Joint/Adjunct Faculty

Ziv Bar-JosephLab Website
Ziv Bar-Joseph – Assist Professor, Carnegie Mellon University, Dept. of Computer Science
Ph.D., Computer Science, Massachusetts Institute of Technology
Our group develops computational methods for understanding the dynamics, interactions and conservation of complex biological systems. As new high-throughput biological data sources become available, they hold the promise of revolutionizing molecular biology by providing a large-scale view of cellular activity. However, each type of data is noisy, contains many missing values and only measures a single aspect of cellular activity. Our computational focus is on methods for large scale data integration. We primarily rely on machine learning and statistical methods. Most of our work is carried out in close collaboration with experimentalists. Many computational tools we develop are available and widely used.
Bar-Joseph Z, Rashid S., Shah S., Pandya Dhaka R. (2018) Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data to appear.

Bar-Joseph Z, Hagood J.S, Ambalavanan N., Ding J. (2018) Interactive visualization of dynamic regulatory networks. PLoS Computational Biology. 14(3): e1006019
Jaime CarbonellLab Website
Jaime Carbonell – Professor, Carnegie Mellon University Dept. of Computer Science
PhD, MPhil, MS, Computer Science, Yale University
Context-Based Machine Translation, Example-Based Machine Translation, Machine Learning in Computational Proteomics, Active and Proactive Machine Learning, Machine Learning and Language Technologies in Biomedicine (e.g. Optimizing HIV Therapy), Wind Energy Optimization.
Wei-Yu A, Kılınç-Karzan F, Carbonell J (2014) Saddle Points and Accelerated Perceptron Algorithms of International Conference on Machine Learning (ICML), Beijing, 2014.

Flanigan J, Thomson S, Carbonell J, Dyer C, Smith N (2014) A Discriminative Graph-Based Parser for the Abstract Meaning Representation In Proc, of the Association for Computational Linguistics Conference (ACL), Baltimore, 2014.
Gregory F. CooperLab Website
Gregory F. Cooper – Associate Professor of Medicine and of Intelligent Systems
PhD, Medical Information Science, MD, Medicine, Stanford University
His research interest is in the application of decision theory, probability theory, and artificial intelligence to address biomedical informatics research questions, with a focus on causal modeling and discovery in medicine and biology, data mining of medical databases, application of Bayesian statistics in medicine, and biosurveillance.
Cooper GF, Bahar I, Becich MJ, Benos PV, Berg JM, Espino JU, Glymour C, Jacobson RC, Kienholz M, Lee AV, Lu X, Scheines RB (2015) The Center for Causal Discovery of biomedical knowledge from Big Data Big Data J Am Med Inform Assoc. 22: 1132-1136

Balasubramanian JB, Cooper GF, Visweswaran S, Gopalakrishnan V (2014) Selective model averaging with Bayesian rule learning for predictive biomedicine Proceedings of the AMIA 2014 Joint Summits in Translational Science (In Press); April 2014; San Francisco, CA, USA2014..
Lance DavidsonLab Website
Lance Davidson – Professor, Department of Bioengineering, Swanson School of Engineering
Ph.D., Biophysics, University of California at Berkeley
We seek to understand how tissues and organs are shaped in the embryo and how principles of self-assembly can be applied to engineer tissues. Our experimental and theoretical approaches are multi-scale, ranging from super-resolution imaging and simulation of intracellular effectors to mesoscale analysis of bulk movements and biomechanics. Such multi-scale analysis is uncovering feedback circuits that make tissue assembly more robust even as structures become more complex.
Miller C.J, Davidson LA, Harris D., Weaver R., Ermentrout B. (2018) Emergent mechanics of actomyosin drive cortical contractions and shape network morphology. PLoS Computational Biology. PLoS Computational Biology.

