Department of Mechanical Engineering and Materials Science, University of Pittsburgh
Ph.D. University of Oxford, 2015

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.

Most Cited Publications

M. Brenes, J. J. Mendoza-Arenas, A. Purkayastha, M. T. Mitchison, S. R. Clark and J. Goold, Tensor-Network Method to Simulate Strongly Interacting Quantum Thermal Machines, Phys. Rev. X 10, 031040 (2020).

J. J. Mendoza-Arenas, M.  Znidaric, V. K. Varma, J. Goold, S. R. Clark and A. Scardicchio, Asymmetry in energy versus spin transport in certain interacting disordered systems, Phys. Rev. B 99, 094435 (2019).

J. J. Mendoza-Arenas, S. R. Clark, S. Felicetti, G. Romero, E. Solano, D. G. Angelakis and D. Jaksch, Beyond mean-field bistability in driven-dissipative lattices: Bunching-antibunching transition and quantum simulation, Phys. Rev. A 93, 023821 (2016).

J. J. Mendoza-Arenas, S. R. Clark and D. Jaksch, Coexistence of energy di usion and local thermalization in nonequilibrium XXZ spin chains with integrability breaking, Phys. Rev. E 91, 042129 (2015).

J. J. Mendoza-Arenas, T. Grujic, D. Jaksch and S. R. Clark, Dephasing enhanced transport in nonequilibrium strongly-correlated quantum systems, Phys. Rev. B 87, 235130 (2013).

Recent Publications

J. J. Mendoza-Arenas and S. R. Clark, Giant rectification in strongly-interacting boundary-driven tilted systems, arXiv:2209.11718 (2022).

J. J. Mendoza-Arenas, Dynamical quantum phase transitions in the one-dimensional extended Fermi-Hubbard model, J. Stat. Mech. (2022) 043101.

J. J. Mendoza-Arenas and B. Buca, Self-induced entanglement resonance in a disordered Bose-Fermi mixture, arXiv:2106.06277 (2021).

R. Guerrero-Suarez, J. J. Mendoza-Arenas, R. Franco and J. Silva-Valencia, Spin-selective insulators in Bose-Fermi mixtures, Phys. Rev. A 103, 023304 (2021).

F. P. M. Méndez-Córdoba, J. J. Mendoza-Arenas, F. J. Gómez-Ruiz, F. J. Rodríguez, C. Tejedor and L. Quiroga, Rényi Entropy Singularities as Signatures of Topological Criticality in Coupled Photon-Fermion Systems, Phys. Rev. Research 2, 043264 (2020).

Department of Electrical and Computer Engineering at CMU; Associate Dean for Research, College of Engineering; Director of the Engineering Research Accelerator
Ph.D., Vienna University of Technology, 2003

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 ( for quantum computing. Details can be found here:

 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].

