About

I am a PhD candidate in Machine Learning at Harvard University, working with Professor Finale Doshi-Velez at the Data to Actionable Knowledge Lab (DtAK). I work on uncertainty quantification and tractable approximate inference for deep Bayesian latent variable models with applications in health-care.

Before joining DtAK, I got a MM in Contemporary Improvisation from the New England Conservatory in 2016 and a AB in Computer Science from Harvard College in 2015. You can find my music page here.

Publications

You can find the most updated list on my google scholar page.

• Y Yacoby, W Pan, F Doshi-Velez. Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks. Pre-Print.

  • Previous Version: Y Yacoby, W Pan, F Doshi-Velez. Failures of Variational Autoencoders and Their Effects on Downstream Tasks. ICML Workshop on Uncertainty & Robustness in Deep Learning (UDL), 2020.

• Y Yacoby, W Pan, F Doshi-Velez. Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables. Pre-Print.

  • Previous Version: Y Yacoby, W Pan, F Doshi-Velez. Mitigating Model Non-Identifiability in BNN with Latent Variables. ICML Workshop on Uncertainty & Robustness in Deep Learning (UDL), 2019. Spotlight Talk.

• Y Yacoby, W Pan, F Doshi-Velez. Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. Advances in Approximate Bayesian Inference (AABI), 2019, Proceedings of Machine Learning Research (PMLR) 118:1-17, 2020. Spotlight Talk.

• S Thakur, C Lorsung, Y Yacoby, F Doshi-Velez, W Pan. Uncertainty-Aware (UNA) Bases for Bayesian Regression Using Multi-Headed Auxiliary Networks. Pre-Print.

  • Previous Version: S Thakur, C Lorsung, Y Yacoby, F Doshi-Velez, W Pan. Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks. ICML Workshop on Uncertainty & Robustness in Deep Learning (UDL), 2020.

• T Guénais, D Vamvourellis, Y Yacoby, F Doshi-Velez, W Pan. BaCOUn: Bayesian Classifiers with Out-of-Distribution Uncertainty. ICML Workshop on Uncertainty & Robustness in Deep Learning (UDL), 2020.

• M Downs, J Chu, Y Yacoby, F Doshi-Velez, W Pan. CRUDS: Counterfactual Recourse Using Disentangled Subspaces. ICML Workshop on Human Interpretability in Machine Learning (WHI), 2020.

• D Vaughan, W Pan, Y Yacoby, EA Seidler, AQ Leung, F Doshi-Velez, D Sakkas. The application of machine learning methods to evaluate predictors of live birth in programmed thaw cycles, Clinical Abstract. American Society of Reproductive Medicine (ASRM), 2019.

Teaching & Mentoring

I’m a co-instructor for CS290, Harvard’s 1st-Year CS PhD Cohort Research Seminar (Fall 2021 - Spring 2022). I created a new syllabus that focuses on skill building (e.g. how to read research papers), soft skill building (e.g. managing advising relationships, how to support your peers), and academic culture (e.g. mental health in academia, normalizing and de-stigmatizing of mental health needs, discussion of power dynamics in scientific communities).

I created and organized a workshop for incoming PhD students at Harvard about managing the multi-faceted challenges of being a PhD student (Fall 2020).  Workshop content can be found here.

I’m a member of InTouch, a peer-to-peer support network for grad students (Spring 2021 - present).

I was a final project mentor, as well as a research mentor for students continuing their research after completing “Stochastic Methods for Data Analysis – Inference and Optimization” (AM207) (Fall 2019 - present).

I was a mentor for the Women in Data Science (WiDS) Cambridge datathon workshop, 2019-2020.

I previously served as a teaching fellow for a number of courses at Harvard: Advanced Machine Learning (CS281) Fall 2018, Systems Programming and Machine Organization (CS61) Fall 2015, and Intro to Computer Science I (CS50) Fall 2012.

Contact

Email: yanivyacoby [AT] g [DOT] harvard [DOT] edu