Our Data Science Team| Our Leadership Team
Meet some of our independent professionals made up of the best and brightest:
Software Engineering and Data Analysis
Steve designs and builds mission critical software systems. He is an expert at making complex ideas clear, precise, and accessible. He uses his analytic design and software engineering skills to help clients derive valuable insights from structured, unstructured or semi-structured rich data sources produced by large enterprises. He has experience in all phases of software development, encompassing requirements definition, design, coding, and alpha and beta testing and has expertise in Java, C, C++, Perl, Windows, Linux/Unix, real-time embedded programming, image processing, and scientific programming. His academic training has made him particularly adept at breaking down complex problems in any domain into manageable pieces; providing detailed, careful analyses of complex systems and procedures; and thinking and writing with mathematical rigor and clarity. In his spare time, Steve enjoys playing classical piano, and has a special affinity for the works of Bach, Beethoven, Chopin, and Brahms.
PhD Mathematical Logic and Cognitive Science, MIT
Haile has a technical background in theoretical condensed matter physics, particularly
in the parameterization and exact solution of integrable matrices. He uses his background in advanced matrix techniques in collaboration with physicists, engineers, and computer scientists to construct classifiers and recommenders for large data sets. He makes extensive use of Mathematica in research and on Big Data projects.
- Linear Algebra: Singular Value Decomposition (SVD), Latent Dirichilet Allocation (LDA), Exact
- Statistical Modeling: Statistical Field Theory, Random Matrix Theory
- Computing: R, MatLab, Excel, Mathematica, LaTex, MySQL; Programming Language: Python
- Machine Learning: random forests, decision trees, Bayesian Additive Regression Trees, Gradient Boosting
PhD Physics, Rutgers University
MSc Mathematical Physics, University of London-Kings College
BA Physics, Yale University
Combinatorial Optimization and Predictive Analytics
Edinah has technical expertise in computer science and applied mathematics particularly
in spectral methods for data analytics. He uses his background in Mathematics in collaboration with physicists, engineers, and computer scientists to implement machine learning algorithms for deriving actionable insights from large high dimensional data sets. Edinah worked at Siemens Corporate Research developing medical image analysis algorithms. Edinah also worked at Microsoft Research New England on developing new algebraic approaches for solving combinatorial problems.
- Linear and Multilinear Algebra: Singular Value Decomposition (SVD), Tensor Decomposition
- Statistical Modeling: Statistical Field Theory, Random Matrix Theory
- Computing: Sagemath, MatLab, Maple, Mathematica, C, C++, Python, R
- Machine Learning: PCA, Linear and non-Linear Regression Trees, Ada boost
PhD candidate in Computer Science, Rutgers University
Data Analytics, Management Consulting, and Aeronautical Engineering
Rory has six years' experience providing high level strategy consulting services to major global companies where he led projects in the telecommunications and retail sectors to mine customer data to assess competitor threats and develop pricing and promotional strategies. He has extensive experience with client and competitor interviews, competitive and market analysis, and quantitative analytics. He has expertise in jet engines and propulsion systems and has led design, development and testing of innovative technologies on DARPA funded US Government projects. He began his career as an engineer at Boeing
PhD & MS Aeronautical and Astronautical Engineering, MIT
Scientific Software Design
Damian is a mechanical engineer with extensive experience in scientific software design for classical-, quantum-, and magneto-fluid turbulence and multiphase flow. He coauthored the textbook Scientific Software Design: The Object-Oriented Way (Cambridge University Press, 2011) and has been contracted to teach a related short-course at supercomputer centers in the U.S. and Europe. He has been a PI or Co-PI on research funded by the National Science Foundation and the Office of Naval Research and has held visiting and tenure-track appointments at universities in the U.S. and in Europe and is currently a Lecturer at Stanford University. He holds a B.M.E. degree from Howard University and M.S. and Ph.D. degrees from Stanford, all in Mechanical Engineering. Most recently, he was a manager in the Combustion Research Facility at Sandia National Laboratories where he led 22 staff and postdoctoral researchers performing basic research at the intersection of fluid dynamics and chemistry and applied research in mathematics and computer science.
PhD & MS Mechanical Engineering, Stanford Univerity
Electrical Engineering and Management Consulting
Kian advises Fortune 500 companies on innovation, strategy, product development, and commercialization. Her background combines strong technical training with several years of consulting at McKinsey & Company, followed by over 10 years of operating experience as a senior executive in the high-tech and healthcare industries. Kian pioneered research in optoelectronics; first to develop integrated smart pixel in InP/InGaAsP and developed ground breaking techniques for novel high-speed transistors and measuring electron mobility as a function of temperature in p-type GaAs.
PhD Electrical Engineering, Princeton University
Product Roadmapping for Advanced Analytic Applications
David is the glue that holds teams and projects together. Adept at spanning the gaps between specialized domains -- business and technology, applications and data, experts and novices, strategy and tactics, abstract and concrete -- he uses the knowledge he has accumulated in a varied career and the skills he has continually developed to bring high-stakes, mission-critical initiatives through to successful completion. David's experience ranges from academic research on the foundations of randomness through teaching across the breadth of the undergraduate CS curriculum to individual contributor and senior manager roles in large enterprises and small start-ups. A prize-winning visionary, he can bring together business executives, applied technologists, and academic researchers to implement Big Data solutions. David has been accountable for data infrastructure for large quantitative investment management operations, product roadmapping and management for advanced analytic applications, and even jack-of-all-trades for a novel metacognitive educational application at a bootstrapping start-up.
PhD & MS Computer Science, Caltech
BS Mathematics, Yale
Bioinformatics and Analytics
Fiona is a data scientist specializing in Bioinformatics. She has unique background that combines data analysis, software development, algorithms, and next-gen sequencing for clinical diagnostics. She is experienced in inter-disciplinary project management with biologists, chemists, and data science professionals. She is fluent in English, Danish, and Dutch.
