Our Data Science Instructors
Our team of experienced, full-time instructional faculty utilize real-world case studies to teach best practices in statistical analysis, machine learning, natural language processing and data visualization that will prepare you for a successful career in Data Science.
Philip Geurin is a Data Scientist specializing in forecasting and simulation. At Uber, he's forecasted trillions of user actions in 60,000 different time series. He was funded by the NSF twice to simulate deterministic game playing and stock market scenarios using GLMs and genetic modeling. As an instructor, he's taught bedside manner to doctors, been a feedback consultant for NovoNordisk technical management, and wraps it up in a 10-years-of-improv-theater burrito.
Hamid received his Ph.D. in computational Physics from University of Waterloo and was the first to predict Quantum Spin Ice using modeling techniques. In 2010, Hamid co-founded and built one of the first robust search engines for apps with the capability to search content inside apps by applying Machine Learning and optimization techniques. Hamid also worked on applying machine learning techniques in IOT space and worked on building a smart tracking device for Alzheimer people and pets. You can learn more about Hamid on his Google Scholar profile here.
Jack has been teaching Data Science at Galvanize since 2016, and is now Head of Curriculum for Data Science Immersive. Prior to that he worked at Microsoft for a decade in performance, forensics, analysis tools, and infrastructure and at a couple startups in bioinformatics and fantasy sports in New York City. He received his BS from Yale and PhD in theoretical physics from Rutgers University, where he built computer models of dislocations in silicon. Jack actively answers data science related questions on Quora and more of his work can be seen on ResearchGate.
Juliana is a data and computational scientist as well as an experienced instructor. She received her Ph.D. in computational chemistry from the University of Texas at Austin where she developed a novel saddle point finding method, applied machine learning techniques to accelerate molecular dynamic simulations, and used computational methods to simulate chemical processes. As an Assistant Professor of Practice at UT Austin, Juliana led and created all curricula for an undergraduate computational science research program. She also led a research group that used high throughput computing and machine learning to find novel materials for catalysis, and explored global optimization methods.
Matt is an experienced data scientist and instructor. He has six years of college-level teaching experience in computer science. He has a PhD in Computer Science with a specialty in artificial intelligence and machine learning from the University of Wisconsin, a second PhD, also from the University of Wisconsin, in Sociology, and an undergraduate honors degree in Physics from Harvard. He also wrote a book on artificial neural network applications to artificial intelligence. He has many years of experience as a researcher both in academia and for the government, focusing on data science research projects involving educational and labor market data. In recent years he has used machine learning libraries in Python and R in this research. He also has a good deal of experience with experimental social science research (randomized controlled trials).
Michelle Hoogenhout is a behavioral data scientist with a background in genetic and cognitive neuroscience. Previously, as the senior data scientist and tech team lead at Umuzi Academy, South Africa, she developed and taught the data science curriculum, implemented the candidate selection and data ETL process and led a team of 7 web developers, data scientist and data engineers. Michelle holds a PhD (Psychology) from the University of Cape Town and a postdoctoral training fellowship from the Broad Institute of Harvard and MIT. Her doctoral work focused on predicting empathy in autism spectrum disorders through measures of autonomic nervous system and muscle reactivity. She has published and blogged on topics such as statistics and data management, data science training methods, cognitive assessment, and child development.
Sean is a Data Scientist with a Masters degree in Economics and an Undergraduate degree in Physics. Sean is a AWS Certified Solutions Architect, a Amazon Web Services SysOps Administrator, and has over ten years of professional experience. To learn more about Sean, check out his CourseReport video comparing Python versus R for Data Science.
Dan graduated with a degree in Computer Science from Lake Forest College. He spent several years developing hospital management systems for the medical field. After his work in the healthcare industry, he turned his interest to data and graduated from the Galvanize Data Science Immersive program.
Prior to Galvanize, Alex graduated with a mathematics degree from Binghamton University. He began his career in the Ecommerce industry before turning his attention within the healthcare sector working as a senior business/data analyst at CareMount Medical and Weill Cornell Medical College. After his work within the healthcare industry, Alex graduated from the Galvanize Data Science immersive program himself and has worked in the industry before jumping back with Galvanize as an instructor. His interests include data engineering, deep learning, recommenders, and student success.
Land received his Ph.D. in Materials Science and Engineering from the University of Wisconsin for his work creating and analyzing nano-scale superconductors and magnetic materials. As an engineer for Intel, GE and other high-tech companies, Land used Data Science and machine learning techniques to develop and control computerized manufacturing processes. After completing the Data science immersive at Galvanize, Land developed a deep learning based ultrasound probe, which is being patented and developed in conjunction with the University of Colorado Medical School.
Kin-Yip Chien is a data scientist and statistician who is passionate about data-driven solutions. He received his MS in Statistics from Texas A&M University and a BA in Neuroscience and Economics from Queens College and has applied statistical and machine learning methods to problems in gene expression analysis, commodity price prediction, automated comment moderation, and fake review detection. He is also an educator, having taught statistics courses and worked one-on-one with students to spread his excitement and knowledge of data science best practices.
Skylar English obtained his B.S. Economics degree with a math minor at Texas A&M before working as a management consultant for Accenture's big data practice. He developed models for Chevron and Shell for logistics and exploration. He was later hired at Comcast where he became an engineering hiring manager over a team of data scientists, data engineers and visualization experts working on many projects including network and product reliability, NPS deployment and analysis, and customer experience for Comcast's top executives.
Ryan Kasichainula is a data scientist with experience in the technology, agriculture, energy, and pharmaceutical industries. They hold an MS in Statistics from Texas A&M and are excited to teach aspiring data scientists the skills necessary to start new careers.
Tomas comes from a math and finance background originally but has been working with online products in the Bay Area for over 8 years now, mostly in product and marketing data science roles. Before joining Galvanize he built and managed a data science team in a mobility startup.
Rongpeng (Ron) Li
Ron is a data science instructor and a senior data scientist at Galvanize. Ron received two master's degrees from USC, one in electrical engineering and another in physics. Before joining Galvanize, Ron was a research programmer at Information Science Institute. Ron has research/engineering experience in computational physics, institutional research, quantum computation, knowledge graph and finance. Ron authored/co-authored several peer-reviewed publications and a book called Statistics for Data Science.
Ron has been an avid mentor and instructor in data science communities both online and offline. Ron is conversant with data science but his happiest teaching experience is solving a student's question that he can't answer immediately.