HyIQPARTNERSFounder & Managing Partner · HyIQ Partners
First principles. Technical depth. Business judgment.
We advise investment firms and life sciences companies on the scientific, technical, and operating assumptions behind consequential decisions. Our experience spans biological AI platforms at NVIDIA, enterprise AI and accelerated computing at Eli Lilly, and bespoke analysis for major investment firms. That background connects scientific evidence with software, hardware, compute economics, and the realities of enterprise adoption.
Our advisory work with investment firms includes technical assessments for private equity, venture capital, and corporate-development teams. The focus is the relationship between scientific claims, technical capability, and the business model.
Questions include differentiation, compute requirements, dependencies, customer adoption, and the evidence that could change the investment thesis.
Discuss an investment question ↗Our advisory work with life sciences companies addresses AI and data strategy, scientific platforms, computing infrastructure, and the conditions needed for adoption across the enterprise.
The work connects technical choices to scientific priorities, resource requirements, operating constraints, and a disciplined cycle of implementation and learning.
Discuss a company challenge ↗Helped create and commercialize BioNeMo and worked with scientific software teams on GPU acceleration for drug-discovery and structural-biology workloads.
Relevance to our advisory workExamining what is distinctive, what the platform depends on, and what enterprise use requires.
Led work on R&D AI and accelerated-computing strategy, infrastructure optimization, and AI-assisted clinical study report authoring.
Relevance to our advisory workConnecting architecture and performance to workflows, governance, delivery cost, and adoption.
Advised a large pharma organization to identify and correct organizational bottlenecks in a major clinical and regulatory writing workflow rather than invest in external AI solutions. The resulting changes consistently reduced cycle time by more than 70% relative to baseline.
Relevance to our advisory workDetermining whether the constraint is technological, organizational, or operational before deciding where the enterprise should invest.
Advised on accelerating a core protein-sequence search workload on GPUs, delivering 20× faster sequence searches and 39% lower compute cost relative to the CPU baseline.
Relevance to our advisory workDetermining when advances in algorithms and hardware materially change the economics and feasibility of scientific workflows.
Led the team that implemented software optimizations for a major cryo-EM research platform, delivering up to 2.1× faster running times and approximately 50% lower storage requirements for end-customers.
Relevance to our advisory workConnecting software architecture, data representation, and hardware infrastructure to customer economics.
Led the team that implemented resource-utilization optimizations for a major molecular-simulation platform, delivering up to 3.04× faster performance and approximately 67% lower GPU compute cost for matching workloads.
Relevance to our advisory workConnecting the technology stack to cloud economics to measurable improvements in scientific throughput and cost per result.
Our perspective starts with the conditions a technology must satisfy in use: the evidence behind its claims, the data it depends on, how the software and hardware interact, and the resources and organizational changes required.
For an investor, those conditions can change the view of differentiation, scalability, and capital requirements. For a company, they can change the architecture, the implementation sequence, or whether to build, buy, or partner.
The aim is not to defend a technology narrative. It is to identify what is supported, what remains uncertain, and which next step would make the decision better informed.
Founder & Managing Partner
Chief Technology and AI Officer
Senior Director & Global Lead, AI & HPC Acceleration
Global Lead, AI Drug Discovery & Structural Biology
Earlier work includes lead roles at Genentech, Illumina and Thermo Fisher Scientific.
Bring the question, its context, and the timeline. We agree the scope, available evidence, deliverables, and commercial terms before substantive work begins.
Engagements can be hourly or project-based. Confidentiality, access, and potential conflicts are addressed before sensitive information is exchanged.
Share the question, its context, and your timeline. Please do not send confidential materials in an initial email.
engage@hyiq.ai