Objective and Summary

My obsession is the development of tools and techniques for the interpretable, safe, and fair deployment of intelligent systems.

Social Impact Work

I am very active in the development of open source code and organizations for social impact, including the engineering and management of the AI Incident Database, which is a collection of AI harms and near harms realized in the real world. The database is funded by private foundation donors and is the sole project of the Responsible AI Collaborative. I also previously developed a federally-recognized 501c3, the Privly Foundation, dedicated to online privacy education. The foundation's activities included developing Open Source software, technology workshops, and supervising student developers.


I strive to cover as much of the machine learning stack as possible from cloud infrastructure to hardware accelerated edge runtimes. Particular strengths of mine are Python, Keras (defining high-level APIs accelerated by hardware), Javascript, React, TensorFlow, CI/CD, and AWS/GCP/Azure. My engineering projects have spanned dataset aquisition, preparation, training, and model analysis tools. A particular engineering joy of mine is building systems (typically on the web stack) explaining the strengths and failings of trained models.

Machine Learning + Silicon Design Startup

My recent development and research interests were in making ultra-low power neural network inference feasible through work at Syntiant. To date, Syntiant has shipped more neural ASICS than any silicon provider in the world for problems ranging from voice interfaces to to sensor fusion. I left my full-time position with Syntiant in January so I could focus on AI assurance. For a full professional history, please view my LinkedIn profile.

PhD Research

My grad school career covered four distinct areas. First, I developed a simulator for fire, forest growth, timber sales, and weather. Second, I developed visual analytic tools for exploring the policy space of Markov Decision Processes (MDPs), including the wildfire simulator. Since many MDPs are defined by computationally expensive simulators, I next developed a surrogate modeling method that brings interactive specification of policy, reward, and optimization functions to large state space Markov Decision Processes. My final area of focus is Bayesian policy search using the surrogate model I developed.

Details on each of these areas are covered in specific cases below.

CV Items

  • Filters

      Filter CV Items by Type

      Filter CV Items by Topic


First Year Results from the IBM Watson AI XPRIZE: Lessons for the "AI for Good" Movement

McGregor, S., Banifatemi, A.

This chapter summarizes the strengths and weaknesses of artificial intelligence in solving problem domains advancing the common wellfare. The chapter will be published with the competition and team details of the NIPS Competition Track.

I will provide the text upon request.

Citation  2018

Connecting Conservation Research and Implementation: Building a Wildfire Assistant

McGregor, S., Houtman, R., Metoyer, R., & Dietterich, T. G.

This chapter binds my PhD dissertation together with other works in AI applied to problems in conservation. If you want to learn more about my PhD work, I recommend starting here.

The attached version is pre-typeset editing from Cambridge Press. A preview of the book is available here.

Paper Citation BibTeX  2019

Introduction to NIPS 2017 Competition Track

(Many Authors)

This chapter surveys the machine learning competitions collected within the NIPS Competition Track. All chapter writers were invited to contribute to this survey chapter.

I will provide the text upon request.

Citation BibTeX  2018


MDPvis: An Interactive Visualization for Testing Markov Decision Processes

McGregor, S., Buckingham, H., Houtman, R., Montgomery, C., Metoyer, R., & Dietterich, T. G.

This paper is an extended abstract of the paper presented at the IEEE Symposium on Visual Languages and Human-Centric Computing.

Paper Presentation Slides Citation BibTeX  2015

Visualizing High-Dimensional MDPs with Model-Free Monte Carlo

McGregor, S., Houtman, R., Montgomery, C., Metoyer, R., & Dietterich, T. G.

Best Paper Nomination

This work extends Model-Free Monte Carlo to large state space Markov Decision Processes (MDPs) by factoring the state space. We successfully generate surrogate trajectories for a large state space MDP (more than 10^1000000 states) with 8 state features. We posted an extended version of the paper to arXiv.

Paper Citation BibTeX  2017

Facilitating Testing and Debugging of Markov Decision Processes with Interactive Visualization

McGregor, S., Buckingham, H., Dietterich, T. G., Houtman, R., Montgomery, C., & Metoyer, R.