Davidson LA, Stuckenholz C., Balakrishnan U. , Kim H. , Jackson T.R (2017) Spatiotemporally controlled mechanical cues drive progenitor mesenchymal-to-epithelial transition enabling proper heart formation and function Current Biology. 27: 1326-1335
Alexander DömlingLab Website
Alexander Dömling – Professor and Chair of Drug Design, University of Groningen, Netherlands
Ph.D., Multicomponent Reaction Chemistry, Technical University of Munich
Prof Alexander Dömling (Chair of Drug Design at the University of Groningen) devotes his academic life to the structure based design and discovery of bioactive compounds for difficult targets such as protein protein interactions. At the University of Pittsburgh he introduced the “google-like” and web-based technology ANCHOR.QUERY together with Carlos Camacho. ANCHOR.QUERY and the congeners NUCLEO.QUERY and TPP.QUERY can screen very large (billions) of virtual compounds in just seconds for pharmacophores and based on key interacting fragments, e.g. large amino acid side chains of amino acids (in PPIs) or nucleotides or the cofactor thiamine. Interestingly the resulting virtual hits can be instantaneously synthesized using convergent and fast multicomponent reaction chemistry (MCR) in order to test the virtually generated hypothesis. Another development are is the technology platform Drug Discover at the Speed of Sound (DDSoS). Here we introduce a fundamentally novel approach towards preclinical drug discovery and development by blending Instant Chemistry, nL dispensing, acoustic-MS, uHTS and artificial intelligence. The indication areas Alexander Dömling is interested in are cancer immunology, infectious diseases and metabolic disorders. He has published more than 150 scientific articles, reviews and patents. Additionally Alexander Dömling is a serial entrepreneur trying to make the expression “from bench to bedside” become true.
Patil P, Khoury K, Herdtweck E, Dömling A (2014) A universal isocyanide for diverse heterocycle syntheses Org Lett. 16(21): 5736-9
G. Bard ErmentroutLab Website
G. Bard Ermentrout – Professor, University of Pittsburgh, Dept. of Mathematics
Ph.D., Biophysics, University of Chicago
Dr. Ermentrout's research program investigates models of neural and muscle physiology. His recent focus has been on the behavior of networks of cortical-like neurons. He is interested in the dynamics of wave propagation in cortical and thalamic slice models, the olfactory lobe of the Limax, and the synchronization of cortical networks, in addition to spatial and temporal patterns in neuronal networks such as those observed during flicker stimulation and localized patterns of working memory. He also studies the effects of various ionic current and synaptic plasticity on the interactions between neural oscillators. He is the author of XPPAUT, a software platform for the simulation and analysis of nonlinear dynamical systems.
Kotani K, Yamaguchi I, Yoshida L, Jimbo Y, Ermentrout GB (2014) Population dynamics of the modified theta model: macroscopic phase reduction and bifurcation analysis link microscopic neuronal interactions to macroscopic gamma oscillation Journal of The Royal Society Interface. 11(95): 20140058

Mochan E, Swigon D, Ermentrout GB, Lukens S, Clermont G (2014) A mathematical model of intrahost pneumococcal pneumonia infection dynamics in murine strains J. Theor. Biol. 353: 44-54
Vanathi GopalakrishnanLab Website
Vanathi Gopalakrishnan – Asst Prof., U. of Pittsburgh, Dept of Biomedical Informatics and Intelligent Systems Program
Ph.D., Computer Science, University of Pittsburgh
We develop and test machine learning and pattern recognition methods for increasing scientific knowledge from biomedical data. We specialize in rule learning algorithms and variants that employ probabilistic scoring for model generation and selection.
Ceschin R, Panigrahy A, Gopalakrishnan V (2015) Open-source software for temporal analysis and visualization of brain tumor diffusion MR using serial functional diffusion mapping Cancer Informatics. 14(Suppl 2): 1-9

Balasubramanian JB, Cooper GF, Visweswaran S, Gopalakrishnan V (2014) Selective model averaging with Bayesian rule learning for predictive biomedicine Proceedings of the AMIA 2014 Joint Summits in Translational Science (In Press); April 2014; San Francisco, CA, USA2014..
Graham HatfullLab Website
Graham Hatfull – Eberly Family Professor of Biotechnology, HHMI Professor
Ph.D. University of Edinburgh
Current lab studies: My lab is interested in bacteriophage diversity, evolution, gene function, and regulation. Integrated research-education programs have provided a collection of over 14,000 phage isolates, of which >2,500 have been completely sequenced encompassing over 250,000 genes. Over 70% of the genes are of unknown function, and we are interested in understand phage gene function, regulation, phage-host dynamics, and exploiting the phages for understanding and controlling mycobacterial infections.
Hatfull GF, Dedrick RM, Jacobs-Sera D, Guerrero-Bustamante CA, Garlena RA, Mavrich TN, Pope WH, Reyes JC, Russell DA, Adair T, Alvey R, Bonilla JA, Bricker JS, Brown BR, Byrnes D, Cresawn SG, Davis WB, Dickson LA, Edgington NP, Findley AM, Golebiewska U, Grose JH, Hayes CF, Hughes LE, Hutchinson KW, Isern S, Johnson AA, Kenna MA, Klyczek KK, Mageeney CM, Michael SF, Molloy SD, Montgomery MT, Neitzel J, Page ST, Pizzorno MC, Poxleitner MK, Rinehard CA, Robinson CJ, Rubin MR, Teyim JN, Vazquez E, Ware VC, Washington J (2017) Prophage-mediated defence against viral attack and viral counter-defence Nature Microbiol. 2:16251:

