Dr. Serre is a Professor of Cognitive and Psychological Sciences as well as Computer Science. He received his Ph.D. in Neuroscience from MIT in 2006 and his M.Sc. in Electrical Engineering and Computer Science from Télécom Bretagne in France in 2000. His research focuses on understanding the neural computations that support visual perception, and it has been featured in various media outlets, including the BBC, The Economist, New Scientist, Scientific American, Technology Review, and Slashdot.
Dr. Serre serves as the Faculty Director of the Center for Computation and Visualization and the Associate Director of the Center for Computational Brain Science. He is also an affiliate of the Carney Institute for Brain Science and the Data Science Institute at Brown University. Additionally, he holds an International Chair in Artificial Intelligence at the Artificial and Natural Intelligence Toulouse Institute in France.
He has actively participated as an area chair and senior program committee member for prestigious machine learning and computer vision conferences, such as AAAI, CVPR, ICML, ICLR, and NeurIPS. Dr. Serre is a Neuroscience section editor for the journal PLOS Computational Biology.
He has received several awards, including the NSF Early Career Award, DARPA’s Young Faculty Award, and the Director's Award. Along 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.
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M.A. Lepori, J. Hu, I. Dasgupta, R. Patel, T. Serre & E. Pavlick.
"Is this just fantasy? Language model representations reflect human judgments of event plausibility." International Conference on Learning Representations, 2026.
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| Calvert, Jonathan S., Parker, Samuel R., Govindarajan, Lakshmi N., Darie, Radu, Shaaya, Elias, Solinsky, Ryan, Del Valle, Lily M., Miranda, Priyanka, Jang, Jaeson, Tiwari, Ekta, Syed, Sohail, Villalobos, Raymond M., Aguiar, Liza M., Taylor, J. Andrew, Tang, Hanlin, McPherson, Sean, Xue, Wenzhe, Carayannopoulos, Alexios G., Oyelese, Adetokunbo A., Gokaslan, Ziya L., Bansal, Arjun K., Resnik, Linda J., Serre, Thomas, Fridley, Jared S., Borton, David A. "Perilesional neuromodulation replaces lost sensorimotor function in persons with spinal cord injury." Nature Biomedical Engineering, 2026. |
| Parker, Samuel R, Calvert, Jonathan S, Darie, Radu, Jang, Jaeson, Govindarajan, Lakshmi Narasimhan, Angelino, Keith, Chitnis, Girish, Iyassu, Yohannes, Shaaya, Elias, Fridley, Jared S, Serre, Thomas, Borton, David A, McLaughlin, Bryan L. "An active electronic, high-density epidural paddle array for chronic spinal cord neuromodulation." Journal of Neural Engineering, vol. 22, no. 2, 2025, pp. 026023. |
| Linsley, Drew, Feng, Pinyuan, Serre, Thomas. "Better artificial intelligence does not mean better models of biology." Trends in Cognitive Sciences, 2025. |
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P. Feng, D. Linsley, T. Boissin, A.K. Ashok, T. Fel, S. Olaiya & T. Serre.
"Beyond adversarial robustness: Breaking the robustness-alignment trade-off in object recognition." ICLR 2025 Workshop on Representational Alignment, 2025.
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| Muzellec S, Alamia A, Serre T, VanRullen R. "Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics." ArXiv, 2025. |
| Pelgrim MH, Raman SS, Serre T, Buchsbaum D. "Evaluating Dogs' Real-World Visual Environment and Attention." Cognitive Science, vol. 49, no. 6, 2025, pp. e70080. |
| Roelfsema PR, Serre T. "Feature binding in biological and artificial vision." Trends in Cognitive Sciences, 2025. |
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L. Béthune, D. Vigouroux, Y. Du, R. VanRullen, T. Serre & V. Boutin.
"Follow the energy, find the path: Riemannian metrics from energy-based models." Neural Information Processing Systems, 2025.
