Baskin School of Engineering

One of the central challenges in quantum computing is finding or approximating the ground-state energy of a local Hamiltonian, a quantum analogue of classical constraint satisfaction problems (CSPs). Among these, the Quantum Max-Cut problem serves as a canonical example, paralleling the classical Max-Cut problem. Despite its foundational importance in both theoretical computer science and condensed […]

Miniaturized spectrometers have the potential to replace bulky and expensive benchtop models. We have previously demonstrated a multimode interference (MMI) waveguide-based spectrometer that achieves high performance while minimizing its footprint. In this talk, the integration of the MMI spectrometer into an optofluidic device is proposed. This integration opens up applications such as the detection of […]

Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However, in the modern era, computational environments have changed drastically, and we […]

Simulation is a coinductive proof technique to assert the behavioral equivalence of computing systems that has seen fruitful application in distributed systems, concurrent process calculi, and programming languages, since the 1970’s. We have also utilized simulation in our prior work, where we formalized and proved a folklore claim that the state-based and operation-based approaches to […]

In this work, we develop numerical methods for conservation laws that explore statistical, structure-preserving, and machine-learning-based approaches, each built on top of traditional numerical solvers. First, we develop a general Gaussian-process-based “recipe’’ for constructing high-order linear operators such as interpolation, reconstruction, and derivative approximations. Building on this recipe, we derive a kernel-agnostic convergence theory for […]

Hardware design flows have become increasingly complex as modern chips integrate billions of transistors and rely on aggressive synthesis optimizations to meet performance, area, and power targets. While these transformations improve circuit efficiency, they also erase the correspondence between gate-level netlists and their originating HDL source lines. The loss of traceability makes post-synthesis debugging, timing […]

Modern applications, such as data center workloads, have become increasingly complex. These applications primarily operate on massive datasets, which involve large memory footprints, irregular access patterns, and complex control and data flows. The processor-memory speed gap, combined with these complexities, can lead to unexpected performance inefficiencies in these applications, preventing them from achieving optimal performance. […]

Reinforcement Learning (RL) is a machine learning paradigm that trains a decision maker, or policy, by learning from interaction with an environment. The power of RL lies in its ability to learn complex strategies without explicit human instruction, which can lead to better solutions that human designers overlook in domains ranging from robotics to scientific […]