Computational

Prashant Krishnamurthy is a professor at the School of Computing and Information. He teaches at both the graduate and undergraduate levels, offering introductory and advanced courses on wireless networks and cryptography. He also was one of the cofounders of the school’s Laboratory for Education and Research in Security Assured Information Systems (LERSAIS), a national Center of Academic Excellence (CAE) in Information Assurance Education (IAC) and Research (IAR).

Krishnamurthy’s research on position/location and security in wireless networks has expanded to consider avant-garde directions for radio spectrum policy and spectrum virtualization, efficient design of location-based social networks, the design of positioning schemes to support wayfinding, and approaches to multi-level information privacy. During his time at Pitt, Krishnamurthy has developed new courses and programs of study for the school, particularly addressing the wireless and security curricula, which has attracted over $5 million in curriculum and education/scholarships grants (for which he served as either the principal investigator or coprincipal investigator) from the National Science Foundation and the Commonwealth of Pennsylvania.

Dr. Harbert’s research focus includes the geomechanical analysis of microseismicity in organic shale, advanced seismic processing and interpretation related to subsurface imaging and analysis, and a rock physics-based determination of dynamic acoustic and mechanical properties of geologic units. He has also been involved with environmental geophysical technologies relevant to remote subsurface water quality and topology analysis.  He and his students and collaborators are actively work on the application of Deep Learning methods to seismic emissions and geophysical object detection and classification. The research of this group is the analysis of microseismic, reflection seismic, VSP and fluid and structure using advanced processing and attributes. The group works to accurately image surface geometry using geophysical techniques and advanced geophysical processing. The goal of this research is to better understand subsurface structures, subsurface pore filling phases and topologies and dynamic processes at a variety of scales, from micro computer tomography (CT) scale to log response scale, to vertical seismic profile and cross well tomography scales and surface seismic response scale.

The Isayev lab works at the interface of theoretical chemistry, pharmaceutical sciences and computer science. In particular, we are using molecular simulations and artificial intelligence (AI) to solve hard problems in chemistry. We are working towards the acceleration of molecular discovery by the combination of AI, informatics and high-throughput quantum chemistry. We also focus on both generative and predictive ML models for chemical and biological data. Details on specific projects can be found below.

Accelerating computational chemistry with deep learning: We are developing fully transferable deep learning potentials for molecular and materials systems. Such atomistic potentials are highly accurate compared to reference QM calculations at speeds 107faster. Neural network potentials are shown to accurately represent the underlying physical chemistry of molecules through various test cases including chemical reactions, kinetics, thermochemistry, structural optimization, and molecular dynamics simulations.

Materials informatics: Material informatics is a rapidly emerging data- and knowledge-driven approach for the identification of novel materials for a range of applications, including solar energy conversion. As the proliferation of high-throughput methods in chemical sciences is increasing the wealth of data in the field, the gap between accumulated-information and derived knowledge widens. We address the issue of scientific discovery in chemical and biological databases by introducing novel analytical approaches based on large-scale data mining and machine learning.

De Novo molecular design: The de novo molecular design problem involves generating novel molecular structures or focused molecular libraries with desirable properties. It solves a so-called inverse design problem. We develop artificial intelligence method that enables the design of chemical libraries with the desired physicochemical and biological properties or both.

The research of the Quantum Computing Group (QCG) at the Tepper School focuses on the creation of radically different types of algorithms to optimize complex large-scale industrial problems startlingly faster, with the ultimate desired outcome of commercialized algorithms that are easily accessible for practical application.

QCG research takes place in three parallel areas:

• Solving practical problems using novel quantum and quantum-inspired algorithms.
• Developing robust and efficient processes of translating a mathematical algorithm into physical instructions executed by the hardware — known as compilers — for quantum computers.
• Understanding and enhancing quantum speedup: how and why speed is increased, and by how much.

Our group designs hypothetical materials to help address energy and environmental challenges. We are interested in creating sophisticated nanostructures; potentially as complex (and useful) as molecular machines found in Nature. Our strategy is to computationally design and study new materials and then work work with our experimental collaborators to synthesize those materials in the lab. We are active software developers, and we build new computational tools to address problems nobody has tackled before.

Dr. Nicholas A. (Nick) Nystrom is the chief scientist of the Pittsburgh Supercomputing Center (PSC), a national computing center founded 1986 that is a joint effort of Carnegie Mellon University and the University of Pittsburgh. He joined PSC in 1992 as scientific programmer. He most recently served as interim director and senior research director. He has held the position of research physicist at Carnegie Mellon University since 2004. He received his Ph.D. in chemistry in 1992 from the University of Pittsburgh.

Dr. Nystorm is the architect, principal investigator (PI), and project director (PD) for “Bridges”, PSC’s flagship platform that was the first to successfully converge HPC, AI, and Big Data. He is also PI for the Data Exacell, a research pilot for enabling high performance data analytics on novel storage; co-PI for Open Compass, which brings emerging AI technologies to important problems in research; co-I for the Center for Causal Discovery, an NIH Big Data to Knowledge (BD2K) Center of Excellence; and co-I for Big Data for Better Health, which applies machine learning to lung and breast cancer research.

Dr. Nystorm's research interest includes data analytics, Big Data, causal modeling, graph algorithms, genomics, machine learning / deep learning, extreme scalability, hardware and software architecture, software engineering for HPC, performance modeling and prediction, impacts of programming models and languages on productivity and efficiency, information visualization, and quantum chemistry. Recent work has focused on enabling data-intensive research in domains new to HPC, scaling diverse computational science codes and workflows to extreme-scale systems, deep hierarchies of parallelism, advanced filesystems, and architectural innovations in processors and interconnects.

Daniel's main research interest is in the allocation of resources (computing and network resources) in the realm of real-time, with main concerns being power management, security, and fault tolerance. He bridges the gap between the operating systems and networking research fields, between practice and theory.

Lately, he has also been focusing on how to increase diversity in computing and how to promote reproducible research in computing.

Dr. 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.

The overarching goals of our research is to develop and use multiscale simulation tools to understand, predict, and design novel materials for applications in energy conversion and storage, surfaces and interfaces, spectroscopy, and nanoparticles. Our group has extensive expertise in different levels of theories in computational materials design that span a wide range of accuracy levels and length scales, including force-field, density-functional theory, quantum Monte Carlo and quantum chemistry methods.

Specific Research Directions:

1. Solar Cells
2. Novel two Dimensional Materials: Graphene and Beyond
3. Innovative Materials for Electrocatalysis
4. Metal Oxidation
5. Surfaces and Interfaces
6. Ferroelectric Materials
7. Raman Spectroscopy
8. Van der Waals interactions

Our research interest is focused on developing analytical and computational multiscale techniques, and applying these techniques to engineering and biomedicine. Some current application topics include (i) response of charged defects to electric fields in solid oxides and ferroelectrics for energy applications; (ii) mechanics of drug transport across biological membranes; (iii) electron transport in deformed biomolecules under stress; (iv) response of nanostructured materials under dynamic loading for impact and blast protection; (v) composite functional materials for high-temperature sensors and actuators for hypersonic aircraft; (vi) quantum mechanical calculation of electromechanical properties of nanomaterials (graphene, nanotubes, chalcogenides)