How to reach me: [email protected]

I am Neuroscience Ph.D. student at Yale University, working in the Cognative and Neural Computation Lab.

I'm on a quest to unravel the mysteries of the mind. My research sits at the intersection of neuroscience, cognitive science, and machine learning, focusing on how complex and dynamic recurrent networks model the world around them to generate abstractions, thoughts, and behavior.

• Dynamic Networks and Systems
• Neural Data Analysis
• Biological and Artificial Intelligence
• Cognitive Modeling
• Attentional, Memory, Inference, and Generative Systems in the Brain

A New Multi-level Modeling Framework Provides Evidence for the Simulation of Object Dynamics in the Dorsomedial Frontal Cortex.
Exploring a Basis Set of Intrinsic Functions Underlying Neural Computation by Symbolically Programming Recurrent Neural Networks

My journey to academia has been anything but linear. It has taken me from the editing rooms of a social impact documentary production company to crossing the Atlantic on the decks of 18th-century sailboats and now to the inner worlds of our minds. But here is a picture—-the best picture I've ever taken--of my old life and the last two boats I worked on:

Today, I'm particularly interested in attentional, memory, inference, and generative systems in the brain. I aim to understand how these networks create generalized yet stable representations and actions—-all from self-orgenized, noisey, and highly reccurent neurons. My research interests extend to exploring mathematical representations of dynamic networks, how they can be applied to inference and probabalistic algorithms, engineering and control theory, and the emergent lexical and semantic structures that arise from attention and predictive coding (yeah who isn't interested in LLMs... but also beyond language; e.g. motor semantics of coordinated muscle fibers and groups to create semantic "movement languages"). Undergirding it all is a particularl interest in choas, self-orginized complexity, and dynamic networks/systems, with all the fascinatingly emergent properties that arise from them.

• 🤖🐵 Current Focus: Programming hypothesis-driven algorithms into recurrent neural networks (RNNs)
• (main) Programming Languages: Matlab, Python, Julia, Bash, HPC/Slurm (does that count?)
• Fun Fact: [Fun Fact]

> Evolving Brains: Solid, Liquid and Synthetic	Attention Approximates Sparse Distributed Memory	The Brain, Determinism, & Cultural Implications
	Bioelectric Networks as the Interface to Somatic Intelligence for Regenerative Medicine

Nate Hagens: The Superorganism & the Future

Daniel Schmachtenberger: Introduction to the Metacrisis

Kate Raworth: The Most Sustainable Economy in the World

• 3blue1brown

Created by Grant Sanderson, 3blue1brown is known for its visually stunning animations that help explain high-level mathematics in an intuitive manner. Topics range from calculus and linear algebra to machine learning and neural networks.

• statquest

Simplifies complex statistics and machine learning topics into fun and easy-to-understand videos. The channel is famous for its "Bam!" moments that clarify confusing concepts.

• Yannic Kilcher

Focuses on explaining recent research papers in machine learning, artificial intelligence, and deep learning. His detailed walkthroughs make cutting-edge research accessible to a broader audience.

• Steven Brunton

Covers a wide range of topics in applied mathematics, including but not limited to control theory, data science, and dynamical systems. His teaching style is clear and methodical, making complex topics easier to grasp.

• Gilbert Strang's Linear Algebra

This is a complete course playlist featuring MIT Professor Gilbert Strang (who quite literally wrote the book on Linear Algebra). It's a comprehensive resource for anyone looking to understand linear algebra at a deep level. Strang's engaging lectures make even the most challenging concepts relatable.

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Author: Daniel Calbick
Last Updated: 2023-10-12