Overview
The Emerging Research Student Cohort is a specialized funding initiative designed to cultivate the next generation of computational experts by supporting 6–8 rising sophomore and junior undergraduate scholars. Participants collaborate with faculty advisors to conduct research that directly leverages High Performance Computing (HPC) resources throughout the academic year. The program provides personalized mentorship from experienced HPC student fellows and requires participation in technical workshops. This transformative opportunity concludes with a detailed progress report, creating a structured pathway for emerging talents to bridge the gap between classroom learning and advanced computational research.
Academic Year 2025–2026 Awarded Grants
Andrew Kai: Assessments of Modern Density Functional Approximations for Fullerenes Using Turbomole on WAVE HPC
Project Overview: This project benchmarks various Density Functional Theory (DFT) methods, specifically local hybrid and range-separated local hybrid functionals, to determine their accuracy in calculating isomerization energies for fullerenes. By leveraging the WAVE HPC to perform over 2,000 complex calculations, the research seeks to identify optimal functionals that balance computational cost with precision. These findings will provide theoretical insights into improving DFT models and support a larger-scale screening of fullerene chemical space for applications in nanotechnology and medicine.
Cole Mitchell: Simulation of UV-Vis Spectra of Transition Metal Complexes with Modern-Day Density Functional Approximations
Project Overview: This research focuses on benchmarking advanced computational functionals within the Time-Dependent Density Functional Theory (TD-DFT) framework to accurately model the UV-Vis spectra of transition metal complexes. The project utilizes the WAVE HPC to handle the high memory and bandwidth requirements of these simulations, aiming to identify the optimal number of energy states needed for accurate spectral representation. Additionally, the researcher will develop an open-source Python script to automate the conversion of discrete transition data into continuous spectra, ultimately contributing to more efficient and sustainable use of high-performance computing resources.
Karina Martinez: Using Scat DNA Samples to Unlock Bird Diet Mysteries and Evaluate Restoration Success in the SF Bay Estuary
Project Overview: This project utilizes "molecular scatology" to analyze bird DNA from scat samples to evaluate the success of habitat restoration in the San Francisco Bay Estuary. By identifying the specific plant species birds consume, the researcher aims to determine if they are utilizing restored native plants or persistent non-native species. The transition to WAVE HPC is essential for this work, as current local systems crash when attempting to process the terabytes of DNA sequence data generated; the HPC will allow for more accurate plant classification through parallel processing.
Neel Mukkavilli: Interpretability of ProteinMPNN via Sparse Autoencoders
Project Overview: This research aims to interpret the inner workings of ProteinMPNN, a neural network used for protein sequence design, by applying sparse autoencoders to identify the features the model learns regarding protein stability. The project involves expanding the neural network’s hidden dimensions to translate complex data into interpretable features, such as the presence of specific amino acids. Leveraging the WAVE HPC will enable the training of these autoencoders on massive datasets—ranging from hundreds of gigabytes to terabytes—providing a novel look into message-passing neural networks.
Sean Wu: Traffic Digital Twin via Scalable Simulation and Machine Learning
Project Overview: This project focuses on the creation of a "traffic digital twin" to analyze and optimize urban mobility through scalable simulation. By utilizing the WAVE HPC to run intensive reinforcement learning algorithms, the research develops more efficient traffic signal control strategies designed to reduce congestion and improve city-wide flow. This computational approach allows for the modeling of highly complex urban environments that would be impossible to process effectively on standard desktop systems.
Academic Year 2026–2027 Awarded Grants
Gabbie Nakamatsu: Benchmarking Density Functional Methods for Singlet Fission Chromophores
Project Overview: This project aims to systematically benchmark the accuracy of modern density functionals against high-level correlated wavefunction reference data to improve organic solar cell efficiency. The researcher will utilize the WAVE HPC to execute "gold standard" DLPNO-CCSD(T) calculations, which are computationally expensive and can take several days or weeks to complete. The goal is to establish efficient computational protocols for prescreening viable chromophores through strategic resource allocation and memory optimization.
