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I was honored to present at the 78th International Symposium on Molecular Spectroscopy (ISMS) this year, sharing my work on high-performance computing for simulations in rotational spectroscopy.
Key takeaways from my talk:
• Hamiltonian matrices enable efficient simulation of rotational spectra.
• Compressed Sparse Row (CSR) format achieves linear memory scaling.
• CUDA-accelerated Jacobi–Davidson diagonalization delivers accurate results in under 1 second.
Looking ahead, I plan to return next year with even faster, simultaneous simulations for a variety of real molecules, along with automatic spectral fitting and automatic extraction of molecular constants.
Our group will also be exploring tensor theory for rovibronic spectra—a direction I’m especially excited about.
SPECVIEWONLINEISMS.pptxConstructing high performance algorithms, managed by Git, in C++/CUDA for sub-second rotational spectroscopy simulation using effective Hamiltonians.
Training machine learning for identifying physical information of molecules using simulated data, solving an important pseudo-inverse problem in the effective Hamiltonian approach.
RaySpy Translational research, applying machine learning models in spectroscopy to hardware.
Currently constructing a web app for large-scale meet ups and symposia with widgets, supporting face-to-face interaction post-COVID. Include LaTeX/PDF autocheck for abstract submissions, file management and distributions, and location/GIS data.
I developed a command-line tool (available on Unix/Linux, MacOS, Windows) for quantitative finance that analyzes stock pairs using both mean-reversion models (Kalman Filter, z-scores, hedge ratios) and copula-based methods for capturing nonlinear dependencies. A report is on the right.
Paris Trade.docxKey features include:
Data pipeline: Automated retrieval of historical stock data via Yahoo Finance API, processed with Pandas and Dask for scalable analysis.
Strategy design: Implemented Paris (positive correlation) and All-Weather (negative correlation) strategies with dynamic spread estimation.
Statistical modeling:
Mean-reversion approach: Kalman Filter to estimate hedge ratios and generate trading signals.
Copula approach: Used Gaussian KDE to fit marginal distributions and applied Frank copulas to capture joint tail dependencies.
Visualization: Generated interpretable plots of spreads, z-scores, and cumulative returns with Matplotlib.
Configurable CLI: JSON-based configuration for tickers, thresholds, partitions, and strategy modes.
Impact: This project demonstrates how mathematical modeling, probability theory, and modern Python libraries can be combined to design and test financial trading strategies. It deepened my expertise in applied statistics, time-series analysis, and algorithmic trading frameworks.
The 37th International Symposium on Free Radicals
As a webmaster, I created the website, payment and abstract management system, and collected metrics for the conference. I had additional duties of assisting in administrative duties, editing photos, and researching the venue.
The 26th International Conference on the Jahn-Teller Effect
This is an international spectroscopy conference addressing the Jahn-Teller effect, and brought together physicists and chemists alike from various countries. I worked as a secretary, assisting in administrative duties and creating information technology (IT) infrastructure, including the website, analytics, and abstract submission system.
History of the conference is covered by the slides I made on the right, showing previous years' conferences.
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