Scott Counts

Seattle, Washington

Scott Counts

Research scientist working at the intersection of computer science, behavioral science, and economics to build agentic AI systems and understand how those systems are reshaping knowledge work.

Senior Principal Research Manager Microsoft
Curriculum vitae Google Scholar LinkedIn Microsoft Research Email
Portrait of Scott Counts
Research

What I work on

AI and the future of knowledge work

How people really use AI systems at work, the value they get from them, and how the shape of work changes with agentic systems in the loop. How we build agentic infrastructure to augment human productivity.

Key paper: AI in the Enterprise: How People Use M365 Copilot Chat

Human–AI collaboration  /  productivity measurement  /  agentic systems

Measurement from naturalistic data

Building novel measures from behavioral trace data using LLM-based and other inference techniques and over population scale data: macroeconomic indicators, health signals, workflows.

Key paper: Abstracting Cross-Domain Action Sequences into Interpretable Workflows

Inferred measurement  /  LLM-based inferencing  /  user behavior modeling

Economic and societal impact

Turning those measures into forecasts and policy-relevant findings — unemployment and migration from internet data, the occupational reach of generative AI, and who is included in or left out of the digital economy.

Key paper: AI and the Democratization of Knowledge Work

Economics of AI  /  macroeconomic forecasting  /  computational social science
Work

Teams and platforms

2025 –

IDEAS Research

Research lead · Microsoft productivity division

A multidisciplinary applied research team on the future of human–AI productivity, combining computer science, behavioral science, and economics. I own organizational vision, research strategy, talent development, cross-company partnerships, and the translation of research into next-generation productivity and AI experiences.

2023 – 2025

Semantic Telemetry

User behavior measurement for the AI era

An analytics platform that characterizes what users are actually doing inside AI systems. I led the development of inferred measurement to improve AI chat systems, and owned both the research agenda and the product improvement pipeline.

2019 – 2023

Project Eclipse

Hyperlocal air quality sensing for cities

A hardware-to-analytics platform for city-scale air quality: state-of-the-art sensing hardware paired with a cloud data stack for ML-based calibration and analytics. I oversaw data and ML engineering, managed public and private partnerships, and led overall design, deployment, and research.

2014 – 2019

Economic Measurement Group

Economic and financial forecasting from internet data

An economic modeling and forecasting system built on internet data that drove an internal trading portfolio of more than $100M, plus a Microsoft product providing forecasts for major macroeconomic data releases. I led data modeling and engineering and served as liaison to the Treasury trading team.

14,000+
Citations
54
h-index
101
i10-index
75+
Publications

Google Scholar, August 2026 · 10 granted U.S. patents

Publications

Selected publications

More than 75 peer-reviewed papers, with a best paper award (CSCW), honorable mentions (CHI), and multiple test-of-time awards (Web Science, ICWSM). The full list lives on Google Scholar.

AI measurement & usage

AI in the Enterprise: How People Use M365 Copilot Chat
Counts, S., et al. · arXiv:2605.23958
2026
How Copilot Changed the Pace of Work in Word — And How We Measured It
Verma, G., Suri, S., & Counts, S. · Microsoft Technical Report MSR-TR-2026-24
2026
AI and the Democratization of Knowledge Work
Daepp, M., Tomlinson, K., Counts, S., & Suri, S. · Nature Computational Science
2026
Working with AI: Measuring the Applicability of Generative AI to Occupations
Tomlinson, K., Jaffe, S., Wang, W., Counts, S., & Suri, S. · arXiv:2507.07935
2025
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Lin, Y., et al. · Proc. ACL
2024
TnT-LLM: Text Mining at Scale with Large Language Models
Wan, M., et al. · Proc. KDD
2024

