Episode 97: Escaping Poverty, Embracing Digital Learning, Benchmarking ML Systems, and Advancing Data-Centric AI with Cody Coleman

Show Notes(01:49) Cody shared his upbringing in New Jersey, his childhood interest in science and technology, and the few people who have made big differences in his story.(09:35) Cody went over his academic experience studying Electrical Engineering and Computer Science at MIT.(17:51) Cody recalled his favorite classes taken at MIT.(22:43) Cody talked about his engagement in serving as the president of MIT’s chapter of Eta Kappa Nu Honor Society and advancing online education at the MIT Office of Digital Learning.(31:25) Cody is bullish on the future of digital learning.(35:43) Cody expanded on his internships with Google throughout his time at MIT — doing local search quality and YouTube analytics.(42:31) Cody described the challenges of dealing with high-frequency trading data from his one year working as a junior data scientist at the Vendor Data Group of Jump Trading in Chicago.(46:50) Cody reflected on his decision to embark on a Ph.D. journey in Computer Science at Stanford University.(51:54) Cody mentioned his participation in the DAWN project, specifically DAWNBench, an end-to-end deep learning benchmark and competition.(54:21) Cody unpacked the evolution of MLPerf, an industry-standard benchmark for the training and inference performance of ML models.(56:52) Cody walked through the motivation and empirical work in his paper “Selection via Proxy: Efficient Data Selection for Deep Learning.”(59:34) Cody discussed his paper “Similarity Search for Efficient Active Learning and Search of Rare Concepts.”(01:06:32) Cody shared his learnings about bringing ML from research to industry from his advisors, Matei Zaharia and Peter Bailis — who were both academics and startup founders simultaneously.(01:09:19) Cody went over key trends in the emerging Data-Centric AI community — given his involvement with the Data-Centric AI workshop at NeurIPS 2021 and the DataPerf benchmark suite.(01:12:19) Cody shared lessons learned about finding product-market fit as the founder of Coactive AI — which brings unstructured data into the world of SQL and the big data tools that teams already love.(01:15:34) Cody emphasized the importance of focusing on the HR function and defining cultural guiding principles for any early-stage startup founder.(01:21:05) Cody provided his perspective on the differences and similarities between being a researcher and a founder.(01:23:47) Closing segment.Cody’s Contact InfoWebsiteTwitterLinkedInGoogle ScholarCoactive AI’s ResourcesWebsiteTwitterLinkedInCulture ValuesMentioned ContentTalk“Digging Deeper: How a Few Extra Moments Can Change Lives” (TEDxStanford 2017)“Data Selection for Data-Centric AI” (Stanford MLSys 2022)Research“Probabilistic Use Cases: Discovering Behavioral Patterns for Predicting Certification” (2015)DAWNBench: An End-to-End Deep Learning Benchmark and Competition (Dec 2017)“MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance” (Feb 2020)“Selection via Proxy: Efficient Data Selection for Deep Learning” (Oct 2020)“Similarity Search for Efficient Active Learning and Search of Rare Concepts” (July 2021)DataPerf, a new benchmark suite for machine learning datasets and data-centric algorithms (Dec 2021)PeopleMatei Zaharia (Cody’s Ph.D. Advisor, Co-Creator of Apache Spark, Co-Founder of Databricks)Fei-Fei Li (Professor of Computer Science at Stanford, Creator of ImageNet Dataset)Michael Bernstein (Professor of Computer Science at Stanford with a focus on Human-Computer Interaction)Books“No Rule Rules: Netflix and the Culture of Reinvention” (by Reed Hastings)“What You Do Is Who You Are: How to Create Your Work Business Culture” (by Ben Horowitz)“The Inner Game of Tennis: The Classical Guide to Peak Performance” (by Timothy Gallwey)Notes

My conversation with Cody was recorded back in January 2022. Since then, many things have happened at Coactive AI. I’d recommend:

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing [email protected].

Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.