We’re bringing the brain’s native intelligence to computing. We’re building an organic computing platform that connects real living neurons with modern AI – making frontier models more stable, scalable and dramatically more efficient. 

Your world doesn’t make sense because it’s large.

Your world makes sense because it’s alive.

Now AI can be too.

We’re bringing the brain’s native intelligence to computing. We’re building an organic computing platform that connects real living neurons with modern AI – making frontier models more stable, scalable and dramatically more efficient. 

Your world doesn’t make sense because it’s large.

Your world makes sense because it’s alive.

Now AI can be too.

A New Computing Lifeform

A New Computing Lifeform

A New Computing Lifeform

Why the neuron?

creating a new industry
Connecting real living neurons with modern AI

We’re bringing the brain’s native intelligence to computing. We’re building an organic computing platform that connects real living neurons with modern AI – making frontier models more stable, scalable and dramatically more efficient. 

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Application 01

    Generative Video & World Models

    BBB improves image resolution, video stability and coherence for less compute.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Application 02

    Computer Vision

    BBB improves classification accuracy while lowering compute.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Application 03

    Biologically-Inspired Compute

    BBB uses learning principles from real biological neural networks to create novel AI architectures.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

Connecting real living neurons with modern AI

We’re bringing the brain’s native intelligence to computing. We’re building an organic computing platform that connects real living neurons with modern AI – making frontier models more stable, scalable and dramatically more efficient. 

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Application 01

    Generative Video & World Models

    BBB improves image resolution, video stability and coherence for less compute.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Application 02

    Computer Vision

    BBB improves classification accuracy while lowering compute.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Application 03

    Biologically-Inspired Compute

    BBB uses learning principles from real biological neural networks to create novel AI architectures.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

Connecting real living neurons with modern AI

We’re bringing the brain’s native intelligence to computing. We’re building an organic computing platform that connects real living neurons with modern AI – making frontier models more stable, scalable and dramatically more efficient.

Application 01

Application 02

Application 03

BBB uses learning principles from real biological neural networks to create novel AI architectures.

Biologically-Inspired Compute

BBB improves classification accuracy while lowering compute.

Computer Vision

BBB improves image resolution, video stability and coherence for less compute.

Generative Video & World Models

Connecting real living neurons with modern AI

We’re bringing the brain’s native intelligence to computing. We’re building an organic computing platform that connects real living neurons with modern AI – making frontier models more stable, scalable and dramatically more efficient.

Application 01

Application 02

Application 03

BBB uses learning principles from real biological neural networks to create novel AI architectures.

Biologically-Inspired Compute

BBB improves classification accuracy while lowering compute.

Computer Vision

BBB improves image resolution, video stability and coherence for less compute.

Generative Video & World Models

The Science
  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 01

    Real Neurons are Incubated

    Living neurons are grown on high density array of electrodes to
    create a biological network.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 02

    Neurons meet Silicon

    Information is encoded through electrical stimulation. Processed information in the form of neural signals is decoded and applied.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 03

    Adaptive Learning

    Biological networks learn through targeted stimulation, optimizing information processing.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 04

    Superior Performance

    Our system delivers exponentially improved computational outcomes, setting a new standard for efficiency and intelligence.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

The Science
  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 01

    Real Neurons are Incubated

    Living neurons are grown on high density array of electrodes to
    create a biological network.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 02

    Neurons meet Silicon

    Information is encoded through electrical stimulation. Processed information in the form of neural signals is decoded and applied.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 03

    Adaptive Learning

    Biological networks learn through targeted stimulation, optimizing information processing.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

  • Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Large Language Models

    Using a biological network of hundreds of thousands of living neurons, we built the world's first closed loop-system, merging biology and AI for LLMs. Our Bio-LLM outperforms in-silico LLMs with massive efficiency gains.

    Phase 04

    Superior Performance

    Our system delivers exponentially improved computational outcomes, setting a new standard for efficiency and intelligence.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

    Image Classification

    We can transform everyday images into high dimensional biological space. Using new features, we can improve on efficiency and accuracy of training and classification
    of images.

    Video

    We harness the organic memory of neurons to encode and decode picture frames across time and space.

Why the neuron?

creating a new industry

Why the neuron?

creating a new industry

Why the neuron?

creating a new industry

Why the neuron?

creating a new industry

Our system delivers exponentially improved computational outcomes, setting a new standard for efficiency and intelligence.

Superior Performance

Neurons learn through targeted stimulation, enhancing their natural network for optimized processing.

Adaptive Learning

We have created a language to encode information into the neurons and decode their response for any application.

Neurons meet Silicon

Neurons are grown on many electrodes to create a biological neural network.

