Junhwan (Chris) Jang
Digital Twins · Simulation · Optimization
INCOMING GRADUATE STUDENT · MARCH 2027
I’m Junhwan (Chris) Jang, preparing to begin graduate study at Dongguk University with Prof. Sojung Kim in the Simulation-Based Optimization Laboratory.
I am interested in digital twins, simulation, and optimization—understanding how complex systems behave and using models to inform better decisions. My background is in software engineering, where I have built and operated production backend services and independent products.
Education
Research Interests
Digital Twins
Representing real-world systems to explore behavior, test scenarios, and support operational decisions.
Simulation
Agent-based modeling, discrete-event simulation, and system dynamics for complex interacting systems.
Optimization
Simulation-based optimization and operations research for evaluating alternatives under uncertainty.
My intended research direction follows Prof. Sojung Kim’s work in digital twins, simulation, and optimization, with applications including supply chains, renewable energy, and smart farming systems.
Publications
Research & Engineering Foundation
Upcoming Graduate Research
Simulation-Based Optimization Laboratory · Dongguk University
Prof. Sojung Kim · Expected start: Mar 2027
My planned graduate research will focus on digital twins, simulation, and optimization, aligned with Prof. Sojung Kim’s research. I aim to build on my software engineering background to implement models, evaluate system behavior, and develop decision-support tools.
| Research direction | Planned focus |
|---|---|
| Digital twins | Modeling real-world systems and connecting models with operational data |
| Simulation | Exploring agent-based models, discrete-event simulation, and system dynamics |
| Simulation-based optimization | Comparing scenarios and operational decisions under uncertainty |
Software Engineering Foundation
I bring practical experience in building event-driven services, preserving data consistency, and evaluating performance. These skills form a software engineering foundation for my transition into simulation and optimization research.
| Capability | Foundation & development focus | Research application |
|---|---|---|
| System implementation | Java, Spring Boot, Spring WebFlux; service design and operation | Implementing system interfaces and the software infrastructure around simulation models |
| Event and state management | Redis, Kafka, concurrency control, distributed locks | Handling events and maintaining consistent system state in data-connected applications |
| Performance evaluation | API profiling, SQL execution plans, indexing, caching | Measuring runtime behavior, locating bottlenecks, and comparing implementation alternatives |
| Experiment delivery | Product planning, deployment, and operation | Turning models and decision-support ideas into usable software prototypes |
| Simulation model implementation | Planned lab development: agent-based models, discrete-event simulation, and system dynamics | Translating system assumptions into executable models |
| Digital-twin data integration | Planned lab development: connecting operational data with model state | Keeping virtual system representations aligned with observed behavior |
| Optimization algorithms | Planned lab development: simulation-based optimization and scenario search | Evaluating operational alternatives under constraints and uncertainty |
| Research experiments & validation | Planned lab development: calibration, sensitivity analysis, and reproducible experiments | Testing model credibility and comparing outcomes systematically |
The planned development areas above are priorities for graduate study with Prof. Sojung Kim, beginning in March 2027.
Industry Experience
Commerce services, caching, concurrency control, and query optimization.
Messaging and chatbot platforms, webhook processing, and reactive services.
Backend development for delivery and fulfillment services.
Systems integration and platform backend development.
Talks & Writing
Data Consistency in High-Concurrency Events: A Redis Distributed Lock Case Study
SIPE 4th Conference · Aug 2, 2025
Production lessons on race conditions, fair access, atomic operations, and the tradeoffs of lock-based designs.