Macroeconomic forecasting

Who Gets to Work in the Digital Economy?
Counts, S., Suri, S., Brown, A., Xu, B., & Raghavan, S. · Harvard Business Review
2022
Forecasting U.S. Domestic Migration Using Internet Search Queries
Lin, A., Cranshaw, J., & Counts, S. · Proc. WWW
2019
Measuring Employment Demand Using Internet Search Data
Chancellor, S., & Counts, S. · Proc. CHI
2018
The Psychology of Job Loss: Using Social Media Data to Characterize and Predict Unemployment
Proserpio, D., Counts, S., & Jain, A. · Proc. Web Science
2016
Quantifying the Effects of Online Bullishness on International Financial Markets
Mao, H., Counts, S., & Bollen, J. · European Central Bank Statistics Papers
2015

Health modeling

Understanding Anti-Vaccination Attitudes in Social Media
Mitra, T., Counts, S., & Pennebaker, J. · Proc. ICWSM
2016
Gender & Ideology in the Spread of Anti-Abortion Policy
Zhang, A., & Counts, S. · Proc. CHI
2016
Unraveling Abstinence and Relapse: Smoking Cessation Reflected in Social Media
Murnane, E., & Counts, S. · Proc. CHI
2014
Social Media as a Measurement Tool of Depression in Populations
De Choudhury, M., Counts, S., & Horvitz, E. · Proc. Web Science
2013

Most cited

Predicting Depression via Social Media
De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. · Proc. ICWSM
2013 · 2,774 cites
Tweeting Is Believing? Understanding Microblog Credibility Perceptions
Morris, M. R., Counts, S., Roseway, A., et al. · Proc. CSCW
2012 · 706 cites
Predicting the Speed, Scale, and Range of Information Diffusion in Twitter
Yang, J., & Counts, S. · Proc. ICWSM
2010 · 681 cites
Predicting Postpartum Changes in Emotion and Behavior via Social Media
De Choudhury, M., Counts, S., & Horvitz, E. · Proc. CHI
2013 · 676 cites
Identifying Topical Authorities in Microblogs
Pal, A., & Counts, S. · Proc. WSDM
2011 · 538 cites
Patents

Granted U.S. patents

Ten granted patents spanning language-model methods, search interfaces, location inference, and mobile social systems.

Dataset Clustering Via Language Model PromptsUS 12,399,955
User Interface for Visualizing Search DataUS 11,416,535
User Behavior Monitoring on a Computerized DeviceUS 9,427,185
Interactive Search Results PageUS 9,292,602
Automatically Generated Highlight View of Electronic InteractionsUS 8,316,315
Computing a Time-Dependent Variable ValueUS 8,190,477
Game Builder for Mobile Device-Based GamesUS 8,123,599
Determining Physical Location Based Upon Received SignalsUS 7,933,612
Implicit Group Formation Around Feed Content for Mobile DevicesUS 7,684,815
Activity Classification from Route and Sensor-Based MetadataUS 7,668,691
Background

Education and service

Education

PhD, Psychology
University of Washington · 2002
“Modeling and Predicting Stable Response Variations Across Situations”
BA, Economics
Claremont McKenna College · 1993 · Cum Laude

Service & recognition

  • Conference leadershipGeneral Chair, CSCW · Program Chair, ICWSM
  • AwardsBest Paper, CSCW · Honorable Mentions, CHI
  • Test of timeWeb Science · ICWSM (multiple)
Practice

How I work

Technical

LLM-based inferencing; machine learning across feature creation, clustering, time series, and model selection and evaluation; exploratory data analysis; data visualization; experimental statistics; R and Python. Working knowledge of NLP (sentiment, embeddings) and deep learning (PyTorch).

Domains

User behavior modeling, agentic systems development, productivity measurement, macroeconomic modeling, and measure development from naturalistic data.

Leadership

Strategic visioning and research agenda development with high business impact, people management, and leadership of multidisciplinary teams.

Contact

Get in touch

I am glad to hear from researchers, collaborators, and teams working on measurement, AI, and the future of knowledge work.

scounts@gmail.com
Curriculum vitae LinkedIn Google Scholar Microsoft Research