Selected Publications: 
  1. "SPIRAL: Extreme Performance Portability." F. Franchetti, T. M. Low, D. T. Popovici, R. M. Veras, D. G. Spampinato, J. R. Johnson, M. Püschel, J. C. Hoe, J. M. F. Moura. Proceedings of the IEEE, Vol. 106, No. 11, 2018. Special Issue on From High Level Specification to High Performance Code
  2. "Discrete Fourier Transform on Multicores: Algorithms and Automatic Implementation." F. Franchetti, Y. Voronenko, S. Chellappa, J. M. F. Moura, and M. Püschel. IEEE Signal Processing Magazine, special issue on “Signal Processing on Platforms with Multiple Cores”, 2009.
Most Cited Publications
  1. "SPIRAL: Code generation for DSP transforms." Markus Puschel, José MF Moura, Jeremy R Johnson, David Padua, Manuela M Veloso, Bryan W Singer, Jianxin Xiong, Franz Franchetti, Aca Gacic, Yevgen Voronenko, Kang Chen, Robert W Johnson, Nicholas Rizzolo. Proceedings of the IEEE.
  2. "Energy-efficient abundant-data computing: The N3XT 1,000 x." Mohamed M Sabry Aly, Mingyu Gao, Gage Hills, Chi-Shuen Lee, Greg Pitner, Max M Shulaker, Tony F Wu, Mehdi Asheghi, Jeff Bokor, Franz Franchetti, Kenneth E Goodson, Christos Kozyrakis, Igor Markov, Kunle Olukotun, Larry Pileggi, Eric Pop, Jan Rabaey, Christopher Ré, H-S Philip Wong, Subhasish Mitra. Computer.
  3. "Data reorganization in memory using 3D-stacked DRAM." Berkin Akin, Franz Franchetti, James C Hoe. ACM SIGARCH Computer Architecture News.
  4. "Mathematical foundations of the GraphBLAS." Jeremy Kepner, Peter Aaltonen, David Bader, Aydin Buluç, Franz Franchetti, John Gilbert, Dylan Hutchison, Manoj Kumar, Andrew Lumsdaine, Henning Meyerhenke, Scott McMillan, Carl Yang, John D Owens, Marcin Zalewski, Timothy Mattson, Jose Moreira. 2016 IEEE High Performance Extreme Computing Conference (HPEC).
  5. "A stencil compiler for short-vector simd architectures." Tom Henretty, Richard Veras, Franz Franchetti, Louis-Noël Pouchet, Jagannathan Ramanujam, Ponnuswamy Sadayappan. Proceedings of the 27th international ACM conference on International conference on supercomputing.
Recent Publications
  1. "A compiler for sound floating-point computations using affine arithmetic." Joao Rivera, Franz Franchetti, Markus Püschel. 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO).
  2. "High performance merge sort with scalable parallelization and full-throughput reduction." Fazle Sadi, Larry Pileggi, Franz FranchettiUS Patent 11,249,720.
  3. "Graph Embedding and Field Based Detection of Non-Local Webs in Large Scale Free Networks." Michael E Franusich, Franz Franchetti2021 IEEE High Performance Extreme Computing Conference (HPEC)
  4. "Optimized Quantum Circuit Generation with SPIRAL." Scott Mionis, Franz Franchetti, Jason Larkin. 2021 IEEE High Performance Extreme Computing Conference (HPEC).
  5. "An Auto-tuning with Adaptation of A64 Scalable Vector Extension for SPIRAL." Naruya Kitai, Daisuke Takahashi, Franz Franchetti, Takahiro Katagiri, Satoshi Ohshima, Toru Nagai. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Software Engineering Institute, Carnegie Mellon University
B.A., University of Pittsburgh, 2017; B.S., University of Maryland

Quantum-Classical Hybrid Systems operate on hardware and software completely different from our everyday von Neumann-esque computers. At the lowest levels of abstraction the difference is stark but becomes more muddled as you move up through the levels of abstraction. At the highest level of abstraction a QC system could potentially be seen as a black boxed module which takes input and returns desired output, no special considerations required. While this is an ideal that may one day come to pass, the inherent complexity and engineering shortcoming of NISQ era computers results in QC systems showing architectural differences throughout the entire stack.  Our research focuses on understanding the software architectural nuances right before QC systems might be seen as a black box, at the intersection of classical software application and quantum compute as a service infrastructure.

Civil and Environmental Engineering, Carnegie Mellon University
Ph.D., Massachusetts Institute of Technology, 2019

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.

Most Cited Publications
  1. "Measurement of τ polarization in W→τν decays with the ATLAS detector in pp collisions at sqrt(s) = 7 TeV." The ATLAS Collaboration, G. Aad, B. Abbott, J. Abdallah, S. Abdel Khalek, G.J. Wang, et al. The European Physical Journal C.
  2. "Hsp90 inhibition increases p53 expression and destabilizes MYCN and MYC in neuroblastoma." Paul L Regan, Joshua Jacobs, Gerald J Wang, Jaime Torres, Robby Edo, Jennifer Friedmann, Xao X Tang. International Journal of Oncology.
  3. "Why are fluid densities so low in carbon nanotubes?" Gerald J Wang, Nicolas G Hadjiconstantinou. Physics of Fluids.
  4. "Molecular mechanics and structure of the fluid-solid interface in simple fluids." Gerald J Wang, Nicolas G Hadjiconstantinou. Physical Review Fluids.
  5. "Layered Fluid Structure and Anomalous Diffusion under Nanoconfinement." Gerald J Wang, Nicolas G Hadjiconstantinou. Langmuir.
Recent Publications
  1. "Dynamic Tracking and Visualization of Thanks-Giving Flows in the Classroom." Gerald WangBulletin of the American Physical Society.
  2. "On the Role of Fluid-Solid Interaction Strength in Anomalous Fluid Diffusion under Nanoscale Confinement." Yuanhao Li, Gerald WangBulletin of the American Physical Society.
  3. "Mo'Mobilities, No Problems: An Excess Entropy Scaling Relation for Diffusivity of an Active Fluid." S Arman Ghaffarizadeh, Gerald WangBulletin of the American Physical Society
  4. "Social distancing slows down steady dynamics in pedestrian flows." Kelby B Kramer, Gerald J WangPhysics of Fluids
  5. "Gratitude and Graph Theory in the Time of Coronavirus." Gerald J Wang2021 ASEE Virtual Annual Conference Content Access.
Department of Informatics and Networked Systems, University of Pittsburgh
PhD, Worcester Polytechnic Institute

