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Computative Reasoning

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Scott Clark
Graduate Student, DOE Computational Science Graduate Fellow 657 Rhodes Hall, Ithaca, NY, 14853

September 19, 2011 sc932@cornell.edu cam.cornell.edu/∼sc932

Education
Cornell University Ph.D. Applied Math (current), M.S. Computer Science Ithaca, NY 2008 - 2012(projected)



– Department of Energy Computational Science Graduate Fellow (Full Scholarship, 4 years) – Emphasis on machine learning/data mining and algorithm design/software development related to bioinformatics and optimization • Oregon State University B.Sc. Mathematics, B.Sc. Computational Physics, B.Sc. Physics Corvallis, OR 2004 - 2008

– Graduated Magna Cum Laude with minors in Actuarial Sciences and Mathematical Sciences – Strong emphasis on scientific computing, numerical analysis and software development

Skills
• Development: C/C++, Python, CUDA, JavaScript, Ruby (Rails), Java, FORTRAN, MATLAB • Numerical Analysis: Optimization, Linear Algebra, ODEs, PDEs, Monte Carlo, Computational Physics, Complex Systems, Iterative Methods, Tomology • Computer Science: Machine Learning, Data Mining, Parallel Programming, Data Structures, Artificial Intelligence, Operating Systems • Discovering and implementing new ideas. Give me an API and a problem and I will figure it out. • Diverse background in Math, Computer Science, Physics and Biology allows me to communicate to a wide scientific and general audience and begin contributing to any group immediately. • I have worked in many places in a myriad of fields. I can readily learn and adapt to a new discipline, area or environment and start pushing real results quickly.

Research and Work Experience
Bloomberg LP Financial Software Development Intern New York, NY Summer 2011



– Developed end-to-end reporting software in C++ and javascript – Implemented statistical models to perform forward and backward portfolio analysis • DOE Joint Genome Institute (Lawrence Berkeley National Lab) Researcher in Analysis Group under Dr. Zhong Wang Walnut Creek, CA Summer 2010

– Created open source genome validation software tool in python and C – Used machine learning to mine TBs of genome data efficiently using novel likelihood function



Los Alamos National Laboratory Researcher in Metagenomics Group under Dr. Nick Hengartner

Los Alamos, NM Summer 2009

– Wrote open source alignment algorithm software tool in python, C and CUDA – Used statistical models to discover sequence alignments using parallel algorithms on GPUs • Oregon State University Research Assistant under Prof. Malgorzata Peszynska and Prof. Rubin Landau – Finite element analysis with uncertainty and web-based teaching in Java • Max Plank Institute for the Physics of Complex Systems NSF REU Research Assistant under Prof. Steven Tomsovic – Research on extreme value statistics in MATLAB and FORTRAN • University of California: Davis NSF REU Research Assistant under Prof. Daniel Cox – Computational biophysics research as applied to protein folding in Java Davis, CA Summer 2006 Dresden, Germany Summer 2007 Corvallis, OR 2005-2008

Selected Open Source Projects and Publications (github.com/sc932)
ALE: Assembly Likelihood Estimator Probabalistic evaluation of genome assemblies – Uses statistical likelihood function to score and rank genome assemblies – Publication being prepared for Genome Research – Presented at several conferences including SIAM Scientific Computing • Velvetrope Python, C, CUDA A parallel statistical algorithm for finding homologous regions within sequences 2009 - 2010 – Profiled in DEIXIS Magazine, Publication submitted to BMC Bioinformatics – Presented at several conferences including Supercomputing and Q-bio • BetaHelix Computes various statistics about a left or right handed beta helix – Published in Prion: Left handed β helix models for mammalian prion fibrils. Java 2006 - 2007 C, Python 2010 - Current



Personal
• Hobbies: Poker, Golf, Racquet-based sports, Snowboarding, Video Games, Building things (physical and/or digital) and drinking good beer or scotch with new and old friends. • Why Industry over Academia? I love exploring new problems on the forefront of research, but academia moves too slowly and bureaucratically for me. I enjoy working in a fast-paced environment pushing measurable results to clients on a daily timescale, using the skills that I have honed throughout my academic career. • My Ideal Position: Working with a fun team solving interesting problems. I enjoy every part of development, from deep backend optimization to client-facing applications and interaction. I would love to make a difference wherever and however I am able while leveraging my machine learning, data mining and mathematical background.

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