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| Serre, Thomas, Pavlick, Ellie. "From prediction to understanding: Will AI foundation models transform brain science?." Neuron, vol. 113, no. 21, 2025, pp. 3504-3508. |
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S. Muzellec, D. Linsley, A.K. Ashok, E. Mingolla, G. Malik, R. VanRullen & T. Serre.
"Tracking objects that change in appearance with phase synchrony." International Conference on Learning Representations, 2025.
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M.A. Lepori, A.R. Tartaglini, W.K. Vong, T. Serre, B.M. Lake & E. Pavlick.
"Beyond the doors of perception: Vision transformers represent relations between objects." Neural Information Processing Systems, 2024.
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| Shahamatdar, Sahar, Saeed‐Vafa, Daryoush, Linsley, Drew, Khalil, Farah, Lovinger, Katherine, Li, Lester, McLeod, Howard T., Ramachandran, Sohini, Serre, Thomas. "Deceptive learning in histopathology." Histopathology, vol. 85, no. 1, 2024, pp. 116-132. |
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S. Muzellec, T. Fel, V. Boutin, L. Andeol, R. VanRullen & T. Serre.
"Gradient strikes back: How filtering out high frequencies improves explanations." International Conference on Machine Learning, 2024.
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V. Boutin, R. Mukherji, A. Agrawal, S. Muzellec, T. Fel, T. Serre & R. VanRullen.
"Latent representation matters: Human-like sketches in one-shot drawing tasks." Neural Information Processing Systems, 2024.
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| Ahuja, Aarit, Yusif Rodriguez, Nadira, Ashok, Alekh Karkada, Serre, Thomas, Desrochers, Theresa M., Sheinberg, David L. "Monkeys engage in visual simulation to solve complex problems." Current Biology, vol. 34, no. 24, 2024, pp. 5635-5645.e3. |
| Cheng YA, Rodriguez IF, Chen S, Kar K, Watanabe T, Serre T. "RTify: Aligning Deep Neural Networks with Human Behavioral Decisions." ArXiv, 2024. |
| Linsley, Drew, Zhou, Peisen, Ashok, Alekh Karkada, Nagaraj, Akash, Gaonkar, Gaurav, Lewis, Francis E, Pizlo, Zygmunt, Serre, Thomas. "The 3D-PC: a benchmark for visual perspective taking in humans and machines." International Conference on Learning Representations, 2024. |
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M.A. Lepori, T. Serre & E. Pavlick.
"Uncovering intermediate variables in transformers using circuit probing." Conference on Language Modeling, 2024.
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T. Fel, L. Bethune, A.K. Lampinen, T. Serre & K. Hermann.
"Understanding visual feature reliance through the lens of complexity." Neural Information Processing Systems, 2024.
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T. Fel, V. Boutin, M. Moayeri, R. Cadene, L. Bethune, L. Andeol, M. Chalvidal & T. Serre.
"A holistic approach to unifying automatic concept extraction and concept importance estimation." Neural Information Processing Systems, 2023.
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| Lepori, Michael A., Serre, Thomas, Pavlick, Ellie. "Break It Down: Evidence for Structural Compositionality in Neural Networks." Neural Information Processing Systems, 2023. |
| Levin, Zachary, Leary, Owen P, Mora, Victor, Kant, Shawn, Brown, Sarah, Svokos, Konstantina, Akbar, Umer, Serre, Thomas, Klinge, Petra, Fleischmann, Alexander, Ruocco, Maria Grazia. "Cerebrospinal fluid transcripts may predict shunt surgery responses in normal pressure hydrocephalus." Brain, vol. 146, no. 9, 2023, pp. 3747-3759. |
| Goetschalckx, Lore, Govindarajan, Lakshmi Narasimhan, Ashok, Alekh Karkada, Ahuja, Aarit, Sheinberg, David L., Serre, Thomas. "Computing a human-like reaction time metric from stable recurrent vision models." Neural Information Processing Systems, 2023. |
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T. Fel, A. Picard, L. Bethune, T. Boissin, D. Vigouroux, J. Colin, R. Cadene & T. Serre.
