Mountain View, CA
Summer Intern (June 2010–September 2010)
- Microsoft Research – Communications and Collaboration Systems Group, Redmond, WA
Summer Intern (June 2006/2009–September 2006/2009)
Awards & Honors
- 2021 Roslyn Abramson Award for Outstanding Undergraduate Teaching, Harvard Faculty
of Arts and Sciences
- 2016 Fellow in Neuroscience of the Alfred P. Sloan Foundation
- Spotlight Presentation at Advances in Neural Information Processing Systems 25 (NIPS
2012) [< 5% acceptance rate]
- ICME 2010 Best Student Paper Award (for summer 2009 work at MS Research)
- University of Maryland Engineering honors citation
- A Scholars Programs for Industry-oriented Research in Engineering
Relevant Coursework
Discrete-time Signal Processing, Stochastic Processes Detection and Estimation, Statistical
Learning and Estimation, High-dimensional Statistics, Dynamic systems and Control, Advanced
Computational Photography, Principles of Digital Communication, Abstract Linear Algebra, Real
Analysis, Functional Analysis.
Presentations
Technical Talks
- “Sparse Coding, Artificial Neural Networks, and the Brain: Toward Substantive
Intelligence.” Center for Brain Science, Harvard University, April 2021.
- “Sparse Coding, Artificial Neural Networks, and the Brain: Toward Substantive
Intelligence.” Institute for Artificial Intelligence and Fundamental Interactions
Colloquium Series, MIT, April 2021.
- “Interpretable AI in Computational Neuroscience: Sparse Coding, Artificial Neural
Networks, and the Brain.” Princeton Neuroscience Institute, Princeton University, April
2021.
- “Interpretable AI in Computational Neuroscience: Sparse Coding, Artificial Neural
Networks, and the Brain.” Center for Theoretical Neuroscience, Columbia University,
March 2021.
- “Interpretable AI in Computational Neuroscience: Sparse Coding, Artificial Neural
Networks, and the Brain.” Computer Science Colloquium, Indiana University
Bloomington, March 2021.
- “Deeply-sparse signal representations.” Physical Mathematics Seminar Series, MIT,
February 2021.
- “Interpretable AI in Computational Neuroscience: Sparse Coding, Artificial Neural
Networks, and the Brain.” Center for Computational Neuroscience, University of
Washington, January 2021.
- “Deeply-sparse signal representations.” Conference of the Mathematical Theory of Deep
Neural Networks (DeepMath), November 2020.
- “Learning deeply-sparse signal representations.” 6.S975 Seminar Series (Advanced Topics
in Signal Processing), MIT, February 2020.
- “Auto-encoders for convolutional dictionary learning.” IBRO-SIMONS Computational
Neuroscience Imbizo, Muizenberg, South Africa, January 2020.
- “AI in computational neuroscience: sparsity, artificial neural networks and the brain.”
Center for Mind, Brain, Computation and Technology Seminar Series, Stanford
University, October 2019.
- “Learning deeply-sparse signal representations.” Applied Mathematics Seminar Series,
Tufts University, September 2019.
- “Population codes, hierarchical sparse coding and connections to artificial neural
networks.” Oxford University Cortexlab Seminar Series, Oxford UK, May 2019.
- “AI in computational neuroscience: sparsity, artificial neural networks and the brain.”
International Brain Lab (IBL) annual meeting, Paris France, May 2019.
- “Learning deeply-sparse signal representations.” Electrical Engineering Seminar Series,
Cornell Tech, April 2019.
- “AI in computational neuroscience: sparsity, artificial neural networks and the brain.”
Amazon AWS AI in Practice – DSP, Audio, Speech and Languages, Electrical Engineering
Seminar Series, Palo Alto CA, March 2019.
- “Learning deeply-sparse signal representations.” Electrical Engineering Seminar Series,
Rice University, March 2019.
- “Learning deeply-sparse signal representations.” Electrical Engineering Seminar Series,
Harvard University, March 2019.
- “Sparse coding, sensory processing in the brain, and artificial neural networks.”
IBRO-SIMONS Computational Neuroscience Imbizo, Muizenberg, South Africa, January
2019.
- “Estimating a separable random field from binary observations.” Department of ECE,
University of Maryland – College Park, March 2017.
- “Estimating a separable random field from binary data.” Center for Brain Science,
Harvard University, November 2016.
