Yaniv Yacoby
Assistant Professor of Computer Science
Links
I'm an interdisciplinary researcher working at the intersection of machine learning (ML) and mental health.
I lead the Model-Guided Uncertainty (MOGU) Lab at Wellesley, where we enable effective and responsible uses of expressive (deep) ML models in safety-critical domains, like precision healthcare. Our research specifically focuses on developing methods to help us better understand, predict, and prevent suicide and related behaviors. We do this by developing new paradigms for clinician/patient-AI collaboration.
I see both my teaching and research as social endeavors. My teaching and research both require a social context to meaningfully center ethics, and require supportive classroom/lab cultures to support holistic growth. I'm therefore excited about creating classroom and mentorship experiences that emphasize community building and interrogation of socio-technical systems and cultures.
Outside of work, I enjoy spending time with my two cats and dog, playing and listening to folk music, trying out new food, and watching reality TV.
For my CV, as well as information about my research, teaching, and service, see my website.
Current and upcoming courses
Data Structures
CS230
An introduction to techniques and building blocks for organizing large programs. Topics include: modules, abstract data types, recursion, algorithmic efficiency, and the use and implementation of standard data structures and algorithms, such as lists, trees, graphs, stacks, queues, priority queues, tables, sorting, and searching. Students become familiar with these concepts through weekly programming assignments using the Java programming language.
This course has a required co-requisite lab - CS 230L.
A student is required to have confirmation of authorization from the CS 111 faculty that is based on the mastery of the CS 111 concepts in order to enroll in CS 230. Students who did not take CS 111 at Wellesley and who wish to enroll in CS 230 should contact the CS department to take a placement questionnaire.
-
In recent years, Artificial Intelligence has enabled applications that were previously not thought possible—from systems that propose novel drugs or generate new art/music, to systems that accurately and reliably predict outcomes of medical interventions in real-time. But what has enabled these developments? Probabilistic Machine Learning, a paradigm that casts recent advances in Machine Learning, like neural networks, into a statistical learning framework. In this course, we introduce the foundational concepts behind this paradigm—statistical model specification, and statistical learning and inference—focusing on connecting theory with real-world applications and hands-on practice. This course lays the foundation for advanced study and research in Machine Learning. Topics include: directed graphical models, deep Bayesian regression/classification, generative models (latent variable models) for clustering, dimensionality reduction, and time-series forecasting. Students will get hands-on experience building models for specific tasks ,most taken from healthcare contexts, using a probabilistic programming language based in Python.. Enrollment in this course is by permission of the instructor only. Students interested in taking this course should fill out this google form prior to registration.