This paper introduces Markov Decision Processes to visualization researchers as a theoretical formulation for sequential optimization problems. We explain the MDP formulation, present a visual analytic system exploring simulator-defined MDPs, and evaluate the system on a high dimensional MDP domain.

Paper Presentation Slides Citation BibTeX  2015

Indexing AI Risks with Incidents, Issues, and Variants

McGregor, S., Paeth, K., & Lam, K.


Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these issues advance human understanding of where AI presents the potential for harm. Similar to databases in aviation and computer security, the AIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a harm or near harm event) and issues (i.e., a risk of a harm event). Further, as some machine learning-based systems will sometimes produce a large number of incidents, the notion of an incident "variant" is introduced. These proposed changes mark the transition of the AIID to a new version in response to lessons learned from editing 2,000+ incident reports and additional reports that fall under the new category of "issue."

Demo Paper Citation BibTeX  2022

Interactive visualization for testing Markov Decision Processes: MDPVIS

McGregor, S., Buckingham, H., Dietterich, T. G., Houtman, R., Montgomery, C., & Metoyer, R.

The development of a computationally expensive MDP simulator demonstrated the absence of adequate tooling for sequential decision making problems. This paper presents a visual analytic system, MDPvis, that is easy to interface with MDP simulators and optimizers and extensible with additional visualizations.

Demo Paper Citation BibTeX  2017

Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database

McGregor, S.

Mature industrial sectors (e.g., aviation) collect their real world failures in incident databases to inform safety improvements. Intelligent systems currently cause real world harms without a collective memory of their failings. As a result, companies repeatedly make the same mistakes in the design, development, and deployment of intelligent systems. A collection of intelligent system failures experienced in the real world (i.e., incidents) is needed to ensure intelligent systems benefit people and society. The AI Incident Database is an incident collection initiated by an industrial/non-profit cooperative to enable AI incident avoidance and mitigation. The database supports a variety of research and development use cases with faceted and full text search on more than 1,000 incident reports archived to date.

You can interact with the website today, which has been the foundational project of the Responsible AI Collaborative.

Demo Paper Citation BibTeX  2021

FlareNet: A Deep Learning Framework for Solar Phenomena Prediction

McGregor, S., Dhuri, D., Berea, A., Muñoz-Jaramillo, A.

This workshop paper and poster introduce the work product of the 2017 NASA Frontier Development Lab (FDL) research team in space weather prediction. We modeled solar activity with deep neural networks to predict solar flares. FlareNet shows promise in predicting natural phenomena that can cause immense damage to human terrestrial infrastructure.

Paper Poster Citation BibTeX  2017

Allowing a Wildfire to Burn: Estimating the Effect on Future Fire Suppression Costs

Houtman, R. M., Montgomery, C. A., Gagnon, A. R., Calkin, D. E., Dietterich, T. G., McGregor, S., & Crowley, M.

This paper outlines the wildfire simulator I collaboratively developed for evaluating wildfire suppression decisions. Wildfires can reduce timber harvests in the present, but suppression decisions must balance firefighting costs, reduced fuel risks post-fire, and timber revenues.

Paper Citation BibTeX  2013

Factoring Exogenous State for Model-Free Monte Carlo

McGregor, S., Houtman, R., Montgomery, C., Metoyer, R., & Dietterich, T. G.

This work shows how to reduce the simulation time of a high-dimensional ecological Markov Decision Process (MDP) from 7 hours to less than a seconds. We apply the method outlined in the RLDM paper.


Toward Visualization Methods for Interactive Improvement of MDP Specifications

McGregor, S., Dietterich, T. G., & Metoyer, R.

This workshop paper presents an early version of MDPvis and discusses the need for stronger visualizations in the reinforcement learning community.

Paper Presentation Slides Citation BibTeX  2014

Fast Optimization of Wildfire Suppression Policies with SMAC

McGregor, S., Houtman, R., Montgomery, C., Metoyer, R., & Dietterich, T. G.

This paper is the first application of random forests for direct policy search. Additionally, it is the first case of optimizing wildfire suppression policies across 100 year time spans. This is a standalone version of the paper incorporated into the book chapter, "Connecting Conservation Research and Implementation: Building a Wildfire Assistant."

Paper Citation  2017

Participation Interfaces for Human-Centered AI

McGregor, S.


Emerging artificial intelligence (AI) applications often balance the preferences and impacts among diverse and contentious stakeholder groups. Accommodating these stakeholder groups during system design, development, and deployment requires tools for the elicitation of disparate system interests and collaboration interfaces supporting negotiation balancing those interests. This paper introduces interactive visual "participation interfaces" for Markov Decision Processes (MDPs) and collaborative ranking problems as examples restoring a human-centered locus of control.

Paper Citation BibTeX  2022

A Scaled Multi-Year Responsible AI Impact Assessment

McGregor, S.

Forthcoming in 2023

This is the publication giving the complete rundown of the AI XPRIZE from start to finish.


The IBM Watson AI XPRIZE was a $5 million competition calling for teams to improve the world for "good" with artificial intelligence. Competing over positive social progress rather than technological progress required innovation in qualitative judging processes that produced the most extensive evaluation of AI system impacts performed to date. Over the course of four years, more than 150 teams were evaluated for their impacts and responsible engineering for social change. This work presents the responsible AI processes developed for the prize, including solutions for judging absolute and relative impacts with (1) a forced ranking method to produce efficient consensus of review panels, (2) a "red judge" process of domain experts supporting the review panels, and (3) an algorithm and user interface for collaborative ranking. Collectively, these developments represent the most comprehensive and scaled impact evaluation of intelligent systems yet conducted.

Citation BibTeX  2023

A taxonomic system for failure cause analysis of open source AI incidents

Pittaras, N. & McGregor, S.


While certain industrial sectors (e.g., aviation) have a long history of mandatory incident reporting complete with analytical findings, the practice of artificial intelligence (AI) safety benefits from no such mandate and thus analyses must be performed on publicly known "open source" AI incidents. Although the exact causes of AI incidents are seldom known by outsiders, this work demonstrates how to apply expert knowledge on the population of incidents in the AI Incident Database (AIID) to infer the potential and likely technical causative factors that contribute to reported failures and harms. We present early work on a taxonomic system that covers a cascade of interrelated incident factors, from system goals (nearly always known) to methods / technologies (knowable in many cases) and technical failure causes (subject to expert analysis) of the implicated systems. We pair this ontology structure with a comprehensive classification workflow that leverages expert knowledge and community feedback, resulting in taxonomic annotations grounded by incident data and human expertise.

Paper Citation BibTeX  2023

Fast Simulation for Computational Sustainability Sequential Decision Making Problems

McGregor, S., Houtman, R., Buckingham, H., Montgomery, C., Metoyer, R., & Dietterich, T. G.,

This paper, presentation, and poster give preliminary results for modeling the wildfire policy MDP with Model-Free Monte Carlo.

Video Paper Poster Presentation Slides Citation BibTeX  2016

Year One of the IBM Watson AI XPRIZE: Case Studies in "AI for Good"

McGregor, S., Banifatemi, A.

This is a summary of judging processes developed for the first milestone competition of the IBM Watson AI XPRIZE, as well as the teams that were nominated for the competition. I will provide the text upon request.

Citation BibTeX  2018

The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media

McGregor, S., Leibowicz, C., and Ovadya, A..

Synthetic media detection technologies label media as either synthetic or non-synthetic and are increasingly used by journalists, web platforms, and the general public to identify misinformation and other forms of problematic content. As both well-resourced organizations and the non-technical general public generate more sophisticated synthetic media, the capacity for purveyors of problematic content to adapt induces a \newterm{detection dilemma}: as detection practices become more accessible, they become more easily circumvented. This paper describes how a multistakeholder cohort from academia, technology platforms, media entities, and civil society organizations active in synthetic media detection and its socio-technical implications evaluates the detection dilemma. Specifically, we offer an assessment of detection contexts and adversary capacities sourced from the broader, global AI and media integrity community concerned with mitigating the spread of harmful synthetic media. A collection of personas illustrates the intersection between unsophisticated and highly-resourced sponsors of misinformation in the context of their technical capacities. This work concludes that there is no "best" approach to navigating the detector dilemma, but derives a set of implications from multistakeholder input to better inform detection process decisions and policies, in practice.