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.

Most Cited Publications
  1. "Modeling of indoor positioning systems based on location fingerprinting." Kamol Kaemarungsi and Prashant KrishnamurthyIeee Infocom 2004. Vol. 2. IEEE, 2004.
  2. "Handoff in hybrid mobile data networks." Pahlavan, K., Krishnamurthy, P., Hatami, A., Ylianttila, M., Makela, J. P., Pichna, R., & Vallstron, J. IEEE Personal Communications 7.2 (2000): 34-47.
  3. "Properties of indoor received signal strength for WLAN location fingerprinting." Kamol Kaemarungsi and Prashant KrishnamurthyThe First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004. IEEE, 2004.
  4. "On indoor position location with wireless LANs." Phongsak Prasithsangaree, Prashant Krishnamurthy, and Panos Chrysanthis, The 13th IEEE international symposium on personal, indoor and mobile radio communications. Vol. 2. IEEE, 2002.
  5. "Wideband radio propagation modeling for indoor geolocation applications." Kaveh Pahlavan, Prashant Krishnamurthy, and A. Beneat, IEEE Communications Magazine 36.4 (1998): 60-65.
Recent Publications
  1. "On configuring radio resources in virtualized fractional frequency reuse cellular networks." Xin Wang, Prashant Krishnamurthy, and David Tipper, Computer Communications 79 (2016): 78-91.
  2. "Socio-spatial affiliation networks." Konstantinos Pelechrinis and Prashant KrishnamurthyComputer Communications 73 (2016): 251-262.
  3. "A collaborative spectrum-sharing framework for LTE virtualization." Xin Wang, Prashant Krishnamurthy, and David Tipper, 2015 IEEE Conference on Collaboration and Internet Computing (CIC). IEEE, 2015.
  4. "Resource allocation for heterogeneous traffic in LTE virtual networks." Ayman AbdelHamid, Prashant Krishnamurthy, and David Tipper, 2015 16th IEEE International Conference on Mobile Data Management. Vol. 1. IEEE, 2015.
  5. "Technologies for positioning in indoor Areas." Prashant KrishnamurthyIndoor wayfinding and navigation 35 (2015).
Department of Geology and Environmental Science
Ph.D. Geophysics, Stanford University, 1987

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.

Selected Publications: 
  1. Kirchen, K., Harbert, W., Apt, J., and Morgan, M. G., 2020, A Solar-Centric Approach to Improving Estimates of Exposure Processes for Coronal Mass Ejections, Risk Analysis, 40, 1020-1039, DOI: 10.1111/risa.13461.
  2. Kumar, A., Bear, A., Hu, H., Hammack, R., Harbert, W., Ampomah, W., Balch, R., Garcia, L, Nolte, A., Tsoflias, G., 2019, Seismic monitoring of CO2-EOR operations in the Texas Panhandle and southern Kansas using surface seismometers, SEG 2019 Annual Meeting, DVD Extended Abstracts.
  3. Larsen, Darren, J., Crump, Sarah E., Abbott, Mark B., Harbert, William, Blumm, Aria, Wattrus, Nigel J., Hebberger, John J. , 2019, Paleoseismic evidence for climatic and magmatic controls on the Teton fault, WY, Geophysical Research Letters, 46, p. 13036-13043.
  4. Shi, Z., Sun, L., Haljasmaa, I., Harbert, W., Sanguinito, S., Tkach, M., Goodman, A., Tsotsis, T. T., and Jessen, K., 2019, Impact of Brine/CO2 Exposure on the Transport and Mechanical Properties of the Mt. Simon Sandstone, Journal of Petroleum Science and Engineering, 117, p. 295-305.
  5. Zorn, Erich, Kumar, Abhash, Harbert, William and Hammack, Richard, 2019, Geomechanical Analysis of Microseismicity in Organic Shale: A West Virginia Marcellus Shale Example, Interpretation, 7, T231-T239.
Most Cited Publications

