Zhaoran Wang
Biographic Data
| ID | 4619101 |
|---|---|
| NAME | Zhaoran Wang |
| GIVEN NAMES | Zhaoran |
| FAMILY NAME | Wang |
| SIGNATURE | WANG Z |
| AFFILIATIONS | Columbia University |
| ORCID | 0000-0002-6617-4842 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Short-term mindfulness intervention mitigates mental fatigue effects on basketball technical performance
Do brief mindfulness-based interventions improve sport-related performance? A systematic review
Background: Mindfulness-based interventions (MBIs) are psychological skills training approaches that are well recognized for enhancing psychological factors that influence sports performance. However, some factors, such as time commitment, cost, and accessibility required for traditional MBIs, can be a barrier to regular practice. As a result, brief MBIs with a duration of ≤ 30 min have gained popularity. This systematic review aims to explore th…
Evidentially Driven Uncertainty Decomposition for Weakly Supervised Point Cloud Semantic Segmentation
Point cloud semantic segmentation is a core component in indoor scene understanding and autonomous driving. Under weak point-level supervision, only a small subset of points is annotated, making effective use of unlabeled points critical yet non-trivial. Many existing approaches rely on prediction confidence to filter pseudo labels or enforce consistency, which can bias training toward easy points and amplify early mistakes. Consequently, confide…
Logit neural-network utility
We introduce stochastic choice models that feature neural networks, one of which is called the logit neural-network utility (NU) model. We show how to use simple neurons, referred to as behavioral neurons, to capture behavioral effects, such as the certainty effect and reference dependence. We find that simple logit NU models with natural interpretation provide better out-of-sample predictions than expected utility theory and cumulative prospect …
Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for Covid-19 pandemic crisis
Purpose: The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of…
How many masks do you buy? A simple dilemma task to differentiate between individual and social rationality
Program Value-Added
We assessed whether an adaptation of value-added analysis (VAA) can provide evidence on the relative effectiveness of interventions implemented in a large school district. We analyzed two datasets, one documenting interventions received by underperforming students, and one documenting interventions received by students in schools benefiting from discretionary funds to invest in specific programs. Results from the former dataset identified several…
Program Value-Added
We assessed whether an adaptation of value-added analysis (VAA) can provide evidence on the relative effectiveness of interventions implemented in a large school district. We analyzed two datasets, one documenting interventions received by underperforming students, and one documenting interventions received by students in schools benefiting from discretionary funds to invest in specific programs. Results from the former dataset identified several…
Program Value-Added
We assessed whether an adaptation of value-added analysis (VAA) can provide evidence on the relative effectiveness of interventions implemented in a large school district. We analyzed two datasets, one documenting interventions received by underperforming students, and one documenting interventions received by students in schools benefiting from discretionary funds to invest in specific programs. Results from the former dataset identified several…
Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for Covid-19 pandemic crisis
Purpose: The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of…
How many masks do you buy? A simple dilemma task to differentiate between individual and social rationality
Logit neural-network utility
We introduce stochastic choice models that feature neural networks, one of which is called the logit neural-network utility (NU) model. We show how to use simple neurons, referred to as behavioral neurons, to capture behavioral effects, such as the certainty effect and reference dependence. We find that simple logit NU models with natural interpretation provide better out-of-sample predictions than expected utility theory and cumulative prospect …
Short-term mindfulness intervention mitigates mental fatigue effects on basketball technical performance
Do brief mindfulness-based interventions improve sport-related performance? A systematic review
Background: Mindfulness-based interventions (MBIs) are psychological skills training approaches that are well recognized for enhancing psychological factors that influence sports performance. However, some factors, such as time commitment, cost, and accessibility required for traditional MBIs, can be a barrier to regular practice. As a result, brief MBIs with a duration of ≤ 30 min have gained popularity. This systematic review aims to explore th…
Evidentially Driven Uncertainty Decomposition for Weakly Supervised Point Cloud Semantic Segmentation
Point cloud semantic segmentation is a core component in indoor scene understanding and autonomous driving. Under weak point-level supervision, only a small subset of points is annotated, making effective use of unlabeled points critical yet non-trivial. Many existing approaches rely on prediction confidence to filter pseudo labels or enforce consistency, which can bias training toward easy points and amplify early mistakes. Consequently, confide…
Artificial Intelligence (3 works) · Computer Science (3 works) · Machine learning (3 works) · Psychological intervention (3 works) · Health care (2 works) · Mathematics (2 works) · Mindfulness and Compassion Interventions (2 works) · Psychology (2 works) · Sport Psychology and Performance (2 works) · 3D Shape Modeling and Analysis (1 work)