Real Neurons are Incubated

The Science

Phase 01

Phase 02

Phase 03

Phase 04

Our system delivers exponentially improved computational outcomes, setting a new standard for efficiency and intelligence.

Superior Performance

Neurons learn through targeted stimulation, enhancing their natural network for optimized processing.

Adaptive Learning

We have created a language to encode information into the neurons and decode their response for any application.

Neurons meet Silicon

Neurons are grown on many electrodes to create a biological neural network.

Real Neurons are Incubated

The Science

Phase 01

Phase 02

Phase 03

Phase 04

Connecting real living neurons with modern AI

We’re bringing the brain’s native intelligence to computing. We’re building an organic computing platform that connects real living neurons with modern AI – making frontier models more stable, scalable and dramatically more efficient. 

Application 01

Application 02

Application 03

BBB uses learning principles from real biological neural networks to create novel AI architectures.

Biologically-Inspired Compute

BBB improves classification accuracy while lowering compute.

Computer Vision

BBB improves image resolution, video stability and coherence for less compute.

Generative Video & World Models

The Evolution of Intelligence

The Evolution of
Intelligence

Evolution has spent 525 million years refining the most sophisticated computer ever made.

Neurons can solve every known computational challenge with less power than a cheese sandwich.

Millions of years in the making
Millions of years in the making

Our system delivers exponentially improved computational outcomes, setting a new standard for efficiency and intelligence.

Superior Performance

Neurons learn through targeted stimulation, enhancing their natural network for optimized processing.

Adaptive Learning

We have created a language to encode information into the neurons and decode their response for any application.

Neurons meet Silicon

Neurons are grown on many electrodes to create a biological neural network.

Real Neurons are Incubated

The Science

Phase 01

Phase 02

Phase 03

Phase 04

Work with us

See Jobs

We are building a new industry of computing - from computational neuroscience, biology, computer science, electrophysiology, machine learning, hardware and software and wetware engineering, physics, philosophy and ethics - to solve the most challenging problems and connect everyone to everything around us.

Work with us

See Jobs

We are building a new industry of computing - from computational neuroscience, biology, computer science, electrophysiology, machine learning, hardware and software and wetware engineering, physics, philosophy and ethics - to solve the most challenging problems and connect everyone to everything around us.

Founders
Our Team
Our Team
Alex Ksendzovsky MD, PhD

CEO & Co-Founder

Neurosurgeon‑scientist turning living neural networks into a new computing paradigm. He led funded research on networked brain disorders and biological neural networks since 2005—bringing deep insight and real-world execution. Previously founded an FDA‑cleared ML wearable platform currently in market.

Cristina Florio, PhD

Senior Scientist and Director of Laboratory Operations
Ex-NIH

Cellular and molecular biologist with cutting-edge in vitro and ex vivo laboratory techniques.

John Wittig, PhD

Machine Learning Engineer, Ex-iBoss, NIH

Computational modeling of electrophysiology via reinforcement learning

Steven Jerjian, PhD

Neuroscientist, Ex-Johns Hopkins, UCL Queen Square

Physiologist with 10+ years of data analysis and visualization.

Jon Pomeraniec MD, MBA

COO & Co-Founder

Neurosurgeon‑scientist developing closed‑loop implantable neurostimulation driven by brain signals of learning, memory and cognition. Award‑winning and nationally recognized, with prior experience of statistical modeling on Wall Street and prior founder of an FDA‑cleared ML wearable platform now in market.

Marisol Cortes, PhD

Biological Scientist
Ex-Johns Hopkins

Expert in Cellular and Molecular Biology with focus on neuronal culturing methods

William Barnes, PhD

VP of Technology,
Ex-Max Planck Society

Computational neuroscientist with 15+ years of large-scale neural dataset experience.

Katie Greenfield

VP Finance and Operations
Ex-Culture, Solugen

Leading finance and operations executive bringing companies from seed to growth stages.

Systems neuroscientist with 20+ years of physics, cognitive and visual neuroscience experience.

Tai-peng Tian, PhD

Head of Computer Vision Ex- Meta, Apple

15+ years of computer vision and deep learning research, bringing cutting edge to production for multiple problem domains.

Simone Chiola, PhD


Biology Researcher Ex-Stanford

Groundbreaking work on development of innovative iPSC-derived 2D/3D in vitro models

Haggai Agmon, PhD


Computational Neuroscientist Ex-Stanford

Expert in dynamical systems, large-scale numerical simulations and complex high-dimensional datasets

Systems neuroscientist with 20+ years of physics, cognitive and visual neuroscience experience.

