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Researcher profile

Aaron Wang

Aaron Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine Learninghep-exQuantitative MethodsArtificial Intelligencecond-mat.mes-hallGenomicsphysics.app-phphysics.ins-det

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Published work

5 published item(s)

preprint2026arXiv

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture Codesign Package (SNAC-Pack), an open-source AutoML framework for hardware-aware neural architecture codesign and end-to-end FPGA deployment. SNAC-Pack runs a multi-objective global search with Optuna and NSGA-II, loading trials to a shared SQLite store that enables parallel workers across compute nodes. A hardware surrogate model outputs per-trial resource and latency estimates, avoiding the synthesis cost that would otherwise dominate the search loop. A local search stage then applies quantization-aware training (QAT) together with iterative magnitude pruning in a combined compression loop, after which the final model is synthesized to FPGA firmware via the hls4ml Python library. A YAML configuration and an optional agentic frontend let users run the pipeline on new datasets without modifying the framework. We demonstrate SNAC-Pack on jet classification at the Large Hadron Collider and superconducting qubit readout, discovering compact architectures that match or exceed strong baselines on the task metric while reducing FPGA resource utilization and, in the qubit readout case, reducing the design space exploration process from months of manual fine-tuning to hours of automated search.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Learning Methods for Protein Family Classification on PDB Sequencing Data

Composed of amino acid chains that influence how they fold and thus dictating their function and features, proteins are a class of macromolecules that play a central role in major biological processes and are required for the structure, function, and regulation of the body's tissues. Understanding protein functions is vital to the development of therapeutics and precision medicine, and hence the ability to classify proteins and their functions based on measurable features is crucial; indeed, the automatic inference of a protein's properties from its sequence of amino acids, known as its primary structure, remains an important open problem within the field of bioinformatics, especially given the recent advancements in sequencing technologies and the extensive number of known but uncategorized proteins with unknown properties. In this work, we demonstrate and compare the performance of several deep learning frameworks, including novel bi-directional LSTM and convolutional models, on widely available sequencing data from the Protein Data Bank (PDB) of the Research Collaboratory for Structural Bioinformatics (RCSB), as well as benchmark this performance against classical machine learning approaches, including k-nearest neighbors and multinomial regression classifiers, trained on experimental data. Our results show that our deep learning models deliver superior performance to classical machine learning methods, with the convolutional architecture providing the most impressive inference performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Machine Learning Prediction of COVID-19 Severity Levels From Salivaomics Data

The clinical spectrum of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the strain of coronavirus that caused the COVID-19 pandemic, is broad, extending from asymptomatic infection to severe immunopulmonary reactions that, if not categorized properly, may be life-threatening. Researchers rate COVID-19 patients on a scale from 1 to 8 according to the severity level of COVID-19, 1 being healthy and 8 being extremely sick, based on a multitude of factors including number of clinic visits, days since the first sign of symptoms, and more. However, there are two issues with the current state of severity level designation. Firstly, there exists variation among researchers in determining these patient scores, which may lead to improper treatment. Secondly, researchers use a variety of metrics to determine patient severity level, including metrics involving plasma collection that require invasive procedures. This project aims to remedy both issues by introducing a machine learning framework that unifies severity level designations based on noninvasive saliva biomarkers. Our results show that we can successfully use machine learning on salivaomics data to predict the severity level of COVID-19 patients, indicating the presence of viral load using saliva biomarkers.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers -- long short-term memory and gated recurrent unit -- within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Elongated Nano Domains and Molecular Intermixing induced Doping in Organic Photovoltaic Active Layers with Electric Field Treatment

The effects of the electric-field-assisted annealing on the bulk heterojunction nano-morphology in the P3HT/PCBM active layer of the organic photovoltaic cells (OPVCs) are presented here. It was widely accepted that the electric-field-assisted annealing will facilitate the P3HT, the polar polymer, to be better crystalline to enhance the charge mobility, hence the improvement of the OPVC performance. The influences on the nano-morphology of the electron donor and accepter domains are not well understood. Here, using the cross-sectional scanning tunneling microscopy and spectroscopy (XSTM/S), the electric-field-assisted annealing treatment is found to influence the molecular domains to be elongated with the orientation near the direction of the external electric field. The elongation of the molecular domains is believed to facilitate the domain percolation, which causes higher charge mobility, hence the higher short-circuit current density (Jsc). On the other hand, it was also observed that the electronic properties of the P3HT-rich and PCBM-rich domains in the electric-field-assisted annealed samples showed smaller energy band gaps and smaller molecular orbital offset between the two domains, which is argued to decrease the open circuit voltage (Voc) and negatively impact the OPVC performance. Based on the X-ray diffraction (XRD) and small angle X-ray scattering (SAXS) results, the altered electronic properties are argued to be due to the molecular intermixing induced doping effects. These results point out competing factors affecting the OPVC performance with the electric-field-assisted annealing treatment.

0 citations0 reviewsNo code linkedOpen access