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
• Epistemology
• Operations research
• Statistics
• Microeconomics
• Mathematics
• Mathematical economics

Selected publications

• Do Large Language Models Mentalize When They Teach?

  ArXiv.org · 2026-04-02

  articleOpen access

  How do LLMs decide what to teach next: by reasoning about a learner's knowledge, or by using simpler rules of thumb? We test this in a controlled task previously used to study human teaching strategies. On each trial, a teacher LLM sees a hypothetical learner's trajectory through a reward-annotated directed graph and must reveal a single edge so the learner would choose a better path if they replanned. We run a range of LLMs as simulated teachers and fit their trial-by-trial choices with the same cognitive models used for humans: a Bayes-Optimal teacher that infers which transitions the learner is missing (inverse planning), weaker Bayesian variants, heuristic baselines (e.g., reward based), and non-mentalizing utility models. In a baseline experiment matched to the stimuli presented to human subjects, most LLMs perform well, show little change in strategy over trials, and their graph-by-graph performance is similar to that of humans. Model comparison (BIC) shows that Bayes-Optimal teaching best explains most models' choices. When given a scaffolding intervention, models follow auxiliary inference- or reward-focused prompts, but these scaffolds do not reliably improve later teaching on heuristic-incongruent test graphs and can sometimes reduce performance. Overall, cognitive model fits provide insight into LLM tutoring policies and show that prompt compliance does not guarantee better teaching decisions.

  PublisherOA PDF

• Do Large Language Models Mentalize When They Teach?

  arXiv (Cornell University) · 2026-04-02

  preprintOpen access

  How do LLMs decide what to teach next: by reasoning about a learner's knowledge, or by using simpler rules of thumb? We test this in a controlled task previously used to study human teaching strategies. On each trial, a teacher LLM sees a hypothetical learner's trajectory through a reward-annotated directed graph and must reveal a single edge so the learner would choose a better path if they replanned. We run a range of LLMs as simulated teachers and fit their trial-by-trial choices with the same cognitive models used for humans: a Bayes-Optimal teacher that infers which transitions the learner is missing (inverse planning), weaker Bayesian variants, heuristic baselines (e.g., reward based), and non-mentalizing utility models. In a baseline experiment matched to the stimuli presented to human subjects, most LLMs perform well, show little change in strategy over trials, and their graph-by-graph performance is similar to that of humans. Model comparison (BIC) shows that Bayes-Optimal teaching best explains most models' choices. When given a scaffolding intervention, models follow auxiliary inference- or reward-focused prompts, but these scaffolds do not reliably improve later teaching on heuristic-incongruent test graphs and can sometimes reduce performance. Overall, cognitive model fits provide insight into LLM tutoring policies and show that prompt compliance does not guarantee better teaching decisions.

  PublisherDOI

• Let's be friends! People work together even when there is no incentive to do so

  PsyArXiv (OSF Preprints) · 2026-05-11

  preprintOpen access1st authorCorresponding

  One distinguishing feature of human social intelligence is our capacity to flexibly coordinate complex behaviors, such as when working together to prepare a meal. What cognitive or motivational processes make such joint action possible? Here, we investigate the role that a \emph{preference to enmesh behaviors} plays in human coordination using a novel two-player virtual baking paradigm. Participants could either independently bake bread by bringing their own water and flour, or jointly bake bread by bringing only water or only flour to share. Depending on the ingredient options available, trials in our experiment could correspond to either the classic Prisoner's Dilemma, in which acting jointly is disincentivized by the payoffs, or a "Friend's Dilemma", in which acting jointly is neither incentivized nor disincentivized (though it remains risky since one's partner might not share). Formal analysis of equilibria and dyadic learning dynamics indicate that people work together even when there is no incentive to do so. These results provide preliminary evidence that a motivation to enmesh behaviors with social partners supports successful coordination.

  Publisher

• Let's be friends! People work together even when there is no incentive to do so

  2026-05-11

  articleOpen accessSenior author

  One distinguishing feature of human social intelligence is our capacity to flexibly coordinate complex behaviors, such as when working together to prepare a meal. What cognitive or motivational processes make such joint action possible? Here, we investigate the role that a \emph{preference to enmesh behaviors} plays in human coordination using a novel two-player virtual baking paradigm. Participants could either independently bake bread by bringing their own water and flour, or jointly bake bread by bringing only water or only flour to share. Depending on the ingredient options available, trials in our experiment could correspond to either the classic Prisoner's Dilemma, in which acting jointly is disincentivized by the payoffs, or a "Friend's Dilemma", in which acting jointly is neither incentivized nor disincentivized (though it remains risky since one's partner might not share). Formal analysis of equilibria and dyadic learning dynamics indicate that people work together even when there is no incentive to do so. These results provide preliminary evidence that a motivation to enmesh behaviors with social partners supports successful coordination.

