About

A brief introduction.


I am a second year PhD student at CSAIL, MIT studying computer vision, machine learning, and AI under Aude Oliva and Phillip Isola. I previously received a degree in Neuroscience, Physics and Math from Bates College.

I enjoy applying methods from computer science, neuroscience and cognitive science to understand and model how perception and cognition are represented in human and machine. My long-term research goals are focused on promoting a self-propagating convergence of brain & cognitive sciences and machine learning.

Some of my research interests include:

  • Computational Neuroscience
  • Representation Learning
  • Generative Models
  • Video and Event Understanding
  • Embodied Intelligence

Email: alexandonian [at] gmail.com OR andonian [at] mit.edu

CV / Google Scholar / GitHub / Twitter

Community Outreach

Some of the organizations I have worked with in the past.




Industry Experience

Places and teams I hope to work with in the future.

Google Brain/DeepMind Team
Facebook AI Research (FAIR)
MIT-IBM Watson AI Lab



Education

Where I've studied over the years.

2013-2017

Bates College

BS, Neuroscience, Physics, Math
Advisors: Jason Castro, Travis Gould

Summer 2017

Stanford University

Visiting student in NeuroAI Lab
Mentor: Dan Yamins

2017-2019

CSAIL, MIT

Principal Research Assistant
Advisor: Dr. Aude Oliva.

2019-Present

MIT

EECS-AI+D, Graduate Student
Schwarzman College of Computing
Advisor: Dr. Aude Oliva.

Projects

A collection of side projects, research studies, and course assignments.

Research

An overview of my research interests, publications, etc.

    Interests

    Machine Learning
    Computer Vision
    Computational Neuroscience


    Application Areas

    Medical disease detection and diagnosis
    Bioimage informatics
    Computational neuroanatomy

  • Past Groups

  • NeuroAI Lab (2017)
    Department of Computer Science
    Stanford University

    Castro Lab (2017)
    Program in Neuroscience
    Bates College

    Rosen Lab (2013)
    Department of Developmental Biology
    Harvard School of Dental Medicine

    Corkey's Lab (2012)
    Obesity Research Center
    Boston University School of Medicine



  • Recent Publications

  • We Have So Much In Common:
    Modeling Semantic Relational Set Abstractions in Videos
    Alex Andonian*, Camilo Fosco*, Mathew Monfort, Allen Lee, Rogerio Feris, Carl Vondrick, Aude Oliva. European Conference on Computer Vision (ECCV), 2020.
    Unsupervised Learning From Video With Deep Neural Embeddings
    Chengxu Zhuang, Tianwei She, Alex Andonian, Max Sobol Mark, Daniel Yamins. Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
    Spatially organized genomic and physiological heterogeneity of the olfactory bulb mitral cell layer
    Daniel Paseltiner, Henry Loeffler, Alex Andonian, Abigail Leberman, Travis J. Gould, Jason B. Castro. bioRXiv preprint https://doi.org/10.1101/2020.01.13.903823, 2020.
    The Algonauts Project: A Platform for Communication
    between the Sciences of Biological and Artificial Intelligence
    Radoslaw Martin Cichy, Gemma Roig, Alex Andonian, Kshitij Dwivedi, Benjamin Lahner, Alex Lascelles, Yalda Mohsenzadeh, Kandan Ramakrishnan, Aude Oliva. arXiv:1905.05675, 2019. PDF Website
    Ganalyze: Toward visual definitions of cognitive image properties
    Lore Goetschalckx* , Alex Andonian*, Aude Oliva, Phillip Isola.
    International Conference on Computer Vision (ICCV), 2019.
    Multi-moments in time: Learning and interpreting models for multi-action video understanding
    Mathew Monfort, Kandan Ramakrishnan, Alex Andonian, Barry A McNamara, Alex Lascelles, Bowen Pan, Dan Gutfreund, Rogerio Feris, Aude Oliva. In revision for IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
    Cross-View Semantic Segmentation for Sensing Surroundings
    Bowen Pan, Jiankai Sun, Ho Yin Tiga Leung, Alex Andonian, Bolei Zhou.
    IEEE Robotics and Automation Letters, 2020.
    A deep learning based method for large-scale classification, registration, and clustering of in-situ hybridization experiments in the mouse olfactory bulb.
    Alex Andonian, Dan Paseltiner, Travis Gould, Jason Castro.
    Journal of Neuroscience Methods, 2018. PDF Code
    Moments in Time Dataset: one million videos for event understanding.
    Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfruend, Carl Vondrick, Aude Oliva. IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2018.
    PDF Website Code
    N-linked glycosylation of the bone morphogenetic protein receptor type 2 (BMPR2) enhances ligand binding.
    Jonathan W. Lowery, Jose M. Amich, Alex Andonian, Vicki Rosen.
    Cellular and Molecular Life Sciences. 2013.
  • In Submission

  • Contrastive Perceptual Loss for Conditional Synthesis
    Alex Andonian, Taesung Park, Bryan Russell, Richard Zhang, Phillip Isola, and Jun-Yan Zhu.
    To be submitted to a 2021 computer vision conference.
    Understanding a Scene-centric BigGAN
    Alex Andonian, David Bau, Aude Oliva.
    To be submitted to a 2021 computer vision conference.
    Deepfake Caricatures: Using Artifact Amplification to Expose Doctoring
    Alex Andonian*, Camilo Fosco*, Xi Wang, Allen Lee, Aude Oliva.
    To be submitted to a 2021 computer vision conference.
    VA-RED2: Video Adaptive Redundancy Reduction
    Bowen Pan, Camilo Fosco, Alex Andonian, Rameswar Panda, Rogerio S, Feris, Yue Meng, Chung-Ching Lin, Aude Oliva.
    Submitted to a 2021 machine learning conference.
  • Presentations and Other Papers

    1. A Deep-Learning Pipeline for Studying Olfactory Bulb Molecular Anatomy at Genomic Scale. Alex J. Andonian, Daniel A. Paseltiner, Jason B. Castro. Presented at Society for Neuroscience. November 2017. Poster
    2. Data Driven Approaches for Investigating Molecular Heterogeneity of the Brain. A. Andonian. Mt. David Summit, Bates College, March 2017.
    3. Informatics Tools for Quantifying Intratumor Heterogeneity in Multiplexed Fluorescence Tissue Data. A. Andonian. Council on Undergraduate Research's Research Experiences for Undergraduates Symposium. National Science Foundation's Atrium, Arlington Virginia. October 2016.
    4. Scalable Informatics Tools for Investigating Intra-Tumor Heterogeneity in Breast Cancer. A. Andonian. Summer Undergraduate Research Symposium, University of Pittsburgh and Duquesne University. July 2016. PDF Poster