Embedded Genomics
Bringing genomic computation closer to where it is needed.
Hardware/software co-design, bioinformatics acceleration and energy-efficient embedded clusters for genomic workloads.
Research
Computing with a purpose.
Our work connects computational efficiency with challenges in biological data, sustainable mobility and intelligent systems.
Intelligent battery systems
Understanding batteries. Extending their useful life.
Battery state estimation, diagnostics and intelligent cell balancing through physical models and data-driven methods.
Efficient & parallel computing
More useful computation from constrained resources.
GPU graph algorithms, heterogeneous scheduling and resource-aware machine learning for embedded and edge systems.
From models
to measured systems.
We ask how hardware and software should work together, and evaluate the trade-offs in accuracy, latency, throughput and energy use.
Model & understand
Develop physical, computational and data-driven models that expose the problem’s structure.
Design & implement
Translate ideas into algorithms, scheduling methods and hardware-aware implementations.
Measure & refine
Benchmark across workloads and architectures, inspect failure cases and improve the design.