Walter S. Lasecki

4.2k total citations · 1 hit paper
113 papers, 2.6k citations indexed

About

Walter S. Lasecki is a scholar working on Computer Science Applications, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Walter S. Lasecki has authored 113 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Computer Science Applications, 62 papers in Artificial Intelligence and 25 papers in Computer Vision and Pattern Recognition. Recurrent topics in Walter S. Lasecki's work include Mobile Crowdsensing and Crowdsourcing (76 papers), Data Stream Mining Techniques (21 papers) and Personal Information Management and User Behavior (17 papers). Walter S. Lasecki is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (76 papers), Data Stream Mining Techniques (21 papers) and Personal Information Management and User Behavior (17 papers). Walter S. Lasecki collaborates with scholars based in United States, South Korea and United Kingdom. Walter S. Lasecki's co-authors include Jeffrey P. Bigham, Ece Kamar, Besmira Nushi, Eric Horvitz, Raja Kushalnagar, Gagan Bansal, Daniel S. Weld, Christopher D. Miller, Juho Kim and Michael S. Bernstein and has published in prestigious journals such as PLoS ONE, Communications of the ACM and interactions.

In The Last Decade

Walter S. Lasecki

110 papers receiving 2.5k citations

Hit Papers

Beyond Accuracy: The Role of Mental Models in Human-AI Te... 2019 2026 2021 2023 2019 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance
    2019 239 citations Ece Kamar, Walter S. Lasecki et al. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Walter S. Lasecki United States 27 1.2k 1.1k 466 365 305 113 2.6k
Lydia B. Chilton United States 23 995 0.8× 1.1k 1.0× 388 0.8× 294 0.8× 503 1.6× 76 2.5k
Brian Magerko United States 24 933 0.8× 789 0.7× 345 0.7× 475 1.3× 293 1.0× 132 2.8k
Juho Kim South Korea 25 1.4k 1.2× 724 0.7× 531 1.1× 367 1.0× 487 1.6× 184 3.4k
Cristina Conati Canada 31 1.1k 0.9× 2.0k 1.8× 857 1.8× 645 1.8× 444 1.5× 138 3.7k
Xiaojuan Ma Hong Kong 28 607 0.5× 973 0.9× 635 1.4× 397 1.1× 241 0.8× 196 2.9k
Michael Terry Canada 26 345 0.3× 928 0.9× 638 1.4× 622 1.7× 429 1.4× 80 2.9k
Roberto Martínez‐Maldonado Australia 32 1.8k 1.5× 912 0.9× 262 0.6× 210 0.6× 513 1.7× 162 3.5k
Saleema Amershi United States 26 546 0.5× 1.9k 1.8× 716 1.5× 400 1.1× 794 2.6× 52 3.7k
Carrie J. Cai United States 20 319 0.3× 1.2k 1.1× 373 0.8× 290 0.8× 308 1.0× 38 2.5k
Kshitij Sharma Norway 28 849 0.7× 661 0.6× 206 0.4× 406 1.1× 391 1.3× 115 2.7k

Countries citing papers authored by Walter S. Lasecki

Since Specialization
Citations

This map shows the geographic impact of Walter S. Lasecki's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Walter S. Lasecki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Walter S. Lasecki more than expected).

Fields of papers citing papers by Walter S. Lasecki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Walter S. Lasecki. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Walter S. Lasecki. The network helps show where Walter S. Lasecki may publish in the future.

Co-authorship network of co-authors of Walter S. Lasecki

This figure shows the co-authorship network connecting the top 25 collaborators of Walter S. Lasecki. A scholar is included among the top collaborators of Walter S. Lasecki based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Walter S. Lasecki. Walter S. Lasecki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chen, Yan, et al.. (2021). Towards Supporting Programming Education at Scale via Live Streaming. Proceedings of the ACM on Human-Computer Interaction. 4(CSCW3). 1–19. 28 indexed citations
2.
3.
Chen, Yan, et al.. (2020). Improving Crowd-Supported GUI Testing with Structural Guidance. 1–13. 15 indexed citations
4.
Kandogan, Eser, et al.. (2019). A Study on Interaction in Human-in-the-Loop Machine Learning for Text Analytics.. 9 indexed citations
5.
Kummerfeld, Jonathan K., et al.. (2018). Analyzing Assumptions in Conversation Disentanglement Research Through the Lens of a New Dataset and Model.. arXiv (Cornell University). 4 indexed citations
6.
Kaur, Harmanpreet, Isaac Johnson, Hannah Miller, et al.. (2018). Oh The Places You'll Share. 1–6. 2 indexed citations
7.
Kaur, Harmanpreet, et al.. (2018). Creating Better Action Plans for Writing Tasks via Vocabulary-Based Planning. Proceedings of the ACM on Human-Computer Interaction. 2(CSCW). 1–22. 14 indexed citations
8.
Yim, Jinyeong, et al.. (2018). EURECA: Enhanced Understanding of Real Environments via Crowd Assistance. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 6. 31–40. 12 indexed citations
9.
Huang, Ting-Hao, Walter S. Lasecki, Amos Azaria, & Jeffrey P. Bigham. (2017). "Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent. arXiv (Cornell University). 79–88. 13 indexed citations
10.
Lasecki, Walter S., Jaime Teevan, & Ece Kamar. (2016). The Cost of Asking Crowd Workers to Behave Maliciously. 6 indexed citations
11.
Peng, Bei, et al.. (2015). Generating real-time crowd advice to improve reinforcement learning agents. National Conference on Artificial Intelligence. 2 indexed citations
12.
Lasecki, Walter S., et al.. (2015). Preserving Privacy in Crowd-Powered Systems. 3 indexed citations
13.
Bigham, Jeffrey P., Jacob O. Wobbrock, & Walter S. Lasecki. (2015). Target Acquisition and the Crowd Actor. 2(2). 3 indexed citations
14.
Lasecki, Walter S., Christopher M. Homan, & Jeffrey P. Bigham. (2014). Architecting Real-Time Crowd-Powered Systems. Figshare. 1(1). 16 indexed citations
15.
Lasecki, Walter S., Jaime Teevan, & Ece Kamar. (2014). Information extraction and manipulation threats in crowd-powered systems. 248–256. 40 indexed citations
16.
Lasecki, Walter S., Raja Kushalnagar, & Jeffrey P. Bigham. (2014). Helping students keep up with real-time captions by pausing and highlighting. 1–8. 26 indexed citations
17.
Naim, Iftekhar, Daniel Gildea, Walter S. Lasecki, & Jeffrey P. Bigham. (2013). Text Alignment for Real-Time Crowd Captioning. North American Chapter of the Association for Computational Linguistics. 201–210. 17 indexed citations
18.
Lasecki, Walter S., et al.. (2013). Answering visual questions with conversational crowd assistants. Figshare. 1–8. 55 indexed citations
19.
Lasecki, Walter S., et al.. (2013). Chorus. 151–162. 103 indexed citations
20.
Lasecki, Walter S., et al.. (2012). Crowd-based recognition of web interaction patterns. 99–100. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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