"CRAFT: Concept Recursive Activation FacTorization for explainability." IEEE Conference on Computer Vision and Pattern Recognition, 2023.
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V. Boutin, T. Fel, L. Singhal, R. Mukherji, A. Nagaraj, J. Colin & T. Serre.
"Diffusion models as artists: Are we closing the gap between humans and machines?." International Conference on Machine Learning, 2023.
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T. Fel, M. Ducoffe, D. Vigouroux, R. Cadène, M. Capelle, C. Nicodème & T. Serre.
"Don't lie to me! Robust and efficient explainability with verified perturbation analysis." IEEE Conference on Computer Vision and Pattern Recognition, 2023.
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A. Nagaraj, A.K. Ashok, D. Linsley, F.E. Lewis, P. Zhou & T. Serre.
"Ecological data and objectives align deep neural network representations with humans." NeurIPS Workshop on Unifying Representations in Neural Models, 2023.
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| Linsley, Drew, Serre, Thomas. "Fixing the problems of deep neural networks will require better training data and learning algorithms." Behavioral and Brain Sciences, vol. 46, 2023. |
| Chalvidal, Mathieu, Serre, Thomas, VanRullen, Rufin. "Learning Functional Transduction." Neural Information Processing Systems, 2023. |
| Linsley, Drew, Rodriguez, Ivan F., Fel, Thomas, Arcaro, Michael, Sharma, Saloni, Livingstone, Margaret, Serre, Thomas. "Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex." Neural Information Processing Systems, 2023. |
| Fel, Thomas, Boissin, Thibaut, Boutin, Victor, Picard, Agustin, Novello, Paul, Colin, Julien, Linsley, Drew, Rousseau, Tom, Cadène, Rémi, Goetschalckx, Lore, Gardes, Laurent, Serre, Thomas. "Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization." arXiv, 2023. |
| Zerroug, Aimen, Vaishnav, Mohit, Colin, Julien, Musslick, Sebastian, Serre, Thomas. "A Benchmark for Compositional Visual Reasoning." Neural Information Processing Systems, 2022. |
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L.N. Govindarajan, R. Kakodkar & T. Serre.
"A practitioner's guide to improve the logistics of spatiotemporal deep neural networks." Workshop on Visual Observation and Analysis of Vertebrate and Insect Behavior, 2022.
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| 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. |
| Boutin, Victor, Singhal, Lakshya, Thomas, Xavier, Serre, Thomas. "Diversity vs. Recognizability: Human-like generalization in one-shot generative models." Neural Information Processing Systems, 2022. |
| Govindarajan, Lakshmi Narasimhan, Calvert, Jonathan S, Parker, Samuel R, Jung, Minju, Darie, Radu, Miranda, Priyanka, Shaaya, Elias, Borton, David A, Serre, Thomas. "Fast inference of spinal neuromodulation for motor control using amortized neural networks." Journal of Neural Engineering, vol. 19, no. 5, 2022, pp. 056037. |
| Vaishnav, Mohit, Serre, Thomas. "GAMR: A Guided Attention Model for (visual) Reasoning." arXiv, 2022. |
| Fel, Thomas, Felipe, Ivan, Linsley, Drew, Serre, Thomas. "Harmonizing the object recognition strategies of deep neural networks with humans." Neural Information Processing Systems, 2022. |
| Tanfous, Amor Ben, Zerroug, Aimen, Linsley, Drew, Serre, Thomas. "How and What to Learn: Taxonomizing Self-Supervised Learning for 3D Action Recognition." 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 2888-2897. |
| Fel, Thomas, Vigouroux, David, Cadene, Remi, Serre, Thomas. "How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks." 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 1565-1575. |
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M. Chalvidal, T. Serre & R. VanRullen.