Mavrich TN, Hatfull GF (2017) Bacteriophage evolution differs by host, lifestyle and genome Nature Microbiol. 2, 17112:
Naftali KaminskiLab Website
Naftali Kaminski – Yale U, Boehringer Ingelheim Pharmaceuticals, Inc. Prof of Medicine (Pulmonary); Section Chief
Medical School of Hadassah and the Hebrew University in Jerusalem
Genomics; microRNAs; Non-coding RNAs; Biomarkers; Idiopathic Pulmonary Fibrosis and other Interstitial Lung Disease; Advanced Lung Disease; Personalized Medicine; Systems Biology; High-throughput technologies; Matrix Mtealloproteases
Benos PV, Tosun BA, Manatakis DV, Vukmirovic M, Nguyen L, Yan X, Hu B, Deluliis G, Woolard T, Maya JD, Homer R, Kaminski N, Chennubhotla CS (2017) Towards Understanding Spatial Lung Tissue Heterogeneity In Idiopathic Pulmonary Fibrosis (IPF) A72. Mechanisms Driving Fibrosis.

Olave N, Lal CV, Halloran B, Pandit K, Cuna AC, Faye-Petersen OM, Kelly DR, Nicola T, Benos PV, Kaminski N, Ambalavanan N (2016) Regulation of alveolar septation by microRNA-489? m J Physiol Lung Cell Mol Physiol. 10: 476-487
Ossama KashlanLab Website
Ossama Kashlan – Research Assistant Professor, University of Pittsburgh, Renal-Electrolyte Division
PhD, University of Pennsylvania
Research interests: Allosteric regulation of ion channels; Regulation of ion channels by proteases; Ion channels selectivity.
Kashlan OB, Blobner BM, Zuzek Z, Tolino M, Kleyman TR (2014) Na + Inhibits the Epithelial Na + Channel by Binding to a Site in an Extracellular Acidic Cleft Journal of Biological Chemistry. 290(1): 568-76

Pearce D, Soundararajan R, Trimpert C, Kashlan OB, Deen PMT, Kohan DE (2014) Collecting Duct Principal Cell Transport Processes and Their Regulation Clinical Journal of the American Society of Nephrology. 10(1): 135-46
Dennis KostkaLab Website
Dennis Kostka – Assistant Professor, U. of Pittsburgh Dept. of Developmental Biology
Ph.D,, Computational Biology, Free University Berlin / Max Planck Institute for Molecular Genetics
Research: How do different organs and tissues arise? What are the genetic and epigenetic mechanisms that drive this development? To address these questions we design statistical methods and algorithms and apply them to large-scale, genome-wide data. Ultimately, our goal is to generate, test, and confirm hypotheses that are relevant to human health.
Capra JA, Kostka D (2014) Modeling DNA methylation dynamics with approaches from phylogenetics Bioinformatics. 30(17): i408-14

Marrone AK, Stolz DB, Bastacky SI, Kostka D, Bodnar AJ, Ho J (2014) Marrone AK1, Stolz DB2, Bastacky SI3, Kostka D4, Bodnar AJ1, Ho J5. Journal of the American Society of Nephrology. 25(3): 1440-52
Maria KurnikovaLab Website
Maria Kurnikova – Associate Professor, Carnegie Mellon University, Department of Chemistry
Ph.D. Physical Chemistry, University of Pittsburgh
Our research is in the area of theoretical/computational chemistry and biophysics. We are especially interested in developing functional models of membrane proteins, such as ion channels, signaling and regulatory proteins.
Flores-Canales JC, Vargas-Uribe M, Ladokhin AS, Kurnikova M (2015) Membrane association of the diphtheria toxin translocation domain studied by coarse-grained simulations and experiment Journal of Membrane Biology. 248(3): 529-543

Ozkan A, Flores-Canales JC, Sitharam M, Kurnikova M (2014) Fast and flexible geometric method for enhancing MC sampling of compact configurations for protein docking problem arXiv:1408.2481.
Christopher James LangmeadLab Website
Christopher James Langmead – Carnegie Mellon U, Assist Prof of Computer Science.
Ph.D. in Computer Science, Dartmouth College, 2003
I am interested in the dynamics of complex biological processes. My research uses a combination of Machine Learning and Formal Methods to model and study the dynamics of a variety of phenomena including: molecular interactions, acute illness, and cancer.
Kamisetty H, Ghosh B, Langmead CJ, Bailey-Kellogg C (2015) Learning sequence determinants of protein:protein interaction specificity with sparse graphical models J Comput Biol. 22(6): 474-86

