Zachary Lipton

Assistant Professor, Machine Learning & Operations Research, CMU

Welcome to my website. My name is Zachary Lipton, and I am currently and Assistant Professor of Machine Learning and Operations Research at Carnegie Mellon University (CMU). I hold appointments in the Machine Learning Department in the School of Computer Science (primary), Tepper school of Business (joint), Heinz School of Public Policy (courtesy) and Societal Computing (courtesy). My research spans core ML methods and theory, their applications in healthcare and natural language processing, and critical concerns, both about the mode of inquiry itself, and the impact of the technology it produces on social systems.

I completed my PhD at the loveliest of universities (in UCSD’s Artificial Intelligence Group), and if I had a time machine, I would go back, take two years longer to graduate, and actually learn to surf.

At CMU, I direct the Approximately Correct Machine Intelligence (ACMI) Lab, a group of wonderful students whose creativity and talent are the primary reasons why I have not made good on my perennial threat to relocate to a small island in the Aegean Sea, where I would tend a flock of goats, slowly acquire the centuries-old craft of distilling spirits from local herbs, and devote the rest of my life to writing third-rate science fiction novels. My lab’s focuses include (i) building robust systems that can cope with a changing world, whether due to natural distribution shift, the strategic manipulations of other agents keen to influence automated decisions; (ii) understanding the social impacts of machine learning in a philosophically coherent way; (iii) the intersection of representation learning and causality; and (iv) leveraging ML to address impactful questions in clinical medicine.

I value clear scientific prose and have (co-)authored two reviews of the literature (on RNNs and Differential Privacy), and more recently an interactive book, (Dive into Deep Learning)[], which teaches deep learning through exposition, math and code, in a fully-interactive textbook written in Jupyter and automatically compiled to HTML and PDF (forthcoming on Cambridge University Press). In Fall 2016, I launched Approximately Correct, a blog aimed at bridging technical and social perspectives on machine learning. We have had some success addressing misconceptions about AI, both in the broader discourse and within the research community, but the problem has only intensified.

For now, I plan to keep this site static and to refer visitors to ACMI lab’s website for dynamically update content, including recent papers, current students, and other miscellany.

Contact: zlipton [at] cmu [dot] edu.

Assistant: To maximize the likelihood that your email receives a response, please CC my assistant, Laura Winter: lwinter [at] cmu [dot] edu.