Software Engineering
Frontend interfaces, clean static sites, automation scripts, and maintainable project structure.
Personal Portfolio
Hi, I’m Qinjingtao.
I’m a Software Engineer
I build thoughtful digital systems for research, learning, and real-world use.
About
Curious, practical, and comfortable connecting ideas across domains.
Hi, I’m Qinjingtao. I’m shaping this site as a personal launchpad for software projects, research notes, and technical experiments connected to my work at UCR.
The portfolio is designed to feel like the reference project you shared: polished, technical, animated, and direct. It is also intentionally simple under the hood, so it can be published from a GitHub Pages repository without a build step.
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Featured project slots
UCR
Academic focus
100%
GitHub Pages ready
Skills
Tools and strengths to customize.
Data & Research
Experiment tracking, analysis workflows, visualization, and translating findings into clear artifacts.
Product Thinking
Organized user flows, readable writing, practical priorities, and interfaces that respect attention.
Projects
Featured work.
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Research Dashboard
A focused dashboard concept for tracking experiments, metrics, and notes across a research workflow.
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Personal Website
This portfolio: a polished, responsive GitHub Pages site inspired by modern developer portfolios.
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Learning Notes
A future home for writeups, technical notes, reading lists, and useful project retrospectives.
Resume
Experience timeline.
Power System Research Scientist -- Hitachi America, Ltd.
Lead R&D on AI-based methods for contingency power flow analysis and voltage compensation in large-scale power systems of the Southwest Power Pool (SPP).
Develop and prototype GPU-based large-scale linear optimization and Woodbury-based sensitivity calculation.
Collabrote with Hitachi's internel and external industry parters (e.g. Nvidia)/researchers to support strategic technology innovation and business incubation.
Ph.D. Student of Electrical Engineering -- UCR
Developed an optimization method-assisted ensemble deep reinforcement learning algorithm to accelerate Unit Commitment (UC) solutions in the day-ahead electricity market.
Developed a hierarchical graph convolution network (GCN) model including spatio-temporal graph convolution network (STGCN) and edge-conditioned graph network (ECGCN) to accelerate Unit Commitment (UC) solutions in the day-ahead electricity market.
Co-developed a power system voltage health index prediction model using the graph convolutional network (GCN).
DERMS Software Intern -- Mitsubishi Electric Research Laboratories, Inc.
Developed a stochastic dynamic hosting capacity assessment module for Distributed Energy Resource Management System(DERMS) using temporal Surrogate Lagrangian Decomposition.
Data Analyst Intern -- National Laboratory of the Rockies
Developed a physics-informed spatio-temporal graph convolution network for dynamic reconfiguration problems to minimize the curtailment of solar generations in unbalanced distribution systems.
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