A5043409810- Advanced Control Systems Optimization
- Fault Detection and Control Systems
- Advanced Breast Cancer Therapies
- Process Optimization and Integration
- Estrogen and related hormone effects
- HER2/EGFR in Cancer Research
- Building Energy and Comfort Optimization
- Breast Cancer Treatment Studies
- Control Systems and Identification
- Cancer Genomics and Diagnostics
- PI3K/AKT/mTOR signaling in cancer
- Cancer Treatment and Pharmacology
- Lung Cancer Treatments and Mutations
- Smart Grid Security and Resilience
- Monoclonal and Polyclonal Antibodies Research
- Microbial Metabolic Engineering and Bioproduction
- Cancer-related Molecular Pathways
- Cancer-related molecular mechanisms research
- Smart Grid Energy Management
- CAR-T cell therapy research
- Refrigeration and Air Conditioning Technologies
- Immunotherapy and Immune Responses
- Gene expression and cancer classification
- Heat Transfer and Optimization
- Microtubule and mitosis dynamics
Guardant (United States)
2025
University of California, Davis
2020-2024
Baylor College of Medicine
2019-2024
Washington University in St. Louis
2006-2023
Jewish Hospital
2023
Barnes-Jewish Hospital
2023
Academy of Public Administration
2023
University of Illinois Urbana-Champaign
2020
Johnson Controls (United States)
2018-2019
University of California, Los Angeles
2013-2016
To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained high fidelity. However, at single-nucleotide level, variable numbers PDX-specific somatic events documented, although they only rarely functionally significant. Variant allele frequencies often preserved in PDXs, demonstrating that clonal...
Economic model predictive control (EMPC) is a feedback technique that attempts to tightly integrate economic optimization and since it scheme formulated with an objective function representing the process economics. As its name implies, EMPC requires availability of dynamic compute actions such may be obtained either through application first principles or system identification techniques. In industrial practice, difficult in general obtain accurate first‐principles process. Motivated by...
Economic model predictive control (EMPC) is a scheme that combines real‐time dynamic economic process optimization with the feedback properties of (MPC) by replacing quadratic cost function general function. Almost all recent work on EMPC involves functions are time invariant (do not explicitly account for time‐varying economics). In present work, we focus development Lyapunov‐based (LEMPC) formulated an First, formulation proposed two‐mode LEMPC given. Second, closed‐loop stability proven...
A stochastic model predictive control (MPC) framework is presented to determine real-time commitments in energy and frequency regulation markets for a stationary battery while simultaneously mitigating long-term demand charges an attached load. The problem multi-scale nature poses challenges on computational tractability of the program forecasting uncertainty quantification (UQ) procedures. deals with by using discounting factor charges, Ledoit–Wolf covariance estimator used overcome UQ...
Fault‐tolerant control methods have been extensively researched over the last 10 years in context of chemical process applications, and provide a natural framework for integrating monitoring aspects way that not only fault detection isolation but also system reconfiguration is achieved event or actuator fault. But almost all efforts are focused on reactive fault‐tolerant control. As another control, proactive has popular topic communication systems aerospace communities years. At this point,...
This work focuses on the development of economic model predictive control (EMPC) systems for transport-reaction processes described by nonlinear parabolic partial differential equations (PDEs) and their applications to a non-isothermal tubular reactor where second-order chemical reaction takes place. The is modeled two PDEs. Galerkin's method used derive finite-dimensional that capture dominant dynamics PDEs which are subsequently EMPC design. formulation uses integral rate along length as...
Multiplicative cyberattacks manipulating data over the process control system (PCS) communication links are that malicious agents may carry out against PCSs. These attacks modeled by multiplying communicated link a factor, and be designed to stealthy without extensive knowledge of dynamics. The current work characterizes relationship between parameters, closed-loop stability, detectability multiplicative sensor–controller attack with respect class residual-based detection schemes. analysis...
Abstract The frequency of cyberattacks against process control systems has increased in recent years. This work considers multiplicative false‐data injection attacks involving the multiplication data communicated over sensor‐controller communication link by a factor. An active detection method utilizing switching between two modes is developed to balance trade‐off closed‐loop performance and attack detectability. Under first mode, parameters are selected using traditional design criteria....
Abstract Introduction: Currently, risk of recurrence in ER+ HER2- breast cancer is determined by pathological stage and histological grade, combination with a Gene Expression Assay (GEA) including Oncotype, Prosigna, Breast Cancer Index EPClin. Higher disease justifies systemic therapy beyond endocrine alone. GEAs need improving as these assays can misclassify risk, i.e., recurrences still occur patients assigned low risk. Thus, approaches to identify high-risk cases more accurately would...
Economic model predictive control (EMPC) is a feedback method that dictates potentially dynamic (time‐varying) operating policy to optimize the process economics. The objective function used in EMPC system may be general nonlinear describes process/system As this not derived on sole basis of classical considerations (stabilization, tracking, and optimal action calculation) but rather economics, selecting appropriate configuration, quantifying influence given input an economic cost important...
Abstract Model predictive control is widely used as a technology for the computation of optimal inputs building heating, ventilating, and air conditioning (HVAC) systems. However, both benefits widespread adoption model (MPC) are hindered by effort creation, calibration, accuracy predictions. In this paper, we apply data-enabled (DeePC) algorithm designing controls HVAC The solely depends on input/output data from system to predict future state trajectories without need identification....
A fundamental problem at the intersection of process control and operations is design detection schemes monitoring a for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify with multiplicative factor could be designed to evading without in-depth knowledge. In prior work, we presented mode switching strategy that enhances FDI in processes operating steady state (when states evolve within small neighborhood state). Control on attack-free steady-state...