Langmead CJ (2014) Generative Models of Conformational Dynamics Adv Exp Med Biol. 805: 87-105
Miler LeeLab Website
Miler Lee – University of Pittsburgh, Biological Sciences
Ph.D., Genomics and Computational Biology, University of Pennsylvania
My research addresses how gene expression programs change, leading to changes to cellular identity. In multicellular organisms, the genetic instructions that guide the initial stages of embryonic development are inherited from the egg as RNA. When the embryonic genome becomes active, new RNAs are transcribed, while maternally provided RNAs are destroyed. During this “maternal-to-zygotic transition,” the embryo is effectively reprogrammed from an oocyte identity to pluripotency.
Yartseva V, Takacs CM, Vejnar CE, Lee MT, Giraldez AJ (2017) RESA identifies mRNA regulatory sequences with high resolution. Nat Methods. 14(2): 201-207

Reischauer S, Stone O, Villasenor A, Chi N, Jin SW, Martin M, Lee MT, Fukuda N, Marass M, Fiddes I, Kuo T, Chung WS, Salek S, Lerrigo R, Alsio J, Luo S, Tworus D, Augustine SA, Mucenieks S, Nystedt B, Giraldez AJ, Schroth GP, Andersson O, Stainier DY (2016) Cloche is a bHLH-PAS transcription factor that drives haemato-vascular specification Nature. 535(7611): 294-8
Natasa Miskov-ZivanovLab Website
Natasa Miskov-Zivanov – Assistant Professor, Electrical and Computer Engineering, University of Pittsburgh
Ph.D., Computer Engineering, Carnegie Mellon University
Dr. Miskov-Zivanov’s research focuses on systems medicine, which represents convergence of (i) systems approach that allows for deep disease mechanism insights, (ii) emerging technologies that lead to large-scale data acquisition and provide means for new treatments, and (iii) analytic tools that can handle the complexity of the disease mechanisms, or billions of data points, and can suggest personalized therapies.
Hawse WF, Sheehan RP, Miskov-Zivanov N, Menk AV, Kane LP, Faeder JR, Morel PA (2015) Cutting Edge: Differential Regulation of PTEN by TCR, Akt, and FoxO1 Controls CD4+ T Cell Fate Decisions J Immunol. 194: 4615-9

Miskov-Zivanov N, Turner MS, Kane LP, Morel PA, Faeder JR (2013) The duration of T cell stimulation is a critical determinant of cell fate and plasticity Science Signaling. 6: ra97
Zoltan Nagy OltvaiLab Website
Zoltan Nagy Oltvai – Associate Professor of Pathology and Computational Biology, University of Pittsburgh, School of Medicine
M.D., Semmelweiss Medical School, Budapest, Hungary
Dr. Oltvai’s research interest is in the area of systems biology of cell metabolism, including the metabolism of prokaryotic and mammalian cells, including tumor cells.
Oltvai ZN, Beckwitt CH, Warita K, Wells K, Benos PV, Raghu V (2018) Biomarker identifica­tion for statin sensitivity of cancer cell lines Biochem. Biophys. Res. Comm.. 495: 659-665

Singh M, Oltvai ZN, Warita K, Warita T, Faeder JR, Lee REC, Sant S (2018) Shift from Sto-chastic to spatially-ordered expression of serine-glycine synthesis enzymes in 3D microtumors Sci. Rep. 8:9388:
John RosenbergLab Website
John Rosenberg – Professor, University of Pittsburgh, Department of Biological Sciences
Ph.D., Massachusetts Institute of Technology
My research philosophy is to bring a physical, problem-ori­ented ap­proach to the acquisition and interpretation of biomolecular structural information. I view biological molecules as amazing machines that have reached the ultimate miniaturization possible in a universe composed of atoms and molecules; my approach is to pick interesting examples where it appears possible to develop understanding of how they work.
Adelman JL, Sheng Y, Adelman JL, Sheng Y, Choe S, Choe S, Abramson J, Abramson J, Wright EM, Wright EM, Rosenberg JM, Rosenberg JM (2014) Structural determinants of water permeation through the sodium-galactose transporter vSGLT Biophysical Journal.

Choe S, Adelman JL, Rosenberg JM, Wright EM, Grabe M (2012) Understanding substrate unbinding from the sodium galactose co-transporter vSGLT based on 16 microseconds of molecular simulation Biophysical Journal. 102(3): 661
Roni RosenfeldLab Website
Roni Rosenfeld – Professor, Carnegie Mellon U, School of Computer Science
Ph.D., Computer Science, and M.Sc., Computer Science, Carnegie Mellon University
My research interests are in: (1) Forecasting Epidemics – the long term vision of our Delphi research group is to make epidemiological forecasting as universally accepted and useful as weather forecasting is today. (2) Information and Communication Technologies for Development – (ICT4D) and specifically Spoken Language Technologies for Development (SLT4D) (3) Machine Learning for Social Good (ML4SG). (4) Data Numeracy for All.
Brooks LC, Farrow DC, Hyun S, Tibshirani RJ, Rosenfeld R (2018) Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions PLoS Computational Biology. 14(6):

