About

Panos Ipeirotis is a professor with a focus on research related to data, crowdsourcing, and machine learning. His work encompasses a broad range of topics within these areas, contributing to the understanding and development of innovative solutions in data-driven fields. The page highlights his role as an academic and researcher, with a history of mentoring students and engaging in scholarly activities. His professional background includes supervising numerous PhD students and participating in research projects that advance knowledge in his areas of expertise.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Economics
• Psychology
• Political Science
• Engineering
• World Wide Web
• Knowledge management
• Social psychology
• Data science

Selected publications

• Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes

  SSRN Electronic Journal · 2025-01-01 · 4 citations

  preprintOpen accessSenior author
  PublisherDOI

• Full Characterization of Adaptively Strong Majority Voting in Crowdsourcing

  2024-06-27 · 1 citations

  preprintSenior author

  In crowdsourcing, quality control is commonly achieved by having workers examine items and vote on their correctness. To minimize the impact of unreliable worker responses, a δ -margin voting process is utilized, where additional votes are solicited until a predetermined threshold δ for agreement between workers is exceeded. The process is widely adopted but only as a heuristic. Our research presents a modeling approach using absorbing Markov chains to analyze the characteristics of this voting process that matter in crowdsourced processes. We provide closed-form equations for the quality of resulting consensus vote, the expected number of votes required for consensus, the variance of vote requirements, and other distribution moments. Our findings demonstrate how the threshold δ can be adjusted to achieve quality equivalence across voting processes that employ workers with varying accuracy levels. We also provide efficiency-equalizing payment rates for voting processes with different expected response accuracy levels. Additionally, our model considers items with varying degrees of difficulty and uncertainty about the difficulty of each example. Our simulations, using real-world crowdsourced vote data, validate the effectiveness of our theoretical model in characterizing the consensus aggregation process. The results of our study can be effectively employed in practical crowdsourcing applications.

  PublisherDOI

• The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets

  Management Science · 2023 · 16 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Machine Learning

  Choosing job applicants to hire in online labor markets is hard. To identify the best applicant at hand, employers need to assess a heterogeneous population. Recommender systems can provide targeted job-applicant recommendations that help employers make better-informed and faster hiring choices. However, existing recommenders that rely on multiple user evaluations per recommended item (e.g., collaborative filtering) experience structural limitations in recommending job applicants: Because each job application receives only a single evaluation, these recommenders can only estimate noisy user-user and item-item similarities. On the other hand, existing recommenders that rely on classification techniques overcome this limitation. Yet, these systems ignore the hired worker’s performance—and, as a result, they uniformly reinforce prior observed behavior that includes unsuccessful hiring choices—while they overlook potential sequential dependencies between consecutive choices of the same employer. This work addresses these shortcomings by building a framework that uses job-application characteristics to provide recommendations that (1) are unlikely to yield adverse outcomes (performance-aware) and (2) capture the potentially evolving hiring preferences of employers (sequence-aware). Application of this framework on hiring decisions from an online labor market shows that it recommends job applicants who are likely to get hired and perform well. A comparison with advanced alternative recommender systems illustrates the benefits of modeling performance-aware and sequence-aware recommendations. An empirical adaptation of our approach in an alternative context (restaurant recommendations) illustrates its generalizability and highlights its potential implications for users, employers, workers, and markets. This paper was accepted by Kartik Hosanagar, information systems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4690 .

  PublisherDOI

• Inscribing Diversity Policies in Algorithmic Hiring Systems: Theory and Empirics

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• On the Usage of Rank Percentile in Evaluating and Predicting Scientific Impacts.

  arXiv (Cornell University) · 2021-03-30

  preprintOpen accessSenior author

  Bibliographic metrics are commonly utilized for evaluation purposes within academia, often in conjunction with other metrics. These metrics vary widely across fields and change with the seniority of the scholar; consequently, the only way to interpret these values is by comparison with other academics within the same field and of similar seniority. We propose a simple extension that allows us to create metrics that are easy to interpret and can make comparisons easier. Our basic idea is to create benchmarks and then utilize percentile indicators to measure the performance of a scholar or publication over time. These percentile-based metrics allow for comparison of people and publications of different seniority and are easily interpretable. Furthermore, we demonstrate that the rank percentile indicators have reasonable predictive power. The publication indicator is highly stable over time, while the scholar indicator exhibits short-term stability and can be predicted via a simple linear regression model. While more advanced models offer slightly superior performance, the simplicity and interpretability of the simple model impose significant advantages over the additional complexity of other models.

  Publisher

• What do crowd workers think about creative work?

