Dr. Serre is a Professor of Cognitive Linguistic & Psychological Sciences and Computer Science. He received a Ph.D. in Neuroscience from MIT in 2006 and an MSc in EECS from Télécom Bretagne (France) in 2000. His research seeks to understand the neural computations supporting visual perception and it has been featured on the BBC and other news articles (The Economist, New Scientist, Scientific American, Technology Review, Slashdot, etc). Dr. Serre is the Faculty Director of the Center for Computation and Visualization and the Associate Director of the Center for Computational Brain Science. He is an affiliate of the Carney Institute for Brain Science and the Data Science Institute at Brown University. He also holds an International Chair in AI within the Artificial and Natural Intelligence Toulouse Institute (France). Dr. Serre has been serving as an area chair and a senior program committee member for top-tier machine learning and computer vision conferences including AAAI, CVPR, ICML, ICLR, and NeurIPS. He also serves as an editor for the journals eLife and PLOS computational biology. He was the recipient of an NSF Early Career Award and DARPA’s Young Faculty Award and Director’s Award. Together with his team, he was awarded the 2021 PAMI Helmholtz Prize and the 2022 PAMI Mark Everingham Prize for their work on human action recognition.
| Spagnuolo EJ, Wilf P, Serre T. "Decoding family-level features for modern and fossil leaves from computer-vision heat maps." American journal of botany, vol. 109, no. 5, 2022, pp. 768-788. |
| Vaishnav M, Cadene R, Alamia A, Linsley D, VanRullen R, Serre T. "Understanding the Computational Demands Underlying Visual Reasoning." Neural Computation, vol. 34, no. 5, 2022, pp. 1075-1099. |
| Wilf P, Wing SL, Meyer HW, Rose JA, Saha R, Serre T, Cúneo NR, Donovan MP, Erwin DM, Gandolfo MA, González-Akre E, Herrera F, Hu S, Iglesias A, Johnson KR, Karim TS, Zou X. "An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning." PhytoKeys, vol. 187, 2021, pp. 93-128. |
| Kreiman G, Serre T. "Beyond the feedforward sweep: feedback computations in the visual cortex." Annals of the New York Academy of Sciences, vol. 1464, no. 1, 2020, pp. 222-241. |
| Serre T. "Deep Learning: The Good, the Bad, and the Ugly." Annual review of vision science, vol. 5, 2019, pp. 399-426. |
| Kott O, Linsley D, Amin A, Karagounis A, Jeffers C, Golijanin D, Serre T, Gershman B. "Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study." European urology focus, vol. 7, no. 2, 2019, pp. 347-351. |
| Goodwill HL, Manzano-Nieves G, Gallo M, Lee HI, Oyerinde E, Serre T, Bath KG. "Early life stress leads to sex differences in development of depressive-like outcomes in a mouse model." Neuropsychopharmacology, vol. 44, no. 4, 2019, pp. 711-720. |
| Mély DA, Linsley D, Serre T. "Complementary surrounds explain diverse contextual phenomena across visual modalities." Psychological Review, vol. 125, no. 5, 2018, pp. 769-784. |
| D. Linsley, J.W. Linsley, T. Sharma, N. Meyers & T. Serre. "Learning to predict action potentials end-to-end from calcium imaging data." IEEE Annual Conference on Information Sciences and Systems, 2018. |
| Kim J, Ricci M, Serre T. "Not-So-CLEVR: learning same-different relations strains feedforward neural networks." Interface Focus, vol. 8, no. 4, 2018, pp. 20180011. |
| M. A. White, E. Kim, A. Duffy, R. Adalbert, B.U. Phillips, O.M. Peters, J. Stephenson, S. Yang, F. Massenzio, Z. Lin, S. Andrews, A. Segonds-Pichon, J. Metterville, L.M. Saksida, R. Mead, R.R Ribchester, Y. Barhomi, T. Serre, M.P. Coleman, Justin Fallon, T.J. Bussey, R.H. Brown Jr & J. Sreedharan. "TDP-43 gains function due to perturbed autoregulation in a Tardbp knock-in mouse model of ALS-FTD." Nature Neuroscience, vol. 21, no. 4, 2018, pp. 52-563. |
