Chief Operating Officer
2022 - PRESENTGhostWrite- Developed GhostWrite, an AI email assistant product, growing from one to 2,000 users in two months with net positive cash flow.
- Managed a team of three engineers and two marketeers in the product's development, rollout, roadmap, and business development.
- Built the entire web and ML stack from zero to one: Heroku-based web services, a Delta Lake data management system, segment IO analytics, and cluster ML deployment for high throughput product delivery.
Technologies: Blitz, Node.js, JavaScript, React, Segment, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Reinforcement Learning, Web ExtensionsResearch Scientist
2020 - 2022OpenAI- Developed methodologies for scaling large language models using human feedback, imitation learning (ExpIt), and reinforcement learning from human feedback. I also uncovered data feedback and computed scaling laws for model alignment.
- Codeveloped Codex, a large language model capable of programming at a human level.
- Cocreated the first release of Copilot, the VS Code AI autocomplete extension which was acquired by GitHub.
- Handled the handover of my acquired project, MineRL, developing large-scale Minecraft AI models, and organized four official NeurIPS conference workshops and competitions.
- Built new imitation learning algorithms based on distance-to-measure techniques from computational topology and applied them to procedural generation environments.
- Conducted extensive testing of state-of-the-art imitation learning algorithms in complex environments.
Technologies: PyTorch, MPI, Deep Learning, Deep Reinforcement Learning, Natural Language Processing (NLP), Clustering, Computer Vision, Cluster, Kubernetes, Docker, GPU Computing, CUDA, Jupyter NotebookDoctoral Researcher
2017 - 2021Carnegie Mellon University- Developed the first method for application of computational topology to deep neural networks: https://arxiv.org/pdf/1802.04443.pdf.
- Created the MineRL project, a large-scale effort to reproduce general human intelligence in open-world domains through internet-scale behavioral cloning. OpenAI later acquired this project.
- Managed 10+ team members in Japan, USA, England, India, and Germany, as well as several research interns across many projects. See the list of publications at: https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=5bB_sFcAAAAJ.
- Created infinite dimensional extensions to deep neural networks: https://arxiv.org/pdf/1612.04799.pdf. I also proved the very first universal approximation theorem for these networks: https://arxiv.org/pdf/1910.01545.pdf.
Technologies: Deep Learning, Torch, React, Node.js, Express.js, Mathematica, Optimization, Computational TopologyVisiting Researcher
2019 - 2019Freie Universität Berlin- Acted as a visiting researcher at the university's Institute of Mathematics, working in the discrete topology and geometry group.
- Developed a theoretical framework for analyzing deep neural networks using algebraic topology.
- Built new theoretical foundations for analyzing neural hyperplane arrangements. These formations are central to neural codes and compression theories of neural network learning.
Technologies: Computational Topology, GPU ComputingChief Technology Officer
2017 - 2019InfoPlay- Worked for InfoPlay, a cryptocurrency hedge fund applying stochastic gradients to markets.
- Developed long-term roadmaps for technology acquisition and strategized the development of proprietary methodologies.
- Created a novel reinforcement learning approach for acting multi-modal non-stationary environments, leveraging multiple asynchronous data sources.
- Led and managed a small team to implement the technology roadmap.
- Managed the development of a deep reinforcement learning infrastructure for online trading in financial markets.
Technologies: Deep Learning, Deep Reinforcement Learning, TensorFlow, PyTorchFounder, Director of Research, and President
2015 - 2017Machine Learning at Berkeley- Successfully launched and managed six research teams studying deep learning theory and applications.
- Researched deep active learning, a bridge between deep learning and active learning, using policy and selection steps inspired by AlphaGo.
- Acted as the project manager on OpenBrain, a massively asynchronous recurrent neurocomputational approach to artificial general intelligence.
- Theorized and implemented a new ML algorithm and generalized artificial neural networks.
- Collaborated with the International Computer Science Institute, researching new layer functions for complex neural networks on Fourier spectrum data and developing reinforcement learning techniques to perform multiple-model car fleet driving.
- Led one of twelve sponsored teams competing to pursue the development of conversational AI and received a $100,000 grant from Amazon for a year-long project (Alexa Prize).
- Built a generative information retrieval model using neural Turing machines and inverse reinforcement learning.
- Managed the organizational recruiting and retention process.
Technologies: Deep Learning, Deep Reinforcement Learning, Computer Vision, Management, Natural Language Processing (NLP)Machine Learning Engineer
2015 - 2017Bonsai- Architected and implemented a new AI/ML back end for classification and deep reinforcement learning.
- Designed and implemented HyperLearner, a generative hyper parameter suggestion back end for metamachine learning optimization using manifold embeddings.
- Built a neural network descriptor language for wrapping a variety of deep neural network models.
- Codeveloped the patent for a "searchable database of trained artificial intelligence objects that can be reused, reconfigured, and recomposed, into one or more subsequent artificial intelligence models" (patent number: US 10,586,173).
Technologies: Python, TensorFlow, Deep Reinforcement Learning, Deep Learning, Clustering