- “Estimating a separable random field from binary data.” Department of ECE, SILO
Seminar Series, University of Wisconsin – Madison, October 2016.
- “Estimating structured state-space models from point-process data.” Neurocontrol
Workshop, Automatic Control Conference, Boston MA, July 2016.
- “Estimating structured state-space models from point-process data.” Second Workshop
on Modelling Neural Activity, Waikoloa HI, June 2016.
- “New time-frequency tools toward a more precise characterization of rhythms from the
brain.” Institute of Applied and Computational Sciences, Harvard University, February
2016.
Conference Proceedings
- Abiy Tasissa, Pranay Tankala, and Demba Ba. Weighted ℓ1 on the simplex:
compressed sensing meets locality. In 2021 IEEE Statistical Signal Processing Workshop
(SSP), pages 1–5.
- Andrew Song, Demba Ba, and Emery Brown. Plso: A generative framework
for decomposing nonstationary timeseries into piecewise stationary oscillatory
components. In 37th Conference on Uncertainty in Artificial Intelligence, 2021.
- Bahareh Tolooshams, Satish Mulleti, Demba Ba, and Yonina C Eldar. Unfolding
neural networks for compressive multichannel blind deconvolution. In ICASSP
2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pages 2890–2894. IEEE, 2021.
- Bahareh Tolooshams, Hao Wu, Paul Masset, Venkatesh Murthy, and Demba Ba.
Unsupervised learning of a dictionary of neural impulse responses from spiking data.
In Computational and Systems Neuroscience (COSYNE), 2021.
- Andrew Song, Francisco Flores, Demba Ba, and Emery Brown. A statistical
framework for extracting time-varying oscillations from neural data. In Computational
and Systems Neuroscience (COSYNE), 2021.
- Bahareh Tolooshams, Andrew H Song, Simona Temereanca, and Demba Ba.
Convolutional dictionary learning based auto-encoders for natural exponential-family
distributions. In Proceedings of the 37th International Conference on Machine Learning
(ICML), 2020. URL: https://arxiv.org/abs/1907.03211.
- Andrew Song, Bahareh Tolooshams, Simona Temereanca, and Demba Ba.
Convolutional dictionary learning of stimulus from spiking data. In Computational and
Systems Neuroscience (COSYNE), 2020.
- Thomas Chang, Bahareh Tolooshams, and Demba Ba. Randnet: deep
learning with compressed measurements of images. In 2019 IEEE 29th
International Workshop on Machine Learning for Signal Processing (MLSP). URL:
https://arxiv.org/abs/1908.09258.
- Javier Zazo, Bahareh Tolooshams, and Demba Ba. Convolutional dictionary
learning in hierarchical hetworks. In 2019 IEEE International Workshop on
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019). URL:
https://arxiv.org/abs/1907.09881.
- Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Pierre Jacob,
and Demba Ba. Clustering time series with nonlinear dynamics: A bayesian
non-parametric and particle-based approach. In International Conference on Artificial
Intelligence and Statistics, 2019. URL: https://arxiv.org/abs/1810.09920.
- Taposh Banerjee, Stephen Allsop, Kay M Tye, Demba Ba, and Vahid Tarokh.
Sequential detection of regime changes in neural data. In 2019 IEEE 9th
International IEEE/EMBS Conference on Neural Engineering (NER). URL:
https://arxiv.org/abs/1809.00358.
- Bahareh Tolooshams, Sourav Dey, and Demba Ba. Scalable convolutional dictionary
learning with constrained recurrent sparse auto-encoders. In 2018 IEEE 28th
International Workshop on Machine Learning for Signal Processing (MLSP).
- Taposh Banerjee, John Choi, Bijan Pesaran, Demba Ba, and Vahid Tarokh.
Classification of local field potentials using gaussian sequence model. In 2018 IEEE
Statistical Signal Processing Workshop (SSP), pages 683–687.
- Taposh Banerjee, John Choi, Bijan Pesaran, Demba Ba, and Vahid Tarokh. Wavelet
shrinkage and thresholding based robust classification for brain-computer interface.
In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pages 836–840.
- Yingzhuo Zhang, Noa Shinitski, Stephen Allsop, Kay Tye, and Demba Ba. A
two-dimensional seperable random field model of within and cross-trial neural spiking
dynamics. In Computational and Systems Neuroscience (COSYNE), 2017.