| D Linsley, S Eberhardt, T Sharma, P Gupta & T Serre. "What are the visual features underlying human versus machine vision?." IEEE ICCV, Workshop on the Mutual Benefit of Cognitive and Computer Vision, 2017. |
| Mély, David A., Kim, Junkyung, McGill, Mason, Guo, Yuliang, Serre, Thomas. "A systematic comparison between visual cues for boundary detection." Vision research, vol. 120, 2016, pp. 93-107. |
| Wilf, Peter, Zhang, Shengping, Chikkerur, Sharat, Little, Stefan A., Wing, Scott L., Serre, Thomas. "Computer vision cracks the leaf code." Proceedings of the National Academy of Sciences, vol. 113, no. 12, 2016, pp. 3305-3310. |
| Cauchoix, Maxime, Crouzet, Sébastien M., Fize, Denis, Serre, Thomas. "Fast ventral stream neural activity enables rapid visual categorization." NeuroImage, vol. 125, 2016, pp. 280-290. |
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S. Eberhardt*, J. Cader* & T. Serre.
"How deep is the feature analysis underlying rapid visual categorization?." Neural Information Processing Systems (NIPS), 2016.
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| Serre, Thomas. "Models of visual categorization." Wiley Interdisciplinary Reviews: Cognitive Science, vol. 7, no. 3, 2016, pp. 197-213. |
| Pascarella, A., Todaro, C., Clerc, M., Serre, T., Piana, M. "Source modeling of ElectroCorticoGraphy (ECoG) data: Stability analysis and spatial filtering." Journal of Neuroscience Methods, vol. 263, 2016, pp. 134-144. |
| Sofer, Imri, Crouzet, Sébastien M., Serre, Thomas. "Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization." PLOS Computational Biology, vol. 11, no. 9, 2015, pp. e1004456. |
| Hofmann, Jeffrey W., Zhao, Xiaoai, De Cecco, Marco, Peterson, Abigail L., Pagliaroli, Luca, Manivannan, Jayameenakshi, Hubbard, Gene B., Ikeno, Yuji, Zhang, Yongqing, Feng, Bin, Li, Xiaxi, Serre, Thomas, Qi, Wenbo, Van Remmen, Holly, Miller, Richard A., Bath, Kevin G., de Cabo, Rafael, Xu, Haiyan, Neretti, Nicola, Sedivy, John M. "Reduced expression of MYC increases longevity and enhances healthspan." Cell, vol. 160, no. 3, 2015, pp. 477-88. |
| Parker, Sarah M., Serre, Thomas. "Unsupervised invariance learning of transformation sequences in a model of object recognition yields selectivity for non-accidental properties." Front. Comput. Neurosci., vol. 9, 2015. |
| Kuehne, Hilde, Arslan, Ali, Serre, Thomas. "The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities." 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. |
| Cauchoix, M., Barragan-Jason, G., Serre, T., Barbeau, E. J. "The Neural Dynamics of Face Detection in the Wild Revealed by MVPA." Journal of Neuroscience, vol. 34, no. 3, 2014, pp. 846-854. |
| Poggio, Tomaso, Serre, Thomas. "Models of visual cortex." Scholarpedia, vol. 8, no. 4, 2013, pp. 3516. |
| Heindel, William, Festa, Elena, Ott, Brian, Sofer, Imri, Serre, Thomas. "Rapid visual categorization as a sensitive measure of early Alzheimer's disease." Alzheimer's & Dementia, vol. 9, no. 4, 2013, pp. P451-P452. |
| Leussis, Melanie P., Berry-Scott, Erin M., Saito, Mai, Jhuang, Hueihan, de Haan, Georgius, Alkan, Ozan, Luce, Catherine J., Madison, Jon M., Sklar, Pamela, Serre, Thomas, Root, David E., Petryshen, Tracey L. "The ANK3 Bipolar Disorder Gene Regulates Psychiatric-Related Behaviors That Are Modulated by Lithium and Stress." Biological Psychiatry, vol. 73, no. 7, 2013, pp. 683-690. |
