Digital Systems
Regular undergraduate teaching connected to logic design, computer organization, hardware description, and the foundations of digital computation.
TEACHING & SUPERVISION
Teaching computing through systems, models and implementation.
My teaching connects theoretical foundations with system architecture, experimentation and practical implementation.
TEACHING AREAS
Core areas of teaching and academic formation.
Operating Systems
Systems-level teaching involving abstractions, resource management, concurrency, scheduling, embedded systems, and software/hardware interaction.
Modeling and Simulation of Systems
Teaching area treating modeling and simulation as an engineering and scientific method across discrete-event models, stochastic processes, and complex systems.
Biological Computation and Computational Biology
Computational perspectives on biological systems and the use of modeling and simulation to reason about living systems.
COURSES
Courses taught
TEACHING APPROACH
Systems, models and implementation.
Abstraction levels first
Courses make explicit how problems move between digital logic, architecture, operating systems, simulation models, software artifacts, and scientific interpretation.
Executable knowledge
Students are encouraged to produce code, models, experiments, documentation, and repositories that can be inspected, reproduced, and improved.
Responsible AI use
AI tools may accelerate programming and literature workflows, but results must remain attributable, testable, explainable, and scientifically defensible.
SUPERVISION
Academic supervision and student work.
Undergraduate final-project supervision.
Undergraduate research supervision across systems, simulation and embedded computing.
Student guidance in university extension projects.
Ongoing student supervision.
OPPORTUNITIES
Research and project areas for students.
Computer systems and architecture
Projects involving computer organization, digital systems, operating systems, embedded systems, cyber-physical systems, and hardware/software integration.
Modeling and simulation
Projects involving simulation tools, discrete-event models, stochastic systems, experiment design, analysis workflows, and domain-specific simulation components.
AI for scientific work
Projects involving AI-assisted scientific software, agents, code generation, literature workflows, simulation automation, and responsible productivity.
Biological computation and BioCompLab topics
Projects involving high-level computational modeling of biological systems, biological computer organization, whole-cell simulation, and conceptual BioCAD workflows.