- Noa Malem-Shinitski, Yingzhuo Zhang, Daniel Gray, Sarah Burke, Anne Smith, Carol
Barnes, and Demba Ba. Can you teach an old monkey a new trick? In Computational
and Systems Neuroscience (COSYNE), 2017.
- Gabriel Schamberg, Demba Ba, Mark Wagner, and Todd Coleman. Efficient
low-rank spectrotemporal decomposition using admm. In 2016 IEEE Statistical Signal
Processing Workshop (SSP), pages 1–5.
- Demba Ba, Behtash Babadi, Patrick L Purdon, and Emery N Brown. Neural
spike train denoising by point process re-weighted iterative smoothing. In 2014
IEEE 48th Asilomar Conference on Signals, Systems and Computers., pages 763–768.
doi:10.1109/ACSSC.2014.7094552.
- Demba Ba, Behtash Babadi, Patrick L Purdon, and Emery N Brown. Exact
and stable recovery of sequences of signals with sparse increments via differential
ℓ1-minimization. Advances in Neural Information Processing Systems, 25, pages
2636–2644, 2012.
- Flavio Ribeiro, Demba Ba, Cha Zhang, and Dinei Florencio. Turning enemies
into friends: Using reflections to improve sound source localization. In 2010 IEEE
International Conference on Multimedia and Expo (ICME), pages 731–736. doi:
10.1109/ICME.2010.5583886.
- Demba Ba, Flavio Ribeiro, Cha Zhang, and Dinei Florencio. L1 regularized room
modeling with compact microphone arrays. In 2010 IEEE International Conference
on Acoustics Speech and Signal Processing (ICASSP), pages 157–160. doi:10.1109/
ICASSP.2010.5496093.
Refereed Journal Articles
- Demba Ba. Deeply-sparse signal representations. IEEE Transactions on Signal
Processing, 2020. URL: https://arxiv.org/abs/1807.01958.
- Bahareh Tolooshams, Sourav Dey, and Demba Ba. Deep residual auto-encoders for
expectation maximization-inspired dictionary learning. IEEE Transcations on Neural
Networks, 2020. URL: https://arxiv.org/pdf/1904.08827.
- Andrew Song, Francisco Flores, and Demba Ba. Fast convolutional dictionary learning
with grid refinement. IEEE Transactions on Signal Processing (Accepted with minor
revisions), 2020. URL: https://arxiv.org/abs/1807.01958.
- Noa Malem-Shinitski, Yingzhuo Zhang, Daniel T Gray, Sara N Burke, Anne C Smith,
Carol A Barnes, and Demba Ba. A separable two-dimensional random field model of
binary response data from multi-day behavioral experiments. Journal of neuroscience
methods, 307:175–187, 2018. doi:10.1016/j.jneumeth.2018.04.006.
- Yingzhuo Zhang, Noa Malem-Shinitski, Stephen A Allsop, Kay M Tye, and Demba
Ba. Estimating a separably markov random field from binary observations. Neural
computation, 30(4):1046–1079, 2018. doi:10.1162/neco_a_01059.
- Gabriel Schamberg, Demba Ba, and Todd Coleman. A modularized efficient
framework for non-markov time series estimation. IEEE Transactions on Signal
Processing, 66(12):3140–3154, 2018. doi:10.1109/TSP.2018.2793870.
- Seong-Eun Kim, Michael Behr, Demba Ba, and Emery N Brown. State-space
multitaper time-frequency analysis. Proceedings of the National Academy of Sciences,
111(50):E5336–E5345, 2018. doi:10.1073/pnas.1702877115.
- Seong-Eun Kim, Demba Ba, and Emery N Brown. A multitaper frequency-domain
bootstrap method. IEEE Signal Processing Letters, 25(12):1805–1809, 2018.
- Gabriela Czanner, Sridevi V Sarma, Demba Ba, Uri T Eden, Wei Wu, Emad
Eskandar, Hubert H Lim, Simona Temereanca, Wendy A Suzuki, and Emery N
Brown. Measuring the singal-to-noise ratio of a neuron. Proceedings of the National
Academy of Sciences, 112(23):E7141–E7146, 2015. doi:10.1073/pnas.1505545112.
- Demba Ba, Behtash Babadi, Patrick L Purdon, and Emery N Brown. Convergence
and stability of iteratively re-weighted least squares algorithms. IEEE Transactions
on Signal Processing, 62(1):183–195, 2014. doi:10.1109/TSP.2013.2287685.