| Zhang, Jun, Barhomi, Youssef, Serre, Thomas. "A New Biologically Inspired Color Image Descriptor." European Conference on Computer Vision, 2012, pp. 312-324. |
| Cauchoix, Maxime, Arslan, Ali Bilgin, Fize, Denis, Serre, Thomas. "The Neural Dynamics of Visual Processing in Monkey Extrastriate Cortex: A Comparison between Univariate and Multivariate Techniques." Neural Information Processing Systems, 2012, pp. 164-171. |
| Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T. "HMDB: A large video database for human motion recognition." 2011 International Conference on Computer Vision, 2011. |
| Zhang, Y., Meyers, E. M., Bichot, N. P., Serre, T., Poggio, T. A., Desimone, R. "Object decoding with attention in inferior temporal cortex." Proceedings of the National Academy of Sciences, vol. 108, no. 21, 2011, pp. 8850-8855. |
| Serre, Thomas, Poggio, Tomaso. "A neuromorphic approach to computer vision." Communications of the ACM, vol. 53, no. 10, 2010, pp. 54. |
| Jhuang, Hueihan, Garrote, Estibaliz, Yu, Xinlin, Khilnani, Vinita, Poggio, Tomaso, Steele, Andrew D., Serre, Thomas. "Automated home-cage behavioural phenotyping of mice." Nature Communications, vol. 1, no. 6, 2010, pp. 1-9. |
| Reddy, Leila, Tsuchiya, Naotsugu, Serre, Thomas. "Reading the mind's eye: Decoding category information during mental imagery." NeuroImage, vol. 50, no. 2, 2010, pp. 818-825. |
| Kliper, Roi, Serre, Thomas, Weinshall, Daphna, Nelkenz, Israel. "The story of a single cell: Peeking into the semantics of spikes." 2010 2nd International Workshop on Cognitive Information Processing, 2010. |
| Jhuang, Hueihan, Garrote, Estibaliz, Edelman, Nicholas, Poggio, Tomaso, Steele, Andrew, Serre, Thomas. "Trainable, vision-based automated home cage behavioral phenotyping." Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - MB '10, 2010. |
| Chikkerur, Sharat, Serre, Thomas, Tan, Cheston, Poggio, Tomaso. "What and where: A Bayesian inference theory of attention." Vision research, vol. 50, no. 22, 2010, pp. 2233-2247. |
| Jhuang, H., Serre, T., Wolf, L., Poggio, T. "A Biologically Inspired System for Action Recognition." 2007 IEEE 11th International Conference on Computer Vision, 2007. |
| Serre, T., Oliva, A., Poggio, T. "A feedforward architecture accounts for rapid categorization." Proceedings of the National Academy of Sciences, vol. 104, no. 15, 2007, pp. 6424-6429. |
| Serre, Thomas, Wolf, Lior, Bileschi, Stanley, Riesenhuber, Maximilian, Poggio, Tomaso. "Robust Object Recognition with Cortex-Like Mechanisms." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 3, 2007, pp. 411-426. |
| Sigala, Rodrigo, Serre, Thomas, Poggio, Tomaso, Giese, Martin. "Learning Features of Intermediate Complexity for the Recognition of Biological Motion." International Conference on Artificial Neural Networks, 2005, pp. 241-246. |
| Serre, T., Wolf, L., Poggio, T. "Object Recognition with Features Inspired by Visual Cortex." 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005. |
My lab seeks to understand the neural computations supporting visual perception. There is little doubt that even a partial solution to the question of which computations are carried out by the visual cortex would be a major breakthrough: It would begin to explain one of our most amazing abilities, vision; and it would open doors to other aspects of intelligence such as language, planning or reasoning. It would also help connect neurobiology and mathematics, making it possible to develop computer algorithms that follow the information processing principles used by biological organisms and honed by natural evolution.
