Author: Alec Linot, Postdoctoral Scholar and IDRE Fellow – Department of Mechanical and Aerospace Engineering In industrial processes involving fluids, vast amounts of energy are lost as dissipation due to turbulent drag. The energy production required to sustain these processes accounts for 5% of all manmade carbon dioxide (Jimenez 2013). Consequently, even small improvements toward...
Read More Optimal perturbations in high-dimensional unsteady aerodynamic flows

IDRE Fellow: Dr. Olivia Sanderfoot (Postdoctoral Scholar) Faculty Mentor:  Dr. Morgan Tingley (Associate Professor) Department: Ecology & Evolutionary Biology Project Description: Climate change coupled with a legacy of fire suppression is driving an increase in the frequency, intensity, and severity of wildfires1,2, prompting the United Nations to declare a worldwide “wildfire crisis3.” The record-breaking 2020 wildfire season in the...
Read More Integrating long-term ecological monitoring data and high-resolution atmospheric models to investigate avian responses to wildfire smoke

Several technological advances in radiotherapy have enabled the use of focused radiation to treat solid tumors within the thoracic cavity. Stereotactic Body Radiation Therapy (SBRT) offers a way to treat patients with high doses of radiation with just a few (three to five) treatments entirely non-invasively, providing excellent tumor control for both early stage non-small...
Read More Building a motion model for radiotherapy

We are working towards creating a joint experimental/theoretical/computational framework to explore the behavior of liquids which have programmable interactions. Inspired by liquid liquid phase separation as a mechanism of organization in biological systems, we are looking in depth at systems containing both chemical reactions (interconversion of species) and phase separation (weak multivalent interactions between species)....
Read More Simulations of Programmable Liquids

As human civilization pours more greenhouse gases into the atmosphere thereby causing and exacerbating climate change, the ocean serves as a temporarily and partial buffer as it absorbs large quantities of the emitted greenhouse gases (e.g., CO2), removing them from the atmosphere. Around Antarctica one of the densest water masses in the ocean is formed...
Read More Remotely Sensing Overturning Circulation Variability in the Southern Ocean

Background: Computerized provider order entry (CPOE) systems have been shown to enhance the safety and efficiency of prescribing chemotherapy over the handwritten ordering process. However, many institutions lack the financial ability, technological capability, or operational flexibility to invest in and implement such a system. In particular, Olive View-UCLA Medical Center (OVMC), a Los Angeles County...
Read More ChemoPalRx: Development and implementation of a chemotherapy mobile provider order entry system in a safety net hospital

Recent studies in nanophotonic structures and metamaterials have showcased remarkable applications such as invisibility cloaks, perfect lenses, radiative cooling, and light trapping for solar cells. However, current design methodologies require computationally-intensive, time-consuming, and iterative processes to realize structurally-complex devices. For example, finite-element analysis (FEA) simulation and other “forward design” methods involve a series of trial-and-error...
Read More Machine Learning-based Inverse Design of Thermal Photonic Metasurfaces

Genomic sequencing has allowed identification of omic features that predict drug sensitivity and their mechanisms of action. However, findings from these associative models have the potential to be more powerful with the availability of data from genome-wide, forward perturbation approaches in large panels of cancer cell lines. Using data generated by RNA interference (RNAi) and...
Read More Unravel Cancer Drug Specificity and Unknown Mechanism of Actions

Accurately quantifying drug response is essential to designing effective anti-cancer agents. Though agents such as chemotherapies have known effects on the cell cycle, these have not been comprehensively characterized or analyzed using a unified quantitative model of pharmacologic response. Here, we used a breast cancer cell line with a stably expressed fluorescent translocation cell cycle...
Read More Mathematical modeling of cell cycle phase-specific drug response in human breast cancer cell lines