Note: All authors contributed equally to this paper. See also earlier work on this topic.

Paper Poster Citation BibTeX  2021


AI for Good via Machine Learning Challenges

Banifatemi, A., McGregor, S., Chklovski, T., and Delisle, L.

In this invited talk I presented with three other people on our individual efforts at improving the world through incentivized AI competitions. I delivered my invited talk in two parts,

Part 1, IBM Watson AI XPRIZE: High Stakes "Academic Review": Here I detailed the collaborative ranking method I developed for the IBM Watson AI XPRIZE, which I will prepare for publication towards the end of 2020. The method was successful in efficiently selecting the strongest set of candidate teams for advancement in the IBM Watson AI XPRIZE.

Part 2, Inverting the Competition: This section was forward looking and explored approaches to competing over the production of useful data rather than useful models. This work is still in the early stages.

You can find more information on the workshop here and the abstract for our invited talk is below.

"AI for Good" efforts (e.g., applications work in sustainability, education, health, financial inclusion, etc.) have demonstrated the capacity to simultaneously advance intelligent system research and the greater good. Unfortunately, the majority of research that could find motivation in real-world "good" problems still center on problems with industrial or toy problem performance baselines. 
Competitions can serve as an important shaping reward for steering academia towards research that is simultaneously impactful on our state of knowledge and the state of the world.  This talk covers three aspects of AI for Good competitions. First, we survey current efforts within the AI for Good application space as a means of identifying current and future opportunities. Next we discuss how more qualitative notions of "Good" can be used as benchmarks in addition to more quantitative competition objective functions. Finally, we will provide notes on building coalitions of domain experts to develop and guide socially-impactful competitions in machine learning.
Citation  2019

Machine Learning in the Real World

McGregor, S.

This was an opportunity to present to a meetup group I founded several years ago in Portland Oregon. I am delighted to see the meetup group outlived my organizing and invited me back after more than 2 years away to present to the group.

Abstract: Companies and governments increasingly deploy intelligent systems to the real world. These systems--depending on the design and purpose--have the capacity to either greatly enhance the general well-being of the world or bring about some form of a science fiction dystopia.

I will cover both aspects of these systems in equal measure. First, I will share examples of leading-edge "AI for Good" research. Then, I will guide us through some real-world harms caused by deployed AI systems as explored from the AI Incident Database now under development with the Partnership on AI. Sean will then wrap up the talk with a discussion of current opportunities and risks in the development of AI systems for the general welfare.

Citation BibTeX  2019

Solving Grand Challenges with Artificial Intelligence

McGregor, S.

I keynoted the Thrival Innovation Festival on the topic of Artificial Intelligence for good. This talk was to a general business and startup audience and centered around the teams competing in the IBM Watson AI XPRIZE. Following the keynote I sat on a panel with David Danks (CMU Philosophy department chair), Amrit Dhir (Google for Entrepreneurs), Jana Eggers (Nara Logics), and Iba Masood (TARA.AI).

Presentation Slides Citation  2017

Making Your Privacy Software Usable

Davidson, J., & McGregor, S.

I collaborated with UX researcher Jen Davidson on a presentation of usability evaluation methods and privacy software ethics.

Citation BibTeX  2014

Digital Transformation and Collaboration in post-COVID world

Arun Rajasekaran, Christine Roberts, Sean McGregor, Sunil Madan

ValleyML is a volunteer-driven cross-industry community for advancing AI to empower people with a global reach of more than 200,000 professionals. In this electronic panel discussion, Arun Rajasekaran (Head, Technology Strategy, Wearables, Poly) led a discussion around electronic collaboration from the perspectives of parts makers (Syntiant), device makers (Poly), and service providers (Sunil Madan from Zoom). The panel was part of a larger convening taking place over the whole summer that covered many aspects of machine learning in industry. A video of the event is available on YouTube.


