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.

View work → Platforms I build Contact
San Diego, California CITO at DiPhyx Software Developer & Data Scientist

“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.

Platforms

DiPhyx & dxflow.

The platform & engine, built to design, run, and manage computational pipelines across cloud, HPC & edge.

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.
Visit DiPhyx → DiPhyx Studio
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.
Visit dxflow → Read the docs

Selected work

Scientific computing across research, data, and platforms.

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.

Learn More →
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.

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 Energy

Scientific 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 Utah

Ph.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.

Email me → Visit DiPhyx
LinkedIn Google Scholar GitHub X (Twitter) DiPhyx dxflow.ai