1. "Evolution of the Mongol-Okhotsk Ocean as constrained by new palaeomagnetic data from the Mongol-Okhotsk suture zone", Siberia
VA Kravchinsky, JP Cogné, WP Harbert, MI Kuzmin, Geophysical Journal International 148 (1), 34-57 (2002)
2. "Late Neogene motion of the Pacific plate" W Harbert, A Cox, Journal of Geophysical Research: Solid Earth 94 (B3), 3052-3064 (1989)
3. "Late Neogene relative motions of the Pacific and North America plates", W Harbert, Tectonics 10 (1), 1-15 (1991)
4. "Plate motions recorded in tectonostratigraphic terranes of the Franciscan Complex and evolution of the Mendocino triple junction, northwestern California", RJ McLaughlin, WV Sliter, NO Frederiksen, WP Harbert, DS McCulloch, US Geological Survey Bulletin 1997 (1994)
5. "An evaluation of fracture growth and gas/fluid migration as horizontal Marcellus Shale gas wells are hydraulically fractured in Greene County, Pennsylvania",R Hammack, W Harbert, S Sharma, B Stewart, R Capo, A Wall, National Energy Technology Laboratory: NETL-TRS-3-2014, 76 (2014)

Recent Publications

1. Wang, Z., Dilmore, R. M., & Harbert, W. (2019). Machine Learning for Leakage Detection at CO 2 Sequestration Sites: Inferring CO 2 Saturation from Synthetic Surface Seismic and Downhole Monitoring Data. AGUFM2019, S31E-0579.
2. Larsen, D. J., Crump, S. E., Abbott, M. B., Harbert, W., Blumm, A., Wattrus, N. J., & Hebberger, J. J. (2019). Paleoseismic evidence for climatic and magmatic controls on the Teton fault, WY. Geophysical Research Letters46(22), 13036-13043.
3. Shi, Z., Sun, L., Haljasmaa, I., Harbert, W., Sanguinito, S., Tkach, M., ... & Jessen, K. (2019). Impact of Brine/CO2 exposure on the transport and mechanical properties of the Mt Simon sandstone. Journal of Petroleum Science and Engineering177, 295-305.
4. Kumar, A., Zorn, E., Hammack, R., & Harbert, W. (2019). Long-period, long-duration seismic events and their probable role in reservoir stimulation and stage productivity. SPE Reservoir Evaluation & Engineering22(02), 441-457.
5. Zorn, E., Kumar, A., Harbert, W., & Hammack, R. (2019). Geomechanical analysis of microseismicity in an organic shale: A West Virginia Marcellus Shale example. Interpretation7(1), T231-T239.

Department of Chemistry, Carnegie Mellon University
PhD, Theoretical Chemistry, Jackson State University

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.