- Demba Ba, Behtash Babadi, Patrick L Purdon, and Emery N Brown. Robust
spectrotemporal decomposition by iteratively reweighted least squares. Proceedings of
the National Academy of Sciences, 111(50):E5336–E5345, 2014. doi:10.1073/pnas.
1320637111.
- Demba Ba, Simona Temereanca, and Emery N Brown. Algorithms for the analysis of
ensemble neural spiking activity using simultaneous-event multivariate point-process
models. Frontiers in computational neuroscience, 8, 2014. doi:10.3389/fncom.2014.
00006.
- Luca Citi, Demba Ba, Emery N Brown, and Riccardo Barbieri. Likelihood methods
for point processes with refractoriness. Neural computation, 26(2):237–263, 2014.
doi:10.1162/NECO_a_00548.
- Flavio Ribeiro, Dinei Florencio, Demba Ba, and Cha Zhang. Geometrically
constrained room modeling with compact microphone arrays. IEEE Transactions on
Audio, Speech, and Language Processing, 20(5):1449–1460, 2012. doi:10.1109/TASL.
2011.2180897.
- Flavio Ribeiro, Cha Zhang, Dinei Florencio, and Demba Ba. Using reverberation to
improve range and elevation discrimination for small array sound source localization.
IEEE Transactions on Audio, Speech, and Language Processing, 18(7):1781–1792, 2010.
doi:10.1109/TASL.2010.2052250.
- Cha Zhang, Dinei Florencio, Demba Ba, and Zhengyou Zhang. Maximum likelihood
sound source localization and beamforming for directional microphone arrays in
distributed meetings. IEEE Transactions on Multimedia, 10(3):538–548, 2008. doi:
10.1109/TMM.2008.917406.
Working Papers
- Andrew H Song, Bahareh Tolooshams,
and Demba Ba. Gaussian process convolutional dictionary learning. Submitted, 2021.
URL: https://arxiv.org/abs/2104.00530.
- Alexander Lin, Andrew H Song, Berkin Bilgic, and Demba Ba. Covariance-free sparse
bayesian learning. Submitted, 2021. URL: https://arxiv.org/abs/2105.10439.
- Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala, Abiy Tasissa, and
Demba Ba. On the convergence of group-sparse autoencoders. Submitted, 2021. URL:
https://arxiv.org/abs/2102.07003.
- Pranay Tankala, Abiy Tasissa, James M Murphy, and Demba Ba. Manifold
learning and deep clustering with local dictionaries. Submitted, 2021. URL:
https://arxiv.org/abs/2012.02134.
- Abiy Tasissa, Emmanouil Theodosis, Bahareh Tolooshams, and Demba Ba.
Dense and sparse coding: theory and architectures. Submitted, 2021. URL:
https://arxiv.org/abs/2006.09534.
Courses Taught
- Harvard Course ES 201: Decision Theory and Model-Based Deep Learning (Lecturer
Spring 2021)
- Harvard Course ES 157: Biomedical Signal Processing and Computing (Lecturer Fall
2020)
- Harvard Course ES 155: Biomedical Signal Processing and Computing (Lecturer Fall
2019)
- Harvard Course ES 201: Decision Theory (Lecturer Spring 2019)
- Harvard Course ES 201: Decision Theory (Lecturer Spring 2018)
- Harvard Course ES 155: Biomedical Signal Processing and Computing (Lecturer Fall
2016)
- Harvard Course ES 201: Decision Theory (Lecturer Spring 2017)
- Harvard Course ES 155: Biomedical Signal Processing and Computing (Lecturer
Spring/Fall, 2016)
- MIT Course 9.073: Statistics for Neuroscience Research (Lecturer Spring 2015)
- MIT Course 9.272J: Topics in Neural Signal Processing (Lecturer Spring 2013/2014)
- MIT Course 6.003: Signals and Systems (Head Teaching Assistant Fall 2007)
- MIT Course 6.002: Circuits and Electronics (Teaching Assistant Fall 2004/2006, Spring
2005/2007)
Skills
- Computer: Proficient: Python, Matlab/Octave. Experienced: C,C++,JavaScript.
- Spoken Languages: French, English, Spanish.
References
Last updated: 2021/06/26 at 15:06:29