Mark A. Jack, Ph.D.

Work: Home:

Associate Professor 501 S. Blairstone Rd, Unit 2801

Physics Department, FH-SRC 301C Tallahassee, FL 32301 Florida A&M University Phone/Cell: 850-***-****/ 850-***-**** Tallahassee, FL 32301, USA Email: acxrjo@r.postjobfree.com Experience

Data scientist and physicist with several years of experience in computational modeling in particle physics, neuroscience, nanoscience and high-performance computing and 1+ year certified training in machine learning and statistical programming in R.

Education

2000 Ph.D. Humboldt-Universität zu Berlin, Berlin, Germany. Theoretical Physics. 1997 M.S. Ludwig-Maximilians Universität München, Munich, Germany. Theoretical Physics. 1994 B.S. Ludwig-Maximilians Universität München, Munich, Germany. Mathematics. 1993 B.S. Ludwig-Maximilians Universität München, Munich, Germany. Physics. Employment

Since 2009 Florida A&M University, Associate Professor, Department of Physics, Tallahassee, FL. 2012-2013 Sabbatical Leave at University of Central Florida, Visiting Professor in Physics, Orlando, FL. 2003-2009 Florida A&M University, Assistant Professor, Department of Physics, Tallahassee, FL. 2003 California Polytechnic University Pomona, Adjunct Faculty, Dept. of Physics, Pomona, CA. 2000 University of California Los Angeles, Postdoctoral Researcher, Neurobiology Department, CA. 1997 DESY Zeuthen, Graduate Student Assistant, Theory Group, Zeuthen/Berlin, Germany. Expertise, Skills, Training and Projects

Data Analytics Training in R:

Oct 11, 2016:

Presentation at Global Big Data Conference, Tampa Convention Center, Tampa, FL, Dec 11, 2016. Title: Introductory BootCamp: Turning Raw Data into a Useful Working Predictive Model. http://globalbigdataconference.com/tampa/big-data-bootcamp/schedule-69.html Oct 9, 2016:

Presentation at Global Big Data Conference, Georgia World Congress Center, Atlanta, GA, Oct 9, 2016. Title: Introductory BootCamp: Turning Raw Data into a Useful Working Predictive Model. http://globalbigdataconference.com/72/atlanta/big-data-bootcamp/schedule.html Aug 25, 2016:

Presentation at Data Science & Business Intelligence Society of Atlanta Meetup, Emory University Continuing Education, Atlanta, GA. Title: Introductory BootCamp: Turning Raw Data into a Useful Working Predictive Model.

https://www.meetup.com/Atlanta-Society-for-Business-Intelligence/events/233286173/ July 28, 2016:

Presentation at Jacksonville Software Architects Group Meetup, Microsoft Store, Jacksonville, FL. Title: R Bootcamp. https://www.meetup.com/JaxArcSIG/events/224705892/ May – July 2016 and Oct – Dec 2016:

R Bootcamp – Introduction to Data Science with R. Instructor with Frank Hasbani at Anova Analytics, LLC in Roswell, GA (Summer 2016, 8 weeks and Fall 2016, 10 weeks). Topics: Introduction to R / RStudio, Getting and Cleaning Data, Data Wrangling and Exploratory Data Analysis, Visualization, Machine Learning, Statistical Inference, Model Building and Estimation, RMarkdown, Presentation Tools, Discussion of Student Data Projects.

Jan 2015 – Present:

• Machine Learning Training: Applied predictive analytics to training and test data sets via linear and logistic regression, decision trees, and different classification techniques (kNN, k-means clustering). Improved model estimation via sampling techniques such as bootstrapping, k-fold cross validation and leave-one-out cross validation. Improved model accuracy further via ensemble methods (random forests, support vector machines) and clustering techniques (boosting, bagging and stacking).

• Statistical Inference Training: Simulated random distributions and analyzed variable dependencies via linear regression fits and Bayesian statistics.