Farrow DC, Brooks L, Hyun S, Rosenfeld R, Tibshirani RJ, Burke DS (2017) A Human Judgment Approach to Epidemiological Forecasting PLoS Computational Biology. 13(3):
Jonathan RubinLab Website
Jonathan Rubin – Professor, University of Pittsburgh, Department of Mathematics
Ph.D. in Applied Mathematics from Brown University
Multiple timescale dynamics with applications to neuronal bursting and rhythms; network dynamics in the basal ganglia including decision-making, parkinsonian dynamics, and effects of deep brain stimulation; mechanisms and implications of synaptic plasticity; the acute inflammatory response; parameter estimation and uncertainty quantification.
Ausborn J, Snyder AC, Rubin JE, Shevtsova NA, Rybak IA (2018) State-dependant rhythmogenesis and frequency control in a half-center locomotor CPG J. Neurophysiol. 119: 96-117

Rubin JE (2017) Computational models of basal ganglia dysfunction: the dynamics is in the details Curr. Opin. Neurobiology. 46: 127-135
Gordon RuleLab Website
Gordon Rule – Professor, Biological Sciences, Carnegie Mellon University
Ph.D., Biological Sciences, Carnegie Mellon University
My research is directed at understanding inter-molecular interactions in biological systems. Our research efforts have been directed at enzyme-substrate interactions, protein-lipid interactions, antibody-antigen interactions, RNA structure, and protein-nucleic acid interactions.
Sinha K, Jen-Jacobson L, Rule GS (2013) Divide and conquer is always best: sensitivity of methyl correlation experiments J Biomol NMR. 56(4): 331-5

Senutovitch N, Stanfield RL, Bhattacharyya S, Rule GS, Wilson IA, Armitage BA, Waggoner AS, Berget PB (2012) A variable light domain fluorogen activating protein homodimerizes to activate dimethylindole red. Biochemistry Biochemistry. 51(12): 2471-85
Hanna SalmanLab Website
Hanna Salman – Assistant Professor, Department of Physics & Astronomy
Ph.D., Weizmann Institute of Science
My research aims to understand the mechanisms of collective behavior and variability in bacterial cultures and their effect on the response of bacteria to changes in the environment. By studying the changes in the behavior of bacteria as a function of their concentration, I am able to detect some of the collective mechanisms that govern the bacterial behavior and allow them to better endure environmental stress. Environmental changes that interest me are temperature and chemical. I utilize various optical microscopy techniques to observe the swimming pattern of bacteria under different conditions. As for the expression level of proteins, proteins of interest are labeled with fluorescent markers and the expression level is measured using fluorescence microscopy or flow cytometry.
Demir M, Salman H (2012) Bacterial Thermotaxis by Speed Modulation Biophysical Journal. 103: 1683-1690

Salman H, Brenner N, Tung CK, Elyahu N, Stolovicki E, Moore L, Libchaber A, Braun E (2012) Universal Protein fluctuation in Populations of Microorganisms Phys. Rev. Letters. 108(23): 238105
Mahadev SatyanarayananLab Website
Mahadev Satyanarayanan – Carnegie Group Professor, School of Computer Science, Carnegie Mellon University
Ph.D., Computer Science, Carnegie Mellon University
Satya's multi-decade research career has focused on the challenges of performance, scalability, availability and trust in information systems that reach from the cloud to the mobile edge of the Internet. In the course of this work, he has pioneered many advances in distributed systems, mobile computing, pervasive computing, and the Internet of Things (IoT). Most recently, his seminal 2009 publication “The Case for VM-based Cloudlets in Mobile Computing” and the ensuring research has led to the emergence of Edge Computing (also known as "Fog Computing"). Satya is the Carnegie Group Professor of Computer Science at Carnegie Mellon University. He received the PhD in Computer Science from Carnegie Mellon, after Bachelor's and Master's degrees from the Indian Institute of Technology, Madras. He is a Fellow of the ACM and the IEEE.
Satyanarayanan M (2017) The Emergence of Edge Computing IEE Computer. 50:

Ferris L, Harkes J, Gilbert B, Winger D, Golubets K, Satyanarayanan M, Akilov O (2015) Computer-aided classification of melanocytic lesions using dermoscopic images Journal of American Academic Dermatology. 73: 769-76
Jason ShoemakerLab Website
Jason Shoemaker – Assistant Professor, University of Pittsburgh, Chemical/Petroleum Engineering
DPhil, Chemical Engineering, University of California, Santa Barbara
Biological information – from molecular events to personal genomics – has exploded. Our group aims to develop computational approaches to exploit large-scale data to promote disease treatment discovery and optimization.
Zhao D, Fukuyama S, Sakai-Tagawa Y, Takashita E, Shoemaker JE, Kawaoka Y (2015) C646, a novel p300/CREB-binding protein-specific inhibitor of histone acetyltransferase, attenuates influenza A virus infection Antimicrob Agents Chemother. 60(3): 1902-6