Most Cited Publications
  1. "Machine learning for molecular and materials science," Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh. Nature, 559, 547-555 (2018)
  2. "ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost," Justin S Smith, Olexandr Isayev, Adrian E Roitberg. Chemical science, 8, 3192-3203 (2017)
  3. "Universal fragment descriptors for predicting properties of inorganic crystals," Olexandr Isayev, Corey Oses, Cormac Toher, Eric Gossett, Stefano Curtarolo, Alexander Tropsha. Nature communications, 8, 1-12 (2017)
  4. "Materials cartography: representing and mining materials space using structural and electronic fingerprints," Olexandr Isayev, Denis Fourches, Eugene N Muratov, Corey Oses, Kevin Rasch, Alexander Tropsha, Stefano Curtarolo. Chemistry of Materials, 27, 735-743 (2015)
  5. "Deep reinforcement learning for de novo drug design," Mariya Popova, Olexandr Isayev, Alexander Tropsha. Science advances, 4, eaap7885 (2018)
Recent Publications
  1. "Crowdsourced mapping of unexplored target space of kinase inhibitors," Anna Cichonska, Balaguru Ravikumar, Robert J Allaway, Sungjoon Park, Fangping Wan, Olexandr Isayev, Shuya Li, Michael J Mason, Andrew Lamb, Minji Jeon, Sunkyu Kim, Mariya Popova, Jianyang Zeng, Kristen Dang, Gregory Koytiger, Jaewoo Kang, Carrow I Wells, Timothy M Willson, Tudor I Oprea, Avner Schlessinger, David H Drewry, Gustavo A Stolovitzky, Krister Wennerberg, Justin Guinney, Tero Aittokallio. bioRxiv, (2020)
  2. "Predicting Thermal Properties of Crystals Using Machine Learning," Sherif Abdulkader Tawfik, Olexandr Isayev, Michelle JS Spencer, David A Winkler. Advanced Theory and Simulations, 1900208, (2019)
  3. "Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery," Marco Fronzi, Mutaz Abu Ghazaleh, Olexandr Isayev, David A Winkler, Joe Shapter, Michael J Ford. arXiv preprint arXiv, 1911.11559 (2019)
  4. "The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules," Justin S Smith, Roman Zubatyuk, Benjamin T Nebgen, Nicholas Lubbers, Kipton Barros, Adrian Roitberg, Olexandr Isayev, Sergei Tretiak. ChemRxiv (2019)
  5. "Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides," Sherif Farag, Rachel M Bleich, Elizabeth A Shank, Olexandr Isayev, Albert A Bowers, Alexander Tropsha. Bioinformatics, 35, 3584-3591, (2019)
Tepper School of Business, Carnegie Mellon University
PhD, Operations Research and Industrial Engineering, Cornell University

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.
Selected Publications: 
  • "A Novel Algebraic Geometry Compiling Framework for Adiabatic Quantum Computations." Raouf Dridi, Hedayat Alghassi, Sridhar TayurarXiv:1810.01440
  • "Graver Bases via Quantum Annealing with Application to Non-Linear Integer Programs." Hedayat Alghassi, Raouf Dridi, Sridhar Tayur. arXiv:1902.04215
  • "Quantum and Quantum-inspired Methods for de novo Discovery of Altered Cancer Pathways." Hedayat Alghassi, Raouf Dridi, A Gordon Robertson, Sridhar Tayur. bioRxiv 845719
  • "Knuth-Bendix Completion Algorithm and Shuffle Algebras For Compiling NISQ Circuits." Raouf Dridi, Hedayat Alghassi, Sridhar TayurarXiv:1905.00129
  • "Enhancing the efficiency of adiabatic quantum computations." Raouf Dridi, Hedayat Alghassi, Sridhar TayurarXiv:1903.01486
Most Cited Publications
  1. "Value of information in capacitated supply chains." Srinagesh Gavirneni, Roman Kapuscinski, Sridhar TayurManagement Science.
  2. "Quantitative models for supply chain management." Sridhar Tayur, Ram Ganeshan, Michael Magazine. Kluwer.
  3. "Models for supply chains in e-business." Jayashankar M Swaminathan, Sridhar R TayurManagement Science.
  4. "Managing broader product lines through delayed differentiation using vanilla boxes." Jayashankar M Swaminathan, Sridhar R TayurManagement Science.
  5. "Sensitivity analysis for base-stock levels in multiechelon production-inventory systems." Paul Glasserman, Sridhar Tayur. Management Science.
Recent Publications
  1. "Integer programming techniques for minor-embedding in quantum annealers." David E Bernal, Kyle EC Booth, Raouf Dridi, Hedayat Alghassi, Sridhar Tayur, Davide Venturelli. arXiv preprint arXiv:1912.08314.
  2. "The Topology of Mutated Driver Pathways." Raouf Dridi, Hedayat Alghassi, Maen Obeidat, Sridhar TayurarXiv preprint arXiv:1912.00108.
  3. "Online-to-Offline Platform Models." Joseph Xu, Hui Li, Sridhar R Tayur. SSRN 3449744.
  4. "Healthcare operations management: A snapshot of emerging research." Tinglong Dai, Sridhar TayurManufacturing & Service Operations Management.
  5. "GAMA: A Novel Algorithm for Non-Convex Integer Programs." Hedayat Alghassi, Raouf Dridi, Sridhar TayurarXiv preprint arXiv:1907.10930.
Department of Chemical & Petroleum Engineering
Ph.D. Chemical Engineering, Northwestern University, 2013

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.