Classical molecular dynamics simulations are widely used to facilitate the modeling and design of silicate glasses. To this end, several empirical have been developed—each of them focusing on distinct features of silicate glasses (e.g., structure, mechanical properties, etc.). Available forcefields relying on fixed partial charges can be divided into “soft” and “hard” depending on the...
Read More Machine-learning-based Molecular Dynamics Simulations of Silica: Soft vs. Hard Forcefields

Receptor tyrosine kinase (RTK)-targeted therapies are invariably limited by drug resistance. A major mechanism of acquired resistance involves “bypass” switching to alternative pathways driven by non-targeted RTKs that reactivate proliferation. These pathways are often centered around Erk/Akt signaling; however, a more fundamental understanding of network-level bypass resistance is required to better inform the design of...
Read More Mapping global signaling state during switched RTK activation to identify the essential features of bypass resistance in cancer

Glasses are often exposed to impact loading during their service life, which may lead to their failure. While in situ experimental studies on impact-induced damage are challenging due to the short timescales involved, continuum-based computational studies are complicated by the discontinuity in the displacement field arising from the propagation of cracks. We’ve used Peridynamic simulations to investigate...
Read More Glass Fracture upon Ballistic Impact: New Insights from Peridynamics Simulations

Developing novel glasses with new, improved properties and functionalities is crucial to address major challenges in energy, communications and infrastructure. A data-driven machine learning (ML) technique is a potential method to accelerate the discovery in novel glass; however, there are some intrinsic limitations embedded in such ML models. For example, the use of traditional ML...
Read More Topology-informed machine learning for the prediction of glass stiffness

In 1946, Professor David Boder from the Illinois Institute of Technology traveled to Europe to interview Holocaust survivors in displaced persons’ camps in France, Germany, Switzerland, and Italy. Using a wire recorder, he collected over 100 interviews in 9 different languages, asking survivors about events which were still relatively unknown in the United States. The...
Read More Analysis of Language Use and Narrative Structures in Genocide Interviews
via Digital Humanities Approaches

We introduce a biomimetic simulation framework for human perceptionand sensorimotor control. Our framework features a biomechanically simulated musculoskeletal human model actuated by numerous skeletal muscles, with two human-like eyes whose retinas contain spatially nonuniform distributions of photoreceptors. Its prototype sensorimotor system comprises a set of 20 automatically-trained deep neural networks (DNNs), half of which comprise...
Read More Learning Biomimetic Perception for Human Sensorimotor Control

Polycrystalline materials, a class which includes most metals and ceramics, are composed of a large aggregate of single crystal pieces called grains. The Kobayashi-Warren-Carter (KWC) model is a fundamental model in material science for simulating grain microstructure evolution. The KWC energy depends on two variables,  which records the orientation of the grains, and ...
Read More A thresholding scheme for the KWC model

Duchenne muscular dystrophy (DMD) is a lethal pediatric disease caused by mutations in the enormous DMD gene. The DMD gene contains 79 exons and spans 2 megabases – one of the largest in the human genome. Multiple messenger RNA isoforms which are “working copies” of the gene, have been described but recent data suggests an...
Read More Leveraging existing RNA-seq data sets to identify alternative isoforms of neuromuscular disease transcripts and aid the diagnosis of muscular dystrophy

Deep learning, a branch of machine learning, can serve as a powerful toolkit for studies in ecology and evolutionary biology. In the era of big data, gaining more insight into complex patterns within a dataset can be transformative for conducting comparative analyses. The field of comparative phylogenetics has become increasingly interested in exploiting massive datasets...
Read More High-Throughput Phenoscaping Using Deep Learning for Accurate Automatic Instance Segmentation of Fish Images

Policy Iteration (PI) is a popular family of algorithms to compute an optimal policy for a given Markov Decision Problem (MDP). Starting from an arbitrary initial policy, PI repeatedly performs locally-improving switches until an optimal policy is found. The exact form of the switching rule gives rise to different variants of PI. Two decades ago,...
Read More Markov Decision Problem