Biological Computing at The Biological Computing Co. (TBC)

I am a Computational Neuroscientist at The Biological Computing Co. (TBC), an AI infrastructure startup investigating biological neurons as a foundation for next-generation computing - with the goal of developing AI models and hardware that go beyond what is possible on silicon alone.

What TBC Is Working Toward

Silicon-based AI is hitting hard walls in energy cost, training expense, and adaptability. Biological brains compute under fundamentally different principles: parallelism, recurrence, dynamic adaptation, and continual integration of new information. TBC is studying biological neurons in order to translate those principles into the next generation of AI systems.

The research program centers on:

• Biological-neuron research pipeline. Studying how biological neuronal cultures respond to and process real-world signals, with the aim of producing richer, more efficient representations than silicon-only approaches.
• Algorithm Discovery. Translating biologically-derived and physically-consistent computational principles into new AI model architectures, in order to achieve faster, cheaper, and more general AI.

Target Application Areas

• General AI-infrastructure improvements (lower training cost, improved performance)

Forward-Looking Research

• Real-time biological compute for continuous processing
• Pattern completion and prediction for time-series data

My Role

I am a Computational Neuroscientist on TBC’s computational neuroscience team, while also contributing in the AI model / ML algorithm space. My work spans the modeling and analysis bridge between biological neuronal activity and machine-learning systems - translating insights from neural data into the design of biologically inspired models and architectures. This is the same intersection of physics-based modeling, computational neuroscience, and ML that has driven my research throughout my Ph.D. and prior industry work.