zoya[at]mit[dot]edu

Some references I put together:

For general audiences and technical audiences.

General

My list of non-fiction book recommendations
Some popular science, history of science, economics, psychology, philosophy, biology and other books I've enjoyed, read cover-to-cover, and can recommend. If it's not your field, you can learn a lot in a light, easy-to-digest, on-the-train-to-work manner. If it is your field, you pick up some great selling points and high-level pictures. I've found that reading about a broad range of topics really faciliates conceptual connections across fields.
Also attached is a list of textbooks I've used.
Recap of 2015 Google I/O conference
This year, I kept up-to-date with Google I/O virtually, but in the same city. I picked out my favorite moments (via video screenshots, paraphrased quotes, and related visual content) from the keynote speech and a set of posted session videos. I augmented these slides with little research hints (like: "this area's going to be pretty big soon, get on it").
Recap of 2014 Google I/O conference
Google flew me and 14 other alumni Anita Borg Scholars to attend this conference and put together a Global committee to increase diversity in computer science. I/O was a blast! The days were packed with information, cool tech, and after-hours networking galore. I presented this slide deck back at MIT for the computer vision group, to initiate a conversation about the emerging tech tools, what we might expect from the future, and the research directions that may prove to be fruitful.
Discussion about future of technology (for high school)
This presentation was prepared for a local high-school class that visited MIT as part of their curriculum unit on smart technology in the context of the social future (they were also reading "Fahrenheit 451" at the time). This presentation was meant to initiate discussion as a class about the cool benefits, but also potential (social and intellectual) issues, with technology. For full effect, we finished the day with a tour around MIT's awesome labs.
20 min intro to computer vision for high school
I gave this presentation two years in a row for MIT's WTP program for gifted high-school girls. The goal of this presentation was to provide a really high-level overview of the types of problems we think about in computer vision, why they are hard, and how we could work towards algorithmic solutions.

Technical

Generalized linear regression derivations
A set of notes prepared for the MIT 6.036 course: "Intro to Machine Learning" (Spring 2015).
These were concepts I covered during recitation (tutorial) when I was a TA for this course.
These notes go into very thorough derivations of the generalized linear regression formulation, demonstrating how to write it out in matrix form.
Some basics of SVM
These notes were prepared as personal study notes (Summer 2012). These notes contain some derivations, details, and explanations that not many SVM tutorials usually delve into. Thus, they're meant to augment primary course material (textbook or lecture notes) on SVMs and to help digest the course material.
Test review of some machine learning concepts
A set of notes prepared for the MIT 6.036 course: "Intro to Machine Learning" (Spring 2015).
These were concepts I covered during an exam review session when I was a TA for this course.
They were designed to bridge some of the concepts students learned in the first half of the course, and to think about some of the connections between different machine learning formulations.
Reading group presentation on SVM+
Presented based on the paper: V. Vapnik and A. Vashist, "A new learning paradigm: Learning using privileged information", Neural Networks, 2009.
I presented this at the MIT computer vision reading group (Fall 2012). The point of this presentation was to clarify the parallels between SVM and SVM+, by carefully taking apart the formulation.
Presentation on visual attention
Presented based on the book:
J. K. Tsotsos, "A computational perspective on visual attention", MIT Press 2011.
I presented this at the University of Toronto lab group meeting (Summer 2012). This presentation was meant to give some background and motivation about visual attention modeling, and some of the computational problems that models must handle.
Paper presentation on saliency models
Presented based on the paper: K. Koehler, F. Guo, S. Zhang, M. P. Eckstein. "What do saliency models predict?" JoV, 2014.
I presented this during an MIT seminar course on computational visual attention taught by J.K. Tsotsos (Spring 2014). All the main results of the paper are reported, but broken down one clean result at a time for easier digestion.
Paper presentation on attentional load
Presented based on the paper: P. Dayan. "Load and Attentional Bayes" NIPS, 2008.
I presented this during an MIT seminar course on computational visual attention taught by J.K. Tsotsos (Spring 2014). This presenation is meant to explain the experiments described in this paper, and put them in the context of related work.
Paper presentation on part and appearance sharing
Presented based on the paper: L. Zhu, Y. Chen, A. Torralba, W. Freeman, A. Yuille "Part and Appearance Sharing: Recursive Compositional Models for Multi-View Multi-Object Detection", CVPR 2010. I presented this at the University of Toronto computer vision reading group meeting (Summer 2012). This presentation slowly worked through and clarified the formulations presented in this paper.
A bit about quantum computation
A paper written for the University of Toronto CSC463 class on Complexity and Computability (Spring 2012). Discusses quantum computing in the context of Turing Machines and Classical Computation.

More to come...