Sarah Tan Email and Phone Number
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Sarah Tan personal email
Currently, I am a Director in Responsible AI at Salesforce, where I work on AI safety. I also hold a Visiting Scientist appointment at Cornell University and am president of the Women in Machine Learning (WiML) nonprofit.Please see my website for more info (http://shftan.github.io/) or my Google Scholar for papers (https://scholar.google.com/citations?user=_tSKmPYAAAAJ).
Salesforce
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Principal Research Scientist, Ai SafetySalesforceUnited States
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Director, Responsible AiSalesforce Jan 2024 - PresentSan Francisco, California, UsAI safety for Salesforce built generative models throughout the entire model development and deployment lifecycle.
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PresidentWomen In Machine Learning 2023 - Present -
DirectorWomen In Machine Learning Apr 2018 - Dec 2022The Women in Machine Learning (WiML) 501c3 nonprofit’s mission is to enhance the experience of women in machine learning. I have been involved with the organization since 2016, from being general chair of workshop at NeurIPS, to joining the board of directors in 2018. As VP of Events in 2019-2020 I oversaw all of WiML's events. Other efforts I led include writing WiML's Code of Conduct and launching a new funding program during the pandemic to fund underrepresented women worldwide to attend virtual conferences. Currently, as President I am focused on streamlining operations and growing the organization to serve the needs of women in different career stages and geographies.
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Visiting ScientistCornell University 2023 - PresentIthaca, Ny, UsI have a visiting appointment in the College of Computing and Information Science to do research in medical imaging and continue my interest in healthcare. -
Ai ScientistCambia Health Solutions 2023 - 2023Portland, Or, UsProjects included building and evaluating retrieval augmented generation LLM systems for healthcare use cases and writing the company’s Responsible AI policy. -
Research ScientistFacebook 2019 - 2023During my time at Meta I was first in the Core Data Science (now Central Applied Science) organization and then the Responsible AI organization. I developed AI bias mitigations and helped product teams apply them to their models. I also worked on harm mitigations for integrity teams, personalized recommendations, and experimentation tooling.Papers:- Error Discovery by Clustering Influence Embeddings. NeurIPS '23- Interpretable Personalized Experimentation. KDD '22- Practical Policy Optimization with Personalized Experimentation. NeurIPS '21 Workshop- Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes. CODE '21 -
Bioinformatics ProgrammerUniversity Of California, San Francisco 2019 - 2019San Francisco, California, UsCollaborated with Zuckerberg hospital to investigate use of interpretable machine learning models for healthcare, and helped teach Biostats 216 (Machine Learning for the Biomedical Sciences) to clinicians and UCSF staff. -
Analytical Studies And Surveys ConsultantNew York City Department Of Health And Mental Hygiene 2018 - 2018Consultant on observational causal inference methods -
Research InternMicrosoft 2017 - 2018Redmond, Washington, UsTwo Microsoft Research internships working with Rich Caruana, Ece Kamar, and Kori Inkpen, resulting in the following papers:- Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES '18- Axiomatic Interpretability for Multiclass Additive Models. KDD '19- Do I Look Like a Criminal? Examining the Impact of Racial Information on Human Judgement. CHI '20- Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. AISTATS '20- How Interpretable and Trustworthy are GAMs? KDD '21- Considerations When Learning Additive Explanations for Black-Box Models. Machine Learning Journal '23- Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL '23 -
Data Scientist (First Data Scientist Hired)Johnson Research Labs 2012 - 2013At this startup research lab, I worked on various NLP problems, such as e-discovery from legal documents, modeling of movie scripts, etc. I also munged a lot of data. Some of the work won best paper awards (https://journals.sagepub.com/doi/abs/10.1177/0003122415598534).
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Data ScientistNew York City Department Of Health And Mental Hygiene 2011 - 2012I developed predictive models for healthcare, such as hospital readmissions, adverse drug interactions, etc. on data from all of New York City's public hospitals. Some of the work resulted in the following papers:- Hospital Readmission Rates: Related To Ed Volume, Population, And Economic Variables. Academic Emergency Medicine 2012.- Using PROC GENMOD to Investigate Drug Interactions: Beta Blockers and Beta Agonists and Their Association with Hospital Admissions. SAS Global Forum 2013.- Two Ways of Modeling Hospital Readmissions: Mixed and Marginal Models. JSM 2013.
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InternUnited Nations 2011 - 2011New York, Ny, UsData mining on customs records to detect aberrant trade transactions. Worked with UN Head of Trade Statistics to update trade transactions reporting guidelines for UN member nations.
Sarah Tan Skills
Sarah Tan Education Details
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Cornell UniversityStatistics -
University Of California, BerkeleyEconomics
Frequently Asked Questions about Sarah Tan
What company does Sarah Tan work for?
Sarah Tan works for Salesforce
What is Sarah Tan's role at the current company?
Sarah Tan's current role is Principal Research Scientist, AI Safety.
What is Sarah Tan's email address?
Sarah Tan's email address is sarahtyl@fb.com
What schools did Sarah Tan attend?
Sarah Tan attended Cornell University, University Of California, Berkeley.
What skills is Sarah Tan known for?
Sarah Tan has skills like Data Analysis, Sql, Business Analysis, Business Intelligence, Databases, Statistics, Research, Sas, Data Mining, Machine Learning, R, C++.
Who are Sarah Tan's colleagues?
Sarah Tan's colleagues are Takayoshi W., Bhumi Damania, Heath Wolfeld, Federico Vella, Chipman Macdonald, Mari Sullivan (Collet), Kelly Boyd.
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