  • Arun Rajasekaran: Head, Technology Strategy, Wearables, Poly
  • Christine Roberts: Senior Vice President and General Manager, Poly’s Enterprise Headset Division
  • Sean McGregor: ML Architect, Syntiant
  • Sunil Madan: Corporate CIO, Zoom
Video Citation  2020

Interdisciplinary Collaboration: Finding new Ways of Extracting Scientific Insights from Solar Observations.

McGregor, S., Munoz-Jaramillo, A., Dhuri, D., & Berea, A.

I spent the summer of 2017 working collaboratively with computer scientists and heliophysicists to predict solar flares from raw Solar Dynamics Observatory images. This presentation was an invited talk to present our preliminary results to the NASA community.

My contributions to this research included defining the software architecture for heliophysics deep learning research, including visualization methods for extracting scientific insights from trained neural network models.

Since the development process was unusual by NASA standards, the first quarter of the presentation focused on our style of agile development methods as a means of conducting interdisciplinary research.

Citation BibTeX  2017

Analog Computing for Deep Neural Networks

McGregor, S.

This talk thematically unifies the four design dimensions of power-efficient neural network computation, including, (1) changes to the neural network specification (e.g., depthwise separable convolutions), (2) changes to the numerical expression of the neural network (e.g., quantization), (3) post-training optimization of the neural network (e.g., pruning), and (4) custom silicon (e.g., analog matrix multiplication). This talk draws from my work at Syntiant developing neural network models for ultra-low power computation on analog silicon.

Citation  2018

Researchers discuss pursuing degrees or careers in AI for social good

Meghana Kshirsagar, Fei Fang, Sean McGregor

A panel of practitioners from the AI for Good space share their experience and insights with young people exploring potential career paths.


  • Meghana Kshirsagar: Senior Applied Scientist, Microsoft AI for Good research
  • Fei Fang, Leonardo Assistant Professor, Institute for Software Research in the School of Computer Science at Carnegie Mellon University
  • Sean McGregor, Founder, Responsible AI Collaborative

More event details

Citation  2022

Syntiant NDP120 (Award Finalist Presentation)

McGregor, S.

Among my greatest professional honors was representing Syntiant at tinyML in competition with industry heavyweights. tinyML is the premiere industry group dedicated to running machine learning solutions on-device,

Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware (dedicated integrated circuits), algorithms and software capable of performing on-device sensor (vision, audio, IMU, biomedical, etc.) data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.

In this presentation, I represented the NDP120 in competition with,

  • Qualcomm: Always-On Vision
  • Samsung Exynos: 2100 NPU
  • Cartesiam: Nanoedge AI Studio

We won!

Video Citation  2021

Panel Discussion on Markets for Global Challenge

Alexandra Amouyel, Moustapha Cisse, Celina Lee, Irene Lo, Sean McGregor, Rudradeb Mitra, Victor Ohuruogu, Bright Simons, Milind Tambe, Nikhil Velpanur, Charity Wayua

A panel discussion broken into two segments. My fellow panelists included,

  • Alexandra Amouyel (MIT Solve)
  • Moustapha Cisse (Google; African Masters of Machine Intelligence)
  • Celina Lee (Zindi)
  • Irene Lo (Stanford University; Mechanism Design for Social Good)
  • Sean McGregor (XPRIZE Foundation and Syntiant)
  • Rudradeb Mitra (Omdena Inc.)
  • Victor Ohuruogu (UN Foundation Global Partnership for Sustainable Development Data)
  • Bright Simons (mPedigree)
  • Milind Tambe (Harvard University; Google)
  • Nikhil Velpanur (Wadhwani AI)
  • Charity Wayua (IBM Research Africa)
Citation  2019

Beneficial AI

United Nations, International Telecommunication Union

I served as panelist and/or rapporteur for this gathering of UN Agencies, NGOs, and academics. The group effort centered on developing guidlines and principles for the beneficial use of Artificial Intelligence. As part of my panel participation I drafted three documents in collaboration with panel moderators. These include one document on beneficial AI with the head of the Montreal Institute for Learning Algorithms (MILA), another document on privacy and security with the director of the United Nations Interregional Crime and Justice Research Institute, and a final document on measuring the positive and negative impacts of AI technologies with the head of UNICEF's venture fund.

