Ethan Ahlquist is pursing a B.S. at California Polytechnic State University. His internship supported the curation and publication of LLNL metal additive manufacturing data.
DSSI Class of 2021
Meet the Data Science Summer Institute Class of 2021, whose internships were conducted remotely this year. Students worked on a variety of projects, attended data science related seminars, and worked on a challenge problem in astronomy.
A PhD student at UC Merced, Jacqueline Alvarez evaluated various neural network architectures on a datasets involving gamma spectroscopy.
Sven Amaya is pursing a Masters degree at California State University, Sacramento. His internship investigated impacts of multi-simulation data on mesh tangling prediction.
Sila Baykal is a student at Portland State University. During her internship, Sila built a logistic regression classifier for MNIST dataset and image classification.
Sam Ade Jacobs
A PhD student at Notre Dame University, Zachariah Carmichael developed new HPC-centric algorithms and methods for neural architectures.
A PhD student at UC Berkeley, Nicolas Castrillon worked on developing fast, accurate physics-informed surrogate models for various physical simulations.
A PhD student at the University of Arizona, Tucson, Pier Fiedorowicz worked on improving particle reconstruction in high-energy physics experiments using ML.
Jose Manuel Marti Martinez
A PhD student at Kyushu University in Japan, Yasuhisa worked on SARS-CoV-2 quasi-species sequence analysis.
Huan He, a PhD student at Emory University, worked on an uncertainty quantification in DL project using approaches like Bayesian neural networks and post-hoc calibration.
A PhD student at UC San Diego, Xiaolong He worked on developing an efficient surrogate model for instability problems by training various neural networks.
A PhD student at the University of Michigan, Ava Hill worked on a project to combine ML and nuclear physics to improve characterization of nuclear model composition.
A PhD student at UC Merced, Alex Ho worked on improving robustness in reinforcement learning agents.
A Masters student at Stanford University, Elyssa Hofgard worked on assessing impact of the incorporation of additional climate variables on DL seasonal predictions of precipitation and temperature over the Western U.S.
A PhD student at Portland State University, Ruizhi Hua worked on a graph clustering project using DL.
Samuel Lewis is pursing a B.S. at Oregon State University. His internship explored adversarial behavior in the Decentralized Autonomous Networks for Cooperative Estimation (DANCE) project.
Daniel Malone is pursing a B.S. at Brigham Young University, majoring in statistics. His internship supported the Joint Test Assembly by developing algorithms to convert and parse raw data into organized, usable data and manipulating large datasets using R or Python.
A PhD student at UC Irvine, Eric Medwedeff worked on applying microscopic physics to the macroscale through DL.
A PhD student at Tulane University, Akshay Mehra worked on exploring advanced ML techniques to analyze the extract material science data to infer general chemistry of classes of materials.
During his internship, Nagoya University PhD student Kentaro Mori worked on developing data-driven optimization models for transportation planning.
Garrett Mulcahy is pursing a graduate degree at Purdue University. His internship supported supervised learning of outermost operator in symbolic regression.
A PhD student at UC Merced, Haoyu Niu worked on discrete optimization tasks.
A PhD student at Florida Agricultural & Mechanical University, Damilola Ologunagba worked on molecular graph neural networks with Gaussian process for property prediction, embedding, and uncertainty-informed sampling of corrosion inhibitor-metal coordination complexes.
Randy Posada is pursuing a B.S. at UC Merced. His internship supported the drug safety project.
Rafael Rivera Soto
A PhD student at UC Merced, Maia Powell worked on a cross-platform authorship attribution project extending DL-based models to generalize between social media platforms to find the same author across different social media and have a model generalize to unseen authors.
Andrei Rekesh is pursuing a B.S. at UCLA. His internship supported drug discovery using feature optimization for safety and pharmacokinetic property prediction.
A PhD student at Pennsylvania State University, Nicholas Rios support the uncertainty quantification for charge-exchange reactions project using Gaussian processes as the target emulator.
Nicole Stiles is pursuing her B.S. degree at the Massachusetts Institute of Technology. Her internship supported the methods for NLP-enabled exploratory analysis of multi-omics datasets project using statistical and ML techniques.
Sam Ade Jacobs
A PhD student at the University of Albany, Tuan Tran worked on a project developing new HPC-centric algorithms and methods for neural architectural search leveraging existing open-source DL and reinforcement learning toolkits.
Jason Van Tuinen
Jason Van Tuinen is pursuing a B.S. from UC Merced. His internship supported ML for pattern-of-life analysis in time series data, which entailed implementing a Python algorithm to enable the user to analyze a data stream and examine patterns found in it.
Yinan Wang is pursing a PhD from Virginia Tech. During his internship, Yinan worked on spatial-temporal dependencies using a data-driven method to predict the “cloud” evolution.
A PhD student at South Dakota State University, James Young supported finding surrogate alternative chemicals and materials for national security applications.
A PhD student at Northeastern University, Zheng Zhan worked on finding accurate binary neural networks by pruning a randomly weighted network.
During her internship, University of Minnesota PhD student Zechen Zhang supported the Numerical Techniques for Multi-scale Machine Learning project.
A PhD student at the University of Michigan, Jing Zhu developed algorithms that can ensure a more favorable balance of algorithmic fairness and performance in graph ML.