"Meta-reinforcement learning with self-modifying networks." Neural Information Processing Systems, 2022.
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A.K. Ashok, L.N. Govindarajan, D. Linsley, D. Sheinberg & T. Serre.
"The emergence of visual simulation in task-optimized recurrent neural networks." NeurIPS Workshop on Shared Visual Representations in Human and Machine Intelligence, 2022.
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| 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. |
| Fel, Thomas, Hervier, Lucas, Vigouroux, David, Poche, Antonin, Plakoo, Justin, Cadene, Remi, Chalvidal, Mathieu, Colin, Julien, Boissin, Thibaut, Bethune, Louis, Picard, Agustin, Nicodeme, Claire, Gardes, Laurent, Flandin, Gregory, Serre, Thomas. "Xplique: A Deep Learning Explainability Toolbox." CVPR workshop on XAI4CV: Explainable Artificial Intelligence for Computer Vision, 2022. |
| Lindsay, Grace W., Serre, Thomas. "Deep Learning Networks and Visual Perception." Oxford Research Encyclopedia of Psychology, 2021. |
| Fel, Thomas, Cadene, Remi, Chalvidal, Mathieu, Cord, Matthieu, Vigouroux, David, Serre, Thomas. "Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis." arXiv, 2021. |
| Ricci, Matthew, Cadène, Rémi, Serre, Thomas. "Same-different conceptualization: a machine vision perspective." Current Opinion in Behavioral Sciences, vol. 37, 2021, pp. 47-55. |
| Linsley, Jeremy W., Linsley, Drew A., Lamstein, Josh, Ryan, Gennadi, Shah, Kevan, Castello, Nicholas A., Oza, Viral, Kalra, Jaslin, Wang, Shijie, Tokuno, Zachary, Javaherian, Ashkan, Serre, Thomas, Finkbeiner, Steven. "Superhuman cell death detection with biomarker-optimized neural networks." Science Advances, vol. 7, no. 50, 2021. |
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G. Malik, D. Linsley, T. Serre & E. Mingolla.
"The challenge of appearance-free object tracking with feedforward neural networks." CVPR Workshop on Dynamic Neural Networks Meets Computer Vision, 2021.
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| Linsley, Drew, Malik, Girik, Kim, Junkyung, Govindarajan, Lakshmi N, Mingolla, Ennio, Serre, Thomas. "Tracking Without Re-recognition in Humans and Machines." Neural Information Processing Systems, 2021. |
| Colin, Julien, Fel, Thomas, Cadene, Remi, Serre, Thomas. "What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods." Neural Information Processing Systems, 2021. |
| 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, Gabriel, Serre, Thomas. "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. |
| Alamia, Andrea, Luo, Canhuang, Ricci, Matthew, Kim, Junkyung, Serre, Thomas, VanRullen, Rufin. "Differential Involvement of EEG Oscillatory Components in Sameness versus Spatial-Relation Visual Reasoning Tasks." eneuro, vol. 8, no. 1, 2020, pp. ENEURO.0267-20.2020. |
| Schuch KN, Govindarajan LN, Guo Y, Baskoylu SN, Kim S, Kimia B, Serre T, Hart AC. "Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics." Journal of Neurogenetics, vol. 34, no. 3-4, 2020, pp. 453-465. |
| Chalvidal, Mathieu, Ricci, Matthew, VanRullen, Rufin, Serre, Thomas. "Go with the Flow: Adaptive Control for Neural ODEs." International Conference on Learning Representations, 2020. |
| Ricci, Matthew, Serre, Thomas. "Hierarchical Models of the Visual System." Encyclopedia of Computational Neuroscience, 2020, pp. 1-14. |
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V. Boutin, A. Zerroug, M. Jung & T. Serre.
