Ishita Prasad
Product Design Lead, Privacy at Meta
San Francisco, California, United States
Summary
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Senior🎓
Top SchoolIshita Prasad is a product design leader based in San Francisco with 11 years of experience blending human-centered design, product strategy, and hands-on engineering for privacy-forward consumer and enterprise products. Currently leading Privacy Product Design at Meta, she has a track record of founding and scaling product-led startups (cofounded Polymer, raising $2M) and shipping factory- and retail-facing tech at Dandelion Chocolate. Her background spans Stanford HCI research and engineering contributions to Stanford CoreNLP tokenization, reflecting a rare mix of visual design, interaction research, and back-end NLP experience. Known for designing monetization and advertiser experiences at Quora and prototyping at Google and Apple, she excels at turning complex technical constraints into elegant user experiences.
11 years of coding experience
4 years of employment as a software developer
ISC Physical Sciences, ISC Physical Sciences at The Cathedral & John Connon SchoolBachelor's degree Computer Science, Bachelor's degree Computer Science at Stanford University
English, Hindi, Spanish, German
Github Skills (5)
javas10
tokenizer10
nlp10
natural-language-processing10
java10
Programming languages (2)
Github contributions (5)
stanfordnlp/CoreNLP
Aug 2014 - Aug 2014
CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits in 1 day
Contributions summary:Ishita's contributions primarily involved modifying the `TokenizerAnnotator` component within the CoreNLP project. These changes included adjusting constructors, updating tokenizer types, and resolving merge conflicts. The code changes appear to focus on refining tokenization functionality and ensuring compatibility across different language configurations within the natural language processing pipeline. The user also made adjustments to several test files to integrate the tokenizer changes.
word-embeddingssentiment-analysisnatural-language-processingstanford-nlpsuite
mayanb/snickerdoodle
Sep 2017 - Aug 2018
Contributions:524 commits in 11 months
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