BS, Computer Science, Mathematics @
University of South Florida
Data Scientist and Computational Biologist, leader of Machine Learning Group at Lawrence Livermore National Laboratory. Formally trained as a Computational Biologist, but with broad interests in statistical modeling and analysis across disciplines, particularly including biological, clinical, and cyber security applications. Specialist in implementing such approaches at scale using Apache Spark, and in streaming contexts using Apache Storm.
Data Scientist and Computational Biologist, leader of Machine Learning Group at Lawrence Livermore National Laboratory. Formally trained as a Computational Biologist, but with broad interests in statistical modeling and analysis across disciplines, particularly including biological, clinical, and cyber security applications. Specialist in implementing such approaches at scale using Apache Spark, and in streaming contexts using Apache Storm. Expert in computational biology, Bayesian and frequentist statistics, and software development. Principal investigator of several research projects involving rigorous modeling in a variety of scientific domains.
Specialties: Bayesian statistics and modeling, big data analytics, Apache Storm, Spark, and Hadoop, systems biology, software development, cyber security, gene regulatory networks, nonparametric statistics, traditional and distributed Markov chain Monte Carlo, hidden Markov models, Scala, C/C++, Python, Ruby, R, Matlab, Perl, Java, sysadmin-level ability with various Unixes including Linux, Mac OS X, and FreeBSD, conversational ability in written and spoken Portuguese language
Machine Learning Group Leader, Data Scientist, Principal Investigator @ Data Scientist and Machine Learning Group Leader in the Computational Engineering Division. Primarily a technical scientist, secondarily delve into management: perform and lead investigations, establish multi-institution collaborations, mentor scientists, postdocs, and students. Focus on derivation and application of machine learning and statistical modeling techniques to a variety of scientific domains ranging from cyber security to biomedicine, as well as development of high performance software to implement these solutions, often built on scalable computing frameworks including Apache Spark and Apache Storm. Oversee group of eight PhD Data Scientists.
Opened new scientific domains and research areas to the benefits of scalable statistics and data science at LLNL. Lead Data Science component of LLNL-led national initiative on predictive medicine. Developed first-of-its-kind scalable streaming nonparametric ensemble modeling approach via Apache Storm. Presently, principal investigator of four-person project developing groundbreaking sepsis prediction models from electronic health records and patient physiology in collaboration with physicians at a major healthcare provider. From February 2012 to Present (3 years 11 months) Postdoctoral Researcher @ From August 2010 to January 2012 (1 year 6 months) Graduate Student @ Ph.D. graduate in Computational Biology and Bioinformatics. Investigated connections between gene promoter occupancy and gene expression in yeast. Developed statistical model of genomic occupancy by nucleosomes and other DNA binding proteins and protein complexes as a function of their mutual thermodynamic competition, driven by sequence affinities and concentrations. Used model to calculate posterior probabilities of binding for each DNA binder across entire yeast genome under a variety of experimental conditions, involving efficient processing of huge datasets in R on several-thousand CPU computing cluster. Implemented model as open source high-performance multithreaded C program capable of processing entire genome with arbitrary sets of DNA binding factors in minutes, outperforming any currently available free toolkits. Developed statistical methods for robustly learning concentration parameters from heterogeneous experimental data. From 2004 to September 2010 (6 years) Associate Software Engineer @ Performed research and development tasks on existing military aggregate-level combat simulators. Developed new virtual reality training simulator allowing an individual to move through and interact with a simulated world on foot. Researched novel methods for implementing widely distributed and scalable physics modeling using existing engines and networking framework. Integrated this simulator with existing aforementioned aggregate-level simulator, allowing physical interaction with off-site networked entities. Implemented artificially intelligent behaviors for simulated military helicopters in accordance with existing real-world doctrine. From October 2002 to July 2004 (1 year 10 months) Software Engineer @ Co-developer of a novel system administration tool for UNIX environments. Tool enabled automatic revision history and rolling back of critical system configuration files across an exponentially scalable number of systems, as well as enabling quick, efficient replication of entire systems, allowing recovery from hardware failures in orders of magnitude less time than would normally be required. From June 2002 to October 2002 (5 months) Software Engineer @ Lead developer of applications ranging from network intrusion detection and forensics to load balancers for use by large clients, primarily on Linux platforms. Delivered products to clients on-site. From February 2002 to June 2002 (5 months)
Ph.D., Computational Biology and Bioinformatics @ Duke University From 2004 to 2010 BS, Computer Science, Mathematics @ University of South Florida From 1997 to 2001 Todd Wasson is skilled in: Machine Learning, Scala, Bayesian statistics, Apache Spark, R, Computational Biology, Bioinformatics, Python, Perl, Linux, C++, Statistical Modeling, C, Network Security, Unix
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