"Iterative VAE as a predictive brain model for out-of-distribution generalization." NeurIPS Workshop on Shared Visual Representations in Human and Machine Intelligence, 2020.
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| Linsley, Drew, Kim, Junkyung, Ashok, Alekh, Serre, Thomas. "Recurrent neural circuits for contour detection." International Conference on Learning Representations, 2020. |
| Linsley, Drew, Ashok, Alekh Karkada, Govindarajan, Lakshmi Narasimhan, Liu, Rex, Serre, Thomas. "Stable and expressive recurrent vision models." Neural Information Processing Systems, 2020. |
| Serre T. "Deep Learning: The Good, the Bad, and the Ugly." Annual Review of Vision Science, vol. 5, no. 1, 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. |
| Kim, Junkyung, Linsley, Drew, Thakkar, Kalpit, Serre, Thomas. "Disentangling neural mechanisms for perceptual grouping." International Conference on Learning Representations, 2019. |
| 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. |
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D. Linsley, D. Schiebler, S. Eberhardt & T. Serre.
"Learning what and where to attend." International Conference on Learning Representations, 2019.
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| Mély, David A., Linsley, Drew, Serre, Thomas. "Complementary surrounds explain diverse contextual phenomena across visual modalities." Psychological Review, vol. 125, no. 5, 2018, pp. 769-784. |
| Linsley, Drew, Kim, Junkyung, Veerabadran, Vijay, Serre, Thomas. "Learning long-range spatial dependencies with horizontal gated-recurrent units." Neural Information Processing Systems, 2018. |
| Linsley, Drew, Linsley, Jeremy W., Sharma, Tarun, Meyers, Nathan, Serre, Thomas. "Learning to predict action potentials end-to-end from calcium imaging data." 2018 52nd Annual Conference on Information Sciences and Systems (CISS), 2018, pp. 1-6. |
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L. Govindarajan, T. Sharma, R. Colwill & T. Serre.
"Neural computing on a raspberry pi: Applications to zebrafish behavior monitoring." 2018.
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| Kim, Junkyung, Ricci, Matthew, Serre, Thomas. "Not-So-CLEVR: learning same-different relations strains feedforward neural networks." Interface Focus, vol. 8, no. 4, 2018, pp. 20180011. |
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Y. Guo, L.N. Govindarajan, B. Kimia & T. Serre.
"Robust pose tracking with a joint model of appearance and shape." 2018.
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M. Ricci, J.K. Kim & T. Serre.
"Same-different problems strain convolutional neural networks." Annual Meeting of the Cognitive Science Society, 2018.
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| White, Matthew A., Kim, Eosu, Duffy, Amanda, Adalbert, Robert, Phillips, Benjamin U., Peters, Owen M., Stephenson, Jodie, Yang, Sujeong, Massenzio, Francesca, Lin, Ziqiang, Andrews, Simon, Segonds-Pichon, Anne, Metterville, Jake, Saksida, Lisa M., Mead, Richard, Ribchester, Richard R, Barhomi, Youssef, Serre, Thomas, Coleman, Michael P., Fallon, Justin R., Bussey, Timothy J., Brown, Robert H., Sreedharan, Jemeen. "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. |
| Linsley, D., Eberhardt, S., Sharma, T., Gupta, P., Serre, T. "What are the visual features underlying human versus machine vision?." 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 2706-2714. |
| 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. |
| Kuehne, Hilde, Gall, Juergen, Serre, Thomas. "An end-to-end generative framework for video segmentation and recognition." 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016, pp. 1-8. |
| 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." WIREs 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. |
| Mély, David A., Serre, Thomas. "Towards a Theory of Computation in the Visual Cortex." Cognitive Science and Technology, 2016, pp. 59-84. |
| 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." Frontiers in Computational Neuroscience, vol. 9, 2015. |
| Serre, Thomas. "Hierarchical Models of the Visual System." Encyclopedia of Computational Neuroscience, 2014, pp. 1-12. |
| Zhang, Yanhao, Zhang, Shengping, Huang, Qingming, Serre, Thomas. "Learning Sparse Prototypes for Crowd Perception via Ensemble Coding Mechanisms." Lecture Notes in Computer Science, 2014, pp. 86-100. |
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D. Reichert & T. Serre.
