School of Engineering and Applied Sciences
33 Oxford st Cambridge, MA 02138
- 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.
- Harvard University, School of Engineering and Applied Sciences, Cambridge, MA
Assistant Professor of Electrical Engineering and Bioengineering (July 2015–Present)
- 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)
- 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
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.
- “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.
- “Why and How to Leverage Amazon Cloud Services to Deploy JupyterHub at Scale?” Keynote, Jupytercon, August 2017 (with Faras Sadek).
- “Labz ‘n da wild: teaching signal processing using wearables and jupyter notebooks in the cloud.” Scientific Computing with Python 2016 (Scipy 2016), July 2016 (with Faras Sadek, Yasha Iravantchi and Yingzhuo (Diana) Zhang).
- “Wearable signal processing using docker notebook containers on AWS.” Jupyter Day Boston, Harvard University, February 2016 (with Faras Sadek, Yasha Iravantchi and Yingzhuo (Diana) Zhang).
- 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 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019. IEEE, 2019. 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 Machine Learning for Signal Processing (MLSP), 2018 IEEE 28th International Workshop on. IEEE, 2018.
- 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. IEEE, 2018.
- 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. IEEE, 2018.
- 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 Statistical Signal Processing Workshop (SSP), 2016 IEEE, pages 1–5. IEEE, 2016.
- Demba Ba, Behtash Babadi, Patrick L Purdon, and Emery N Brown. Neural spike train denoising by point process re-weighted iterative smoothing. In 48th Asilomar Conference on Signals, Systems and Computers., pages 763–768. IEEE, 2014. 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 Multimedia and Expo (ICME), 2010 IEEE International Conference on, pages 731–736. IEEE, 2010. doi:10.1109/ICME.2010.5583886
- Demba Ba, Flavio Ribeiro, Cha Zhang, and Dinei Florencio. L1 regularized room modeling with compact microphone arrays. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pages 157–160. IEEE, 2010. doi:10.1109/ICASSP.2010.5496093
- 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. Audio, Speech, and Language Processing, IEEE Transactions on, 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. Audio, Speech, and Language Processing, IEEE Transactions on, 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. Multimedia, IEEE Transactions on, 10(3):538–548, 2008. doi: 10.1109/TMM.2008.917406
- Bahareh Tolooshams, Sourav Dey, and Demba Ba. Constrained recurrent sparse auto-encoders: Expectation-maximization based architectures for convolutional dictionary learning. In Preperation for IEEE Transcations on Neural Networks, 2019.
- Demba Ba. Deeply-sparse signal representations. arXiv preprint arXiv:1807.01958, 2018. URL: https://arxiv.org/abs/1807.01958.
- Andrew H Song, Francisco Flores, and Demba Ba. Spike sorting by convolutional dictionary learning. arXiv preprint arXiv:1806.01979, 2018.
- 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)
- Spoken Languages: French, English, Spanish.
- Prof. Emery N Brown (Harvard/MIT) firstname.lastname@example.org
- Prof. Jonathan Victor (Cornell) email@example.com
- Prof. Todd P Coleman (UCSD) firstname.lastname@example.org
Last updated: 2019/01/28 at 11:18:10