Tim Sainburg

  Curriculum Vitæ
Email: timsainb@gmail.com
Phone: 1-814-574-7780
Website: http://timsainburg.com
Github: github.com/timsainb
220 Longwood Avenue
Department of Neurobiology
Harvard Medical School
Boston, MA 02115 USA

Biography

I am interested in the intersection between biology, cognition, and machine intelligence. My research experiences range from computational approaches such as machine perception and statistical modeling, to behavioral approaches such as field ethology with chimpanzees and operant conditioning with songbirds, to physiological approaches such as freely behaving ephys with songbirds and rodents.

Research Interests

neuroethology, neurophysiology/neural data analysis, sensory/motor neuroscience, language, animal cognition/communication, sequence and generative models, computational ethology, machine perception & AI, dimensionality reduction

Research Positions

2022–now
Postdoctoral researcher / Schmidt Science Fellow,
Harvard / Harvard Medical School, Boston, MA
Advisor: Sandeep Robert Datta, Hopi Hoekstra
2015–2021
PhD Student, Auditory Neurscience Laboratory
University of California, San Diego, La Jolla, CA
Advisor: Timothy Q Gentner
2016–2017
Student Contractor, Machine learning and Artificial Perception Laboratory
Space and Naval Warfare Systems Center, San Diego, CA
Advisor: Benjamin Migliori
2015
R&D Engineer, Cognitive Perception and Computational Intelligence Laboratory
Applied Research Laboratory, State College, PA
Advisor: John Sustersic and Brad Wyble
2014
Field Research Technician, Waibira Research Project
Budongo Conservation Field Center, Budongo Forest, Uganda
Advisor: Catherine Hobaiter
2012–2014
Research Assistant, Comparative Communication Laboratory
The Pennsylvania State University, State College, PA
Advisor: Daniel Weiss

Grants and Awards

2024
Leonard and Isabelle Goldenson Fellow
Harvard Medical School
2023
AI for Interspecies Communication
Earth Species Project
2023
Dean’s Innovation award for the Use of Artificial Intelligence
Harvard Medical School
2022
Schmidt Science Fellow
Schmidt Futures
2022
Glushko Dissertation Prize
Cognitive Science Society and the Glushko-Samuelson Foundation
2020
Dissertation Fellowship in the Cognitive, Clinical, and Neural Foundations of Language
William Orr Dingwall Foundation
2020
Abstract Merit Award Honorable Mention
Society for the Neurobiology of Language
2020
CARTA Fellowship
Center for Research and Training in Anthropogeny
2020
Institute for Neural Computation (INC) Training Fellowship (5T32MH020002-20)
NIMH
2020
Summer Graduate Teaching Scholars Fellowship
UC San Diego
2019
Annette Merle-Smith Fellowship
Center for Research and Training in Anthropogeny
2018
Norman Henry Anderson Scholar
UC San Diego, Psychology Dept.
2018
Student Travel Award
Computational Cognitive Neuroscience (CCN)
2016
Graduate Research Fellowship (2017216247)
National Science Foundation (NSF)
2012
President's Fund for Undergraduate Scholars
The Pennsylvania State University

Publications

Tim Sainburg, Trevor S McPherson, Ezequiel M. Arneodo, Srihita Rudraraju, Michael Turvey, Brad Theilman, Pablo Tostado Marcos, Marvin Thielk, Timothy Q Gentner, “Expectation-driven sensory adaptations support enhanced acuity during categorical perception”, Nature Neuroscience, 2025 (to appear).
Bernard Koch, Tim Sainburg, Pablo Geraldo, Song Jiang, Yizhou Sun, and Jacob Gates Foster, “Deep Learning of Potential Outcomes”, Sociological Methods and Research, 2024.
Tim Sainburg, “American postdoctoral salaries do not account for growing disparities in cost of living”, Research Policy, 2023.
Jeffrey Xing, Tim Sainburg, Hollis Taylor, and Timothy Q. Gentner, “Syntactic modulation of rhythm in Australian pied butcherbird song”, Royal Society Open Science, 2022.
Mara Thomas, Frants Jensen, Baptiste Averly, Vlad Demartsev, Marta B Manser, Tim Sainburg, Marie Roch, and Ariana Strandburg-Peshkin, “A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations”, Journal of Animal Ecology, 2022.
Tim Sainburg, Anna Mai, and Timothy Q. Gentner, “Long-range sequential dependencies precede complex syntactic production in language acquisition”, Proceedings of the Royal Society B, 2022.
Tim Sainburg, Timothy Q. Gentner, “Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions”, Frontiers in Behavioral Neuroscience, 2021.
Tim Sainburg, Leland McInnes, Timothy Q. Gentner, “Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning”, Neural Computation, 2021.
Tim Sainburg, Marvin Thielk, and Tim Gentner, “Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires”, PLOS Computational Biology, 2020.
Willem Zuidema, Robert M. French. Raquel G. Alhama, Kevin Ellis, Timothy J. O'Donnell, Tim Sainburg, Timothy Q. Gentner, “Five ways in which computational models can help advancing Artificial Grammar Learning research”, Topics in Cognitive Sciences (TiCS), 2019.
Tim Sainburg, Brad Theilman, Marvin Thielk, and Tim Gentner, “Parallels in the sequential organization of birdsong and human speech”, Nature Communications, 2019.

