Visualization of Gene Publications
HIDSI Fellow Amihan Camson is developing a visualization of gene publications on a timescale. The current paradigm in genomics filters a target gene by the most well-characterized to then formulate a mechanistic approach to the gene’s function in disease. Expanding research beyond these few established genes can identify novel disease-gene relationships. Further analysis using modeling will be utilized to investigate bias affecting experimental design, hypothesis generation, and funding.
AI in Astronomy
Eric Gaidos (Earth Sciences), Peter Sadowski (ICS), and Dan Huber (IfA) have been awarded $50k from NASA to identify rare dimming events in stars observed by the Transiting Exoplanet Survey Telescope (TESS). They are developing a new artificial intelligence algorithm to systematically identify sudden, subtle and irregular dimming of stars caused by dust that is the building-block of — or debris from — systems of orbiting planets. These observations will help us understand the compositions of planets around other stars and how they form.
Deep Learning for Particle Physics
Data Science Fellow Yannik Glaser is collaborating with the Belle II particle physics experiment to investigate the fundamental nature of matter using deep learning to search for faint signals in a torrent of experimental data. Together with Kurtis Nishimura in Physics and Peter Sadowski in ICS, they are developing new data science methods for accelerating the process of scientific discovery.
Center for Microbiome Analysis through Island Knowledge and Investigation (C-MĀIKI)
The Hawaii Data Science Institute is collaborating with C-MAIKI on the development of robust and efficient workflows and pipelines for the analysis of the microbial data generate by C-MAIKI, a consortium addressing the diversity and function of microbiota across organisms and ecosystem
An Early Tsunami Notification System
This project is a collaboration with Dr. James Foster to create an active monitoring system of open ocean data to use as early detection of tsunamis. The system leverage sensors on commercial ships and onboard, low-cost computational resources (Raspberry Pi) to identify early trends of tsunamis.
Cloud Dynamics and Machine Learning
Giuseppe Torri of the Department of Atmospheric Sciences studies the physics of clouds and storms through computationally-intensive simulations of these complex dynamical systems. He and Peter Sadowski are investigating the use of machine learning to speed up and improve these computational models.