Roger
Wang

• Led design of event recommendation system using artist similarity and user behavioral data
• Applied regression analysis and A/B testing with Product Analytics to optimize UI/UX
• Maintained predictive models for ticket demand and pricing trends

• Led development of deep learning models (LSTM, Transformers, GNN) boosting AI performance
• Deployed scalable AI solutions for evolving business needs
• Optimized and maintained AI platform for reliability and efficiency

• Owned Dynamic Pricing end-to-end: demand prediction + price optimization, generating $3M additional revenue
• Built AI chatbots for internal search, client service, and call center
• Refined Broker Detection Model; built automated data pipelines with third-party vendors

• Designed and maintained NEMESIS, a next-gen DS tool; built ML solutions for churn and segmentation
• Connected engine to IBM DB2 and MS SQL Server, helping secure a new client contract

• Built CodeBox64 — tactile device for visually impaired students to access Scratch via Python & Raspberry Pi
• 2nd Place, 2019 ACM SIGCSE Student Research Competition (Undergraduate)

Identified how YTHDC1 recognizes METTL16-dependent m⁶A modifications on chromosome-associated RNAs and coordinates cotranscriptional splicing regulation.

CodeBox64: a peripheral enabling visually impaired students to access Scratch via six buttons and auditory cues. User study confirmed usability and efficiency.

Tactile input modality using Raspberry Pi and Python for accessible block-based programming. Experiments confirmed proper function and accessibility.