Lopes TJS, Shoemaker JE, Matsuoka Y, Kawaoka Y, Kitano H (2015) Identifying problematic drugs based on the characteristics of their targets Front Pharmacol. 6: 186
Alexander SorkinLab Website
Alexander Sorkin – Professor and Chair, University of Pittsburgh, Dept. of Cell Biology and Physiology
Ph.D., Cell Biology, Institute of Cytology,Academy of Sciences of the U.S.S.R.
B.A., Biology and Chemistry, Leningrad Pedagogical Institute, Leningrad, U.S.S.R.
My research focuses on the mechanisms of endocytosis of growth factor receptors and neurotransmitter transporters, and the role of endocytosis in regulation of the function of these proteins. We use systems biology approaches to address basic questions of cell biology. We are also interested in the quantitative image analysis, computational modeling of endocytosis and signaling networks, and modeling of the molecule dynamics of transporters and receptors.
Ma S, Cheng MH, Guthrie DA, Newman AH, Bahar I, Sorkin A (2017) Targeting of Dopamine Transporter to Filopodia Requires an Outward-facing Conformation of the Transporter. Sci Rep. 7: 5399

Kaya C, Cheng MH, Block ER, Sorkin A, Faeder JR, Bahar I (2017) Effect of Spatial Complexity on Dopaminergic Signaling Revealed from Multiscale Simulations Biophysical Journal. 112(3): 135a
Robert SwendsenLab Website
Robert Swendsen – Professor, Physics, Carnegie Mellon University
Ph.D., Physics, University of Pennsylvania
My main area of research is solid state physics and statistical mechanics, with an emphasis on computer simulations. I have worked especially on thermodynamic phase transitions combining computer simulations with a renormalization-group analysis. I have also been involved in the development of new algorithms for more efficient simulations, to enable the study of problems that would otherwise involve prohibitively long computer runs.
Swendsen RH (2014) Unnormalized probability: A different view of statistical mechanics Am. J. Phys. 82: 941

Klatzky RL, Gershon P, Shivaprabhu V, Lee R, Wu B, Stetten G, Swendsen RH (2013) A model of motor performance during surface penetration: from physics to voluntary control Experimental Brain Research. 230(2): 251-60
David SwigonLab Website
David Swigon – Associate Professor, U of Pittsburgh, Dept. of Mathematics
Ph. D.and M.S., Theoretical Mechanics, Rutgers University, Piscataway, NJ.
Mgr., Applied Mathematics(equivalent of M.S.), Charles University, Prague, Czech Republic
My research interests are in the area of mathematical biology, in particular, construction of mathematical models of biological systems within the framework of theories of continuum mechanics, dynamical systems, and stochastic dynamics. Structure of the Lac Repressor-DNA Complex; Flexible docking of DNA to RNA polymerase; Control of transcription by designed DNA bending drugs; Naturally Discrete Model for DNA; Theory of Elastic Rods and Its Application to DNA
Stepien T, Swigon D (2014) Traveling waves in one-dimensional elastic continuum model of cell layer migration with stretch-dependent proliferation SIADS. 13: 1489-1516

Mochan E, Swigon D, Ermentrout GB, Lukens S, Clermont G (2014) A mathematical model of intrahost pneumococcal pneumonia infection dynamics in murine strains J. Theor. Biol. 353: 44-54
Pei TangLab Website
Pei Tang – Professor of Anesthesiology, Pharmacology and Chemical Biology, & Computational Biology
PhD, Physical Chemistry, SUNY at Stony Brook
Tang’s laboratory focuses on three related research areas: (1) structure-based discovery and development of a new class of potent non-opioid painkillers. The research integrates various computational approaches with in vitro and in vivo experiments and has reached a promising milestone. (2) determining structures and functions of pentameric ligand-gated channels (pLGICs) that are targets of therapeutics, including for pain and drug addiction. The newly determined structures and new functional information provide the basis for rational drug discovery and development. (3) understanding the molecular mechanisms and action of various drugs on pLGICs by utilizing different biophysical tools, such as NMR, X-ray crystallography, electrophysiology functional measurements and molecular dynamics simulations.
Chen QC, Wells MM, Tillman TS, Kinde MN, Cohen A, Tang P, Xu Y (2017) Structural Basis of Alcohol Inhibition of the Pentameric Ligand-gated Ion Channel ELIC Structure. 25(1): 180-187

