Computational

My research is devoted to the computational study of the properties of quantum materials. These systems consist of many interacting particles (e.g., electrons, impurities, phonons), are highly sensitive to external perturbations, and feature spectacular states of matter such as magnetic order and high-temperature superconductivity. This makes them very attractive for implementing and controlling diverse novel technologies. However, understanding their properties is extremely challenging, and many questions regarding their nature remain open.

To unravel the phenomenology of quantum materials, I rely on simple yet rich theoretical models (such as Heisenberg or Hubbard Hamiltonians) to capture their underlying physics, and on powerful tensor network algorithms to calculate their quantum states. In particular, my work focuses on ground-state phase diagrams of correlated systems, out-of-equilibrium (coherent and dissipative) control of superconductivity and magnetism, optimization of electric and energy transport, and quantum thermal machines. Furthermore, I am interested in the analysis of quantum materials through state-of-the-art quantum computers and simulators.

Currently I am also aiming at applying recently-developed tensor network approaches to solve partial differential equations, with particular applications to fluid turbulence and combustion.

Franz Franchetti is the Kavčić-Moura Professor of Electrical & Computer Engineering at Carnegie Mellon University. He received the Dipl.-Ing. (M.Sc.) degree in Technical Mathematics and the Dr. techn. (Ph.D.) degree in Computational Mathematics from the Vienna University of Technology in 2000 and 2003, respectively. In 2006 he was member of the team winning the Gordon Bell Prize (Peak Performance Award) and in 2010 he was member of the team winning the HPC Challenge Class II Award (most productive system). In 2013 he was awarded the CIT Dean's Early Career Fellowship by the College of Engineering of Carnegie Mellon University.

Dr. Franchetti's research focuses on automatic performance tuning and program generation for emerging parallel platforms and algorithm/hardware co-synthesis. He targets multicore CPUs, clusters and high-performance systems (HPC), graphics processors (GPUs), field programmable gate arrays (FPGAs), FPGA-acceleration for CPUs, and logic-in-memory and 3DIC chip design.  Within the Spiral effort, his research goal is to enable automatic generation of highly optimized software libraries for important kernel functionality. In other collaborative research threads, Dr. Franchetti is investigating the applicability of domain-specific transformations within standard compilers and the application of HPC in smart grids and material sciences. He has led four DARPA projects in the BRASS, HACMS, PERFECT, and PAPPA programs and is Co-PI in the DOE ExaScale Project and XStack program as well as DARPA DPRIVE. Recent interests include leveraging the SPIRAL system (www.spiral.net) for quantum computing. Details can be found here: http://spiral.net/software/qco.html.

 Open Source SPIRAL System 

Open Source SPIRAL is available here under non-viral license (BSD-style license). This is an initial version, and there will be an ongoing effort to open source whole system. Please let us know which parts of SPIRAL you are most interested in. Commercial support is available via SpiralGen, Inc.
SPIRAL tutorial at HPEC 2019. See also [Overview], [Walk-Through], and [SPIRAL User Manual].

Dr. Wang's research interests involve using mechanics, statistical physics, and high-performance computing to understand nanoscale structural and transport phenomena, with the goal of developing very small solutions for very big problems in the water-energy nexus.

The M5 Lab's research is centered around the use of theory and high-performance computation to address problems in micro- and nanoscale mechanics; their core motivation is to inform and inspire the design of materials and devices for CEE applications, including higher efficiency molecular-scale separation processes, more resilient structural materials, more recyclable polymers, and tunable thermal interfaces. Their tools of choice include statistical physics, molecular mechanics, fluid mechanics, thermodynamics and heat transfer, and a wide range of computational methods for modeling small-scale phenomena, including (in almost all cases) particle simulations and (in appropriate cases) techniques from machine learning. They are also interested in developing efficient simulation methods for simulating micro- and nanoscale phenomena.

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.