"Neuronal synchrony in complex-valued deep networks." International Conference on Learning Representations, 2014.
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| 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, pp. 780-787. |
| Cauchoix, M., Barragan-Jason, G., Serre, T., Barbeau, E. J. "The Neural Dynamics of Face Detection in the Wild Revealed by MVPA." The 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. |
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C. Tan, J. Singer, T. Serre, D. Sheinberg & T. Poggio.
"Neural representation of action sequences: How far can a simple snippet-matching model take us?." Neural Information Processing Systems, 2013.
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| 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." Lecture Notes in Computer Science, 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." Lecture Notes in Computer Science, 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, pp. 2556-2563. |
| 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. |
| S.M. Crouzet & T. Serre. "What are the visual features underlying rapid object recognition?." Frontiers in Psychology, vol. 2, 2011. |
| 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. |
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T. Serre & M. Giese.
"Elements for a neural theory of the processing of dynamic faces." Dynamic Faces: Insights from Experiments and Computation, 2010.
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| 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, pp. 281-286. |
| 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." Proceedings of the Eleventh IEEE 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. |
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T. Serre, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich & T. Poggio.
"A quantitative theory of immediate visual recognition." Progress in Brain Research, 2007.
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| 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. |
| Heisele, Bernd, Serre, Thomas, Poggio, T. "A Component-based Framework for Face Detection and Identification." International Journal of Computer Vision, vol. 74, no. 2, 2006, pp. 167-181. |
| Sigala, Rodrigo, Serre, Thomas, Poggio, Tomaso, Giese, Martin. "Learning Features of Intermediate Complexity for the Recognition of Biological Motion." Lecture Notes in Computer Science, 2005, pp. 241-246. |
| Serre, T., Wolf, L., Poggio, T. "Object Recognition with Features Inspired by Visual Cortex." IEEE Conference on Computer Vision and Pattern Recognition, 2005. |
| Ivanov, Y., Heisele, B., Serre, T. "Using component features for face recognition." Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., 2004, pp. 421-426. |
| Heisele, Bernd, Serre, Thomas, Prentice, Sam, Poggio, Tomaso. "Hierarchical classification and feature reduction for fast face detection with support vector machines." Pattern Recognition, vol. 36, no. 9, 2003, pp. 2007-2017. |
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B. Heisele, T. Serre, M. Pontil, T. Vetter & T. Poggio.
"Categorization by learning and combining object parts." Advances in Neural Information Processing Systems, 2002.
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| Serre, Thomas, Riesenhuber, Maximilian, Louie, Jennifer, Poggio, Tomaso. "On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision." Lecture Notes in Computer Science, 2002, pp. 387-397. |
| Heiselet, B., Serre, T., Pontil, M., Poggio, T. "Component-based face detection." Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, 2001, pp. I-657-I-662. |
| Heisele, B., Serre, T., Mukherjee, S., Poggio, T. "Feature reduction and hierarchy of classifiers for fast object detection in video images." Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 2, 2001, pp. II-18-II-24. |
My research focuses on understanding the computational principles of biological vision and developing brain-inspired models of visual intelligence. I bridge computational neuroscience and artificial intelligence to quantify gaps between human and machine vision, develop cognitive benchmarks that reveal fundamental limitations in AI systems, and reverse-engineer how cortical feedback enables robust visual reasoning. Through explainable AI tools and rigorous comparisons between biological and artificial systems, my work aims to create AI models that genuinely see the world as humans do while advancing our understanding of brain mechanisms that support visual cognition.
