Raphael Taiwo Alabi

VP Ai & Machine Learning @SAP

Santa Clara, CA, US
EMAILS
r••••••••@sap.com
MOBILE NUMBERS
+14•••••••99

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WORK HISTORY

Mar 2026 — Present

VP Ai & Machine Learning @SAP

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Palo Alto, CA, US

EDUCATION

2005 — 2008

Mendeleyev University of Chemical Technology of Russia

Bachelors of Science Degree, Chemical Engineering and Biotechnology

2009 — 2012

Georgia Institute of Technology

Doctor of Philosophy (PhD), Materials Science & Engineering (minors: Chemistry and Materials)

N/A

Stanford University

Artificial Intelligence Professional Certificate, Artificial Intelligence

2007 — 2007

NIIT

Sun Java Architect, Java Programming

N/A

Stanford University

Advanced Cybersecurity Certificate, Cybersecurity

N/A

Stanford University Graduate School of Business

Stanford LEAD Program, Strategic Leadership

2012 — 2013

Bowling Green State University

None, Chemistry-Photochemistry and Photophysics

2017 — 2018

University of California, Berkeley

Master's degree, Information and Datascience

2020 — 2021

Stanford University

Artificial Intelligence Graduate Certificate, Artificial Intelligence

SKILLS

SemMicrosoft R ProgrammingLatexCharacterizationSpectroscopyTgaMaterialsPhysicsXpsMaterials ScienceMicrosoft Sql ProgrammingExperimentationDesign of ExperimentsPolymersMathematicaTga, DscSemiconductorsR&DPowder X-Ray DiffractionScanning Electron MicroscopyScripting and AutomationAfmResearch and Development (R&D)PhotolithographyNanomaterialsSiliconUv-VisUv/VisAdvanced Programming for Data Analytics and Data Presentation with Jmp Scripting/ Visual Basic and Python ProgrammingBiomaterialsChemistryEngineeringNanotechnologyFluorescence MicroscopyHtmlLabviewSolidworksResearchPython ProgrammingJmp

ABOUT RAPHAEL TAIWO ALABI

10+ Years experience in Computer hardware and 10 + years in the software industry. Expertise in hardware computer eng. and Machine Learning/Artificial Intelligence including a Master\'s degree in Infor. and Datascience from U.C Berkeley. Expertise in ML/AI range from Deep learning with CNN |RNN| BiLSTM/LSTM | GRU | to using conventional ML tools including Linear, Polynomial & Logistic Regression; KNN, Naive Bayes, Decision trees, Random-Forest, K-Means and Gaussian Mixture Models to name a few. Machine learning software toolkits are TensorFlow, PyTorch, Scikit learns, Python | R | Java-script | Visual-Basic | JMP | SQL | Pyspark | C++. Distributed computing expertise using AWS EMR| EC2 frameworks.Graph NN - GAT| GraphSAGEMeta-learning - 1-shot, few-shotFoundation Models - LLMsHugging Face Cybersecurity

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