Scientific Depth
Physics-based modeling, simulation and computational science: a Ph.D. foundation that lets me direct deep technical work, not just manage it.
Scientific Computing, AI Platform, and R&D Data Leader
A physics-trained leader who turns complex computational workflows, AI, and data pipelines into enterprise-grade solutions.
I help R&D and engineering teams modernize how they run, scale, and automate computational work.
“From equations to architecture to shipped products: I deliver scientific computing and data platforms at enterprise scale.”
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, Databricks integration, and global computational workflows. As founder of 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
a builder of solutions that connect scientific computing, AI, data, and enterprise execution.
Platform Execution
APIs, MSP, workflow orchestration, HPC execution, AI, reproducibility, and observability: from concept to architecture to shipped product.
Enterprise Leadership
Merck leadership, cross-functional teams, regulated R&D, roadmap ownership, and executive communication across technical, scientific, and business stakeholders.
Platforms
DiPhyx & dxflow.
As founder of DiPhyx, I am building the platform and workflow engine that make computational pipelines easier to design, run, monitor, and reproduce across cloud, HPC, and AI infrastructure.
Platform
One platform for scientific computing with AI.
DiPhyx is an AI-driven scientific computing platform that streamlines the design, execution, and monitoring of complex computational pipelines across scientific and engineering domains. It manages the lifecycle of computational workloads in a cloud-agnostic environment, turning fragmented, failure-prone workflows into reproducible, optimized runs.
- Automates pipeline design: AI helps configure workflows across data processing, simulation, analytics, and scientific computing use cases.
- Optimizes compute: dynamically selects the most cost- and time-efficient infrastructure across AWS, Azure, GCP, and on-prem HPC.
- Monitors in real time: reads outputs, logs, and resource usage to catch failures early, suggest fixes, and guarantee reproducibility.
Engine
A workflow engine for Docker, Slurm, and more: built for scientific and engineering computation.
dxflow is the distributed computing engine born inside DiPhyx to tame scattered scripts, containerized tools, and cluster execution. It orchestrates workflows across environments, turning accessible machines into a unified computational fleet with CLI and API interfaces for seamless integration.
- Container & HPC orchestration: Docker integration and Slurm cluster management from one engine.
- CLI, REST API & Web UI: automate it, integrate it programmatically, or manage workflows in the browser.
- Enterprise-grade: built-in authentication and authorization for teams.
Selected work
Scientific computing across research, data, and platforms.
Representative work across computational science, AI-enhanced data pipelines, platform architecture, and enterprise R&D technology.
Live visualization · Computational science foundation
Interactive
Two-Phase One-Dimensional Turbulence (ODT) Model
An interactive walkthrough of my doctoral work at the Institute for Clean and Secure Energy. This project shows the technical foundation behind my platform work: modeling complex physical systems, reducing computational cost, and translating advanced numerical methods into usable scientific software.
Focus: computational modeling · numerical methods · HPC · scientific software
Open visualization →
Enterprise R&D
Data platforms
AI and Data Pipelines for Scientific R&D
At Merck, I worked across data science, NLP, machine learning, and computational workflow initiatives for nonclinical drug safety. The work connected scientific users, enterprise data platforms, and production computational pipelines in regulated R&D settings.
Focus: ML workflows · NLP · Databricks · R&D data systems
Founder-led platform
Product execution
DiPhyx and dxflow Scientific Computing Platform
Through DiPhyx and dxflow, I built platform concepts, product workflows, and execution patterns for running computational pipelines across local machines, containers, cloud, and HPC infrastructure. The work reflects my broader focus: make complex computation usable, reproducible, and valuable for teams.
Focus: workflow orchestration · cloud/HPC · APIs · reproducibility
Explore dxflow →
Experience
From scientific code to enterprise platforms.
Chief Information & Technology Officer
DiPhyx Inc.Present
- Built AI-enhanced scientific computing products: dxflow, DiPhyx Platform, and DiPhyx Studio.
- Led product strategy, platform architecture, design partners, and engineering execution.
- Focused on computational pipelines, cloud and HPC execution, reproducibility, product packaging, and executive communication.
Associate Director, Data Science
Merck & Co.Enterprise R&D
- Led scientific data and computational workflow initiatives in regulated R&D.
- Worked on capacity planning, ML and NLP pipelines, Databricks integration, and internal AI/data platforms.
- Connected scientists, engineers, data teams, and leadership across international collaborations.
Senior Software Developer
Institute for Clean and Secure EnergyScientific computing
- Built and optimized scientific computing codes for large-scale multiphysics simulations.
- Developed GPU-enabled and cloud-enabled computational workflows.
- Delivered major performance improvements in compute-intensive scientific workflows.
Computational Scientist · Ph.D. Researcher
University of UtahPh.D., Chemical Engineering
- Conducted research in computational modeling, CFD, thermochemistry, and HPC.
- Developed numerical and computational approaches for complex reacting-flow systems.
- Peer-reviewed publications in computational science, CFD, and high-performance scientific computing, Google Scholar →
Let’s talk
Scientific computing, AI platforms, R&D data systems, computational pipelines, HPC/cloud infrastructure, and platform strategy. If your team builds complex technical systems, I would like to hear from you.