About

Jin Wang is an Affiliated Professor in the Departments of Physics and Chemistry at Stony Brook University. His research group is associated with the Department of Physics and Astronomy, and he is involved in academic activities within the College of Arts and Sciences. His contact information includes an email address at stonybrook.edu, and his office is located in Physics B-103 and Chemistry 104. The page references the late Prof. C.N. 'Frank' Yang, indicating a connection to the department's history and academic community. No further biographical details, research focus, or key contributions are provided in the page text.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data Mining
• Natural Language Processing
• Algorithm
• Theoretical computer science
• Mathematical optimization
• Mathematics
• Real-time computing

Selected publications

• Parameterized algorithms of fundamental NP-hard problems: a survey

  Human-centric Computing and Information Sciences · 2020 · 83 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Algorithm

  Abstract Parameterized computation theory has developed rapidly over the last two decades. In theoretical computer science, it has attracted considerable attention for its theoretical value and significant guidance in many practical applications. We give an overview on parameterized algorithms for some fundamental NP-hard problems, including MaxSAT, Maximum Internal Spanning Trees, Maximum Internal Out-Branching, Planar (Connected) Dominating Set, Feedback Vertex Set, Hyperplane Cover, Vertex Cover, Packing and Matching problems. All of these problems have been widely applied in various areas, such as Internet of Things, Wireless Sensor Networks, Artificial Intelligence, Bioinformatics, Big Data, and so on. In this paper, we are focused on the algorithms’ main idea and algorithmic techniques, and omit the details of them.

  DOI

• Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

  IEEE Transactions on Systems Man and Cybernetics Systems · 2020 · 384 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.

  DOI

• Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

  IEEE Access · 2020 · 553 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

  DOI

Recent grants

• Collaborative Research: New Formulation and Algorithms for Fluid-Structure Interaction, with Application to Tumor Growth

  NSF · $150k · 2012–2016

• A Multidisciplinary Platform for Wave Energy Education

  NSF · $199k · 2013–2016

Frequent coauthors

• Meichun Liu

  City University of Hong Kong

  2210 shared

• Chu‐Ren Huang

  Hong Kong Polytechnic University

  2210 shared

• Yanhui Gu

  2209 shared

• Shu-Ping Gong

  Inner Mongolia University

  2209 shared

• Chan-Chia Hsu

  National Taipei University of Business

  2209 shared

• Yingjie Han

  2209 shared

• Chin-Chuan Cheng

  Hong Kong Polytechnic University

  2209 shared

• Lei Zhang

  Shanghai Ocean University

  2209 shared

Awards & honors

• C.N. Yang 1957 Nobel Prize

Similar researchers at Stony Brook University

• Scott McLennanh-145
• Anatoly I. Frenkelh-101
• Vitaly Citovskyh-98
• Michael R. Douglash-91
• Minghua Zhangh-89