About

Many of the world’s greatest challenges begin with stressed, poorly managed or misbehaving systems. Momacs is at the forefront of a new science of interacting systems and it leads the development of technologies for modeling and managing these systems.

Many of the world’s greatest challenges begin with stressed, poorly managed or misbehaving systems. Momacs is at the forefront of a new science of interacting systems and it leads the development of technologies for modeling and managing these systems.

Our Mission

Science seeks knowledge of the whole by studying its parts, but it is difficult to assemble models of the whole from knowledge of the parts.  Modeling the whole — whether it’s the whole economy, the whole microbiome, the whole climate — has never been more important than today, when these systems are increasingly interdependent and stressed.  At momacs, computing and information technologies help humans to gather data and assemble and test models of unimaginable scale and fidelity, far exceeding anything that can be done by humans working alone. Momacs fosters collaboration among computational and domain experts and intelligent machines, pushing the boundaries of systems science and technology with each new project. 

About

Paul R. Cohen,
Founding Director, Dean and Professor

Dr. Paul Cohen is the founding Dean of School of Computing and Information. Prior to becoming the Dean, Dr. Cohen was a Program Manager at the Information Innovation Office at DARPA.

There he promoted AI techniques to help people to model and manage complicated, interacting systems. Cohen was a professor and founding director of the University of Arizona’s School of Information: Science, Technology and Arts. He also served as the head of the university’s department of computer science. He has held research and faculty positions at the University of Southern California’s Information Sciences Institute and at the University of Massachusetts. Much of Cohen’s earlier research was about algorithms to find patterns in time series, with the intent of modeling human conceptual development on robot platforms. Dr. Cohen holds a Doctor of Philosophy degree in Computer Science and Psychology from Stanford University, a Master of Science degree in Psychology from the University of California, Los Angeles and a Bachelor of Science degree in Psychology from the University of California, San Diego.

Bruce R. Childers
Executive Director

Bruce Childers is the Senior Associate Dean in the School of Computing and Information, Chair of the Department of Information Culture and Data Stewardship, and a Professor in the Computer Science (CS) Department at the University of Pittsburgh. He also serves as Special Assistant to the Provost for Data Science. Previously, he served as the Associate Dean for Strategic Initiatives in SCI and led faculty recruitment and development in this role. Childers has also served as the Co-director of the Graduate Computer Engineering program and the Director of Graduate Studies for Computer Science. He graduated from the University of Virginia with a PhD (CS, 2000) and from the College of William and Mary with a BS (CS, 1991). His most recent work focuses on technology and cultural changes to advance transparency, reuse, and reproducibility in computationally-driven science. Childers is a passionate advocate of increasing accountability in computer systems research for more reproducible and open experimentation. His research focuses on the intersection of the software-hardware boundary for improved energy, performance, and reliability in computer systems design, with an emphasis on embedded systems. He has developed techniques at both the software layer (dynamic binary translation, compiler optimization, debugging and software testing) and the hardware layer (GPU resource management, asynchronous custom processors, speed scaling, reliable cache design, and storage class memory). Childers participates in numerous international and national activities, including past steering committee chair of the ACM SIGPLAN and SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (2012-2015), program chair for LCTES (2010) and PPPJ (2014), member of the Editorial Advisory Board for the Computer Languages, Systems and Structures Journal, member of the organizing commmittee for the Workshop on Modeling and Simulation of Systems and Applications, member of the steering committee for the Managed Programming Languages and Runtimes conference, and Associate Editor for IEEE Transactions on Computers. He participates in ACM task forces on issues about scientific reproducibility in computer science research.

Seong Jae Hwang
Affiliate

Dr. Seong Jae Hwang joined the Department of Computer Science at the University of Pittsburgh School of Computing and Information in 2019. He received his BS in Computer Science from the University of Illinois at Urbana-Champaign in 2011, his MSE in Robotics from the University of Pennsylvania in 2013, and PhD in Computer Sciences from the University of Wisconsin-Madison in 2019.

His research is focused on developing statistical machine learning and deep neural network methods for analyzing imaging modalities in computer vision, machine learning, and medical imaging. On the technical side, he develops algorithms for cross-sectional and sequential data from small to large scales with statistical machine learning and deep learning models. On the application side, his interests range from neuroscientific discoveries including understanding the pathological progression of Alzheimer’s disease to machine learning/computer vision applications.

Xulong Tang
Affiliate

Dr. Xulong Tang joined the Department of Computer Science in the fall of 2019. He received his PhD degree from Penn State in 2019. He obtained his MS from the University of Science and Technology of China, USTC (2013) and his BS from Harbin Institute of Technology (China, 2010), both in Computer Science.

His research interests lie in the fields of high-performance computing and parallel computer architectures and systems. In particular, he is interested in effective and efficient system optimization for complicated real world applications (deep learning, graph analytics, and scientific applications).