TAP's network is now expanding into hardware, advanced materials, and quantum systems — connecting domain experts across semiconductors, materials science, and quantum engineering through the same distributed collaboration infrastructure that underpins our other verticals. Key stakeholders are being brought in selectively as this area develops.
TAP's HPC infrastructure creates a meaningful opportunity in materials discovery. Computational simulation at scale can surface candidate materials — for semiconductors, quantum devices, and energy applications — that would take years to find through conventional experimental approaches alone. LLMs are also beginning to reshape how hardware software is written: generating and optimizing code at a level that directly addresses the complexity of modern chip design and the coordination challenges of large-scale distributed compute environments.
As the network deepens here, TAP's compounding knowledge platform will grow with it — capturing what is learned across materials families, device architectures, and simulation approaches, and making that knowledge available to the next program. The most valuable insights in this domain come from pairing computational scale with expert physical intuition, and that is the model we are bringing in.
TAP's HPC infrastructure enables computational simulation of materials at a scale and speed that conventional lab-based approaches cannot match. Candidate materials for semiconductors, quantum devices, and energy applications can be identified, screened, and prioritized computationally — with expert scientists directing the search and interpreting results at each stage.
LLMs are beginning to materially change how hardware software is written — generating, optimizing, and stress-testing code at a level that addresses the complexity constraints of modern chip design and the coordination challenges that arise when managing large numbers of chips in distributed compute environments. TAP is investing in this intersection.
Quantum systems represent one of the most consequential long-range bets in deep tech — with near-term relevance in simulation, optimization, and cryptography. TAP's network includes quantum engineering expertise, and we are actively building the relationships needed to participate meaningfully as the hardware and error correction landscape matures.
The most important advances in hardware and materials come from the combination of computational scale and expert physical intuition. TAP's model pairs AI-driven simulation and search with domain scientists who can interpret anomalous results, redirect experiments, and form the kind of non-obvious hypotheses that computational systems alone cannot generate.
Management Systems • High Performance Organization • Senior Lecturer, MIT Sloan
Machine Learning, Deep Learning • AI Researcher/Instructor, Berkeley, NASA • Founder, Silicon Valley Bank AI Lab
Corporate and Technology Strategy • Entrepreneur, Co-Founder ValueAI Institute • Investor, Venture Partner
Electrical Engineer • Sr TPM, WhatsApp (Meta) & Amazon Alexa AI • Founder, RoutineHub (50K → 1.4M users) • Thinks across physical systems, software, and scale • USC MBA
Principal Scientist, O'Donnell Data Science & Research Computing Institute, SMU • HPC Applications Scientist, SMU • Adjunct Professor of Data Science, SMU • Computational Chemistry & Molecular Dynamics
Quantum Computing • Senior Engineer, IonQ • Machine Learning & Physics • Financial Industry Quant
Founder, Ember Agentic Labs • Component Design Engineer, Intel • Analytics & Data Science Engineering, Google Cloud • Berkeley MIDS