Scientific Depth
A Ph.D. foundation in physics-based modeling and simulation: I direct deep technical work, not just manage it.
Scientific Computing, AI Platform, and R&D Data Leader
A physics-trained leader who transforms complex computational, AI, and data challenges into enterprise-grade solutions.
I help R&D and engineering teams run, scale, and automate computational work.
“From equations to architecture to shipped products.”
DiPhyx
Merck & Co.
Inst. for Clean & Secure Energy
University of Utah
About
Scientific depth, platform execution, and enterprise delivery
I am a Ph.D. chemical engineer and scientific computing leader with deep expertise in computational modeling, high-performance computing, GPU acceleration, AI, cloud infrastructure, and enterprise data platforms. My career sits at the intersection of science, software, and product strategy: I have built simulation tools, machine-learning workflows, data pipelines, scientific platforms, and AI-enhanced products for complex R&D environments.
At Merck, I led data science and computational platform initiatives supporting nonclinical drug safety, NLP-driven report analysis, vendor solutions integration (e.g. Databricks), and global computational workflows. At DiPhyx, I build products that help scientists, engineers, and data teams run, manage, automate, and scale computational workloads across cloud, HPC, and AI infrastructure.
Value
Three strengths
Platform Execution
APIs, workflow orchestration, HPC, AI, and observability: from concept to shipped product.
Enterprise Leadership
Cross-functional teams, regulated R&D, roadmap ownership, and executive communication.
Platforms
DiPhyx & dxflow.
The platform and workflow engine I build to design, run, and reproduce computational pipelines across cloud and HPC.
Platform
One platform for scientific computing with AI.
An AI-driven platform for designing, running, and monitoring computational pipelines — turning fragmented, failure-prone workflows into reproducible, optimized runs in any cloud.
- AI-assisted pipeline design across simulation, analytics, and data processing.
- Optimized compute across AWS, Azure, GCP, and on-prem HPC.
- Real-time monitoring that catches failures early and keeps runs reproducible.
Engine
A workflow engine for Docker, Slurm, and beyond.
Born inside DiPhyx to tame scattered scripts, containers, and clusters, dxflow turns accessible machines into a unified computational fleet.
- Container & HPC orchestration: Docker and Slurm from one engine.
- CLI, REST API & Web UI for automation and integration.
- Enterprise-grade: built-in authentication and authorization.
Selected work
Scientific computing across research, data, and platforms.
Live visualization · Computational science foundation
Interactive
Two-Phase One-Dimensional Turbulence (ODT) Model
An interactive walkthrough of my doctoral work: modeling complex physical systems, cutting computational cost, and turning numerical methods into usable software.
Focus: computational modeling · numerical methods · HPC · scientific software
Open visualization →
Enterprise R&D
Data platforms
AI and Data Pipelines for Scientific R&D
Data science, NLP, and computational workflows for nonclinical drug safety at Merck — connecting scientists, data platforms, and production pipelines in a regulated setting.
Focus: ML workflows · NLP · Databricks · R&D data systems
Founder-led platform
Product execution
DiPhyx and dxflow Scientific Computing Platform
Product workflows and execution patterns for running computational pipelines across containers, cloud, and HPC — making complex computation usable and reproducible.
Focus: workflow orchestration · cloud/HPC · APIs · reproducibility
Explore dxflow →
Experience
From scientific code to enterprise platforms.
Associate Director, Data Science
Merck & Co.Enterprise R&D
- Led scientific data and computational workflow initiatives in regulated R&D.
- ML/NLP pipelines, Databricks integration, and internal AI and data platforms.
Chief Information & Technology Officer
DiPhyx Inc.AI-Enhanced Scientific Computing Platform
- Built dxflow, the DiPhyx Platform, and DiPhyx Studio.
- Led product strategy, platform architecture, and engineering execution.
Senior Software Developer
Institute for Clean and Secure EnergyScientific computing
- Built and optimized codes for large-scale multiphysics simulations.
- Developed GPU- and cloud-enabled workflows with major performance gains.
Computational Scientist · Ph.D. Researcher
University of UtahPh.D., Chemical Engineering
- Research in computational modeling, CFD, thermochemistry, and HPC.
- Peer-reviewed publications in computational science — Google Scholar →
Let’s talk
If your team builds complex technical systems — scientific computing, AI platforms, R&D data — I would like to hear from you.