Oliver Y. Chén bio photo

Oliver Y. Chén

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Reviews

  • The roles of statistics in human neuroscience. Brain Sci. 9, 194 (2019). Dedicated to Michael Jacroux in Honour of his Emeritus Retirement. [Paper] [Erratum].

  • Big data in omics and imaging: integrated analysis and causal inference. The Journal of the American Statistical Association 115, 487-488 (2020) [Paper].

  • Thou shalt not reject the P-value. arXiv 2002.07270 (2020) (with R. Saraiva, G. Nagels, H. Phan, T. Schwantje, H. Cao, J. Gou, J. Reinen, B. Xiong, and M. de Vos) [Paper].

  • The Bayesian-Laplacian brain. European Journal of Neuroscience 51, 1441-1462 (2019) (with S. Zeki) [Paper].

Book chapters

  • Critical boundary refinement in a group sequential trial when the primary endpoint data accumulate faster than the secondary endpoint. In ICSA Book Series in Statistics. Springer, Berlin, Germany (2018) (with J. Gou) [Chapter] [Book].

  • Unsupervised learning methods to proteomic data from colon cancer. In Contemporary Topics in Mathematics and Statistics with Applications. New Delhi, India (2013) (with N. Dasgupta, R. Basu, and S.S. Daoud) [Chapter].

Papers

  • Building a machine-learning framework to remotely assess Parkinson’s disease using smartphones. IEEE Transactions on Biomedical Engineering (2020) (with F. Lipsmeier, H. Phan, J. Prince, K.I. Taylor, M. Lindemann, C. Gossens, and M. de Vos) [Paper].

  • Resting-state brain information flow predicts cognitive flexibility in humans. Scientific Reports 9, 3879 (2019) (with H. Cao, J. Reinen, T. Qian, J. Gou, H. Phan, M. de Vos, and T. Cannon) [Paper].

  • Identifying Neural Signatures Mediating Behavioral Symptoms and Psychosis Onset: High-Dimensional Whole Brain Functional Mediation Analysis. bioRxiv 2020.04.15.043034 (2020) (with H. Cao, H. Phan, J. Reinen, J. Gou, J. Di, T. Qian, J. Prince, T. Cannon, and M. de Vos) [Paper][Supp. Materials].

  • The causal effect of priming. (2019) (with H. Phan, T. Qian, and M. de Vos).

  • Cerebello-thalamo-cortical hyperconnectivity: a state-independent functional neural signature for psychosis prediction and characterization. Nature Communications 9, 3836 (2018) (with H. Cao, Y. Chung, S.C. McEwen, C.E. Bearden, J. Addington, B. Goodyear, K.S. Cadenhead, H. Mirzakhanian, B.A. Cornblatt, D.M. Olvet, D.H. Mathalon, T.H. McGlashan, D.O. Perkins, A. Belger, L.J. Seidman, H. Thermenos, M.T. Tsuang, T.G.M. van Erp, E.F. Walker, S. Hamann, A. Anticevic, S.W. Woods, and T.D. Cannon) [Paper][Supp. Materials].

  • The human cerebral cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychotic illness. Nature Communications 9, 1157 (2018) (with J. Reinen, J. Baker, T. Yeo, K. Anderson, R. Hutchison, M. Sabuncu, D. Öngür, J. Roffman, J. Smoller, and A. Holmes) [Paper] [Supp. Materials].

  • On the use of semi-folding in regular blocks two-level factorial designs. Communications in Statistics - Theory and Methods 44, 2473-2506 (2015) (with M.A. Jacroux) [Paper].

  • The biological basis of mathematical beauty. Frontiers in Human Neuroscience 12, 467 (2018) (with S. Zeki and J. Romaya) [Paper].

  • High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics 19, 121–136 (2015) (with E. Ogburn, C. Crainiceanu, B. Caffo, T. Wager, and M. Lindquist) [Paper] [Asymptotic Theory].

  • SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 400-410 (2018) (with H. Phan, F. Andreotti, N. Cooray, and M. de Vos) [Paper] [Journal Cover].

  • Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering 66, 1285-1296 (2018) (with H. Phan, F. Andreotti, N. Cooray, and M. de Vos) [Paper].

  • Automatic sleep staging: personalization with single-night data via KL-divergence regularization. Physiological Measurements (2020) (with H. Phan, K. Mikkelsen, P. Koch, A. Mertins, P. Kidmose, and M. de Vos).

  • A longitudinal functional data analysis for underlying daily physical activity change. (2015) (with L. Xiao, J. Schrack, L. Ferrucci, and C. Crainiceanu) [Paper].

  • Automatic sleep stage classification using single-channel EEG: learning sequential features with attention-based recurrent reural networks. In IEEE Engineering in Medicine and Biology Society (EMBC), 1452-1455 (2018) (with H. Phan, F. Andreotti, N. Cooray, and M. de Vos) [Paper].

  • Towards more accurate automatic sleep staging via deep transfer learning. arXiv 1907.13177 (2019) (with H. Phan, P. Koch, Z. Lu, I. McLoughlin, A. Mertins, and M. de Vos) [Paper].

  • Cross-paradigm connectivity: reliabiltiy, stability, and utility. Brain Imaging and Behavior (2020) (with H. Cao, S. McEwen, J. Forsyth, G. Dylan, C. Bearden, J. Addington, B. Goodyear, K. Cadenhead, H. Mirzakhanian, B. Cornblatt, R. Carrión, D. Mathalon, T. McGlashan, D. Perkins, A. Belger, H. Thermenos, M. Tsuang, T. van Erp, E. Walker, S. Hamann, A. Anticevic, S. Wood, and T.D. Cannon).

  • Improving GANs for speech enhancement. arXiv 2001.05532 (2020) (with H. Phan, I. McLoughlin, L. Pham, P. Koch, M. de Vos, and A. Mertins) [Paper]

  • Revisiting convolutional neural networks for automated sleep staging. EMBC (2020) (wiht H. Phan, P. Koch, L. Pham, I. McLoughlin, A. Mertins, and M. de Vos).

  • Spatial-temporal attention pooling for audio scene classification. Interspeech (2019) (with H. Phan, L. Pham, P. Koch, M. de Vos, I. McLoughlin, and A. Mertins) [Paper].

  • Deep transfer learning for single-channel automatic sleep staging with channel mismatch. EUSIPCO (IEEE European Association for Signal Processing (2019) (with H. Phan, P. Koch, A. Mertins, and M. de Vos) [Paper].

  • Fusion of end-to-end deep learning models for sequence-to-sequence sleep staging. IEEE Engineering in Medicine and Biology Society (2019) (with H. Phan, P. Koch, A. Mertins, and M. de Vos) [Paper].

  • Unifying isolated and overlapping audio event detection with multi-label multi-task convolutional recurrent neural networks. In ICASSP 51-55 (2018) (with H. Phan, P. Koch, L. Pham, I. McLoughlinz, A. Mertins, and M. de Vos) [Paper].

  • Beyond equal-length snippets: how long is sufficient to recognize an audio scene? (2018) (with H. Phan, P. Koch, L. Pham, I. McLoughlin, A. Mertins, and M. de Vos) [Paper].

  • Altered brain activation during memory retrieval precedes and predicts conversion to psychosis in individuals at clinical high risk. Schizophrenia Bulletin 45, 924-933 (2018) (with H. Cao H, S.C. McEwen, Y. Chung, C.E. Bearden, J. Addington, B. Goodyear, K.S. Cadenhead, H. Mirzakhanian, B.A. Cornblatt, D.M. Olvet, D.H. Mathalon, T.H. McGlashan, D.O. Perkins, A. Belger, L.J. Seidman, H. Thermenos, M.T. Tsuang, T.G.M. van Erp, E.F. Walker, S. Hamann, A. Anticevic, S.W. Woods, and T.D. Cannon) [Paper].