• Merged and cleaned data sets, created tidy data sets with dplyr. Visualized data sets via ggplot2. Deep Learning in Python:

July 2016 – Present:

Online training in Deep Learning algorithms in Python based on support vector machines and neural networks in Python using libraries Scikit_learn, Tensorflow, Theano, and Caffe on GPUs and cloud computing resources.

Big Data Training:

August 2016:

XSEDE ‘Workflows’ workshop at Georgia State University, Atlanta, GA. Workflow tools presented: Galaxy, Copernicus, Makeflow/WorkQueue, Pegasus, Radical Cybertools, Swift. https://sites.google.com/a/illinois.edu/workflows-workshop/home August 2016:

One-week summer institute ‘HPC for the Long Tail of Science’ in Big Data management and analysis at the San Diego Supercomputing Center (SDSC), University of California, San Diego, CA. Training in data-intensive computing including SPARK, Parallel Computing, Performance Optimization, Predictive Analytics, Scalable Data Management, Visualization, Workflow Management, GPU’s/CUDA and Python for Scientific Computing. https://github.com/sdsc/sdsc-summer-institute-2016 High-Performance Computing:

2009 – Present (Research Computing and Computational Science Curriculum at Florida A&M University):

• Formulated research computing and cyber-infrastructure strategy for the Office of the President and the Office of VP of Research (2014-16).

• Collaborated with regional networks Sunshine State Education and Research Computing Alliance

(SSERCA) and Florida Lambda Rail (FLR) on topics in HPC and data storage (2009-16).

• Submitted proposals to NSF, Air Force and DoD: Cloud Computing with Florida Atlantic University; large data storage with 6 SSERCA member institutions (2013-15).

• Attended workshops in research computing, HPC, data management and computational science. Organizations: NSF, DoD, DoE, XSEDE, SURA, SIAM, Supercomputing Conferences (2009-2016). 2009 – 2014 (Student Training and Mentorship at Florida A&M University):

• Created graduate-level 'Computational Physics' course in parallel computing (C++, MPI, PETSc).

• Mentored summer students: Leon Durivage, Winona State University (2010); Adam Byrd, Florida A&M University (2011-12); both BlueWaters Undergraduate Petascale Computing and Education Project. Ben Prather, Arizona State University, XSEDE Summer Student Program (2014). 2011 – Present (Outreach in High-Performance Computing at Florida A&M University): Emerging Researchers Network 2015; Institute for Teaching and Mentoring 2013; SACNAS 2011. Computational Nanoscience:

2003 – 2012 (Research in Computational Nanoscience at Florida A&M University):

• Electrons on a curved manifold: Electronic properties of nanoscale toroidal structures.

• Electron-phonon coupling with low-energy phonon modes in carbon nanodevices.

• Metamaterials and quantum control theory in carbon nanotube rings.

• Exciton generation, charge separation and charge transport in organic photovoltaic materials. 2010 – 2012 (Nanoscience Education Initiative at Florida A&M University): Interdisciplinary collaboration in nanoscience with Electrical Engineering, Physics, Chemistry and Computer & Information Sciences with proposal submissions to NSF, DoD, Air Force. Computational Neuroscience:

2000 – 2002 (Postdoctoral research at UCLA):

• Designed and created C code to statistically analyze electrical spike trains data recorded from retinal ganglion cells to study encoding of visual information by neurons in mice retina.

• Applied information theory and pattern recognition to successfully reconstruct full image from recorded electrical data.

Particle Physics Modeling:

1997 – 2000 (Ph.D. research at DESY/CERN):

• Calculated experimentally realistic 2-loop QED radiative corrections to particle pair production cross sections at LEP/CERN and Linear Collider energies.

• Developed, implemented, tested and benchmarked QED section in large Fortran software package ZFITTER for theoretical fitting of particle physics data at the LEP/CERN experiments. Software Developed

2010-2015: QRing - a scaleable, parallel software tool for quantum transport simulations in carbon nanodevices using linear solvers on Intel Xeon/Phi and GPU architectures (PHY120028). Awards and Honors

2016 NSF XSEDE education allocation at Texas Advanced Computing Center. Title: Applications of Parallel Computing. Computational Physics course PHY 6157C at Florida A&M University. 2015 OLCF Director’s Discretionary Allocation, Oak Ridge Leadership Computing Facility, Titan Cluster: 2 million CPU hours (Mar-Dec 2015).

2015 Argonne Training Program for Extreme-Scale Computing (ATPESC), IL. Two-week intensive workshop in petascale computing (Aug. 2-14, 2015). 2014 ORISE Visiting Summer Faculty Program, Oak Ridge National Laboratory, Oak Ridge, TN. 2012 NSF XSEDE research allocation at Texas Advanced Computing Center. Title: Quantum transport simulation with electron-phonon coupling ... magnetic multipole and toroidal moments. 2010/ NSF Pathways TeraGrid Summer Fellowship Program and BlueWaters UPEP Student Internships. 2011 Title: Exciton dynamics on carbon nanorings under microwave excitation for metamaterials apps. 2009 ASEE Summer Faculty Award, AFRL/RX Wright Patterson Air Force Base, Dayton, OH. Program: Quantum interference in a biopolymer-functionalized carbon nanoring with magnetic flux. 2008 ASEE Summer Faculty Award, AFRL/RX Wright Patterson Air Force Base, Dayton, OH. Program: Theory and Computation for the Design of Molecular-to-Bio/Nanomaterials. 2005 Summer Visiting Faculty Appointment, Physics Department, Boston University, MA. Selected Publications

1. M. Jack et al., QRing - a scaleable, parallel software tool for quantum transport simulations in carbon nanodevices using linear solvers on Intel Xeon/Phi and GPU architectures. To be submitted. 2. A. Byrd, M. Jack and M. Encinosa, An HPC approach to modeling electron-phonon interactions in carbon nanotori. Submitted to J. Comp. Sci. Ed., Apr. 2012. 3. L.W. Durivage, M. Jack and M. Encinosa, Modeling electron transport in carbon nanotori. Submitted to J. Comp. Sci. Ed., Feb. 2011.

4. M. Jack and M. Encinosa, Quantum electron transport in toroidal carbon nanotubes with metallic leads. J. Mol. Simul. 34 (1), pp. 9-16, 2008.

5. M. Encinosa and M. Jack, Elliptical tori in a constant magnetic field. Phys. Scr. 73, pp. 439 – 442, 2006. 6. M. Encinosa and M. Jack, Dipole and solenoidal magnetic moments of electronic surface currents on toroidal nanostructures. J. Computer-Aided Materials Design, Vol. 14 (1), pp. 65-71, 2007. 7. M. Encinosa and M. Jack, Excitation of surface dipole and solenoidal modes on toroidal structures. E-print archive: physics/0604214.

Technical Skills

DATA SCIENCE – 1. R Programming: Model Building and Estimation, Machine Learning, Statistical Inference, Regression Models, Reproducible Research, Exploratory Data Analysis, Data Cleaning. Certifications by Coursera, Johns Hopkins University, School of Public Health and Biostatistics. 2. Deep Learning in Python using support vector machines and neural networks in Python, Scikit_learn, Tensorflow, Theano, and Caffe. 3. Big data training: Spark, platforms, workflow engines.

COMPUTING – MPI, C/C++, FORTRAN, PETSc, ScaLAPACK, LAPACK, MATHEMATICA, MATLAB. LANGUAGES – English (native), German (native), French (fair), Spanish (beginner). WORK STATUS – US and EU/German citizenship. Immediately available and willing to relocate. Web

https://www.linkedin.com/in/mark-jack-9445908

https://github.com/mjgrav2001

https://www.researchgate.net/profile/Mark_Jack/contributions

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