  University of Oulu Repository (University of Oulu) · 2020-01-01 · 1 citations

  preprintOpen access

  Crowdsourcing platforms are a powerful and convenient means for recruiting participants in online studies and collecting data from the crowd. As information work is being more and more automated by Machine Learning algorithms, creativity $-$ that is, a human's ability for divergent and convergent thinking $-$ will play an increasingly important role on online crowdsourcing platforms. However, we lack insights into what crowd workers think about creative work. In studies in Human-Computer Interaction (HCI), the ability and willingness of the crowd to participate in creative work seems to be largely unquestioned. Insights into the workers' perspective are rare, but important, as they may inform the design of studies with higher validity. Given that creativity will play an increasingly important role in crowdsourcing, it is imperative to develop an understanding of how workers perceive creative work. In this paper, we summarize our recent worker-centered study of creative work on two general-purpose crowdsourcing platforms (Amazon Mechanical Turk and Prolific). Our study illuminates what creative work is like for crowd workers on these two crowdsourcing platforms. The work identifies several archetypal types of workers with different attitudes towards creative work, and discusses common pitfalls with creative work on crowdsourcing platforms.

  OA PDF

• Demand-Aware Career Path Recommendations: A Reinforcement Learning Approach

  Management Science · 2020 · 57 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  A skill’s value depends on dynamic market conditions. To remain marketable, contractors need to keep reskilling themselves continuously. But choosing new skills to learn is an inherently hard task: Contractors have very little information about current and future market conditions, which often results in poor learning choices. Recommendation frameworks could reduce uncertainty in learning choices. However, conventional approaches would likely be inefficient; they would model previous (often poor) observed contractor learning behaviors to provide future career path recommendations while ignoring current market trends. This work proposes a framework that combines reinforcement learning, Bayesian inference, and gradient boosting to provide recommendations on how contractors should behave when choosing new skills to learn. Compared with standard recommender systems, this framework does not learn from previous (often poor) behaviors to make future recommendations. Instead, it relies on a Markov decision process to operate on a graph of feasible actions and dynamically recommend profitable career paths. The framework uses market information to identify current trends and project future wages. Based on this information, it recommends feasible, relevant actions that a contractor can take to learn new, in-demand skills. Evaluation of the framework on 1.73 million job applications from an online labor market shows that its implementation could increase (1) the marketplace’s revenue by up to 6%, (2) contractors’ wages by 22%, and (3) the diversity of new skill acquisitions by 47%. A comparison with alternative recommender systems highlights the limitations of approaches that make recommendations based on previously observed learning behaviors. This paper was accepted by Chris Forman, information systems.

  PublisherDOI

• Creativity on paid crowdsourcing platforms

  2020 · 40 citations

  • Computer Science
  • Data science
  • Political Science

  Crowdsourcing platforms are increasingly being harnessed for creative work. The platforms’ potential for creative work is clearly identified, but the workers’ perspectives on such work have not been extensively documented. In this paper, we uncover what the workers have to say about creative work on paid crowdsourcing platforms. Through a quantitative and qualitative analysis of a questionnaire launched on two different crowdsourcing platforms, our results revealed clear differences between the workers on the platforms in both preferences and prior experience with creative work. We identify common pitfalls with creative work on crowdsourcing platforms, provide recommendations for requesters of creative work, and discuss the meaning of our findings within the broader scope of creativity-oriented research. To the best of our knowledge, we contribute the first extensive worker-oriented study of creative work on paid crowdsourcing platforms.

  DOI

• Demand-Aware Career Path Recommendations: A Reinforcement Learning Approach

  SSRN Electronic Journal · 2020-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Gender and Race Preferences in Hiring in the Age of Diversity Goals: Evidence from Silicon Valley Tech Firms

  SSRN Electronic Journal · 2020-01-01 · 5 citations

  articleOpen accessSenior author
  PublisherDOI

Recent grants

• CAREER: Towards a Text-Centric Database Management System

  NSF · $500k · 2007–2012

Frequent coauthors

• Luis Gravano

  44 shared

• Anindya Ghose

  New York University

  33 shared

• Foster Provost

  15 shared

• Jing Wang

  15 shared

• Beibei Li

  14 shared

• Mehran Sahami

  11 shared

• Marios Kokkodis

  10 shared

• Victor S. Sheng

  Texas Tech University

  9 shared

Labs

• Panos Ipeirotis LabPI

Education

• Ph.D., Computer Science

  Columbia University

  2005

• M.S., Computer Science

  Columbia University

  2002

• B.S., Computer Science

  National Technical University of Athens

  1998

Awards & honors

• 2015 Lagrange Prize in Complex Systems
• 2020 Test of Time award at KDD
• More than ten “Best Paper” awards and nominations
• CAREER award from the National Science Foundation