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Dakuo Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers
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5

JBHI Journal 2025 Journal Article

DoctorPupil: A Virtual Reality System for Parkinson's Diagnosis Through Task-Evoked Pupil Response

  • Xucheng Zhang
  • Zhirong Wan
  • Jing Zhao
  • Xinjin Li
  • Anfeng Liu
  • Xiangmin Fan
  • Wei Sun
  • Feng Tian

Parkinson's Disease (PD) is one of the most critical neurodegenerative diseases, yet there is no cure for it, and the state-of-the-art treatment is to slow its progression. Thus, the earlier a patient with PD is recognized, the better he can be treated. Our project joins the research effort that aims to support early PD diagnosis by designing a Virtual Reality (VR)-based system to monitor pupil diameter patterns as new biomarkers (e. g. , Pupil Light Reflex and Task-evoked Pupil Response) and provide early warning of potential PD onset. A follow-up experiment with 55 participants shows that the accuracy of recognizing early PD from healthy controls could reach 0. 8942. Our study shows early results of a promising research direction that leverages VR-based technology to non-intrusively recognize patterns and provide alerts to early PD patients who would otherwise not know their symptoms until much later.

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AAAI Conference 2022 System Paper

Semantic Feature Discovery with Code Mining and Semantic Type Detection

  • Kavitha Srinivas
  • Takaaki Tateishi
  • Daniel Karl I. Weidele
  • Udayan Khurana
  • Horst Samulowitz
  • Toshihiro Takahashi
  • Dakuo Wang
  • Lisa Amini

In recent years, the automation of machine learning and data science (AutoML) has attracted significant attention. One under-explored dimension of AutoML is being able to automatically utilize domain knowledge (such as semantic concepts and relationships) located in historical code or literature from the problem’s domain. In this paper, we demonstrate Semantic Feature Discovery, which enables users to interactively explore features semantically discovered from existing data science code and external knowledge. It does so by detecting semantic concepts for a given dataset, and then using these concepts to determine relevant feature engineering operations from historical code and knowledge.

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AAAI Conference 2021 System Paper

AutoText: An End-to-End AutoAI Framework for Text

  • Arunima Chaudhary
  • Alayt Issak
  • Kiran Kate
  • Yannis Katsis
  • Abel Valente
  • Dakuo Wang
  • Alexandre Evfimievski
  • Sairam Gurajada

Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e. g. , domain experts) can quickly generate performant NLP models with support to both control (e. g. , via specifying constraints) and understand learned models.

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IJCAI Conference 2021 Conference Paper

Graph-Augmented Code Summarization in Computational Notebooks

  • April Wang
  • Dakuo Wang
  • Xuye Liu
  • Lingfei Wu

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code and neglect the creation of the documentation in a notebook. In this work, we present a human-centered automation system, Themisto, that can support users to easily create documentation via three approaches: 1) We have developed and reported a GNN-augmented code documentation generation algorithm in a previous paper, which can generate documentation for a given source code; 2) Themisto also implements a query-based approach to retrieve the online API documentation as the summary for certain types of source code; 3) Lastly, Themistoalso enables a user prompt approach to motivate users to write documentation for some use cases that automation does not work well.

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AAAI Conference 2020 Conference Paper

An ADMM Based Framework for AutoML Pipeline Configuration

  • Sijia Liu
  • Parikshit Ram
  • Deepak Vijaykeerthy
  • Djallel Bouneffouf
  • Gregory Bramble
  • Horst Samulowitz
  • Dakuo Wang
  • Andrew Conn

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML & OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.

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