Research
My research spans two main areas: Quantum Computing Systems, which makes quantum computation practical to simulate, characterize, and apply to real scientific problems, and Resilient HPC and AI Systems, which makes large-scale computing and learning systems dependable in the presence of faults.
Area 1
Quantum Computing Systems
Quantum hardware is noisy and its behavior is hard to predict, so turning quantum computation into a usable scientific instrument requires systems research. This area covers quantum-HPC driven scientific discovery, large-scale quantum circuit simulation on HPC platforms, quantum noise characterization and mitigation on NISQ devices, reproducibility of quantum results, and quantum machine learning and applications.
Publications: Quantum Sampling for Protein Structure (SC ‘26), QDockBench (SC ‘25), BMQSim (ICS ‘25), Red-QAOA (ASPLOS ‘24), PQML (QCE ‘24), Reproducibility on NISQ Devices (QCE ‘23), Hierarchical State Vector Simulation (Cluster ‘22), NISQ Reliability Degradation (QCE ‘22), SV-Sim (SC ‘21), QuGAN (QCE ‘21)
Area 2
Resilient HPC and AI Systems
Silent data corruption, soft errors, and storage faults quietly undermine the correctness of large-scale scientific and machine learning workloads. This area builds methodologies and tools that measure, predict, and tolerate those failures across the stack: error resilience characterization for GPU and HPC applications, fault injection frameworks from the IR level to mixed-precision accelerators, fault tolerance for LLM training and inference, and precision-aware recovery.
Publications: Demystifying LLM Inference Resilience (SC ‘25), FT2 (HPDC ‘25), ATTNChecker (PPoPP ‘25), Parallel File System Metadata Corruption (IPDPS ‘25), MPGemmFI (Cluster ‘24), Storage Faults in HPC (Cluster ‘21), BonVoision (ICS ‘19), LetGo (HPDC ‘17), ePVF (DSN ‘16), GPU-Qin (ISPASS ‘14)