Ion BF, Wells MM, Chen Q, Xu Y, Tang P (2017) Ketamine Inhibition of the Pentameric Ligand-Gated Ion Channel GLIC Biophysical Journal. 113: 605-612
George C. TsengLab Website
George C. Tseng – Associate Professor, U of Pittsburgh, Dept. of Biostatistics
ScD, Biostatistics, Harvard University; MS, Mathematics, National Taiwan University
We are a statistical group with major applications on genomics and bioinformatics. Our vision is to develop rigorous, timely and useful statistical and computational methodologies to help understand disease mechanisms and improve disease diagnosis and treatment.
Ding Y, Tang S, Liao SG, Jia J, Oesterreich S, Lin Y, Tseng GC (2014) Bias correction for selecting the minimal-error classifier from many machine learning models. Bioinformatics. 30(22): 3152-8

Liao SG, Lin Y, Kang DD, Kaminski N, Sciurba FC, Tseng GC (2014) Missing value imputation in high-dimensional phenomic data: Imputable or not? And how? BMC Bioinformatics. 15: 346
Ben Van HoutenLab Website
Ben Van Houten – Professor, Pharmacology and Chemical Biology
PhD (Biomedical Sciences – Genetics), Oak Ridge Graduate School of Biomedical Sciences/University of Tennessee
The Van Houten laboratory studies the formation and repair of DNA damage in nuclear and mitochondrial genomes. We are particularly interested in the structure and function of proteins that mediate nucleotide excision repair and the role of oxidative stress in human disease. Our research group uses a wide range of cellular and biochemical tools including atomic force microscopy and single particle tracking of quantum dot labeled proteins to follow the dynamics of repair.
Liu L, Kong M, Gassman NR, Freudenthal BD, Prasad R, Van Houten B, Zhen S, Watkins SC, Wilson SH (2017) PARP1 changes from three-dimensional DNA damage searching to one-dimensional diffusion after auto-PARylation or in the presence of APE1 Nucleic Acids Res. 45(22): 12834-12847

Kong M, Liu L, Chen X, Driscoll K, Van Houten B, Mao P, Böhm S, Watkins S, Bernstein K, Wyrick J, Min JH (2016) Single-molecule imaging reveals that Rad4 employs a dynamic DNA Damage Recognition Process Molecular Cell. 64(2): 376-387
Yoram VodovotzLab Website
Yoram Vodovotz – Director, Center for Inflammation and Regenerative Modeling, Professor of Surgery, Immunology, Communication Sci and Disorders, and Comp Biology U of Pittsburgh
PhD, Immunology, Cornell University Graduate School of Medical Sciences
Dr. Vodovotz is a Professor of Surgery, Immunology, Computational and Systems Biology, Clinical and Translational Science, Bioengineering, and Communication Science and Disorders at the University of Pittsburgh, as well as serving as the Director of the Center for Inflammation and Regenerative Modeling at the University of Pittsburgh’s McGowan Institute for Regenerative Medicine. His group employs computational and systems biology approaches to inflammation in multiple disease states (sepsis/trauma, wound healing, chronic inflammatory diseases, and cancer), coupled to biochemical, cellular, animal, and clinical studies, with the goal of rational inflammation reprogramming
Vodovotz Y, An G (2014) Translational Systems Biology: Concepts and Practice for the Future of Biomedical Research New York, NY: Elsevier ISBN: 9780123978844.
Alan WellsLab Website
Alan Wells – Thomas J. Gill III Professor of Pathology
M.D., Brown University
The Wells’ lab research program, in close collaboration with its partners, aims to understand the communication between cells and their microenvironment, including the matrix. This is used to probe the physiology and pathology of organogenesis in the adult, including the physiologic response of wound healing and pathologic situations of cancer dissemination and metastasis. For these studies, we use all model situation, not limited to in silico modeling, in vivo testing, and microphysiological engineered tissue and organ systems.
Clark AM, Kumar MP, Wheeler SE, Wells AU, Young CL, Venkataramanan R, Stolz DB, Griffith LG, Lauffenburger DA (2018) A model of dormant-emergent metastatic breast cancer progression enabling exploration of biomarker signatures. Molecular and Cellular Proteomics. 17: 619-630

Dioufa N, Clark AM, Ma B, Beckwitt C, Wells AU (2017) Bi-directional exosome-driven intercommunication between the hepatic niche and cancer cells Molecular Cancer. 16: e172
Erik S. WrightLab Website
Erik S. Wright – Assistant Professor, Biomedical Informatics
PhD, Microbiology, University of Wisconsin-Madison
Developing new strategies for treating pathogens in the clinic, ultimately turning the tide against increasing antibiotic resistance.
Wright ES (2017) Getting my feet wet Science. 356(6333):106:

