ISE has high quality facilities to support research across the range of industrial systems and engineering disciplines including physical and cognitive ergonomics research, data intensive/computational research, and advanced manufacturing.
The Additive Manufacturing and Design Lab focuses on developing the next generation of additive manufacturing (3D printing) technologies. We aim to understand the fundamental mechanism of the additive manufacturing processes and elucidate its parameter-process-structure-property relations. The research is conducted by leveraging design, analysis, optimization and simulation tools through a combined experimental and modeling methodology. The lab houses a variety of additive manufacturing technologies including stereolithography, direct ink writing and inkjet printing. The ultimate goal is to promote its widespread applications in areas such as energy (supercapacitors and all-solid-state batteries), health (human bones and tissues), environment (water treatment, gas separation, pollutant removal), aerospace (thermal and acoustic insulation), automotive (smart windows), and consumer products (wearable devices, smart houses). For more information please contact Dr. Chi Zhou.
The Applied Cognitive Engineering Lab conducts research on understanding and improving human behavior and performance in dynamic, complex systems, such as transportation and healthcare. We aim to understand and leverage individual differences in demographics, cognitive resources, and social-psychological factors to support human machine interactions. The laboratory is equipped with a NADS MiniSim – a high fidelity, fixe-based quarter-cab driving simulator that has an autonomous driving module, wearable eye trackers, and various physiological sensors. A comprehensive analytical platform supports multi-channel data syncing and includes specialized analysis modules for eye tracking, physiological and video data. The lab utilizes various research approaches, including the use of laboratory studies, simulation, participative assessment, and naturalistic data collection. For mor information contact Dr. Winnie Chen.
The Healthcare Engineering Research Center at University at Buffalo's Industrial and Systems Engineering Department focuses on developing methods and applications using systems engineering and human factors. Since 2000, faculty members have worked on research in analysis and design of health care information technology in hospitals, evaluation of health care IT implementation, efficiency improvement in hospitals, financial analysis of health care systems, in primary, acute and home care settings. Research at the center has been funded by health care agencies at the federal level (e.g., Agency for Health care Research and Quality, AHRQ), state level (New York State Department of Health) and local county health department. A large number of efficiency and financial improvement projects have also been completed at several hospitals (e.g., Erie County Medical Center, Mercy Hospital of Buffalo, Sisters of Charity Hospital, Kenmore Mercy Hospital, and St. Vincent Health Center in Erie, PA). Research collaborators in many of the center's research projects are from MedStar (Washington, D.C.), Cornell Weill Medical College, University of Florida Medical School, Virginia Common University Medical School, and University of Rochester Medical School. Students graduated from the center are employed by health systems (e.g., Catholic Health, Mayo Clinic), insurance firms (e.g., Progressive Insurance), and consulting companies (e.g., Milliman). For more information please contact Dr. Li Lin.
This laboratory is used for research activities in the area of physical ergonomics, safety, and occupational biomechanics. Research in this lab involves the investigation of workplace injury mechanisms, human capacity, and physical performance along with the development and evaluation of ergonomic assessment methods, controls and interventions. Students utilize the laboratory equipment to simulate work conditions and to identify and assess the physical risks of work environments. For more information contact Dr. Lora Cavuoto.
This laboratory is configured to support research in collaborative/team decision-making, visualization for decision support, and human factors studies. This facility supports research on individual and team decision making and system control. The laboratory contains several large screen displays and three configurable individual workstations, each equipped with positionable flat panel displays, networked computers, microphones and high quality headphones for team communication or control over the auditory environment. The room includes partitions to create individual or team workspaces. Data collection is supported through screen and voice recording software via and experimenter control room equipped with one-way glass. There are several additional workstations supporting experimental control and data analysis. For more information contact Dr. Victor Paquet.
This is a collaborative team consisting of faculty, graduate students, undergraduate students, and visiting scholars. We work on challenging topics related to decision, risk, and big data analytics. The tools that will be used include: game theory, network analysis, decision analysis, mathematical modeling, data analysis, machine learning, and programming (e.g., using Matlab, R, PHP, Java, Python). Examples of recent projects include the modeling of misinformation spreading and debunking on social media, fire risk forecasting and resource management, human trafficking and smuggling, border security, and protection of Soft Targets and Crowded Places (ST-CPs). For more information please contact Dr. Jun Zhuang.
Dedicated to solving engineering domain specific challenges by integrating process and sensor data via data analytics and machine learning, and generate actionable knowledge/decision-making. Our research focuses on:
· Data Analytics/Machine Learning for Advanced Manufacturing Processes Modeling, Monitoring, and Control: analysis of heterogeneous data such as functional, graph, and point cloud data, with applications in semiconductor manufacturing, additive manufacturing, flexible electronics, etc.
· Data Fusion Modeling and Design Optimization for Health and Energy Systems: integrating process data, domain knowledge, physical models, etc. for system modeling, forecasting, and control, with applications in warehouse worker fatigue, microbial fuel cell, 2D material synthesize, etc.
The Social Optimization Laboratory supports research efforts in social network analysis, social influence, social behavior modeling, crowdsourcing, collaborative learning, and more broadly, inference and mining of large social media, transportation system and healthcare data. For more information, contact Dr. Alex Nikolaev.
The OPTIMATOR Lab focuses on optimization for industrial and military applications through operations research. Students in the lab design algorithms and systems to solve difficult vehicle routing and scheduling problems. The OPTIMATOR Lab features a flight simulator for unmanned aerial vehicles (UAVs, also more popularly known as drones), as well as a fleet of seven quadcopter UAVs. Student work stations and a group meeting space facilitate collaborative research within the lab. For more information contact Dr. Chase Murray.
The Industrial and Systems Engineering department owns and operates a Dell PE high-performance computing cluster, composed of 12 shared-memory computer nodes, each having a 12-core Intel Xeon E5-2620 v3 2.4GHz processor, 128 GB of RAM, and QDR Infiniband. The cluster is currently hosted and maintained by the University at Buffalo’s Center for Computational Research (CCR) and is used to support the research endeavors of the ISE students and faculty members, serving as a useful computational platform for testing complex algorithms and processing large amounts of data. For more information contact Dr. Jose Walteros, or Dr. Jee Eun Kang.
In addition to research centers and laboratories organized within the ISE department, ISE faculty and students contribute to the following UB centers and strategic initiatives: