R&D Chief Scientist

20+ years identifying and delivering solutions for DoD’s most challenging problems in machine learning, high performance computing, quantum sciences, materials, data science, and cybersecurity. Using HPC, and AI/ML to push the possible and meet the governments unique challenges both unclassified and classified. 10+ years leading teams to deliver bespoke data analysis tools for a variety of unstructured and structured data sets. Work was focused on technical leadership and management of programs at academic institutions, startup companies, AFRL, NRL, ARL and the Army C5ISR center of excellence.

SKILL SETS:

• Leading teams of technical experts from fields covering: machine learning, physical sciences, nanophotonics, high performance computing, cyber modeling.
• Planning and executing on multi-year (2 -10 yr), multi stakeholder (foreign and domestic) projects.
• Massively parallel computing (1k-100k procs) coding / debugging / optimization.
• Sustained international collaborations.
• Managed both unclassified and classified solutions development.
• Science: quantum mechanics, nanotechnology, electrodynamics, photonics, plasmons, non- adiabatic molecular dynamics, finite difference time domain, Mie theory, finite element method, discrete dipole approximation, discrete event modeling, deep neural networks, natural language processing, support vector machine, gaussian process regression, ensemble learning, adversarial learning, reinforcement learning, game theory decisions support

WORK EXPERIENCE:

Chief Scientist, DCI (Supporting C5ISR) 

April 2016 - Present

Management area: Deep learning, ensemble learning, cyber operations

Senior Scientist, Army Research Lab

August 2013 – April 2016

Army Research Laboratory, Energetic Materials Science Branch. Management area: HPC, linear scaling algorithms, Genetic algorithms

Scientist III, High Performance Technology group

November 2011 – August 2013

Dynamic Research Corporation, HPTg (formerly HPTi), PETTT. Management area: QM linear scaling, molecular dynamics, Genetic algorithms

Postdoctoral Fellow, Northwestern University       

April 2009 – Novermber 2011

Northwestern University - Research advisor: Prof. George Schatz Research area: nanophononics, QM/ED, SERRS

Research Assistant, Iowa State University and Ames Laboratory

October 2000 – April 2009

Research advisor for (B.S. , M.S. , Ph.D.) : Prof. Mark S. Gordon Research advisor for (M.S.) : Prof. Richard Honzatko

Research area: QM linear scaling, molecular dynamics, solvation of biomolecules

ACCOMPLISHMENTS: (reverse chronological)

• Building teams from the ground up. Joining DCI solutions as the fifth employee I have had to shape and grow from a team of one to several teams operating in multiple domains and countries with a current employee count over 100.
• Coordinating efforts to bring university research and commercial products into the C5ISR center. Forming the basis for proof-of-concept prototypes and tech transfer to product managers for fielding to the warfighter.
• Identified unmet requirements and initiated a program based on developing an ML assisted fuzzing approach which achieves at least 30% better bug detection and code coverage than current state of the art.
• Led development of a homology approach based on binary visualization to achieve nonsignature-based malware detection.
• Initiated program based on natural language-based model for unsupervised learning of computer logs to flag anomalous behavior. Used GANs to harden the base system.
• Led team for cyber modeling and simulations using behavioral agents, and AI agents to develop scenarios relating actual assets as they interact with cyber effects.
• Developed ML approach to identify encrypted network traffic on a per packet basis.
• Identified a tech transfer for my university research of hybrid quantum mechanics and electrodynamics methods. This formed the basis of startup which focused on real time high sensitivity explosive detection using regression and neural networks which were trained on simulation data allowing limited hardware (ARMv8) to signal process raw data from a sensor.
• Initiated and coordinated international collaboration for the development of sparse matrix density based linear scaling algorithms based on linear algebra. Enabled ARL to achieve 100x increase in size and speed of simulations.
• Developed multiple objective evolutionary strategies approach to fit approximate physical models from ab into data.
• Expansion of the molecular dynamics (MD) code in GAMESS, VASP, CP2K.
• Created new linear scaling approaches in GAMESS, CP2K.
• Developed resonance Raman packages in NWCHEM required for analytical coupling quantum mechanics to classical electrodynamics calculations.

EDUCATION:

• Iowa State University, Ames, Iowa.
• Biochemistry (B.S, M.S.) Physical Chemistry (Ph.D.) ’04 / ’05 / ‘09

SECURITY CLEARANCE:

TS/SCI (TS adjudicated AUG.2018 / SCI indoctrination FEB.2019)

COMMUNICATIONS OVERVIEW:

• PAPERS: DoD Reports:15+. Open Literature: Total: 13. First author: 8. Featured: 1.
• Times cited: 597 h-index 12, i-10: 12, i-20: 11 (Sept 2020)
• TALKS: 23, Invited: 8, POSTERS: 20

GRANTS:

• LUCI – Machine learning for material discovery. - 600K (2016)
• HPCMP/CAP: Chem + Physics of Energetic Materials - ~50M computer hours. (2015)
• PETTT: Assessment of linear scaling approaches for DoD applications - 100K (2013)
• PETTT: Development of python code for grain boundary descriptions - 30K (2012)
• PETTT: Machine learning fitting of reactive potentials - 100K (2012)