Welcome
The Laboratory for Intelligent Systems and Analytics (LISA) has been an integral part of the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at Penn State over the years.
Originally founded by Dr. Soundar Kumara, Allen E. Pearce and Allen M. Pearce Professor of Industrial engineering, in 1987 as the Intelligent Design and Diagnostics Research Laboratory (IDDL), LISA has grown to be an interdisciplinary laboratory that spans many academic departments. During the mid-nineties Professors Kumara, Barton, and Chandra joined together to take the AI lab to the next step. Professor Gautam teamed up with the lab in 1996.
The LISA lab is located in 233 Leonhard Building at Penn State University Park Campus.
” Our rich background work naturally took us to the unexplored territory of large-scale systems. In the futuristic world whether it is the shop floor of a manufacturing system, trucks in a transportation network, medical facilities in healthcare, or the nodes in an Internet – all will be a part of the larger integrated distributed system. Individual sensors collect data and use their intelligence to communicate with different geographically distributed entities (people, equipment, and computers), forming complex networks. Though each entity may individually exhibit simple behaviors, collectively complex behaviors will manifest. The fundamental question that will be faced by the futuristic world is How will we make these large-scale systems adaptive so that their survivability is ensured ? ”
LISA Lab Research
LISA Lab is focused on fundamental research problems in various domains and integrates mathematics, Artificial Intelligence, pattern recognition, advanced computing, statistical physics, and operations research, to solve problems in complex networks, product design, and real-time monitoring of manufacturing and logistics systems.
Research Objectives
- Sensor data representation, modeling, and analysis
- Nonlinear Complex Systems analysis
- Applications of AI and ML to Manufacturing and Healthcare
- To develop AI&ML based implementation paradigms for smart manufacturing related to small and medium manufacturing enterprises (SMME)
- Healthcare Data Analytics
- Real-time supply chain resilience analysis
Educational Objectives
- To provide an environment for graduate students and faculty to exchange ideas and share learning related to the mission statement
- To utilize pooled resources to provide a better research environment for our Lab members, and to bring publicity to our research efforts and successes
- Provide graduate students with a smaller (20 instead of 120) graduate community in which to be known and establish friendships
- Gain research experience and technological awareness by interacting/collaborating with other Lab members
- To develop professional communication skills (e.g., communication of research funding via papers, presentations, posters boards, etc.)