Lawson Fuller, PhD

Computational Neuroscientist Ex-Imagia, UCSD

Leading computational physicist with specialization in neural network architecture design and recurrent neural networks

Jacob Jaffe, PhD


AI Research Scientist Ex-MIT, Stanford

Specialist in reinforcement learning with cutting edge computational methods to analyze large systems

Vik Chaudhary


VP of Product and Partrnerships

Ex-Meta, Dropbox, MIT

World leader in Product Management and Corporate Development for AI developer platforms, data infrastructure and conversational AI

Systems neuroscientist with 20+ years of physics, cognitive and visual neuroscience experience.

Jason Ngo


AI Researcher

Ex-UCSD

Expert in end-to-end retrieval-augmented generation (RAG) pipeline and developing new machine learning architecture

Alex Ksendzovsky MD, PhD

CEO & Co-Founder

Neurosurgeon‑scientist turning living neural networks into a new computing paradigm. He led funded research on networked brain disorders and biological neural networks since 2005—bringing deep insight and real-world execution. Previously founded an FDA‑cleared ML wearable platform currently in market.

Jon Pomeraniec MD, MBA

COO & Co-Founder

Neurosurgeon‑scientist developing closed‑loop implantable neurostimulation driven by brain signals of learning, memory and cognition. Award‑winning and nationally recognized, with prior experience of statistical modeling on Wall Street and prior founder of an FDA‑cleared ML wearable platform now in market.

Katie Greenfield

VP Finance and Operations
Ex-Culture, Solugen

Leading finance and operations executive bringing companies from seed to growth stages.

Katie Greenfield

VP Finance and Operations
Ex-Culture, Solugen

Leading finance and operations executive bringing companies from seed to growth stages.

Steven Jerjian, PhD

Neuroscientist Ex-Johns Hopkins, UCL Queen Square

Computational neuroscientist with 10+ years of experimental neuroscience, data analysis and machine learning.

Cristina Florio, PhD

Senior Scientist and Director of Laboratory Operations
Ex-NIH

Cellular and molecular biologist with cutting-edge in vitro and ex vivo laboratory techniques.

John Wittig, PhD

Machine Learning Engineer, Ex-iBoss, NIH

Computational modeling of electrophysiology via reinforcement learning.

Marisol Cortes, PhD

Biological Scientist
Ex-Johns Hopkins

Expert in Cellular and Molecular Biology with focus on neuronal culturing methods

Tai-peng Tian, PhD

Head of Computer Vision

Ex- Meta, Apple

15+ years of computer vision and deep learning research, bringing cutting edge to production for multiple problem domains.

Tai-peng Tian, PhD

Head of Computer Vision

Ex- Meta, Apple

15+ years of computer vision and deep learning research, bringing cutting edge to production for multiple problem domains.

Simone Chiola, PhD

Biology Researcher

Ex-Stanford

Groundbreaking work on development of innovative iPSC-derived 2D/3D in vitro models

Simone Chiola, PhD

Biology Researcher

Ex-Stanford

Groundbreaking work on development of innovative iPSC-derived 2D/3D in vitro models

Lawson Fuller, PhD

Computational Neuroscientist

Ex-Imagia, UCSD

Leading computational physicist with specialization in neural network architecture design and recurrent neural networks

Lawson Fuller, PhD

Computational Neuroscientist

Ex-Imagia, UCSD

Leading computational physicist with specialization in neural network architecture design and recurrent neural networks

Jacob Jaffe, PhD

AI Research Scientist

Ex-MIT, Stanford

Specialist in reinforcement learning with cutting edge computational methods to analyze large systems

Jacob Jaffe, PhD

AI Research Scientist

Ex-MIT, Stanford

Specialist in reinforcement learning with cutting edge computational methods to analyze large systems

Haggai Agmon, PhD

Computational Neuroscientist

Ex-Stanford

Expert in dynamical systems, large-scale numerical simulations and complex high-dimensional datasets

Haggai Agmon, PhD

Computational Neuroscientist

Ex-Stanford

Expert in dynamical systems, large-scale numerical simulations and complex high-dimensional datasets

Vik Chaudhary

VP of Product and Partnerships

Ex-Meta, Dropbox, MIT

World leader in Product Management and Corporate Development for AI developer platforms, data infrastructure and conversational AI

Vik Chaudhary

VP of Product and Partnerships

Ex-Meta, Dropbox, MIT

World leader in Product Management and Corporate Development for AI developer platforms, data infrastructure and conversational AI

Jason Ngo

AI Researcher

Ex-UCSD

Expert in end-to-end retrieval-augmented generation (RAG) pipeline and developing new machine learning architecture

Jason Ngo

AI Researcher

Ex-UCSD

Expert in end-to-end retrieval-augmented generation (RAG) pipeline and developing new machine learning architecture

William Barnes, PhD

VP of Technology,
Ex-Max Planck Society

Computational neuroscientist with 15+ years of large-scale neural dataset experience.

