Bio


Michael Bernstein is an Associate Professor of Computer Science and STMicroelectronics Faculty Scholar at Stanford University, where he is a member of the Human-Computer Interaction group. His research focuses on the design of social computing and crowdsourcing systems. Michael has received eight best paper awards at premier computing venues, and he has been recognized with an NSF CAREER award and an Alfred P. Sloan Fellowship. His Ph.D. students have gone on both to industry (e.g., Adobe Research, Facebook Data Science, entrepreneurship) and faculty careers (e.g., Carnegie Mellon, UC Berkeley). Michael holds a bachelor's degree in Symbolic Systems from Stanford University, as well as a master's degree and a Ph.D. in Computer Science from MIT.

Academic Appointments


Honors & Awards


  • STMicroelectronics Faculty Scholar, Stanford University
  • Sloan Research Fellowship, Sloan Foundation
  • NSF CAREER award, National Science Foundation
  • Best paper award, CHI 2019, CHI 2017, CSCW 2017, CHI 2016, UIST 2014, ICWSM 2011, UIST 2010, UIST 2006
  • Outstanding Academic Title, "Handbook of Collective Intelligence", American Library Association, Choice
  • Robert N. Noyce Family Faculty Scholar, Stanford University
  • George M. Sprowls Award for best doctoral thesis in Computer Science, MIT

Program Affiliations


  • Symbolic Systems Program

Professional Education


  • PhD, MIT, Computer Science (2012)
  • SM, MIT, Computer Science (2008)
  • BS, Stanford University, Symbolic Systems (2006)

2019-20 Courses


Stanford Advisees


  • Doctoral Dissertation Reader (AC)
    Pranav Rajpurkar, Sherry Ruan, Ana Saavedra
  • Postdoctoral Faculty Sponsor
    Amy Zhang
  • Orals Evaluator
    Pranav Rajpurkar
  • Master's Program Advisor
    Will Kenney, Matilda Nickell, Andrej Safundzic, Johnson Song
  • Doctoral Dissertation Co-Advisor (AC)
    Zhiyuan Jerry Lin
  • Doctoral (Program)
    Mitchell Gordon, Catherine Mullings

All Publications


  • Scene Graph Prediction with Limited Labels. Proceedings. IEEE International Conference on Computer Vision Chen, V. S., Varma, P., Krishna, R., Bernstein, M., Ré, C., Fei-Fei, L. 2020; 2019: 2580–90

    Abstract

    Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete relationships. All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each. Hiring human annotators is expensive, and using textual knowledge base completion methods are incompatible with visual data. In this paper, we introduce a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few' labeled examples. We analyze visual relationships to suggest two types of image-agnostic features that are used to generate noisy heuristics, whose outputs are aggregated using a factor graph-based generative model. With as few as 10 labeled examples per relationship, the generative model creates enough training data to train any existing state-of-the-art scene graph model. We demonstrate that our method outperforms all baseline approaches on scene graph prediction by 5.16 recall@ 100 for PREDCLS. In our limited label setting, we define a complexity metric for relationships that serves as an indicator (R2 = 0.778) for conditions under which our method succeeds over transfer learning, the de-facto approach for training with limited labels.

    View details for DOI 10.1109/iccv.2019.00267

    View details for PubMedID 32218709

    View details for PubMedCentralID PMC7098690

  • Conservation of Procrastination: Do Productivity Interventions Save Time or Just Redistribute It? Kovacs, G., Gregory, D., Ma, Z., Wu, Z., Emami, G., Ray, J., Bernstein, M. S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions Alkhatib, A., Bernstein, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Eevee: Transforming Images by Bridging High-level Goals and Low-level Edit Operations Lam, M. S., Young, G. B., Xu, C. Y., Krishna, R., Bernstein, M. S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Ink: Increasing Worker Agency to Reduce Friction in Hiring Crowd Workers ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION Salehi, N., Bernstein, M. S. 2018; 25 (2)

