Displaying All Categories from All Departments
Office: SEIR 342
Research: My laboratory uses various molecular and bioinformatics approaches to study antibiotic resistance in nosocomial pathogens. We also utilize high-throughput sequencing and computational analysis to screen and develop alternative therapeutics to fight multidrug resistant pathogens
Office: ERB 433
Research: Genomics, population genetics, bioinformatics
Office: SEIR 325B
Research: My research group is interested in employing GPU-based Monte Carlo simulation, AI-based methods and mathematical modeling to study the radiation-induced biological effects, relevant hardware design and clinic-oriented problem, like motion management in radiotherapy.
Office: CPB 352
Research: The focus of our research is developing mass spectrometry-based proteomics technology, bio-markers discovery in inflammatory diseases using quantitative proteomics, system-level protein-protein/protein ligand interactions and protein posttranslational modifications. Our lab also develops bioinformatcs software tool for novel mass spectrometry-based proteomics technology.
Office: LS 416
Research: Experimental psychology methods and statistics; Experimental analysis of behavior
Office: ERB 430
Research: We use a variety of molecular and bioinformatics approaches to study the evolution of genes, genomes and organisms. Since the growing wealth of genomic data poses exciting opportunities as well as unique challenges, much of our research is integrated with the development of computational tools necessary to analyze evolutionary hypotheses on a genomic scale.
Office: CPB 340
Research: High Energy Physics, including the ATLAS experiment at CERN, where I work with the Tile Calorimeter group and the Computing group
Office: LS B28
Research: population and community modeling, phylogenetic comparative methods, global change
Office: LS 468
Research: My research on aquatic ecosystems uses mathematical and statistical methods to simulate and predict the abundance and dynamics of natural populations, especially harmful algae
Office: SH 132C
Research: My research involves (1) iterative reconstruction methods for medical imaging data, such as X-ray Computed Tomography (CT), Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI); (2) spatiotemporal processing and analysis of medical data and space physics observations; and (3) machine learning methods for prediction and regression
Office: PKH 403
Research: My scientific research covers the fields of mathematical biology and numerical analysis, and is strongly motivated and oriented toward applications. I am primarily interested in the accurate mathematical modeling and theoretical analysis of biological phenomena, in particular the modeling of population dynamics, microbial biofilms, bone and muscle formation and growth, and the human immune system. I am also interested in the formulation, analysis and implementation of non-standard numerical methods for solving nonlinear differential equations.
Office: CPB 353
Research: My group works on various challenges in materials chemistry using computational methods and tools. A major field are amorphous ceramics and glasses, elucidating structure formation and properties of disordered materials. We apply data harvesting and optimization strategies (some call this "machine learning") to compute observable parameters (e.g. NMR shielding) or to develop new reactive force-fields.
Office: LS 523
Research: Further activity is in high-pressure chemistry, especially in nitrogen-rich compounds. We research paths of structural phase transitions, and compute high-temperature/high-pressure phase diagrams to support experimental endeavors.
Office: SEIR 217
Research: The current focus of my research is to develop novel statistical models and associated estimation algorithms that will help clinicians and researchers to identify which patients are most likely to benefit from specific therapies, thereby providing important insight for optimizing treatment approaches and defining rational combination therapies.
Associate Professor of Instruction
Office: LS 453
Research: My research focuses on phylogenetic systematics and taxonomy of neotropical amphibians and reptiles. I use multivariate and tree-based statistical methods to infer species boundaries in poorly studied taxonomic complexes.
Office: LS 517
Research: My work uses statistical approaches to assess the impact of technology on workplace behavior, especially in the context of web-based job applicant screening, virtual freelance work, and online employee misbehavior.
Office: SEIR 317
Research: My research involves the development of machine learning algorithms and their applications together with probability theory in computational and data-intensive fields, including Proteomics, Biomedical Imaging, and High-Energy Astrophysics.
Office: PKH 464
Research: My research areas include (1) nonparametric statistics (2) Asymptotic theorems (3) Statistical applications in remaining useful life estimation
Office: GS 217
Research: My research utilizes (1) statistical methods to find environmental risk factors of human diseases and (2) simulation techniques to predict how environmental contaminants are degraded and transported in indoor and outdoor environments.
Office: NH 241
Research: sensemaking, cognition, AI/HI integration, analytics methods and models
Office: PKH 438
Research: My research follows three main directions. The first one is unsupervised graph structure learning, which has wide applications in fields such as computer vision and bioinformatics. The second one is dimensionality reduction, which is the first and critical step for dealing with very high dimensional datasets. The third one is developments of efficient data analysis and optimization methods for big data.
Office: SH 201
Research: My research involves combining large scale numerical simulations of the near-Earth space environment with expansive ground- and space-based data sets. With advanced numerical methods, such as adaptive mesh grids and ensemble and assimilative simulations, the scale of our data grows every day.