Work in my laboratory has sought to identify the neural computations supporting visual perception. We have made significant contributions towards the development of a unified, mechanistic theory of visual processing which spans cortical areas, visual domains and functions. We have developed computational neuroscience models to emulate the very first feedforward pass of information through the visual cortex and to describe subsequent interactions with feedback attentional and contextual processes. We have exposed the remarkable capabilities of these feedforward processes but also their key limitations, which has motivated much of our experimental work. In the process, we have also identified “canonical” computations that are shared across visual domains and functions, suggesting that different visual cortices may tackle a common set of computational problems with a shared toolbox of computations, and paving the way for the identification of their neural substrate.
Project title: Brain-inspired deep learning models of visual reasoning
Funding agency: ONR
Grant type: Research grant
Grant number: N00014-19-1-2029
Role: PI
Duration: 2018–2023
Project title: Oscillatory processes for visual reasoning in deep neural networks
Funding agency: NSF
Grant type: CRCNS US-France Research grant
Grant number: IIS-1912280
Role: co-PI (PIs: Serre/VanRullen)
Duration: 2019–2022
Project title: Intelligent Spine Interface (ISI)
Funding agency: DARPA
Grant type: Research grant
Grant number: D19AC00015
Role: Co-I (PI: Borton)
Duration: 2019–2021
Project title: Origins of southeast Asian rainforests from paleobotany and machine learning
Funding agency: NSF
Grant type: Collaborative research grant in Frontier Research in Earth Sciences (FRES)
Grant number: EAR-1925481
Role: co-PI (Wilf/Gandolfo/Serre)
Duration: 2019–2024
Project title: Next-generation machine vision for automated behavioral phenotyping of knock-in ALS-FTD mouse models
Funding agency: NIH
Grant type: R21
Role: co-PI (Fallon/Serre)
Duration: 2020–2022
| Year | Degree | Institution |
|---|---|---|
| 2006 | PhD | Massachusetts Institute of Technology |
| 2000 | MS | Université de Rennes |
| 2000 | MS | École Nationale Supérieure des Telecommunications de Bretagne |
| 1997 | BS | Lycee Pasteur |
| Postdoctoral Associate | MIT, Mc Govern Institute for Brain Research | 2006-2010 | Cambridge, MA |
2022 PAMI Mark Everingham Prize
2021 PAMI Helmholtz Prize
2019 International Chair in AI, ANR-3IA ANITI
2016 DARPA Director's Award
2014 DARPA Young Faculty Award
2013 Manning Assistant Professor
2013 NSF early career award
2012 Teaching with Technology Course Design Award
2011–2012 Sheridan Junior Faculty Teaching Fellow
| Name | Title |
|---|---|
| Borton, David | Associate Professor of Engineering, Associate Professor of Neurosurgery, Associate Professor of Brain Science |
| Fallon, Justin | Professor of Medical Science, Professor of Psychiatry and Human Behavior |
| Frank, Michael | Edgar L. Marston Professor of Psychology, Professor of Brain Science |
| Pavlick, Ellie | Briger Family Distinguished Associate Professor of Computer Science, Associate Chair of Computer Science |
| Sheinberg, David | Professor of Neuroscience, Graduate Program Director for the Neuroscience Graduate Program |
Carney Institute for Brain Science
Data Science Initiative
| CLPS 0950 - Introduction to programming |
| CLPS 1291 - Computational Methods for Mind, Brain and Behavior |
| CLPS 1950 - Deep Learning in Brains, Minds and Machines |
