DJ

I am currently a Computer Science undergraduate at UC Berkeley and a researcher with Stanford Medicine’s Gevaert Lab. My work focuses on computer vision, medical imaging, and machine-learning systems.

My current research interests lie in robust visual learning, synthetic data, and biomedical AI. I also build scientific and creative software, including research workspaces, agent tools, forecasting interfaces, and tools for DJs.

News

  • : Completed my Canary CREST research internship and presented the final research poster. Poster
  • : Our cutaneous neurofibroma digital-twin paper is published at the inaugural MICCAI Workshop on Medical World Models. Paper

Publications

Education

Experience

Stanford Medicine
Research · Computational Medicine & Dermatology
Gevaert Lab
Research Intern
Medical imaging, segmentation robustness, and physiology-informed augmentation.
Canary CREST
Research Intern
Developed and presented a physics-guided cNF medical digital-twin project.
Sarin Lab
Research Collaborator
Cutaneous neurofibroma imaging and longitudinal burden estimation.
Machine Learning at Berkeley
Internal & Research Committee
Machine learning engineer building applied ML systems for industry partners.
Logitech
Industry Project · Machine Learning Engineer
Video-conferencing intelligence and meeting-room readiness modeling.
Infrastruct
CEO & Founder
Building AI-agent workflows for customer-support teams.

Side Projects

Openleaf

Research WorkspacemacOS
WebsiteDocs

A local research workspace built primarily in JavaScript, combining LaTeX, live PDF, PowerPoint, Python, GitHub, SSH, and coding agents.

BsCode

Agent ToolingDesktop
WebsiteCode

A desktop built primarily in JavaScript for coordinating Codex, Claude, and shell agents across local and remote workspaces.

LanternTrace Explorer

Scientific SoftwareForecasting
WebsiteCode

A physics-based approach to track and predict the invasion of the spotted lanternfly across the U.S. East Coast.

Misc.

CS184 Homework 1: Rasterizer

Research use only

Research Software Tools

Download the expert viewer for macOS or Windows, or copy the matching one-line installer.

CT-PET Viewer

Inspect co-registered CT, PET, and segmentation volumes in a linked four-view workstation. The case folder is downloaded separately. Source repository.

macOS Download Apple silicon DMG (v1.1.2)
curl -fsSL https://alex-dils.com/ct-pet-viewer/install.sh | bash
Windows 10/11 Download x64 installer
curl.exe -fsSL https://alex-dils.com/ct-pet-viewer/install.ps1 | powershell -NoProfile -ExecutionPolicy Bypass -Command -

Case data: download the folder containing task021–task030 separately. The app finds it under Downloads automatically, or use Open folder.

CT-PET Inference AI

Run local AI inference and compare generated PET masks alongside the source imaging.

curl -fsSL https://alex-dils.com/ct-pet-inference/install.sh | bash

For research use only. These tools are not cleared for clinical diagnosis.