Guangyi Liu: Learning-Driven RSMA Optimization in HPC-Enabled Sionna-Based 6G Wireless Environments
Project Overview: This research investigates the use of Deep Reinforcement Learning (DRL) to adaptively optimize Rate-Splitting Multiple Access (RSMA) parameters in complex 6G wireless systems. By leveraging the NVIDIA Sionna simulation platform on WAVE HPC, the project will generate physically grounded channel conditions via ray tracing in urban environments. High-performance resources are essential to manage the massive computational burden of training DRL agents, which would otherwise require up to 18 days of local compute time per configuration.
Emily Chen: RL-Driven Parameter Optimization of Instant-NGP for Semantic Video Streaming
Project Overview: This research proposes RNGP, a framework that integrates Hybrid Neural Video Representation with a Reinforcement Learning controller to minimize latency in semantic communication. The system utilizes HPC resources to host the RL training loops and enable parallel execution across 4 to 8 high-memory GPUs, ensuring real-time optimization of hash grid parameters. The goal is to bridge the gap between theoretical semantic models and practical hardware constraints for 4K video streaming.
Derek Chui: Scaling RL-Based Optimization for PASS-NOMA in 6G Networks
Project Overview: This research scales an optimization framework for Reconfigurable Pinching Antenna Systems (PASS) to mass deployment scenarios like stadiums with over 50,000 simultaneous connections. The project employs Deep Q-Networks (DQN) and computationally intensive 3D ray tracing on HPC GPU nodes to manage exponentially large state spaces that would take weeks to process on standard laptops. The final outputs include peer-reviewed manuscripts and educational labs for future SCU students.
Tianbao Yang: High-Performance Computing for Multi-Agent Radiogenomic Analysis of Glioblastoma
Project Overview: This project automates the radiogenomic analysis of brain tumors by coordinating MRI preprocessing, segmentation, and mutation prediction through LLM-based agents. High Performance Computing is critical to train deep learning models on 3D medical images from over 1,000 patients and to execute thousands of independent feature extraction jobs in parallel. The researcher aims to gain proficiency in Slurm job scheduling while producing benchmarked ML models for clinical reporting.
Evan Hackstadt: Bioinformatics for the Conservation of an Endangered Succulent
Project Overview: This research builds a containerized bioinformatics pipeline to analyze the genetic diversity and reproduction methods of the federally endangered succulent Dudleya setchellii. Utilizing the WAVE HPC, the project aims to process approximately 280 GB of raw DNA sequences to generate whole-genome annotations and phylogenetic trees that inform regional conservation efforts. The findings will be submitted to peer-reviewed journals to chart a path for future computational genomics work at SCU.
Agastya Brahmbhatt: Memcapacitor-Based Reservoir Computing for High-Speed Nonlinear Equalization
Project Overview: This proposal investigates memcapacitor-based reservoir computing as a power-efficient alternative to traditional digital nonlinear equalization in ultra-high-speed fiber networks. The researcher will utilize the Ray distributed computing framework on HPC to simulate and compare the bit error rate (BER) and silicon area requirements of reservoir models versus Volterra and neural equalizers. The study aims to establish analog dynamical processing as a viable hardware acceleration method for data rates up to 400 Gb/s.
James Wilson: Edge-Deployed Vision-Language Navigation on a Jetson Orin Nano Robot
Project Overview: This project focuses on deploying vision-language large models (VLLMs) for robotic navigation on constrained edge hardware. SCU HPC resources will be used for computationally intensive model training, adaptation, and quantization loops that are infeasible on the robot's onboard processor. The project aims to produce a reproducible benchmark and a practical baseline for robust, language-conditioned navigation in real-world scenes.
Heidi Chen: Computational Analysis of ATXN2-Associated Gene Expression in SCA2
Project Overview: This research employs a multi-stage bioinformatics workflow to identify gene expression signatures associated with ATXN2 dysfunction across neurodegenerative diseases like SCA2, Parkinson’s, and ALS. Utilizing Python- and R-based tools on HPC, the project will analyze large microarray datasets containing over 41,000 probes to perform pathway enrichment and differential gene analysis. The ultimate goal is to develop a network model to prioritize candidate therapeutic targets for underserved communities.