PhD Bioinformatics-in progress
Datamatician, diploma course in Advanced Computer Studies
Software Development, Artificial Intelligence, Machine Learning
Dimitri is a Java Architect with expertise is all phases of software development: Requirements, Architecture, Recruiting, Training, Development, Management, Testing, Deployment, and Support. In addition to his software expertise, Dimitri has a wide scientific background in Mathematics, Artificial Intelligence, Machine Learning, Finance, and Physics and is as comfortable in communicating with Research and Business departments as he is with Technology organizations.
Software Expertise: Certified Scrum Master, Java, C, C++, J2EE, EJB, JMS, Apache commons, Ant, Lucene, Spring, log4j, slf4j, XStream, JFreeChart, Jide, Swing, JGoodies, GWT, Vaadin, GlazedLists, JSP, Velocity, Spring MVC, Hibernate, iBatis, JDBC, JTA, Quartz, SQL, MySQL, Oracle, Sybase, Eclipse, NetBeans, Tomcat, Weblogic, Coherence, LaTex and more.
Science Expertise: Probability and Stochastic Processes, Functional Analysis, Complex Analysis, Differential Geometry, Abstract Algebra, Dynamical Systems, Optimization, AI, OCR, Image Processing, Classical Mechanics, Quantum Mechanics, Relativity, Electrodynamics
MS Electrical Engineering and Computer Science, MIT
BS Electrical Engineering and Physics, Tufts (Graduated first in class)
Statisitical Analysis and Data Visualization
Andrew is the Senior Biostatistician at U.C. Berkeley’s Clinical Research Center, as well as a statistical consultant. He has over 15 years’ experience in advanced statistical modeling, including multivariate linear and non-linear regression, mixed effects analysis of variance, logistic regression, survival analysis, Receiver Operating Characteristic (ROC) analysis and survey research methods. He has proven expertise in all aspects of clinical trials, particularly in the field of ocular surface/vision research, from study design to conduct to analysis to reporting. He is also highly experienced in data management, quality assurance and regulatory compliance for clinical trials, and is certified by the National Institutes of Health (NIH) for ethical conduct of research involving human subjects. Andrew is experienced statistical programmer (e.g., R, SAS) and has consulted on projects ranging from automating and validating data entry for large clinical trials to the development and testing of algorithms for pressure-sensitive touchpad devices. He has a substantial list of scientific publications in highly-refereed journals, as well as invited lectures, technical reports and invited review articles. Andrew has served as a peer reviewer for international journals in optometry, vision science and physiological optics, and has chaired the organizing committee for the annual Bay Area Vision Research Conference.
MA Biostatistics, University of Californa, Berkeley
Bioinformatics, Chemical Engineering, Project Management
Ann has led design, development, and implementation of innovative technologies and analytical methodologies to support the US Government in making informed investment decisions that satisfy organizational requirements, performance objectives, and budget constraints. She has managed analytic teams and projects across multiple domains including chemical and biological defense, bioinformatics, medical countermeasure development, biometrics, mission analysis, and homeland security.
BS Chemical Biological Engineering, MIT
Data Science and Programming
Venu has expertise in extracting patterns and insights from large data sets through data science. His research experience is in distributed computing and in memory processing. He is strong in Java, C++, Python, SQL, Unix, NoSQL, Machine Learning, Natural Language Processing among others. He uses Bayesian analysis and recommendation engine clustering from mahout.
He recently worked on a project that obtained genomic and clinical trial data from openly available data sources and corporate data sets and then worked on finding patterns in the data. As a data engineer/ scientist he writes MapReduce code that can be scaled and uses machine learning techniques to extract insight from the data. He also worked on a project analyzing massive data coming in at 120 Hz from the Linear Coherent Light Source (LCLS) at the SLAC National Accelerator Laboratory. A typical experiment would take 5 days costing about $1million dollars. To improve performance and economics, he worked to process the data in realtime thus reducing the experimentation data while simultaneously executing preliminary data analysis. He wrote C++ distributed code that crystallographers would call from Python to analyze.
PhD Computer Science-in progress
Product Development and Project Management
Brian is a product development professional. He has over 14 years of hands-on operational experience in industries including social media, gaming, and consumer software and services. He has a special focus on delivering exceptional user experience.
Most recently, Brian led product innovation and marketing at a leading social gaming company. In this role, he was responsible for product strategy and managed a cross-functional team to build, design, launch, and optimize the company's primary revenue-generating product. He also developed and executed best-in-class marketing campaigns that significantly increased game playing engagement and grew overall website traffic.
Brian began his career in management consulting.
BA Stanford: Economics
Neurofeedback for Mission Critical Projects
Thomas is a clinical psychologist focused on helping data science leaders enhance brain performance to better execute mission critical projects. He is a licensed psychologist in the state of California helping people unlock their brain's potential and achieve peak performance through analysis of neurobiofeedback data, a specialty requiring expertise in psychology, physiology, electroencephalography (EEG) and digital signal processing. He helps leaders use Neurofeedback to literally retrain brainwave patterns to realign them for peak performance to achieve goals and address factors limiting potential such as anxiety, chronic stress, insomnia, depression, headaches and migraines, and chronic fatigue.
He is currently preparing a study for publication in the Journal or Neurotherapy, the official journal of the International Society for Neurofeedback Research (ISNR) and is working on his upcoming book "Psychophysiology and Medicine."
He served as an Adjunct Professor in neuroscience and applied pshychophysiology at the University of California, Berkeley and has served as President of the Biofeedback Society of California.
Ph.D., Psychology, California Institute of Integral Studies: