Conduct JMP Software demonstrations/presentations to enhance the probability of deal closures and provide seminars/webinars to boost user software proficiency.• Assisted clients in unlocking value from their data, specializing in translating complex data analytics capabilities into compelling demonstrations and seminars tailored for diverse audiences.• Guided client analysis processes using JMP, leading engagements that successfully introduced and implemented JMP software, culminating in a notable 145.14% user growth.• Collaborated intimately with clients, understanding their unique challenges, and offering custom software solutions that fostered lasting business relationships.• Played a pivotal role in assisting a leading semiconductor manufacturing company, designing optimal experiments that drastically reduced the defective product rate.• Provided expertise to a renowned chemical manufacturing company in generating automated reports using advanced statistical methods, ensuring anomaly detection.• Supported advanced analysts at a prominent battery manufacturing company, facilitating the integration of open-source languages like R and Python with JMP. This integration enabled the deployment of state-of-the-art machine learning algorithms and tailored statistical models.• Joined hands with the global enablement team, delivering top-notch technical support to an elite global chemical film manufacturing company.• Assisted client engineers in pinpointing key elements by leveraging contemporary machine learning algorithms and implementing Monte-Carlo simulations, guaranteeing optimal process capability.

• Led seminars/webinars to broaden the JMP user community.• Established a Notion blog offering insights on diverse aspects of JMP software utilization.• Adapted and localized the free online course "Statistical Thinking for Industrial Problem Solving (STIPS)" for enhanced accessibility.

Developed a graph learning algorithm using relaxed assumptions and provided assistance for three college courses, including mathematical statistics and introductory Bayesian probability theory.• Developed a new identifiable directed acyclic graphical (DAG) model capable of learning Gaussian structural equation models (SEMs).• Scripted modern SEM learning algorithms using R and Python and conducted numerical simulations.• Conducted comprehensive research on state-of-the-art SEM learning algorithms, such as the Greedy DAG Search, and analyzed real data on Seoul house prices for a research paper.• Utilized DBSCAN, K-Means Clustering, and other advanced methods to detect clusters from Seoul's geographical data using R.• Conducted extensive research on cutting-edge machine learning algorithms and performed real data analyses for a research paper.• Provided teaching assistance for over 200 courses, including mathematical statistics and introductory Bayesian probability.