Master’s Degree in Applied Data Science Applied Data Science Faculty

Online M.S. in Applied Data Science Faculty

The faculty members selected for the M.S. in Applied Data Science program possess a unique combination of government, academic, and industry experience spanning enterprises focused on technology, engineering, business, marketing, education and more. All courses in the M.S. in Applied Data Science degree program are taught by these real world experts in the applied data science field. Their expertise, experience and insight about this emerging industry provides students with relevant information and expectations about careers in data science.

Ebrahim K. Tarshizi, PhD

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

Ph.D., Geo-Engineering – University of Nevada, Reno
M.S., Data Science – Michigan Tech
Master of Business Administration (MBA) – University of Nevada, Reno
M.S., Mining Engineering, with graduate minor in Business – University of Nevada, Reno
B.S., Exploration Engineering – Azad University

Areas of Interest in Data Science

Applications of Data Science in Occupational Health and Safety, Text and Web Mining Applications, and Ethics in Data Science.

About

Dr. Ebrahim Tarshizi is the Professor of Practice, faculty lead, and program coordinator for the Applied Data Science program at the University of San Diego’s Shiley-Marcos School of Engineering, and he serves on the scientific advisory board for a tech start-up company in San Diego, CA. He is an Adjunct Professor of Data Science and Business Analytics at National University, where he has taught a variety of analytics courses. He is also an instructor for the UCSD Extension’s Business Intelligence Analysis. Ebrahim is a former senior technical advisor for the CDC National Institute for Occupational Safety and Health’s (NIOSH) Mining Research Program. He was a visiting scholar at UCSD’s San Diego Supercomputer Center (SDSC) and an Assistant Professor at Michigan Tech, where he spent over two years to co-develop a new engineering curriculum.

Ebrahim received a Ph.D. degree in Geo-Engineering from the University of Nevada, Reno (UNR). He obtained an M.Sc. in Data Science from Michigan Tech and an M.B.A. from UNR College of Business. He earned a B.Sc. in Exploration Engineering in Iran. Ebrahim has published several articles in international journals and symposium proceedings and has delivered many technical reports and presentations at professional meetings and conferences. He has received a number of University, national, and international awards and honors.

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Satya Varaprasad Allumallu, MS

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

M.S. in Statistics, School of Statistics, University of Minnesota
M.S. in Mechanical Engineering, Department of Mechanical Engineering, University of Minnesota
Bachelor of Engineering in Mechanical Engineering, M.V.S.R. Engineering College, Hyderabad, India

Areas of Interest in Data Science

Anomaly Detection, Interpretable Machine Learning & Data Science in Health Care

About

Data Science team lead at Becton, Dickinson & Company (BD) with 14+ years of industry experience with expertise in applied predictive modeling, statistical methods, engineering understanding and business acumen. Recipient of highest corporate technical achievement awards at Seagate & BD for Data Science product development with significant impact. Played active role in building data science teams and mentoring junior data science professionals. Continuous learner with dual master’s degrees in mechanical engineering & Statistics, data science professional development certifications from MIT & Stanford.

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Jarod Daming, BS

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

B.S., Computer Science- University of Southern Indiana

Areas of Interest in Data Science

Data Engineering

About

Jarod Daming is a Senior Data Engineer at Teradata specializing in database design, performance, and query development. He works in the Service Analytics team with a mission to ensure the team and company have access to the information for different data-driven projects. In this effort, he has improved query response times by 40-200x, data processing volume increase 100x, and increased schema flexibility. His current projects are to reach how to automate feature generation while staying inside the Teradata database nodes, as well as create an application-versioned, auto-upgraded database schema to allow Teradata’s data scientists to be more flexible in their research and development efforts. Jarod holds a Bachelor of Science of Computer Science degree from the University of Southern Indiana in 2013, and worked in different programming roles at OneMain Financial, a financial services company, for almost 7 years.

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Bharath Inti, MS

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

M.S., Data Science- Michigan Technological University
Bachelor of Technology, Electronics and Communication Engineering- Jawaharlal Nehru Technological University, Hyderabad, India

Areas of Interest in Data Science

Machine Learning, Data Science Programming, Natural Language Programming, Neural Networks and Deep Learning.