Citation  2017

Impactful AI: Solving for Sustainable Development Goals

McGregor, S.

In this presentation I walked the UN World Food Program through the opportunity in applying AI to programs in food security and conservation. My goal was to simultaneously express the opportunities afforded by deployment of intelligent software products, while emphasizing that these solutions still require careful thought and clever formulations.

Presentation Slides Citation  2017

Moving Cloud Level Deep Learning Into Always On Battery Powered Edge Devices With Analog Neural Networks

McGregor, S.

This is a talk to the mems and sensors community, which is generally concerned with parts that go into devices. I presented within the emerging technologies track because our (Syntiant's) analog neural network is something both completely different and potentially transformative to the sensor community that produces more data than can conceivably be stored.

Citation  2019

AI for Good in Action

Banifatemi, A., McGregor, S., McConaghy, T.

This presentation surveyed recent results and organizational directions in the AI for Good space. My contribution to the presentation was to introduce a few examples of teams within the IBM Watson AI XPRIZE and to give the attendees an outline of the conference proceedings for which "AI for Good" is an explicit or implicit topic. I was later invited to reprise elements of the presentation at the Common Model Infrastructure Expo Workshop organized by Baidu.

Citation  2018

Incentives within Competitions for Social Good

McGregor, S.

The main idea of this workshop is that we should more deliberately think about the problem of facilitating interdisciplinary collaborations as a market design problem. We believe this is our research community’s path to greatest societal impact—we should focus on building the engine that allows people across disciplines to tackle important social issues, and in the process, expand the technical expertise of aspiring researchers in parts of the world where it’s currently lacking. Our goal is to take a project-based approach and provide apprenticeship learning to effectively mentor people into having novel intersections of domain and technical expertise.

My contribution to this topic is the attached video, which had live follow up questions. It covers

Part 1, IBM Watson AI XPRIZE: High Stakes "Academic Review": Here I detailed the collaborative ranking method I developed for the IBM Watson AI XPRIZE. The method was successful in efficiently selecting the strongest set of candidate teams for advancement in the IBM Watson AI XPRIZE.

Part 2, Inverting the Competition: This section was forward looking and explored approaches to competing over the production of useful data rather than useful models. This work is still in the early stages.

You can find more information on the workshop here and the complete video of the presentation is attached.

Video Citation  2020

Preventing Repeated AI Harms by Sharing AI Failures

McGregor, S.

Mature industrial sectors (e.g., aviation) collect their real world failures in incident databases to improve design and process, but the AI industry lacks similar systematization. As a result, companies repeatedly make the same mistakes in the design, development, and deployment of intelligent systems. The AI Incident Database (AIID) is the start of formal record keeping of AI harms realized in the real world. The AIID dataset highlights several issues in human-machine collaboration through an analytic web front end for more than 1,000 incident reports archived to date. Insights from the project's data and collaboration architecture will be presented.

Session details

Citation BibTeX  2021

Evaluating problems with AI

United Nations, International Telecommunication Union

In this talk I contextualized machine learning engineering processes for the mixed crowd of machine learning researchers and representatives of international organizations.

Citation  2019

Production Machine Learning Systems

McGregor, S.

Abstract: Over the last half century, the field of software engineering has produced tools and techniques for efficiently developing software. Some examples include unit/integration testing, integrated development environments, formal verification methods, and more. More recent machine-learned or "intelligent systems" pose distinctive challenges that are poorly served by these existing software engineering tools and techniques. This talk will cover the elements making intelligent system engineering distinctive from traditional software engineering and highlight tools and techniques tailored to the engineering challenges they introduce. This topic will be explored in the context of several motivating examples, including the development of a "production grade" speech recognition model at Syntiant, a system for making recommendations in response to wildfires, and collaborative work at the Partnership on AI.