Founders
founders
Alex Ksendzovsky MD, PhD

CEO & Co-Founder

Neurosurgeon‑scientist turning living neural networks into a new computing paradigm. He led funded research on networked brain disorders and biological neural networks since 2005—bringing deep insight and real-world execution. Previously founded an FDA‑cleared ML wearable platform currently in market.

Jon Pomeraniec MD, MBA

COO & Co-Founder

Neurosurgeon‑scientist developing closed‑loop implantable neurostimulation driven by brain signals of learning, memory and cognition. Award‑winning and nationally recognized, with prior experience of statistical modeling on Wall Street and prior founder of an FDA‑cleared ML wearable platform now in market.

Cristina Florio, PhD

Senior Scientist and Director of Laboratory Operations
Ex-NIH

Cellular and molecular biologist with cutting-edge in vitro and ex vivo laboratory techniques.

John Wittig, PhD

Machine Learning Engineer, Ex-iBoss, NIH

Computational modeling of electrophysiology via reinforcement learning.

Marisol Cortes, PhD

Biological Scientist
Ex-Johns Hopkins

Expert in Cellular and Molecular Biology with focus on neuronal culturing methods

William Barnes, PhD

VP of Technology,
Ex-Max Planck Society

Computational neuroscientist with 15+ years of large-scale neural dataset experience.

Haggai Agmon, PhD

Computational Neuroscientist

Ex-Stanford

Expert in dynamical systems, large-scale numerical simulations and complex high-dimensional datasets

Haggai Agmon, PhD

Computational Neuroscientist

Ex-Stanford

Expert in dynamical systems, large-scale numerical simulations and complex high-dimensional datasets

Katie Greenfield

VP Finance and Operations
Ex-Culture, Solugen

Leading finance and operations executive bringing companies from seed to growth stages.

Katie Greenfield

VP Finance and Operations
Ex-Culture, Solugen

Leading finance and operations executive bringing companies from seed to growth stages.

Tai-peng Tian, PhD

Head of Computer Vision

Ex- Meta, Apple

15+ years of computer vision and deep learning research, bringing cutting edge to production for multiple problem domains.

Tai-peng Tian, PhD

Head of Computer Vision

Ex- Meta, Apple

15+ years of computer vision and deep learning research, bringing cutting edge to production for multiple problem domains.

Simone Chiola, PhD

Biology Researcher

Ex-Stanford

Groundbreaking work on development of innovative iPSC-derived 2D/3D in vitro models

Simone Chiola, PhD

Biology Researcher

Ex-Stanford

Groundbreaking work on development of innovative iPSC-derived 2D/3D in vitro models

Lawson Fuller, PhD

Computational Neuroscientist

Ex-Imagia, UCSD

Leading computational physicist with specialization in neural network architecture design and recurrent neural networks

Lawson Fuller, PhD

Computational Neuroscientist

Ex-Imagia, UCSD

Leading computational physicist with specialization in neural network architecture design and recurrent neural networks

Jacob Jaffe, PhD

AI Research Scientist

Ex-MIT, Stanford

Specialist in reinforcement learning with cutting edge computational methods to analyze large systems

Jacob Jaffe, PhD

AI Research Scientist

Ex-MIT, Stanford

Specialist in reinforcement learning with cutting edge computational methods to analyze large systems

Steven Jerjian, PhD

Neuroscientist Ex-Johns Hopkins, UCL Queen Square

Computational neuroscientist with 10+ years of experimental neuroscience, data analysis and machine learning.

Vik Chaudhary

VP of Product and Partnerships

Ex-Meta, Dropbox, MIT

World leader in Product Management and Corporate Development for AI developer platforms, data infrastructure and conversational AI

Vik Chaudhary

VP of Product and Partnerships

Ex-Meta, Dropbox, MIT

World leader in Product Management and Corporate Development for AI developer platforms, data infrastructure and conversational AI

Jason Ngo

AI Researcher

Ex-UCSD

Expert in end-to-end retrieval-augmented generation (RAG) pipeline and developing new machine learning architecture

Jason Ngo

AI Researcher

Ex-UCSD

Expert in end-to-end retrieval-augmented generation (RAG) pipeline and developing new machine learning architecture

Our Team
Baltimore, MD &
San Francisco, CA
Sign up to stay informed on how we are changing the landscape of computing.

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Baltimore, MD &
San Francisco, CA
Sign up to stay informed on how we are changing the landscape of computing.

Please enter a valid email address

Baltimore, MD &
San Francisco, CA
Sign up to stay informed on how we are changing the landscape of computing.

Please enter a valid email address