The Computational Optimization and Applications Laboratory (COALa) focuses on the theory, methodology and applications of optimization, as well as on interdisciplinary research with other quantitative subjects, e.g., statistics, and machine learning/AI.
It also supports the development of software tools for optimization along with their implementation. Researchers affiliated with this lab work on a wide range of optimization topics ranging from traditional linear, nonlinear and integer programming to combinatorial, data-driven, network, bilevel and AI related methods.
The focus of the Data Science Lab is on:
- Using data science to improve decision making in applications domains such as healthcare and electric grid management.
- Developing tools to make data science reliable, fast and accessible.
Researchers in this group aim both to build practical open source software and develop theoretical underpinning to machine learning algorithms and models. They are also working on developing novel, data-driven solutions to traditional problem areas as well as sociotechnical systems.
The Human Factors Engineering (HFE) Laboratory is a team-based teaching and research laboratory for undergraduate and graduate students. The laboratory focuses on cognitive, ergonomic, and environmental aspects of human factors, and their influence on product design, productivity and quality. The lab has a wide array of hardware and software.
The Laboratory for Advanced Materials at Pittsburgh (LAMP) under the direction of Professor Paul W. Leu, focuses on designing and understanding advanced materials by computational modeling and experimental research. Simulations and experiments are used in a synergistic manner to study the mechanical and electronic properties of nanomaterials and surfaces for various applications. Take a virtual tour of our lab, which resides right across the hallway from the Pitt Nanoscale Fabrication and Characterization Facility.
The goal of LAMP is to design better material systems through a combined modeling and experimental approach. Much in the same way that finite element modeling is now foundational in mechanical engineering design, multiscale and device modeling will become increasingly important in the prediction and understanding of nanomaterial and surface properties. In addition, these techniques can be used as a design tool for evaluating and optimizing various design metrics. Current areas of interest include:
- Ab initio modeling of nanomaterials and surfaces
- Combining physical simulations with optimization methods
- Nanomaterial manufacturing and characterization
- Solar cells
The goal of the Operations Research and Analytics in Healthcare (ORAH) Lab is twofold: (1) to identify significant challenges and pressing issues in healthcare operations, health policy, and medical decision making, and (2) to develop innovative Operations Research and Analytics methods to provide quantitative, evidence-based solutions that address these challenges. Operations, decisions and policymaking in healthcare involve complex issues such as high degrees of uncertainty, difficult trade-offs, and conflicting objectives that dynamically evolve over time. Researchers in the ORAH Lab focus on modeling and analysis of such complex healthcare problems by utilizing an array of advanced quantitative methods such as stochastic modeling, mathematical optimization, simulation and machine learning. The ORAH Lab emphasizes both the development of novel and creative methodology and interdisciplinary research via active collaboration with health researchers and practitioners. These methodologies and collaborations generate rigorous and viable solutions to inform medical decisions, operations strategies and public health policy in broad healthcare settings.
Research projects in the ORAH Lab are primary funded by the National Science Foundation, the National Institutes of Health, and other federal agencies. Past and ongoing projects include:
- Medical decision making applications in organ transplantation, cardiac device care and therapy sequencing in chronic disease management;
- Healthcare operations applications in vaccine administration, donor milk processing, COVID-induced drug supply shortages, biomanufacturing, and cancer therapy design and delivery;
- Health policy applications in organ allocation, incentive design, and paid sick days legislation.
The Sociotechnical Systems Research lab is led by Prof. Amin Rahimian, and the focus is on questions that help society navigate the age of data. On the one hand, the landscape for scientific research is itself changing: the combined force of high-end data analytics and high-performance computing opens new ways for scientific discovery; more and more data from various sources and in novel forms are available to facilitate scientific inquiries. On the other hand, to overcome the trust barriers and embrace the increasing role of data and algorithms in our lives, we need a scientific understanding of the algorithmic, data-driven and platform-based economies that algorithms enable. The lab’s research into large-scale, sociotechnical systems is aimed at helping society in this transition by deepening an understanding of the emerging, data-enabled infrastructure within its societal context.
The primary mission of the Stochastic Modeling, Analysis and Control (SMAC) Lab is to address challenges encountered in the mathematical modeling, analysis, and control of engineering, service, and other systems that have inherently stochastic elements. Examples of such systems include energy, production, telecommunications, inventory, and healthcare. Research in the lab emphasizes analytical and computer-based modeling of such systems and their optimization by exploiting applied probability, stochastic processes, and stochastic optimal control techniques. This collaborative laboratory’s aim is to generate, analyze and provide viable solutions to complex, often sequential, decision-making problems in uncertain environments. The SMAC Lab is primarily funded through grants from the National Science Foundation, the U.S. Department of Defense, and other governmental agencies. Current research thrusts in the laboratory include:
- the modeling, analysis, and optimization of energy storage systems;
- mission abort decision making;
- degradation-based reliability modeling and evaluation;
- data-driven, adaptive maintenance planning models;
- spare parts inventory modeling and control;
- healthcare applications;
- sequential analysis and quickest change detection in science and engineering.
Structural Nanomaterials Laboratory
Ravi Shankar Meenakshisundaram, PhD
This lab is directed by Dr. Ravi Shankar and its objective is to characterize, control and exploit physical phenomena that are operative at the nanometer length-scale to engineer material systems with unprecedented properties. To this end, we focus on understanding the fundamental mechanics of deformation at the nano-scale, elucidation of kinetics of atomic transport in nanostructured domains and characterization of phase-transformations in nanomaterials. Facilities include sample preparation capabilities for electron microscopy and micromechanical characterization, microhardness and tensile testing and capabilities for the creation of ultra-fine grained multi-phase materials. Current research is focused on the elucidation of microstructure evolution and behavior of multi-phase materials subjected to severe thermo-mechanical deformation and investigations of development of environmentally benign machining processes.
This research group, headed by Dr. Youngjae Chun, is a collaboration between the Departments of Industrial Engineering and Bioengineering. Current research interests include artificial biomaterials, composites, endovascular devices, diagnostic vascular implants, and micro-bio-systems, as well as fundamental device-associated biocompatibility and development of experimental techniques. Specifically, the work in the area of:
- Designing and Manufacturing Medical Devices for Treating Vascular Diseases
- Development of Artificial Biomaterials and Bio-hybrid Composites
- Micro Fabrication and Nanoscale Characterization
- Studies on Hemocompatible Surface Modification of Biomaterials
- In-vitro tests: Device functionality, Hemocompatibility, Inflammatory Response
- Investigation on Hemodynamics using MEMS transducer arrays
- In-vivo Animal Testing and Post-mortem Examination