About

Bharath Inti works as a data analyst at KS Industries, an Oil and Gas company in Bakersfield, CA. He provides data science solutions in accident mitigation, employee safety, risk assessment, and operations management among other things, on the firm’s massively outgrowing data. He has developed an automated NLP system that tracks occupational employee exposure to unsafe substances based on CAL OSHA regulations. On a day to day basis Bharath uses Python for most of his data science workflow involving data acquisition, preparation, model building, and deployment. Algorithms vary from machine learning, neural networks to deep learning and AI. SQL and R programs are also widely used to communicate with databases and build statistical hypotheses. Bharath holds a Master’s degree in Data Science from Michigan Technological University and a Bachelor’s in Electronics and Communication Engineering from Jawaharlal Nehru Technological University. He has over 5 years of experience in data analysis and analytics and worked on a wide range of industry projects from building prospect conversion and customer segmentation models for marketing teams to GPU accelerated neural networks for near real-time image recognition on streaming data.

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Vicente Malave, MS

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

M.S., Cognitive Science – University of California, San Diego
B.S., Computer Science – Carnegie Mellon University
B.S., Cognitive Science – Carnegie Mellon University

Areas of Interest in Data Science

IoT and Sensor networks, Data Science in healthcare, and Bayesian statistics.

About

Vicente Malave is a Data Scientist and currently working for a start-up company in San Diego, California. Vicente obtained a Master’s Degree from UC San Diego’s Department of Cognitive Science, specializing in Data Science. He is currently on leave (ABD) from the Ph.D. program at UCSD. He also holds a degree in Computer Science (BS) Carnegie Mellon University and has worked on machine learning research at UCLA’s Institute for Pure Applied Math, The New Mexico Consortium/Los Alamos, and Nokia Research.

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Jules Malin, MS

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

M.S., Data Science (formerly Predictive Analytics)- Northwestern University
B.A., Organizational Studies- University of California, Davis

Areas of Interest in Data Science

IoT, Smart Device, Smart App Analytics, and Applications of Deep Learning in Computer Vision in Digital Marketing.

About

Jules Malin is the Director of Data Science and Analytics at GoPro and regularly speaks on the topics of Big Data, Analytics, Data Science, and Machine Learning in different workshops and conferences. Jules holds a BSc in Organizational Studies from the University of California, Davis, and a Master’s Degree in Data Science from Northwestern University. He has built multiple analytics teams that deliver actionable insights, influencing product development, and marketing efforts.

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Ion Nemteanu, MS

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

M.S., Data Science (formerly Predictive Analytics)- Northwestern University
B.S., Business Administration/High Technology Management- California State University, San Marcos

Areas of Interest in Data Science

Text Mining, Recommender Systems, Forecasting, Scaling Data Science, Experimentation, and Outlier Detection.

About

Ion Nemteanu is the Senior Director of Data Science for Thermo Fisher Scientific’s Life Sciences Group. His leadership spans global commercial, marketing, and operational functions, where he builds and deploys data science and advanced analytic capabilities to support business growth and innovation. Ion also instructs Text Mining for the University of California, San Diego Data Science Certificate program. Previously, Ion led the Analytic Services team for Becton Dickinson comprised of data scientists and analytics consultants. His leadership and data science expertise focused specialized in building algorithms that were embedded in BD’s hosted products featuring clinical medication management, diversion, and laboratory software products. Prior to BD, Ion has also held leadership and analytic positions spanning across the cybersecurity, telecommunication, and entertainment industries. Ion earned a Bachelor of Science in Business Administration from California State University, San Marcos, and a Master of Science in Predictive Analytics from Northwestern University.

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Brandon Quach, PhD, ME

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

Ph.D. Bioengineering, Caltech

M.Eng. Bioengineering, U.C.S.D.

B.S. in Bioengineering, U.C. Berkeley

Areas of Interest in Data Science

Customer service automation, machine vision, and credit risk.