  • DNN filter bank improves 1-max pooling CNN for automatic sleep stage classification. IEEE Engineering in Medicine and Biology Society (EMBC), 453-456 (2018) (with H. Phan, F. Andreotti, N. Cooray, and M. de Vos) [Paper].

  • Towards better practice of deep learning for automatic sleep staging. IEEE (2018) (with H. Phan, F. Andreotti, N. Cooray, and M. de Vos) [Paper].

  • Penalised iterative sparse partial correlation estimation (Π-SPaCE) - with an application to whole-brain graph estimation. (2015) (with J. Di).

  • A generalized and drifting time corrected approach using Wiener-Granger causality and VAR(p) process for detecting high-dimensional directed functional communication between brain regions and predicting behavior.

  • The generative representational similarity analysis.

  • Comparison of clustering algorithms: an example with proteomic data. Advances and Applications in Statistics 33, 63 (2013) (with N. Dasgupta, R. Basu, and S.S. Daoud) [Paper].

  • Comparison of methods for unsupervised learning methods – an applied study using proteomic data from colon cancer and simulations. In CIAS, Indian Statistical Institute (2012) (with N. Dasgupta, R. Basu, and S.S. Daoud).

Lectures and talks

I have made many errors and mistakes in research and in ways of thinking; I am very grateful to scholars from various of fields who have pointed out my shortcomings and helped me learn and grow.

  • John von Neumann Institute, Vietnam National University. Ho Chi Minh City, Vietnam. December, 2018

  • Roche Innovation Center. Basel, Switzerland. September, 2018

  • Mathematical Institute, University of Oxford. Oxford, UK. February, 2018

  • Institute of Biomedical Engineering, University of Oxford. Oxford, UK. February, 2018

  • Cognitive Neuroscience Laboratory, Yale University. New Haven. CT. June, 2017

  • Bernstein Center for Computational Neuroscience. Berlin, Germany. August, 2016.

  • Department of Psychology, Yale University. New Haven, CT. July, 2016.

  • Si-Shui Lecture. He University and He Eye Hospital. Shen-Yang, China. September, 2016.

  • Department of Psychology, Stanford University. Serra Mall, Stanford, CA. January, 2016.

  • MRC Brain Network Dynamics Unit, University of Oxford. Oxford, United Kingdom. December, 2015.

  • Engineering Department, University of Cambridge. Cambridge, United Kingdom. December, 2015.

  • The 8th International Conference of the ERCIM WG on Computational and Methodological Statistics, London, United Kingdom. December, 2015.

  • Spiegelman Student Finalist Speaker. The 142nd Annual Meeting and Exposition of the American Public Health Association, Chicago, IL. October, 2015.

  • ETH Zürich and University of Zürich. Institut für Neuroinformatik, Zürich, Switzerland, August, 2015.

  • Mathematics Colloquium. Department of Mathematics and Statistics, Washington State University. Pullman, WA. August, 2015.

Honors and awards

  • Si-Shui Lecture.

  • Joseph Zeger Award.

  • Louis I. and Thomas D. Dublin Award for the Advancement of Epidemiology and Biostatistics.

  • Univeristy College London Visiting Lectureship.

  • Fritz Hoffmann-La Roche Award.

  • Statistical and Applied Mathematical Sciences Institute Award.

  • Award of Excellence for Outstanding Performance and Lasting Contributions as a Teaching Assistant.

  • First Place. Student Research Competition, the Applied Public Health Statistics Section, the American Public Health Association.

Unpublished Reviews, Essays, Handbooks, and Notes

  • Chén O. Y. Comments on Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity (Pfister and Gerstner, 2006). [Link]

  • Chén O. Y. A Handbook to Conquer Casella and Berger Book in Ten Days. [Link]

  • Chén O. Y. A Brief Study Guide for Full, Blocking, and Fractional Factorial Experimental Designs. [Link]

  • Chén O. Y. Notes for Time Series Data Analysis. [Link]