How to Encrypt Your Content on Any Website

McGregor, S., Karve, S., & Davidson, J.

This presentation explained the technical foundations of the priv.ly project.

Citation BibTeX  2012

Panel Introducing the Facebook Deepfake Detection Challenge

Joaquin Quiñonero Candela, Cristian Canton Ferrer, Irina Kofman, Claire Leibowicz, Sean McGregor

As part of my service on the Partnership on AI's AI and Media Integrity Steering Committee, I sat on a panel for the launch of Facebook's Deepfake Detection Challenge. The panel focused on the importance of the deepfake detection problem and how we can mitigate the immediate impacts through the development of detector models. The central premise I presented to the panel was that we can build detector models for the current generation of deepfakes, but these detectors are brittle and will fail to detect with each generation of improved deepfake generator models. See my discussion of the topic here.

Citation  2019

Privacy (or not) in a Digital Age

McGregor, S.

This talk covered threats to electronic privacy and mitigation strategies. Topics covered included encryption, browser extensions, networking, and malware.

Citation BibTeX  2015

The Open Privacy Stack: Privly

McGregor, S., & Davidson, J.

OSCON is the premiere Open Source industry conference. This presentation focuses on our work on the priv.ly project's implementation.

Presentation Slides Citation BibTeX  2013

Future of Privacy: Challenges and Directions

Zubair Shafiq, Bryan Cunningham, Steven Englerhardt, Sean McGregor, Vikas Mishra, Ben Moskovitz, Henning Schulzrinne, Claire Vishik

A panel of advisers and guests from both industry and policy sectors discussed the “Future of Privacy: Challenges and Directions.”


  • Zubair Shafiq (moderator): associate professor of computer science at UC Davis
  • Bryan Cunningham: UCI Cybersecurity Policy and Research Institute
  • Steven Englerhardt, DuckDuckGo
  • Sean McGregor, Syntiant
  • Vikas Mishra, Eyeo
  • Ben Moskovitz, Consumer Reports
  • Henning Schulzrinne, Columbia
  • Claire Vishik, Intel.

More event details

Citation  2021


Incorporating Future Values into Analysis of Current Wildfires

McGregor, S., Houtman, R., Metoyer, R., Dietterich, T. G., Montgomery, C., & Crowley M.

Optimization and forestry are not often combined. We created this poster to gather input from forestry practitioners regarding the reports and visualizations that are useful in their work. The idea is to present our computer science research in a way that will lead to its adoption in a field that emphasizes field work over simulation students.

Poster Citation BibTeX  2014


Mentor, Google Code In

I mentored students through the FOSSASIA mentoring organization in the 2016 Google Code In, a "...global, online contest introducing teenagers to the world of open source development" with variety of bite-sized tasks. My responsibility was to help students accomplish a variety of tasks advancing their skills and understanding of open source development. Mentoring for this program is both inspiring and tremendously valuable for developing cultural insights. Teenagers from around the world typically have not developed an international style of professional work so you see unfiltered communication and help develop a professional sense.


Organizer, Panel Moderator, Reviewer

I was an organizer for the NeurIPS Workshop focusing on social problems for which artificial intelligence has the potential to offer meaningful solutions. I additionally moderated the panel on "Academia, Corporations, Society, Responsibility", with Zoubin Ghahramani (Uber), David Danks (CMU), Lisa Di Jorio (Imagia), and Julien Cornebise (Element AI).

The workshop reach a peak attendance of more than 700 people during the lunchtime demonstration from acclaimed cellist Yo Yo Ma.


Co-Chair, Safety Critical AI Expert Group

The Safety Critical Expert Group brings together representatives from AI industrial organizations with persons from civil society organizations to address developing issues in AI safety. I serve as co-chair with Ashish Kapoor and Amanda Askell. My passion project within the working group is a database cataloging where intelligent systems have produced harms within the world.


General Organizer

I was an organizer for the NeurIPS Workshop focusing on social problems for which artificial intelligence has the potential to offer meaningful solutions. I worked to broaden the scope of the workshop from the prior iterations and include elements of producing good, characterizing bad, and policy for maximizing the former while minimizing the later.