Most Cited Publications
  1. "Nanoscale forces and their uses in self‐assembly." Kyle JM Bishop, Christopher E Wilmer, Siowling Soh, Bartosz A Grzybowski. small.
  2. "Metal–organic framework materials with ultrahigh surface areas: is the sky the limit?." Omar K Farha, Ibrahim Eryazici, Nak Cheon Jeong, Brad G Hauser, Christopher E Wilmer, Amy A Sarjeant, Randall Q Snurr, SonBinh T Nguyen, A Özgür Yazaydın, Joseph T Hupp. Journal of the American Chemical Society.
  3. "Review and analysis of molecular simulations of methane, hydrogen, and acetylene storage in metal–organic frameworks." Rachel B Getman, Youn-Sang Bae, Christopher E Wilmer, Randall Q Snurr. Chemical reviews.
  4. "Large-scale screening of hypothetical metal–organic frameworks." Christopher E Wilmer, Michael Leaf, Chang Yeon Lee, Omar K Farha, Brad G Hauser, Joseph T Hupp, Randall Q Snurr. Nature Chemistry.
  5. "Light-harvesting and ultrafast energy migration in porphyrin-based metal–organic frameworks." Ho-Jin Son, Shengye Jin, Sameer Patwardhan, Sander J Wezenberg, Nak Cheon Jeong, Monica So, Christopher E Wilmer, Amy A Sarjeant, George C Schatz, Randall Q Snurr, Omar K Farha, Gary P Wiederrecht, Joseph T Hupp. Journal of the American Chemical Society.
Recent Publications
  1. "The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation." Arni Sturluson, Melanie T Huynh, Alec R Kaija, Caleb Laird, Sunghyun Yoon, Feier Hou, Zhenxing Feng, Christopher E Wilmer, Yamil J Colón, Yongchul G Chung, Daniel W Siderius, Cory M Simon. Molecular Simulation.
  2. "Heat flux for many-body interactions: Corrections to LAMMPS." Paul Boone, Hasan Babaei, Christopher E Wilmer. Journal of chemical theory and computation.
  3. "Intelligent selection of metal-organic framework arrays for methane sensing via genetic algorithms." Jenna Ann Gustafson, Christopher E Wilmer. ACS sensors.
  4. "Designing a SAW Sensor Array with MOF Sensing Layers for Carbon Dioxide and Methane." Jagannath Devkota, Paul R Ohodnicki, Jenna A Gustafson, Christopher E Wilmer, David W Greve. 2019 Joint Conference of the IEEE International Frequency Control Symposium and European Frequency and Time Forum (EFTF/IFC).
  5. "High-throughput calculations of metal organic frameworks: Mixed matrix membranes for carbon capture." Jan Steckel, Samir Budhathoki, Paul Boone, Christopher Wilmer. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY.
Pittsburgh Supercomputing Center and Dept. of Physics, Carnegie Mellon University
Ph.D. in Chemistry, University of Pittsburgh, 1992

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.

Most Cited Publications
  • "Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets," Lu, S., Lu, K., Cheng, S.-Y., (...), Nystrom, N., Lu, X., BIBM (2015).
  • "Bridges: A uniquely flexible HPC resource for new communities and data analytics," Nystrom, N.A., Levine, M.J., Roskies, R.Z., Scott, J.R., International Conference Proceeding Series (2015).
  • "Porting third-party applications packages to the Cray T3D: Programming issues and scalability results," Wimberly, F.C., Lambert, M.H., Nystrom, N.A., Ropelewski, A., Young, W., Parallel Computing (1996).
Recent Publications
  • "Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets," Lu, S., Lu, K., Cheng, S.-Y., (...), Nystrom, N., Lu, X., BIBM (2015).
  • "Bridges: A uniquely flexible HPC resource for new communities and data analytics," Nystrom, N.A., Levine, M.J., Roskies, R.Z., Scott, J.R., International Conference Proceeding Series (2015).
  • "Porting third-party applications packages to the Cray T3D: Programming issues and scalability results," Wimberly, F.C., Lambert, M.H., Nystrom, N.A., Ropelewski, A., Young, W., Parallel Computing (1996).