Jesseba Fernando

I’m Jesseba, a Network Science PhD student at Northeastern University. I’m advised by Dr. Sam Scarpino. I’m broadly interested in learning and adaptation in both biological and artificial systems and how one can inform the other.

My research focuses on understanding how networks reorganize during learning and adaptation, bridging neuroscience and artificial intelligence through the lens of network science and statistical physics. I’m particularly interested in how information sharing patterns and network motifs evolve during learning and what governs this reorganization process.

Before joining Northeastern, I explored problems in systems neuroscience, studying how motivational states influence attention to sensory cues in the Andermann Lab. I investigated domain adaptation of medical imaging models with William Lotter at Dana Farber Cancer Institute. This interdisciplinary background, combining neuroscience, machine learning, and network science, has shaped my current approach to understanding complex adaptive systems.

Feel free to reach out if you’d like to discuss potential collaborations or just chat about network science!

news

Jan 14, 2025	I gave a contributory talk at the NetSciX 2025 conference in Indore, India on “From Neurons to Networks: Unraveling Adaptive Learning Mechanisms in Mice and Machines”
Nov 04, 2024	I presented a poster on “Information Processing Dynamics in Biological and Artificial Neural Systems During Meta-learning” at the Institute of Pure and Applied Mathematics (IPAM) at UCLA for their workshop on “Naturalistic Approaches to Artificial Intelligence”
Aug 04, 2024	I attended the Brains, Minds, and Machines Summer School in Woods Hole, MA and met some amazing people!

selected publications

2024

Nature

Cortical reactivations predict future sensory responses

Nghia D Nguyen, Andrew Lutas, Oren Amsalem, Jesseba Fernando, Andy Young-Eon Ahn, Richard Hakim, and 5 more authors

Nature, 2024

URL Bib

@article{2024-nguyen,
  title = {Cortical reactivations predict future sensory responses},
  author = {Nguyen, Nghia D and Lutas, Andrew and Amsalem, Oren and Fernando, Jesseba and Ahn, Andy Young-Eon and Hakim, Richard and Vergara, Josselyn and McMahon, Justin and Dimidschstein, Jordane and Sabatini, Bernardo L and Andermann, Mark L},
  journal = {Nature},
  volume = {625},
  number = {7993},
  pages = {110--118},
  year = {2024},
  publisher = {Nature Publishing Group UK},
  url = {https://www.nature.com/articles/s41586-023-06810-1}
}

MIDL

Beyond Structured Attributes: Image-Based Predictive Trends for Chest X-Ray Classification

Jesseba Fernando, Katharina V Hoebel, and William Lotter

Medical Imaging with Deep Learning, 2024

URL Bib

@article{2024-fernando,
  title = {Beyond Structured Attributes: Image-Based Predictive Trends for Chest X-Ray Classification},
  author = {Fernando, Jesseba and Hoebel, Katharina V and Lotter, William},
  journal = {Medical Imaging with Deep Learning},
  year = {2024},
  publisher = {MIDL},
  url = {https://openreview.net/forum?id=Y15taNvfFN}
}