Technical data science executive and advisor with a passion for turning data into products, actionable insights, and meaningful stories. At Jawbone, I built & lead a team focusing on developing data products that promote a healthier lifestyle and on finding stories in the UP wristband data. Prior to Jawbone, I was one of the early members of the LinkedIn data science team, where I developed and improved some of LinkedIn’s key data products for matching jobs to passive candidates, discovering people you may know, and recommending groups you may like. Strong background in applied machine learning (CMU CS PhD), data science, wearables and health, social network analysis, recommender systems, statistical text mining and multilingual information retrieval (search). Fast Company's 100 most creative people in business; Fortune Magazine 2014 Big Data All-Stars.

Equity Partner @ Technical due diligence and advising. From March 2013 to Present (2 years 10 months) Advisor @ From 2012 to Present (3 years) VP of Data @ I built a team of world class, full stack data scientists and engineers. At the intersection of wearables, data and the Internet of Things, we focused on developing products that promote a healthier lifestyle. My team used data to make smart, personalized and contextualized suggestions to help you achieve your goals. We automatically detect if you've been working out, automatically classify the type of workout, suggest foods you've eaten, show you interesting correlations in your activity/sleep/food data, and give you actionable advice. We built and maintained product & analytics infrastructure, shipped production code, designed & implemented the metrics, experiments and instrumentation, built interactive visualizations -- all in record time. Understanding sleep, activity and nutrition at scale led us to previously unimagined insights -- discoveries you couldn't make in a lab or with a survey. We’ve now established Jawbone as one of the top resource for understanding health implications of activity, sleep and nutrition. Here are a few examples: https://jawbone.com/blog/napa-earthquake-effect-on-sleep/ https://jawbone.com/blog/jawbone-up-data-by-city/ https://jawbone.com/blog/weight-loss/ https://jawbone.com/blog/what-makes-people-happy/ https://jawbone.com/blog/how-temperature-affects-exercise/ https://jawbone.com/blog/sleep-week/ https://jawbone.com/blog/circadian-rhythm/ https://jawbone.com/blog/jawbone-up-common-pairings/ https://jawbone.com/blog/jawbone-up-food-data/ https://jawbone.com/blog/sleep-china/ https://jawbone.com/blog/2014-year-review/ https://jawbone.com/blog/new-years-eve/ From July 2013 to June 2015 (2 years) Senior Data Scientist @ How do you know Darth Vader’s LinkedIn account is not real? How do you train a recommender system *before* it goes live? What are the hottest companies this year, and more importantly, how do you leverage terabytes of data to define “hot”? What algorithms will find your next sharp CTO in the 10^8 LinkedIn profile haystack? I answer these types of questions every day - and more importantly, I ask them. The hats: applied machine learning scientist, hands-on prototyper, product manager, data miner, talent high-pass filter, mentor, spokesperson, fraud fighter, crowdsourcer, acquisition due diligence, namer … and distributed database guinea pig. Impact: ✔ Created and implemented the initial LinkedIn job-to-candidate matching system (Talent Match), now a multi-million dollar product. ✔ Developed the first machine learning model for LinkedIn’s People You May Know, immediately doubling invitation rates. ✔ Created and implemented the "Groups You May Like" product (v1.0) resulting in a double digit engagement rate. ✔ Coaxed stories out of data -- they brought us to Good Morning America, the front page of the Wall Street Journal, The Economist and hundreds of other media outlets. This includes the top shared story in LinkedIn’s history (on overused buzzwords), job title trends, successful first names, “hot” companies and industries, labor market movements etc. ✔ Pioneer, scientist, and evangelist for the LinkedIn’s recommender system line of products: Jobs You May Be Interested In, Similar Jobs, Talent Match etc. Technical contributions include: solving the cold start training problems, a generalized metrics / evaluation framework, feature engineering, model building & visualization. ✔ Crowdsourcing specialist (Mechanical Turk, Crowdflower, Samasource). ✔ Fraud detection (fake accounts, spam, TOS violations). ✔ Co-organized the LinkedIn external tech talk series & the data science summer intern program From May 2008 to May 2013 (5 years 1 month) Research Assistant @ Advisors: Yiming Yang and Jaime Carbonell. Feature selection for text classification. Cross-lingual information retrieval using parallel corpora, including full system implementation. Multilingual search competitions, named entity detection, event detection and tracking. Thesis work on domain adaptation of translation models, as applied to Cross-Language Information Retrieval, Event Tracking and Machine Translation. Crucial role in the CMU DARPA GALE project: grant proposal co-writing, end-to-end system architecture and design, project coordinator across CMU, UPitt & IBM; user study design, evaluation dataset design. From September 2000 to May 2008 (7 years 9 months) Research Intern @ From May 2005 to August 2005 (4 months) Research Intern @ Unsupervised learning for Arabic stemming. Work published: ACL'03. From May 2003 to August 2003 (4 months) Research Intern @ Text mining and machine learning for the DARPA Communicator. Work published : NAACL'01, ACL'01. From May 2000 to August 2000 (4 months) Undergraduate Teaching Assistant & Software Developer @ Teaching Assistant, UNM CS Department (multiple classes, starting as a sophomore) Scheme, C++, intro to algorithms. Developed an automatic grader application to evaluate student projects and e-mail a progress report. Software Developer, UNM CS Department Lead a team developing a web-based grade database (SQL, C++) used by the three largest classes in the CS department. From August 1997 to December 1999 (2 years 5 months) Logic Design Assistant @ Developed a packetizing chip software simulation and a multiplexer usage optimization for multiple outputs boolean functions; disassembled and redesigned a microcontroller test bench. Responsible for student assignment evaluation for a logic design class. From 1996 to 1999 (3 years)

Ph.D., Computer Science @ Carnegie Mellon University From 2003 to 2008 MS, Computer Science @ Carnegie Mellon University From 2000 to 2003 BS, Computer Science @ The University of New Mexico From 1996 to 2000 Computer Science @ Tudor Vianu Computer Science High School / Liceul de Informatica Monica Rogati is skilled in: Recommender Systems, Social Networking, Analytics, Crowdsourcing, Information Retrieval, Machine Learning, Text Mining, Data Mining, Text Classification, Text Analytics, Personal Finance, Machine Translation, Natural Language Processing, Collaborative Filtering, Information Extraction