  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 analyses capture both the structure and temporal dynamics of social interactions, which are then directly tested using computational models sequentially predicting each foraging decision. These results reveal that adaptation mechanisms of both asocial foraging and selective social learning are driven by individual foraging success (rather than social factors). Furthermore, it is the degree of adaptivity-of both asocial and social learning-that best predicts individual performance. These findings not only integrate theories across asocial and social domains, but also provide key insights into the adaptability of human decision-making in complex and dynamic social landscapes.

  PublisherOA PDFDOI

• Heuristics for meta-planning from a normative model of information search

  2025-06-23

  preprintOpen access

  Planning, the process of evaluating the future consequences of actions, is typically formalized as search over a decision tree. More search means higher expected rewards, but tree search is computationally expensive. Most approaches designed to mitigate the costs associated with tree search have been driven by researcher-specified heuristics, and only recently have normative solutions been applied to the domain of planning. An open question is how people approximate the values associated with potential plans while they are planning. In this work, we propose to abstract planning as an information search problem to produce heuristics for meta-planning, or to determine which action to plan for. Specifically, we model a metacognitive process where evaluating candidate actions is viewed as gaining noisy measurements of the value of each action. This statistical estimate is then combined with prior experience in a Bayesian manner to decide whether and in which direction to continue sampling. This Bayesian meta-planner makes intuitive predictions across a range of parameters and acts as a more valuable, informed method for guiding search when compared to best-first and breadth-first search. Additionally, the Bayesian meta-planner qualitatively accounts for response time trends in a complex planning task. Thus, we provide a principled framework for information search that directs simulations towards the most promising actions, deriving heuristics that generalize to people's behavior while planning.

  PublisherOA PDFDOI

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

  Nature Communications · 2025-06-20 · 1 citations

  erratumOpen access
  PublisherOA PDFDOI

• Multimodal Chip Physical Design Engineer Assistant

  ArXiv.org · 2025-07-02

  preprintOpen access

  Modern chip physical design relies heavily on Electronic Design Automation (EDA) tools, which often struggle to provide interpretable feedback or actionable guidance for improving routing congestion. In this work, we introduce a Multimodal Large Language Model Assistant (MLLMA) that bridges this gap by not only predicting congestion but also delivering human-interpretable design suggestions. Our method combines automated feature generation through MLLM-guided genetic prompting with an interpretable preference learning framework that models congestion-relevant tradeoffs across visual, tabular, and textual inputs. We compile these insights into a "Design Suggestion Deck" that surfaces the most influential layout features and proposes targeted optimizations. Experiments on the CircuitNet benchmark demonstrate that our approach outperforms existing models on both accuracy and explainability. Additionally, our design suggestion guidance case study and qualitative analyses confirm that the learned preferences align with real-world design principles and are actionable for engineers. This work highlights the potential of MLLMs as interactive assistants for interpretable and context-aware physical design optimization.

  PublisherOA PDFDOI

• Reinforcement Learning-Driven Window Selection for Enhanced Window-Based Rip-up and Reroute in Chip Detailed Routing

  2025-06-22

  article

  With increasingly complex design rules and pin density in advanced technology nodes, achieving a violation-free layout has become more challenging, also making rip-up and reroute (RUR) the most runtime-intensive component of detailed routing. We propose a novel reinforcement learning (RL)based approach to enhance the window-based RUR process. Our method features a dynamic window generation strategy that adjusts window size and position based on the distribution of design rule violations (DRV), enabling efficient targeting of congested areas. By leveraging the predictive capabilities of RL, our approach aims to minimize DRVs and achieve high-quality routing results. Experimental results demonstrate that our method outperforms the state-of-theart detailed routers, TritonRoute, achieving a DRV-free solution, averagely improving wirelength by 0.07%, via count by 2.42%, and consuming almost the same average runtime.

  PublisherDOI

• A timeline of cognitive costs in decision-making

  Trends in Cognitive Sciences · 2025-05-19 · 10 citations

  review
  PublisherDOI

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