About

Dr. Brandon Quach holds a Ph.D. from Caltech, an M.Eng from UCSD, and a B.S from UC Berkeley all in bioengineering. Brandon has almost a decade of industry experience as a data scientist and is a passionate learner who thrives in multi-disciplinary team environments. He codes and owns machine learning algorithms, from conception to production. At Teradata, he is currently a Principal Data Scientist and Manager leading the charge to modernize the customer experience by applying machine learning to customer support. Before that, he was at Lytx working on algorithms that detect vehicles, determine risky vehicle maneuvers, and detect risky driver behaviors in-cab. He began his career at Opera Solutions, where he was an Analytics Manager leading small teams applying data science in a wide variety of fields including credit risk, insurance, counter-terrorism, and conversion marketing.

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Matthew C. Vanderbilt, MS

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

M.S., Business Analytics – National University
B.S., Accountancy – National University

Areas of Interest in Data Science

Applications of Data Science in Finance and Operations, Business Analytics, and Probabilistic Depreciation.

About

Matthew Vanderbilt is the Director of Business Analytics & Fiscal Affairs for the UC San Diego School of Medicine, Department of Medicine, where he is responsible for financial and accounting oversight and development of finance and operations business intelligence solutions across the academic, clinical, and research missions. Previously, he represented San Diego Gas & Electric, a Sempra Energy utility, as a State and Federal expert witness on probabilistic depreciation and legal matters pertaining to the jointly-owned San Onofre Nuclear Generating Station. He has more than two decades of experience providing leadership and expertise in business intelligence solutions for accounting, finance, engineering, and operations. Mr. Vanderbilt holds with distinction a Master of Science in Business Analytics and Bachelor of Science in Accountancy from National University, as well as an Associate of General Studies and Associate of Arts earned from Scottsdale Community College at age 16. He has previously built and operating a small-business accounting consultancy, Vanderbilt Ventures, Inc., and a technology company, Vanderbilt Technologies, Inc.

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Jeffrey Yau, PhD, MA

Faculty

Degree Program

Master of Science in Data Science

Academic Degrees

Ph.D., Economics (focus: Econometrics)- University of Pennsylvania
M.A., Economics (focus: Econometrics)- University of Pennsylvania
B.S., Mathematics- University of California, Los Angeles

Areas of Interest in Data Science

Time Series Forecasting, Time Series Data Mining, Reinforcement Learning, Stochastic Dynamic Programming, and Econometrics.

About

Dr. Jeffrey Yau joins Walmart Labs as a Data Science leader, where he leads a data science and engineer team. He is also an adjunct professor at UC Berkeley, where he has designed, developed, and taught the advanced statistics course for the online Masters of Information and Data Science program. His former and existing students come from a wide array of companies and other organizations, such as Microsoft, Amazon, Apple, Cisco, Moody’s Analytics, Goldman Sachs, Bank of America, Disney, KPMG, and Singapore Military. Dr. Jeffrey appears frequently as a speaker in Data Science and A.I. conferences, such as Starta, Spark & AI Summit, and ODSC. Prior to WalmartLabs, he was the Chief Data Scientist and Global Head of Data Science at AllianceBernstein (AB), a global investment management firm that manages over $550 billion in assets.

Dr. Jeffrey taught machine learning courses at NYU and Cornell Tech while he lived in New York City. Before AB, he held various leadership roles in data science and quantitative research in companies such as Silicon Valley Data Science, a boutique data science and data engineering consulting firm where he led a team of Ph.D. computer scientists, statisticians, and scientists from various domains to help Fortune 500 companies transformed their businesses using data science and emerging data technologies, Charles Schwab Corporation, as the officer leading all of the quantitative modeling and research for investment risk analytics, and KPMG, as a director specializing in the quantitative modeling practice. He also trained initially as an economist/econometrician from the University of Pennsylvania, where he received his Ph.D. and M.A. in economics. His first job was an assistant professor of economics at Virginia Tech, where he conducted econometric research and taught econometrics. During his doctoral study and the last two years of his undergraduate study at UCLA where he studied math and economics, Dr. Jeffrey worked in research teams at the RAND Corporation, the World Bank, the Wharton School, and University of Pennsylvania School of Medicine.

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