Under Review

Tim Sainburg, Asaf Zorea, “Noisereduce: omain General Noise Reduction for Time Series Signals”, arXiv, 2024 (to appear).

Conference Papers

Jeffrey Xing, Tim Sainburg, Timothy Q. Gentner, and Hollis Taylor, “Examining song complexity of Australian pied butcherbirds”, Acoustics 2021, 2021.
Bernard Koch, Tim Sainburg, Song Jiang, Jacob G. Foster, Yizhou Sun, “Deep learning of potential outcomes”, American Sociological Association (ASA) Annual Meeting, 2020.
Tim Sainburg, Marvin Thielk, and Timothy Q. Gentner, “Learned context dependent categorical perception in a songbird”, Computational Cognitive Neuroscience (CCN), 2018.
Marvin Thielk, Tim Sainburg, Tatyana Sharpee, and Timothy Q. Gentner, “Combining Biological and Artificial Approaches to Understand Perceptual Spaces for Categorizing Natural Acoustic Signals”, Computational Cognitive Neuroscience (CCN), 2018.

Preprints

Tim Sainburg, Marvin Thielk, Brad Theilman, Benjamin Migliori, and Timothy Q. Gentner, “Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convexlatent distributions”, arXiv. 1807.06650, 2018.

Datasets

Zeke Arneodo, Tim Sainburg, James Jeanne, & Timothy Q Gentner, “An acoustically isolated European starling song library”, Zenodo, 2019.

Conference Presentations (Oral)

Tim Sainburg, Anna Mai, and Timothy Q. Gentner, “Long-range sequential dependencies precede syntactically-rich vocalizations in humans”, Society for the Neurobiology of Language, 2020.
Tim Sainburg, Anna Mai, and Timothy Q. Gentner, “Long-range sequential dependencies precede syntactically-rich vocalizations in humans”, Evolang, 2020.
Tim Sainburg, Marvin Thielk, and Timothy Q. Gentner, “Generative modelling for birdsong: learned acoustic spaces for generating stimuli, labelling syllables, and visualizing sequences”, Birdsong (SfN Satellite), 2018.
Tim Sainburg, Marvin Thielk, and Timothy Q. Gentner, “Learned context dependent categorical perception in a songbird”, Computational Cognitive Neuroscience (CCN), 2018.
Tim Sainburg and Timothy Q. Gentner, “Analyzing birdsong with neural networks”, SSC Pacific Workshop on Naval Applications of Machine Learning, 2016.

Conference Presentations (Poster)