    View details for DOI 10.1145/3177882

    View details for Web of Science ID 000431849700003

  • Engagement Learning: Expanding Visual Knowledge by Engaging Online Participants Krishna, R., Lee, D., Li, F., Bernstein, M., ACM ASSOC COMPUTING MACHINERY. 2018: 87–89
  • Mechanical Novel: Crowdsourcing Complex Work Through Reflection and Revision DESIGN THINKING RESEARCH: MAKING DISTINCTIONS: COLLABORATION VERSUS COOPERATION Kim, J., Sterman, S., Cohen, A., Bernstein, M. S., Plattner, H., Meinel, C., Leifer, L. 2018: 79–104
  • Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress DESIGN THINKING RESEARCH: MAKING DISTINCTIONS: COLLABORATION VERSUS COOPERATION Kim, J., Agrawala, M., Bernstein, M. S., Plattner, H., Meinel, C., Leifer, L. 2018: 105–29
  • Referring Relationships Krishna, R., Chami, I., Bernstein, M., Li Fei-Fei, IEEE IEEE. 2018: 6867–76
  • Shared Autonomy for an Interactive AI System Zhou, S., Mu, T., Goel, K., Bernstein, M., Brunskill, E., ACM ASSOC COMPUTING MACHINERY. 2018: 20–22
  • Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations INTERNATIONAL JOURNAL OF COMPUTER VISION Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L., Shamma, D. A., Bernstein, M. S., Li Fei-Fei, F. F. 2017; 123 (1): 32-73
  • Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions Cheng, J., Bernstein, M., Danescu-Niculescu-Mizil, C., Leskovec, J., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2017: 1217–30

    Abstract

    In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.

    View details for PubMedID 29399664

  • Examining Crowd Work and Gig Work Through The Historical Lens of Piecework Alkhatib, A., Bernstein, M. S., Levi, M., ACM ASSOC COMPUTING MACHINERY. 2017: 4599–4616
  • MyriadHub: Efficiently Scaling Personalized Email Conversations with Valet Crowdsourcing Kokkalis, N., Fan, C., Roith, J., Bernstein, M. S., Klemmer, S., ACM ASSOC COMPUTING MACHINERY. 2017: 73–84
  • Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations Valentine, M., Retelny, D., To, A., Rahmati, N., Doshi, T., Bernstein, M. 2017