Wright ES, Vetsigian KH (2016) DesignSignatures: a tool for designing primers that yields amplicons with distinct signatures Bioinformatics. 15;32(10):1565-7:
Xiang-Qun (Sean) XieLab Website
Xiang-Qun (Sean) Xie – Professor, U of Pittsburgh, Dept of Pharmaceutical Sciences and Drug Discovery Institute, Associate Dean of the School of Pharmacy, Director of CDAR and CCGS centers
Ph.D., Medicinal Chemistry, School of Pharmacy, University of Connecticut, CT
Xie’s group focuses on development of diseases-specific chemogenomics knowledgebase, an integrated platform of “Big Data to Knowledge” target identification and system pharmacology for drug discovery translational research. The innovation includes GPU-accelerated cloud computing machine-learning TargetHunter programs for drug target identification and system pharmacology. His lab was the first discovered/patented INK4C-targeting small molecule inhibitors for hematopoietic stem cell expansion (Nature Comm 2015), and was the first discovered/patented p62ZZ chemical inhibitors for multiple myeloma (Nature Leukemia 2015) .
Liu HB, Wang L, Su WW, Xie XQ (2014) ALzPlatform: An Alzheimer’s Disease Domain-Specific Chemogenomics Knowledgebase for Polypharmacology and Target Identification Research J Comput Info Modeling. 54(4): 1050-60

Wang L, Ma C, Wipf P, Liu H, Su W, Xie XQ (2013) TargetHunter: An In Silico Target Identification Tool for Predicting Therapeutic Potential of Small Organic Molecules Based on Chemogenomic Database AAPS J.. 15: 395-406
Eric XingLab Website
Eric Xing – Associate Professor, Carnegie Mellon University, Department of Computer Science
PhD,Computer Science, U.C. Berkeley; PhD, Molecular Biology, Rutgers University, NJ
My principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and infrastructure, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in social and biological systems.
Yuan J, Gao F, Ho Q, Dai W, Wei J, Zheng X, Xing EP, Liu TY, Ma WY (2014) LightLDA: Big Topic Models on Modest Compute Clusters arXiv:1412.1576.

Zheng X, Kim JK, Ho Q, Xing EP (2014) Model-Parallel Inference for Big Topic Models arXiv:1411.2305.
Da YangLab Website
Da Yang – Assistant Professor, Pharmaceutical Sciences
M.D., Harbin Medical University in China; Ph.D., Pharmacology and Genomics
The research of Yang lab is focusing on using bioinformatics and experimental tools to identify novel cancer-driving none-coding RNAs (ncRNAs), modeling ncRNA down-stream regulatory network, and characterizing ncRNAs’ function in tumor initiation and progression using cancer cell line and mouse models.
Wang Y, Wang Z, Xu J, Li J, Li S, Zhang M, Yang D (2018) Systematic Identification of Non-coding Pharmacogenomic Landscape in Cancer Nat commun (in press).

Yang D, Yang B, Zhang M, Guo W, Wu Z, Jia L, Wang Y, Wang Z, Li S, Xie W (2018) LncRNA epigenetic landscape analysis identifies EPIC1 as an oncogenic lncRNA that interacts with MYC and promotes cell cycle progression in cancer Cancer Cell. 33(4): 706-720
Daniel M. ZuckermanLab Website
Daniel M. Zuckerman – Professor, Oregon Health & Science University, Dept of Biomedical Engineering
Ph.D. in Physics, University of Maryland, College Park
Research: Computational Study of Protein Binding, Allostery, and Molecular Machines. Molecular-level biology is inherently dynamic. The Zuckerman group pursues ensembles of protein structures and pathways of conformational change that play a key role in binding, allostery and molecular machines. The group has been active in developing scale-free “weighted ensemble” (WE) software, which is now in use around the world. Zuckerman and colleagues developed WE methods for sampling steady-state and equilibrium conditions that have been applied to all-atom protein binding and to highly complex systems biology models of cell signaling.
Shanhang J, Miedel MT, Ngo M, Hessenius R, Wang P, Bahreini A, Li Z, Ding Z, Chen N, Shun TY, Zuckerman DM, Taylor DL, Puhalla SL, Lee AV, Oesterreich S, Stern AM (2017) Clinically observed estrogen receptor alpha mutations within the ligand-binding domain confer distinguishable phenotypes indicative of Darwinian-like somatic evolution Oncology.

Donovan RM, Tapia JJ, Sullivan DP, Faeder JR, Murphy RF, Dittrich M, Zuckerman DM (2016) Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories PLoS Comput Biol. 12(2): e1004611