We believe that understanding the computational strategies evolved by biological vision systems will lead to transformative advances in both neuroscience and artificial intelligence. Our mission is to develop a unified, mechanistic theory of visual processing that not only explains human visual perception and its neural basis, but also inspires the next generation of machine vision algorithms. By identifying canonical computations shared across visual domains and cortical areas, we aim to bridge the gap between biological and artificial intelligence, creating brain-inspired models that are both interpretable and capable of human-like visual reasoning.
A central theme of our work is quantifying and closing the gap between human and machine vision. Despite impressive performance on standard benchmarks, current deep learning models process visual information fundamentally differently from humans. We develop rigorous cognitive-psychology-inspired benchmarks that reveal systematic failures in modern AI—from contour integration to compositional reasoning and visual perspective-taking. These benchmarks not only expose limitations in current AI systems but also help identify which computations require the recurrent, feedback-driven processing characteristic of biological vision. By identifying where feedforward networks fail, we can pinpoint the computational role of cortical feedback and develop brain-inspired recurrent models that achieve human-like performance on challenging visual reasoning tasks. Our goal is to build models that are not merely descriptive, but predictive of both neural responses and behavioral outcomes across diverse visual tasks.
Our research emphasizes explainability, interpretability, and scientific discovery. Through explainable AI tools like our CRAFT framework and open-source Xplique toolbox, we peer inside the "black box" of deep learning to understand what models learn and identify when they rely on spurious correlations rather than meaningful features. This work is critical for building trustworthy AI systems and advancing scientific understanding—as we move from prediction to understanding in brain science, we need methods to identify the computational mechanisms learned by foundation models. Our work has direct applications in developing robust computer vision systems, automated behavioral phenotyping for neurodegenerative disease research, and advancing our fundamental understanding of how neural computations give rise to intelligent behavior. Through close collaboration between theorists and experimentalists, we strive to build models that bridge biological and artificial intelligence, creating systems that are both interpretable and capable of human-like visual reasoning while advancing our understanding of the brain mechanisms that enable robust visual cognition.
| 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 |
– Elected fellow in the ELLIS Program, Natural Intelligence • 2024
– Brown Mid‐Career Research Achievement Award • 2024
– Awarded Thomas J. Watson, Sr. Professor of Science endowed Chair • 2023
– Awarded Manning Assistant Professorship • 2010
– PAMI Mark Everingham Prize for pioneering human action recognition datasets. • 2022
– PAMI Helmholtz Prize for significant impact on computer vision research. • 2021
– Awarded International Chair in AI (ANITI, France) • 2019–present • 2019
– DARPA Director’s Award • 2016
– Distinguished Speaker in Behavioral and Brain Sciences, Cornell University (Ithaca, NY) • 2016
– DARPA Young Faculty Award • 2014
– Professeur Invité, Lorient University (Lorient, France) • 2014
– NSF Early Career Award • 2013
– Manning Assistant Professorship • 2013
– Teaching with Technology Course Design Award • 2012
– Sheridan Junior Faculty Teaching Fellows Program • 2011–2012
| Name | Title |
|---|---|
| Borton, David | Associate Professor of Engineering, Associate Professor of Neurosurgery, Associate Professor of Brain Science |
| Domini, Fulvio | Professor of Cognitive and Psychological Sciences |
| 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 Professor of Cognitive and Psychological Sciences |
| Sheinberg, David | Professor of Neuroscience, Graduate Program Director for the Neuroscience Graduate Program |
| Associate Director. Brown Unversity , 2020- |
| Faculty Director. brown, 2018- |
| CLPS 0950 - Introduction to programming |
| CLPS 1291 - Computational Methods for Mind, Brain and Behavior |
| CLPS 1950 - Deep Learning in Brains, Minds and Machines |
| CPSY 1291 - Computational Methods for Mind, Brain and Behavior |
| CPSY 1950 - Deep Learning in Brains, Minds and Machines |
