Prof. Qu’s research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high-dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, his major interest is in understanding deep networks through the lens of low-dimensional modeling.
I work with the Space Weather Modeling Framework, which is a software tool to couple several models describing (in my case) the different plasma-physical processes in the near-Earth space. The aim of our research group is to use the simulations to understand the dynamic evolution of the plasmas and electromagnetic fields as driven by solar variability. The fundamental science questions concern magnetic reconnection, particle acceleration, and wave-particle interactions in tenuous, non-thermalized, fully collisionless plasmas. The societal importance arises from the space weather hazards the fields and high-energy particle fluxes pose on spacecraft, the communication and positioning errors and disruptions that arise between ground and spacecraft, and the harm on ground-based power systems and other infrastructure sensitive to electromagnetic fields.
Kai Zhu has a strong interest in global change biology, ecological modeling, and environmental data science. He enjoys exploring the intersection between ecological theory and advanced tools in statistics and computer science, including Bayesian inference and machine learning. Currently, Kai’s research is focused on studying plant and soil responses to environmental changes within coupled natural and human systems. His work encompasses meter-scale experiments to global-scale analyses. In recent projects, Kai has quantified the impacts of climate change on forest geographic distribution and growth in North America, synthesized a multi-factor global change experiment in California grassland, investigated soil fungi and tree mutualisms across geographical gradients in the United States, and detected land surface phenology change in the Northern Hemisphere.
Max’s research interests lies in the design, management, and optimization of large-scale infrastructure systems, focusing on the air transportation system and emerging aerial mobility systems. He is interested in the application of methods applicable to networked systems, especially with resource constraints (e.g., airspace and airport capacity), diverse stakeholders (e.g., passenger-centric, airline-centric), and complex dynamics (e.g., changing temporal behaviors). Max has worked on a variety of data-driven problems related to analyzing flight delays across airport networks, strategic/tactical air traffic management and delay assignments, privacy and routing in drone-based applications, and uncertainty-aware traffic management. He is interested in methods such as graph signal processing and signal processing over non-Euclidean domains, data-driven optimization, mixed-integer/integer/combinatorial programs, resilient network design, and stochastic optimization. Broadly, Max hopes to contribute to a safe, resilient, and efficient air transportation system (inclusive of intra- and inter-city modalities) within the context of a passenger’s (or cargo’s) door-to-door journey.
My research broadly revolves around extending, specializing, and developing novel ML/AI methods for computational mechanics. My primary focus is data-driven physics-based modeling that utilizes approaches like Variational System Identification and PDE-constrained optimization. I apply these methods for inferring PDE models for complex physical phenomena, for instance, foldings during brain growth, deformation mechanics in soft matter (human tissue and ligaments), and migration and proliferation in biological cells. I also develop graph-based approaches for Machine Learning and NISQ (Noisy Intermediate Scale Quantum) computing. These methods are rooted in classical physics and mathematical analysis but simultaneously developed in concert with real-life experimental data.
Fadhl Alakwaa is a computational biologist working on large biological datasets such as transcriptomics, metabolomics, and proteomics. He used bioinformatics tools such as Seurat and Scanpy to analysis the data. He used other tools such as Slingshot to predict cell trajectories and Cellchat to predict ligand-receptor connections. These cutting-edge tools have helped him to make significant contributions to our understanding of biological systems.
My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes.
My lab takes a user-centered and data-driven approach. We aim to understand user needs and the effect of data on users’ decision making through qualitative research, such as focus groups or workshops. We then design new information systems through geospatial/GIS analysis, risk analysis, and statistical modeling techniques. We often work with earth observation, sensor, and survey data. We consider various aspects of disaster information, whether it be the hazard, its physical impacts, its social impacts, or a combination of the three.
I also focus on the communication of information, through data visualization techniques, and host a Risk and Resilience DAT/Artathon to build data visualization capacity for early career professionals.
Mathematical representations of big data, spacekime analytics, computational statistics, , longitudinal morphometric studies of development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s disease, Parkinson’s disease). Developing, validating, and disseminating novel methods (e.g., spacekime analytics) and technologies (e.g., CBDA, DataSifter, TCIU) for mathematical modeling, statistical computing, biomedical applications, scientific education, and active learning.
Our research can be summarized in two words: Matter and Machine. On the Matter side, Z lab studies far-from-equilibrium physics. They synergistically combine and push the boundaries of statistical and stochastic thermodynamic theories, accelerated molecular simulations, understandable AI/ML/DS methods, and neutron scattering experiments, with the goal of significantly extending our understanding of a wide range of long timescale phenomena and rare events. Particular emphasis is given to the physics and chemistry of liquids and complex fluids, especially at interfaces, driven away from equilibrium, or under extreme conditions. On the Machine side, leveraging their expertise in materials and modeling, his group advances the development of soft robots and human-compatible machines, swarm robots and collective intelligence, and robots in extreme environments, which can lead to immediate societal impact.
The Multisensory Perception Lab studies how information from one sensory system influences processing in other sensory systems, as well as how this information is integrated in the brain. Specifically, we investigate the mechanisms underlying basic auditory, visual, and tactile interactions, synesthesia, multisensory body image perception, and visual facilitation of speech perception. Our current research examines multisensory processes using a variety of techniques including psychophysical testing and illusions, fMRI and DTI, electrophysiological measures of neural activity (both EEG and iEEG), and lesion mapping in patients with brain tumors.
Our intracranial electroencephalography (iEEG/ECoG/sEEG) recordings are a unique resource that allow us to record neural activity directly from the human brain from clinically implanted electrodes in patients. These recordings are collected while patients perform the same auditory, visual, and tactile tasks that we use in our other behavioral and neuroimaging studies, but iEEG measures have millisecond temporal resolution as well as millimeter spatial precision, providing unparalleled information about the flow of neural activity in the brain.