Tim Sainburg, Trevor McPherson, Zeke Arneodo, and Timothy Q. Gentner, “Rapid expectation-driven sensory changes guide vocal categorical perception”, Society for Neuroscience, 2022.
Tim Sainburg, Anna Mai, and Timothy Q. Gentner, “Long-range sequential dependencies precede complex syntactic production in language acquisition”, Cognitive Science Society 2021 (CogSci), 2021.
Tim Sainburg, Marvin Thielk, and Timothy Q. Gentner, “Animal Vocalization Generative Network (AVGN): A method for visualizing, understanding, and sampling from animal communicative repertoires”, Cognitive Science Society 2019 (CogSci), 2019.
Tim Sainburg, Marvin Thielk, Brad Theilman, and Timothy Q. Gentner, “Sequential organization in birdsong and language: implications for neuroscience and AI”, Center for Englineered Natural Intelligence Symposium (CENI 2018), 2018.
Tim Sainburg, Marvin Thielk, and Timothy Q. Gentner, “Learned context dependent categorical perception in songbirds”, Society for Neuroscience (SfN), 2018.
Marvin Thielk, Tim Sainburg, Tatyana Sharpee, and Timothy Q. Gentner, “Combining Biological and Artificial Approaches to Understand Perceptual Spaces for Categorizing Natural Acoustic Signals”, Cognitive Computational Neuroscience (CCN), 2018.
Tim Sainburg, Marvin Thielk, and Timothy Q. Gentner, “Latent manifold analysis reveals parallels in hierarchical patterning of birdsong and human speech”, Computational and Systems Neuroscience (COSYNE), 2017.
Marvin Thielk, Tim Sainburg, Tatyana Sharpee, and Timothy Q. Gentner, “Shared perceptual spaces for high-dimensional natural acoustic signals”, Computational and Systems Neuroscience (COSYNE), 2017.
Tim Sainburg, Marvin Thielk, Brad Theilman, and Timothy Q. Gentner, “Latent manifold analysis reveals parallels in hierarchical patterning of birdsong and human speech”, Computational and Systems Neuroscience (COSYNE), 2017.
Tim Sainburg, Brad Theilman, and Timothy Q. Gentner, “Mechanisms for category learning in songbirds”, Society for Neuroscience, 2016.

Invited Talks

2024
A computational neuroethology of vocal and motor behavior
University of Vienna, University of Bochum, Harvard University
2022
American postdoctoral salaries do not account for growing disparities in cost of living
Boston Postdoctoral Association
2021
Parametric UMAP embeddings for representation and semi-supervised learning
G Research Machine Learning Seminar Series, Tutte Institute for Mathematics and Computing
2021
The production and perception of syntactic vocal behavior in songbirds
UZH Language Sciences Departmental Seminar Series
2021
Towards a computational neuroethology of vocal communication
Bridging Brains and Bioacoustics
2018
Sequential structure of learned vocal communization
Anderson Prize Address, UC San Diego

Software Contributions

noisereduce, (python package) Noise reduction in Python using spectral gating (speech, bioacoustics, time-domain signals).
birdbrain, (python package) MRI atlas visualization tool for several songbirds and bats.
UMAP, (python package, contributer) Contributed Parametric UMAP submodule.
AVGN, (bioacoustics tools) Latent space models for visualizing and manipulating animal communization signals.
generative models, (Tensorflow tutorials) Colab implementations of a number of audio and image generative models.

Teaching

2020
Instructor: Sensory Neuroscience, University of California, San Diego
2018
Teaching Assistant: Brain, Behavior, and Evolution, University of California, San Diego
2018
Teaching Assistant: Sensory Neuroscience, University of California, San Diego
2017
Teaching Assistant: General Psychology, University of California, San Diego
2016
Teaching Assistant: Cognitive Psychology, University of California, San Diego
2013–2014
Peer Leader/Tutor: Biology: Basic Concepts and Biodiversity, The Pennsylvania State University

Mentorship

2024
Glory Kalu, Northeastern COOP
2024
Minea Stayman, Northeastern COOP
2023
Jack Lovell, Harvard Medical School Computer Scientist
2020–2022
Jeffrey Xing, Music and Composition Undergraduate at UCSD
2019
Mingxuan Zhang, Computer Science Post-Bac at UCSD
2019
Meixuan Jiang, Computer Science Undergraduate at UCSD
2019
Yijia Yan, Computer Science Undergraduate at UCSD
2019
Jimmy Lozono, Cognitive Science Undergraduate at UCSD
2016–2018
Darvesh Gorhe, Neuroscience Undergraduate at UCSD
2017
Hector Ponce, Neuroscience Undergraduate at UCSD
2016
Katherine Hernandez, Ecology, Behavior, and Evolution Undergraduate at UCSD

Academic Service

2017–2019
External Funding Officer, UCSD Psychology

Education

2015–2021
University of California, San Diego, CA
Ph.D. in Psychology (Behavioral Neuroscience) with a Specialization in Anthropogeny
'Temporal organization in vocal communication: sequential structure, perceptual integration, and neural foundations'
December 9, 2021
Advisor: Timothy Q. Gentner
2010–2014
The Pennsylvania State University, State College, PA
B.A. in Psychology with a Minor in Biology

Additional Coursework

2019
Diverse Intelligences Summer Institute (DISI), St. Andrews, Scotland
2014
Data Science Specialization, Johns Hopkins / Coursera
2014
Machine Learning, Stanford / Coursera

Employment and Business Ventures

2007–2012
Brambling Design, Co-Founder/Lead Developer