AI and Media Integrity Steering Committee

A group originally called together to steer Facebook's Deepfake Detection Challenge, we have continued meeting weekly after the completion of the competition to inform the Partnership on AI's efforts in ensuring deepfake detectors are appropriately developed.

  • Laura Ellis, Head of Technology Forecasting (BBC)
  • Sam Gregory, Program Directory (Witness)
  • Irina Kofman, Director & Business Lead (Facebook AI)
  • Marc Lavallee, Head of Research & Development (New York Times)
  • Bruce MacCormack, Senior Advisor, Business Strategy, (CBC)
  • Sean McGregory, Technical Lead (IBM Watson AI XPRIZE)
  • Pietro Perona, Amazon Fellow & Allen E. Puckett Professor of Electrical Engineering, California Institute of Technology (Amazon)
  • Jay Stokes, Research Software Engineer (Microsoft)
  • Claire Wardle, US Director, (First Draft)

Note: this is the public member list at launch. We have several new members replacing people that have moved on.


Reviewer, NIPS

I reviewed reinforcement learning papers for this top-tier conference.


Reviewer, IJCAI

I served as a reviewer for the "AI for Good" track of the conference.


Steward, Coalition of Graduate Employees

The Coalition of Graduate Employees (CGE) is a labor union representing more than 1,600 graduate employees. During my time in graduate school I helped double the size of the bargaining unit and budget, bargain a multi-million dollar contract, collaborate with university human resources on the implementation of our health plan, serve as ranking member of our delegation to the state federation, and serve as secretary-treasurer.


Senior Program Committee Member, Session Chair, AAAI

I served on the senior program committee for the "AI for Social Impact" track (2020 - 2023) and the main track (2022) of the conference.


General Organizer of NeurIPS Social

In 2019 the Neural Information Processing Systems conference added a peer-reviewed "socials track." The idea was to bring the social life of the conference back from sprawling corporate parties to gatherings organized by and for the research community. In this social event I submitted an abstract describing the event with my colleagues at the XPRIZE Foundation, then hosted a gathering attended to capacity.


Organizer, ICML Workshop on AI for Social Good

I was an organizer for the ICML Workshop focusing on social problems for which artificial intelligence has the potential to offer meaningful solutions. The organizing group shares responsibilities for chairing the review, schedule, and other event tasks.


Program Committee Member, Visualization for Social Good

I served on the program committee for this workshop focused on producing social impacts with information visualizations.


Organizer, ICLR Workshop on AI for Social Good

I was an organizer for the ICLR Workshop focusing on social problems for which artificial intelligence has the potential to offer meaningful solutions. The organizing group shares responsibilities for chairing the review, schedule, and other event tasks.

You can find videos from the event on Slideshare.


Program Committee Member, IAAI

I served on the program committee for this conference focused on the deployment concerns of artificial intelligence models.


Expert Group Member, Fairness, Transparency, Accountability Expert Group

The FTA brings together representatives from AI industrial organizations with persons from civil society organizations to address developing issues in AI fairness and accountability.


Founder, EECS Graduate Student Association

Despite having more than 400 graduate students in the department, I am the only one in my 6+ years to have organized events for the whole grad student population. I formed a student association and recruited a slate of officers to continue building on the social events I organized. I am told the organization has continued to hold events.


Founder, Privly Foundation

The Privly Foundation is a side project I kickstarted during my second year of graduate school. It is unrelated to my graduate studies, but has seen significant successes, including:

  • Designed and built method for posting private content to third-party social media: The Priv.ly Project
  • Gained attention in The Atlantic, Der Spiegel, Hacker News, and numerous other outlets.
  • Obtained 501(c)(3) tax exemption
  • Organization has mentored ten students in the Google Summer of Code (2013-2015)
  • Initiated online privacy meetup group in Portland, Oregon featuring presentations from leaders in security, incident response, citizen journalism, censorship circumvention, and cryptography
  • Funded at 260 percent of a Kickstarter campaign and raised total financial and in-kind support in excess of $100,000