Faculty & Staff

We believe that students need to hear from a wide array of perspectives to be successful data scientists. The faculty in the program are experts, appointed from across the world to provide a comprehensive experience across different fields and industries. Our faculty and advisors are leaders in the industry with professional and academic backgrounds in the field of data science.

Faculty and Instructors

Alessandro Gagliardi


Alessandro worked as a data scientist for both Path and Glassdoor, where he analyzed terabytes of customer activity logs to provide insights for product development. He also applied machine learning to user-generated content such as salaries and reviews. He received a BA in Computer Science from UCSC and MS in Behavioral & Neural Science at Rutgers University. He taught statistics, psychology, and neuroscience at Lehman College of the City of New York, Notre Dame de Namur University, the California Institute of Integral Studies, and the University of San Francisco, the last of which awarded him the Innovation in Teaching Award in 2011.

Nir Kaldero

Program Director & Visiting Scholar

Having served as a principal data scientist and economist, Nir is experienced in leading teams to deliver industry applications and creating value. Nir was the lead instructor for data science at General Assembly, a scholar in the Economics Department at the University of California, Berkeley, and an independent data science consultant. Nir completed PhD coursework in Economics, with application to Industrial Organizations, Finance and Marketing at the University of California, Berkeley. He holds a BA in Economics and Business Administration from the IDC Herzliya, Israel.

Brian Spiering

Visiting Assistant Professor

Brian has taught statistics at both the undergraduate and graduate level for over 10 years. He received his PhD in Cognitive Neuroscience from the University of California, Santa Barbara in 2009. He was a post-doctoral research associate fellow and lecturer at Colorado State University and The University of Maryland. He was named a Maryland Neuroimaging Center Summer Institute Fellow in 2013 for his work in the statistical analysis of large-scale fMRI (functional magnetic resonance imaging) data.His academic research used mathematics and computer programming to better understand the human brain and behavior. More recently, he has been working as a data scientist building scalable machine learning solutions and leading analytic teams. He is active in the San Francisco data science community through volunteering, mentoring and organizing talks.

Vince Corvo

Visiting Assistant Professor

Vince was a pioneer in Deep Machine Learning before the field even had an official name. He has extensive experience in both academic and industrial realms, solving a variety of theoretical as well as practical problems in the technical marketplace. He is also the founder of four startups in the local software industry, with an emphasis on Natural Language Processing applications. Vince holds a Ph.D. in Mathematics from UC Berkeley, and both a M.A. in Philosophy and B.S. in Mathematics from Stanford University.

Donatella Taurasi

Adjunct Instructor

Donatella earned her bachelor’s degree in Economics and Finance and master’s degree in Financial Markets and Institutions at the University of Bologna, Italy. She then earned her PhD in Finance, Financial Markets, and Institutions in the Department of Business and Economics of Bologna, Italy. During her graduate studies, Donatella worked as a visiting scholar at the Westminster Business School in London, Stern School of Business at New York University, and Haas School of Business at the University of California, Berkeley. Donatella is currently a lecturer at Haas School of Business and at the Fung Institute for Engineering Leadership at the University of California and a faculty member at Hult International Business School in San Francisco.

Conor Murphy

Associate Instructor

Conor joins the MSDS program with four years of experience leveraging data for more impactful humanitarian interventions in developing countries with a focus on business development. In the nonprofit sector, he managed a multi-million dollar portfolio of grants for The Rotary Foundation focusing on developing and analyzing impact measurements in economic development initiatives, evaluating program participation, and translating academic research into institution policies. His education background includes a masters in philosophy from Belgium’s KU Leuven and undergrad degrees in both philosophy and French. He further refined his knowledge of data through Galvanize’s Data Science Immersive program as a Data Science Fellow and then as a Data Scientist in Residence.

Edward Banner

Associate Instructor

Edward received a MS of computer science from The University of Texas at Austin, where his research focused on developing tools to assist researchers in authoring clinical systematic reviews (SR) more efficiently. In particular, Edward leveraged techniques from deep learning to create representations of full-text clinical trials optimized for workflows common among SR researchers. Over the years, Edward has been able to share his passion for education with others by tutoring both peers and adult learners in math, serving as a teaching assistant for computer science, web development, and software engineering courses, and leading a seminar for graduate students on deep learning for natural language processing. Edward has a passion for empowering others through education and helping them reach their potential.




Bonny Xie

Assistant Director

Prior to Galvanize, Bonny advised New York University's Program Board, and worked in NYU's MBA Admissions and Graduate Financial Aid. Additionally, Bonny served as Director of Operations at the Institute for China-US Law & Policy Studies, where she work closely with the leadership at Peking University's School of Transnational Law. Bonny holds a bachelor's in psychology from the University of San Francisco, and a master's degree in Higher Education from New York University.

Advisory Board

Paco Nathan

Paco Nathan, is a "player/coach" who has led innovative Data teams building large-scale apps for several years. He has expertise in distributed systems, machine learning, functional programming, and cloud computing, and is an O'Reilly author, Apache Spark open source evangelist with Databricks, and an advisor for Amplify Partners and GalvanizeU. He received his BS Math Sci and MS Comp Sci degrees from Stanford University, and has 30+ years technology industry experience ranging from Bell Labs to early-stage start-ups.

Lyle H. Unger

Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania. He received a B.S. from Stanford University and a Ph.D. from MIT. Dr. Ungar directed Penn's Executive Masters of Technology Management (EMTM) Program for a decade, and served as Associate Director of the Penn Center for BioInformatics (PCBI). He has published over 200 articles and holds eleven patents. His current research focuses on statistical natural language processing, spectral methods, and the use of social media to understand the psychology of individuals and communities.

Alexy Khrabrov

Alexy Khrabrov is a computer scientist working at the intersection of startups, “big" data, and functional programming. Alexy founded Scala for Startups, a meetup in San Francisco focused on doing more startups with fewer but more effective people, and merged SF Scala with it to grow the largest Scala meetup in the world, sfscala.org. He was early adopter of Apache Spark, which he used at Klout, and a co-founder of the Spark Users meetup, spun off of the Scala for Startups and now one of the largest meetups in technology. Alexy founded and runs SF Scala yearly conference, called Scala By the Bay (scala.bythebay.io), and organizes new conferences on applied Natural Language Processing (text.bythebay.io) and Big Data Scala (bigdatascala.org).

Adam Gibson

Adam Gibson is the co-founder of Skymind, an enterprise deep-learning and NLP firm, and creator of the distributed, open-source frameworks Deeplearning4j.org and ND4J.org. Adam has taught machine-learning at Zipfian Academy and is currently deep-learning specialist in residence at GalvanizeU. Adam is the author of the forthcoming O'Reilly book “DeepLearning: A Practicioner's Approach”