Mark Ho

About

Mark Ho is an Assistant Professor at Stevens Institute of Technology in the Department of Computer Science. His research focus and contributions are not detailed in the provided page text. The page primarily lists alumni outcomes and placements of faculty fellows at the NYU Center for Data Science, including their subsequent positions across academia, industry, and government, but does not include specific biographical or research information about Mark Ho.

Research topics

• Artificial Intelligence
• Computer Science
• Cognitive psychology
• Psychology
• Social psychology
• Cognitive science
• Management science
• Economics
• Econometrics
• Human–computer interaction

Selected publications

• Building machines that learn and think with people

  Nature Human Behaviour · 2024-10-22 · 46 citations

  review
  PublisherDOI

• Adaptive mechanisms of social and asocial learning in immersive collective foraging

  Nature Communications · 2025-04-25 · 9 citations

  articleOpen access

  Human cognition is distinguished by our ability to adapt to different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial and social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task in a virtual Minecraft environment to integrate these two fields, by leveraging automated transcriptions of visual field data combined with high-resolution spatial trajectories. Our behavioral…

  PublisherOA PDFDOI

• Bayesian Reinforcement Learning With Limited Cognitive Load

  Open Mind · 2024-01-01 · 9 citations

  articleOpen access

  Abstract All biological and artificial agents must act given limits on their ability to acquire and process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent’s learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This bod…

  PublisherOA PDFDOI

• Exploring the hierarchical structure of human plans via program generation

  arXiv (Cornell University) · 2023-11-30 · 4 citations

  preprintOpen access

  Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically structured plans, using an experimental paradigm with observable hierarchical representations: participants create programs that produce sequences of actions in a language with…

  PublisherOA PDFDOI

• Building Machines that Learn and Think with People

  arXiv (Cornell University) · 2024-07-22 · 2 citations

  preprintOpen access

  What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,'' systems built to meet our e…

  PublisherOA PDFDOI

Frequent coauthors

• Thomas L. Griffiths

  60 shared

• Michael L. Littman

  41 shared

• Fiery Cushman

  Harvard University

  27 shared

• Theodore R. Sumers

  15 shared

• Robert D. Hawkins

  13 shared

• Arunima Sarin

  University College London

  13 shared

• David Abel

  13 shared

• Joseph L. Austerweil

  12 shared

Awards & honors

• CDS Faculty Fellows and Moore-Sloan Fellows at CDS
• DIRAC Fellow

Similar researchers at New York University

• Yiding (Sophie) Haoh-28
• Qing Quh-25
• Najoung Kimh-18