A dense block of serotonergic neurons in the DRN. Imaged in 2014, Warden Lab

Background

I am a passionate problem solver with broad interests spanning both the humanities and technology.

Recently, I completed my Ph.D. in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology. With my mentor Steve Flavell, I investigated the neural circuits underlying behavior in C. elegans using cutting-edge optical, genetic, and computational tools (such as deep learning, and tailored Bayesian inference). My thesis project was published in Cell and covered by Scientific American

Before grad school, I spent a year doing optical engineering (biomedical) and systems neuroscience with Drew Robson and Jennifer Li at the Rowland Institute at Harvard. Prior to that, I was at Cornell, where I learned how to solve challenging problems, primarily in biological contexts (Biological Engineering). While there, I worked in Melissa Warden's lab, where I developed a deep interest in neuroscience.

Outside of work, I enjoy reading, cycling, swimming, meditating, examining both my own life and that of others (e.g. reading biographies), following various topics (such as American history, investing, macroeconomics, and law), and listening to music. I love working with people to solve challenging problems and learning about how various businesses operate and grow.

Degrees

Ph.D. in Neuroscience, Brain and Cognitive Sciences, Massachusetts Institute of Technology

B.S. in Biological Engineering, College of Engineering, Cornell University

Thesis Project

Brain-wide representations of behavior spanning multiple timescales and states in C. elegans

I led, together with Adam Atanas, a pioneering neuroscience project that was published in Cell and featured in Scientific American. Our work involved developing innovative hardware and software solutions to image neural activity in freely behaving C. elegans. We then used probabilistic modeling to create the first comprehensive neural encoding dictionary of an organism.

graphical abstract of the Cell 2023 paper

Simultaneous recordings of brain-wide activity and diverse behaviors in C. elegans
Using a custom-engineered microscope, we recorded brain-wide neural activity simultaneously with diverse motor behaviors in C. elegans. We developed a custom software to extract neural traces at the cellular level with high SNR.

Probabilistic encoder model describes how each neuron encodes behavior
We developed a probabilistic encoder model alongside tailored inference algorithms to analyze neural data. This approach provides detailed, interpretable descriptions of how individual neurons encode specific behaviors.

Results mapped to connectome, yielding encoding atlas of C. elegans nervous system
By integrating our models with the connectome, we generated a comprehensive encoding atlas illustrating how genetically identified neuron classes in C. elegans encode various behaviors.

Defined neurons flexibly change encoding in a state-dependent manner
Interestingly, we discovered that some neurons exhibit flexibility in encoding, dynamically changing these properties depending on the behavioral state.

Projects

A quick look at some of my engineering and science projects

Microscope diagram and calcium imaging examples

Brain-wide cellular-resolution imaging in freely behaving animal

World's first cellular-resolution, brain-wide neural activity imaging of freely-behaving vertebraes

  • Real-time image procesing and model predictive control to perform motion cancelling
  • DIFF, a structural illumination technique, to perform optical sectioning (3D imaging)
Neural imaging pipeline software screenshot

TB-scale Neural Data Processing Pipeline

An end-to-end software stack to process and extract nerual activity from 10s of TBs of data

  • Deep learning models for 3D tasks, custom GPU-accelerated motion correction and denoising
  • HPC-deployed parallel processing
Probabilistic modeling visualization

Probabilistic Generative Encoding Models

Probabilistic encoding models that link neural activity to rich behavioral state representations.

  • Non-linear models to capture key neuronal encoding motifs
  • Tailored inference algorithms using SMC with MCMC with HMC and MH with custom proposals
Connectome-aware atlas artwork

Mapping Functional Properties onto Connectome

Mapping learned encoding features onto the wiring diagram

  • Inferred encoding properties from functional data mapped onto the connectome
Connectome-aware atlas artwork

WormWideWeb

An interactive web app to explore and visualize C. elegans neural datasets and connectomes

  • Won the MIT Prize for Open Data
  • Explore, search, plot, and download neural datasets
Connectome-aware atlas artwork

Deep-learning pipeline for 3D neural image processing

Pipeline to register, segment, and identify neurons

  • 3D segmentation, volumetric non-rigid registration, and neuronal identity classification

Publications

Deep Neural Networks to Register and Annotate Cells in Moving and Deforming Nervous Systems
eLife, 2025
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Adam A. Atanas, Alicia Kun-Yang Lu, Brian Goodell, Jungsoo Kim, Saba Baskoylu, Di Kang, Talya S. Kramer, Eric Bueno, Flossie K. Wan, Karen L. Cunningham, Brandon Weissbourd, Steven W. Flavell

Brain-wide representations of behavior spanning multiple timescales and states in C. elegans
Cell, 2023
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Adam A. Atanas*, Jungsoo Kim*, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Talya S. Kramer, Flossie K. Wan, Saba Baskoylu, Ugur Dag, Elpiniki Kalogeropoulou, Matthew A. Gomes, Cassi Estrem, Netta Cohen, Vikash K. Mansinghka, Steven W. Flavell

* equal contribution

Dissecting the functional organization of the C. elegans serotonergic system at whole-brain scale
Cell, 2023
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Ugur Dag*, Ijeoma Nwabudike*, Di Kang*, Matthew A. Gomes, Jungsoo Kim, Adam A. Atanas, Eric Bueno, Cassi Estrem, Sarah Pugliese, Ziyu Wang, Emma Towlson, Steven W. Flavell

* equal contribution

Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish
Nature Methods, 2017
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Dal Hyung Kim, Jungsoo Kim, João C. Marques, Abhinav Grama, David G. C. Hildebrand, Wenchao Gu, Jennifer M. Li, Drew N. Robson