The Penn Institute for Computational Science

Excellence in Multidisciplinary Research

The current and emerging research themes among the core PICS faculty include engineering soft matter and living systems, physical spatial and single cell biological engineering, development of advanced functional materials, molecular and chemomechanical engineering, and thermomechanical and energy engineering. PICS research also highlights emerging concepts in theory and algorithms in statistical mechanics, fluid dynamics, solid mechanics, complex systems, multiscale multiphysics modeling, machine learning methods, and computer graphics and visualization.

We strive to launch centers of thematic collaborations in the different domains we represent in partnership with experimental investigators. We will further strengthen partnerships within and outside Penn and synergize with other existing centers at Penn through multi-PI collaborative grant initiatives (e.g., through initiatives within NSF, DOE, NASA, NIH), and training grant initiatives (e.g., NSF NRT, NIH-T32). PICS brings together internationally reputed scholars by regularly hosting seminars, workshops, and symposia. PICS will work with leadership in all SEAS departments to successfully recruit future leaders, in the developments of Theory, Algorithms, and Computing.

Excellence in Graduate Education

PICS has established an interdisciplinary training environment to foster the growth of a thriving community of scientists and scholars by providing multifaceted graduate training in the fundamentals and applications of scientific computing. PICS enables graduate (MSE and Ph.D.) students from all SEAS graduate groups and those from the AMCS graduate group to achieve scholarship and expertise in core engineering and advanced scientific computing principles.

The SCMP Masters program and the PICS certificate in Advanced Scientific Computing have been created to build a cohesive community of graduate scholars who will benefit from the PICS training initiatives. The trainees will learn to: (1) develop and apply concepts fundamental to science and engineering; (2) learn to exploit elements of data science, including machine learning methods and tools of data integration; (3) adopt best practices in software architecture to leverage modern computational infrastructure. Through the SCMP MSE as well as the Advanced Scientific Computing certificate programs, the trainees will interact with the PICS environment and also benefit from individualized professional development activities such as improved communication, team building, and honing other soft skills, through group activities and events, such as seminars, hackathons, annual symposia, internships, and undergraduate research mentoring.

High-Performance Computing Infrastructure

PICS is committed to providing high-performance computing infrastructure and education through the following modalities.

• By enabling facilitating access to supercomputing platforms and high-performance computing infrastructure for parallel and scientific computing via HPC grants (e.g., NSF/XSEDE, DOE/INCITE).
• By providing sustainable access to in-house high-performance computing clusters via large instrumentation grants (e.g., NSF MRI).
• By enabling training and access to next-generation platforms for Exascale computing, cloud computing, and GPU-based computing.

It will continue to sponsor software architecture and coding boot camps for trainees ranging from foundational coding and software workshops to more advanced workshops on scientific visualization and parallel programming. PICS is looking to partner with XSEDE and other commercial partners (Google) in offering virtual training in HPC and cloud platforms to members of the Penn community.

Outreach and Community

PICS will strive to establish relationships and partnership agreements with Industry and National labs to facilitate external collaborations and provide access to high-quality internships to PICS scholars. It strives to create an influential culture for recruiting, mentoring, and retaining trainees with diverse cultural and disciplinary backgrounds by leveraging several existing programs and partnerships at Penn (e.g., SWE, SBE).

PICS seeks to initiate new partnerships (e.g., Quality Education Minority Network) to build capacity at minority-serving institutions in computationally focused STEM research. PICS will adopt and implement robust evaluation, outreach, and dissemination elements to deliver maximal impact, and with the aim towards program sustainability. It will establish an External Advisory Board (EAB) comprising of scientists from Academia, Industry and National Labs with diverse disciplinary backgrounds. PICS will also confirm an Internal Advisory Board (IAB) to complement the EAB that includes student members, Penn faculty, and representatives from Computing and Technology Services. The EAB and IAB will provide the necessary perspective and breadth to help achieve our goals.