    View details for DOI 10.1145/3025453.3025811

  • ImageNet Large Scale Visual Recognition Challenge INTERNATIONAL JOURNAL OF COMPUTER VISION Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., Fei-fei, L. 2015; 115 (3): 211-252
  • Soylent: A Word Processor with a Crowd Inside COMMUNICATIONS OF THE ACM Bernstein, M. S., Little, G., Miller, R. C., Hartmann, B., Ackerman, M. S., Karger, D. R., Crowell, D., Panovich, K. 2015; 58 (8): 85-94
  • Learning Perceptual Kernels for Visualization Design IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Demiralp, C., Bernstein, M. S., Heer, J. 2014; 20 (12): 1933-1942
  • Catalyst: Triggering Collective Action with Thresholds Cheng, J., Bernstein, M. 2014
  • Ensemble: Exploring Complementary Strengths of Leaders and Crowds in Creative Collaboration Kim, J., Cheng, J., Bernstein, M. 2014
  • Quantifying the Invisible Audience in Social Networks Bernstein, Michael, S., Bakshy, E., Burke, M., Karrer, B. 2013
  • EmailValet: Managing Email Overload through Private, Accountable Crowdsourcing Kokkalis, N., Köhn, T., Pfeiffer, C., Chornyi, D., Bernstein, Michael, S., Klemmer, Scott, R. 2013
  • Crowd-scale Interactive Formal Reasoning and Analytics Fast, E., Lee, C., Aiken, A., Bernstein, M., Koller, D., Smith, E. 2013
  • The Future of Crowd Work Kittur, A., Nickerson, Jeffrey, V., Bernstein, Michael, S., Gerber, Elizabeth, M., Shaw, A., Zimmerman, J. 2013
  • Leveraging Online Populations for Crowdsourcing IEEE INTERNET COMPUTING Chi, E. H., Bernstein, M. S. 2012; 16 (5): 10-12
  • Who Gives A Tweet? Evaluating Microblog Content Value Andre, P., Bernstein, M., Luther, K. 2012
  • Direct Answers for Search Queries in the Long Tail Bernstein, M., Teevan, J., Dumais, S., Liebling, D., Horvitz, E. 2012
  • Analytic Methods for Optimizing Realtime Crowdsourcing CI: Collective Intelligence 2012 Bernstein, M., Karger, D., Miller, R., Brandt, J. 2012
  • The Trouble with Social Computing Systems Research Bernstein, M., Ackerman, M., Chi, Ed, H., Miller, R. 2011
  • Crowds in Two Seconds: Enabling Realtime Crowd-Powered Interfaces Bernstein, M., Brandt, J., Miller, R., Karger, D. 2011
  • PingPong++: Community Customization in Games and Entertainment Xiao, X., Bernstein, M., Yao, L., Lakatos, D., Gust, L., Acquah, K. 2011
  • TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration Marcus, A., Bernstein, M., Badar, O., Karger, D., Madden, S., Miller, R. 2011
  • 4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community Bernstein, M., Monroy-Hernandez, A., Harry, D., Andre, P., Panovich, K., Vargas, G. 2011
  • Eddi: Interactive Topic-Based Browsing of Social Status Streams Bernstein, M., Suh, B., Hong, L., Chen, J., Kairam, S., Chi, Ed, H. 2010
  • Short and Tweet: Experiments on Recommending Content from Information Streams Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E. 2010
  • Personalization via Friendsourcing ACM Transactions on Computer-Human Interaction 2010 Bernstein, M., Tan, D., Smith, G., Czerwinski, M., Horvitz, E. 2010
  • Who Am I? Two-Four-Six-Oh-One! Bernstein, M., Marcus, A., Karger, D., Miller, R. 2010
  • Enhancing Directed Content Sharing on the Web Bernstein, M., Marcus, A., Karger, D., Miller, R. 2010
  • A Torrent of Tweets: Managing Information Overload in Online Social Streams Bernstein, M., Kairam, S., Suh, B., Hong, L., Chi, Ed, H. 2010
  • Soylent: A Word Processor with a Crowd Inside Bernstein, M., Little, G., Miller, R., Hartmann, B., Ackerman, M., Karger, D. 2010
  • Collabio: A Game for Annotating People within Social Networks Bernstein, M., Tan, D., Smith, G., Czerwinski, M., Horvitz, E. 2009
  • Note to Self: Examining Personal Information Keeping in a Lightweight Note-Taking Tool Van Kleek, M., Bernstein, M., Panovich, K., Vargas, G., Karger, D., schraefel, m. c. 2009
  • CHIstory Bernstein, M., Andre, P., Luther, K., Poole, E. S., Solovey, E., Paul, S. 2009
  • Wicked Problems and Gnarly Results: Reflecting on Design and Evaluation Methods for Idiosyncratic Personal Information Management Tasks MIT-CSAIL-TR-2008-007 2008 Bernstein, M., Kleek, M. V., Khushraj, D., Nayak, R., Liu, C., Karger, D. 2008
  • Simplifying Knowledge Creation and Access for End-Users on the Semantic Web Van Kleek, M., Bernstein, M., Andre, P., Pertunnen, M., Karger, D., schraefel, m. c. 2008
  • Evolution and Evaluation of an Information Scrap Manager Bernstein, M., Van Kleek, M., schraefel, m. c., Karger, D. 2008
  • Inky: A Sloppy Command Line for the Web with Rich Visual Feedback Miller, R., Chou, V., Bernstein, M., Little, G., Van Kleek, M., Karger, D. 2008
  • Information Scraps: How and Why Information Eludes our Personal Information Management Tools ACM Transactions on Information Systems 2008 Bernstein, M., Kleek, M. V., Karger, D., schraefel, m. 2008
  • Taskpose: Exploring Fluid Boundaries in an Associative Window Visualization 21st Annual ACM Symposium on User Interface Software and Technology Bernstein, M., Shrager, J., Winograd, T. ASSOC COMPUTING MACHINERY. 2008: 231–234
  • Management of Personal Information Scraps Bernstein, M., Van Kleek, M., schraefel, m. c., Karger, D. 2007
  • GUI — Phooey!: The Case for Text Input Van Kleek, M., Bernstein, M., Karger, D., schraefel, m. c. 2007
  • Personal Information Management, Personal Information Retrieval? Bernstein, M., Van Kleek, M., Karger, D., schraefel, m. c. 2007
  • Diamond's Edge: From Notebook to Table and Back Again Ubicomp: Posters 2006 Bernstein, M., Robinson-Mosher, A., Yeh, R., Klemmer, S. 2006
  • Reflective Physical Prototyping through Integrated Design, Test, and Analysis Hartmann, B., Klemmer, Scott, R., Bernstein, M., Abdulla, L., Burr, B., Robinson-Mosher, A. 2006
  • D.tools: Integrated prototyping for physical interaction design IEEE PERVASIVE COMPUTING Hartmann, B., Klemmer, S. R., Bernstein, M. 2005; 4 (4): 79-79
  • d.tools: Visually Prototyping Physical UIs through Statecharts UIST: Extended Abstracts 2005 Hartmann, B., Klemmer, S. R., Bernstein, M., Mehta, N. 2005