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David C. Anastasiu - IEEE Xplore Author Profile
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David C. Anastasiu

Also published under: David Anastasiu

Publications
26
Citations
923
Publications by Year
20132025
Co-Authors:
Show All Co-Authors (73)
Image of author David C. Anastasiu

David C. Anastasiu

Also published under: David Anastasiu

Affiliation

Computer Science and Engineering Department
Santa Clara University
Santa Clara, CA, USA

Publication Topics

Biography

David C. Anastasiu is a PhD candidate at the University of Minnesota and was a research assistant at Texas State University at the time this research was conducted. His research interests include data mining and information retrieval. Anastasiu has an MS in computer science from Texas State University. Contact him at anast021@umn.edu.(Based on document published on 29 April 2013).
Publications
26
Citations
923
Publications by Year
20132025
Co-Authors:
Show All Co-Authors (73)
Author's Published Works

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MC-ANN: A Mixture Clustering-Based Attention Neural Network for Time Series Forecasting

Yanhong Li;David C. Anastasiu

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Time Series Forecasting (TSF) has been researched extensively, yet predicting time series with big variances and extreme events remains a challenging problem. Extreme events in reservoirs occur rarely but tend to cause huge problems, e.g., flooding entire towns or neighborhoods, which makes accurate reservoir water level prediction exceedingly important. In this work, we develop a novel extreme-ad...Show More

MC-ANN: A Mixture Clustering-Based Attention Neural Network for Time Series Forecasting

Yanhong Li;David C. Anastasiu

Year: 2025 | Early Access Article |

Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting

Yanhong Li;David C. Anastasiu

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
Accurate time series forecasting is critical in a variety of fields, including transportation, weather prediction, energy management, infrastructure monitoring, and finance. Forecasting highly skewed and heavy-tailed time series, particularly in multivariate environments, is still difficult. In these cases, accurately capturing the relationships between variables is critical for successful model d...Show More

Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting

Yanhong Li;David C. Anastasiu

Year: 2024 | Volume: 12 | Journal Article |

The 8th AI City Challenge

Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Yue Yao;Liang Zheng;Mohammed Shaiqur Rahman;Meenakshi S. Arya;Anuj Sharma;Pranamesh Chakraborty;Sanjita Prajapati;Quan Kong;Norimasa Kobori;Munkhjargal Gochoo;Munkh-Erdene Otgonbold;Fady Alnajjar;Ganzorig Batnasan;Ping-Yang Chen;Jun-Wei Hsieh;Xunlei Wu;Sameer Satish Pusegaonkar;Yizhou Wang;Sujit Biswas;Rama Chellappa

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (28)
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC)...Show More

The 8th AI City Challenge

Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Yue Yao;Liang Zheng;Mohammed Shaiqur Rahman;Meenakshi S. Arya;Anuj Sharma;Pranamesh Chakraborty;Sanjita Prajapati;Quan Kong;Norimasa Kobori;Munkhjargal Gochoo;Munkh-Erdene Otgonbold;Fady Alnajjar;Ganzorig Batnasan;Ping-Yang Chen;Jun-Wei Hsieh;Xunlei Wu;Sameer Satish Pusegaonkar;Yizhou Wang;Sujit Biswas;Rama Chellappa

Year: 2024 | Conference Paper |

SEED: An Effective Model for Highly-Skewed Streamflow Time Series Data Forecasting

Yanhong Li;Jack Xu;David C. Anastasiu

2023 IEEE International Conference on Big Data (BigData)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Accurate time series forecasting is crucial in various domains, but predicting highly-skewed and heavy-tailed univariate series poses challenges. We introduce the Segment-Expandable Encoder-Decoder (SEED) model, designed for such time series. SEED incorporates segment representation learning, Kullback-Leibler divergence regularization, and an importance-enhanced sampling policy. We tested our mode...Show More

SEED: An Effective Model for Highly-Skewed Streamflow Time Series Data Forecasting

Yanhong Li;Jack Xu;David C. Anastasiu

Year: 2023 | Conference Paper |

Enhancing Retail Checkout through Video Inpainting, YOLOv8 Detection, and DeepSort Tracking

Arpita Vats;David C. Anastasiu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (18)
The retail industry has witnessed a remarkable upswing in the utilization of cutting-edge artificial intelligence and computer vision techniques. Among the prominent challenges in this domain is the development of an automated checkout system that can address the multifaceted issues that arise in real-world checkout scenarios, including object occlusion, motion blur, and similarity in scanned item...Show More

Enhancing Retail Checkout through Video Inpainting, YOLOv8 Detection, and DeepSort Tracking

Arpita Vats;David C. Anastasiu

Year: 2023 | Conference Paper |

The 7th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Yue Yao;Liang Zheng;Mohammed Shaiqur Rahman;Meenakshi S. Arya;Anuj Sharma;Qi Feng;Vitaly Ablavsky;Stan Sclaroff;Pranamesh Chakraborty;Sanjita Prajapati;Alice Li;Shangru Li;Krishna Kunadharaju;Shenxin Jiang;Rama Chellappa

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (48)
The AI City Challenge’s seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countries. Track 1 was a brand new track that ...Show More

The 7th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Yue Yao;Liang Zheng;Mohammed Shaiqur Rahman;Meenakshi S. Arya;Anuj Sharma;Qi Feng;Vitaly Ablavsky;Stan Sclaroff;Pranamesh Chakraborty;Sanjita Prajapati;Alice Li;Shangru Li;Krishna Kunadharaju;Shenxin Jiang;Rama Chellappa

Year: 2023 | Conference Paper |

On-Device Prediction for Chronic Kidney Disease

Alex Whelan;Soham Phadke;Alessandro Bellofiore;David C. Anastasiu

2022 IEEE Global Humanitarian Technology Conference (GHTC)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
The number of people diagnosed with advanced stages of kidney disease has been rising every year. Although early diagnosis and treatment can slow, if not stop, the progression of the disease, many lower income individuals are unable to afford the high cost of frequent testing necessary to keep the disease progression at bay. To address this issue, we designed a kidney health monitoring system that...Show More

On-Device Prediction for Chronic Kidney Disease

Alex Whelan;Soham Phadke;Alessandro Bellofiore;David C. Anastasiu

Year: 2022 | Conference Paper |

The 6th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Yue Yao;Liang Zheng;Mohammed Shaiqur Rahman;Archana Venkatachalapathy;Anuj Sharma;Qi Feng;Vitaly Ablavsky;Stan Sclaroff;Pranamesh Chakraborty;Alice Li;Shangru Li;Rama Chellappa

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (45)
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and mortar retail businesses. The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries....Show More

The 6th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Yue Yao;Liang Zheng;Mohammed Shaiqur Rahman;Archana Venkatachalapathy;Anuj Sharma;Qi Feng;Vitaly Ablavsky;Stan Sclaroff;Pranamesh Chakraborty;Alice Li;Shangru Li;Rama Chellappa

Year: 2022 | Conference Paper |

Key Point-Based Driver Activity Recognition

Arpita Vats;David C. Anastasiu

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
We present a key point-based activity recognition framework, built upon pre-trained human pose estimation and facial feature detection models. Our method extracts complex static and movement-based features from key frames in videos, which are used to predict a sequence of key-frame activities. Finally, a merge procedure is employed to identify robust activity segments while ignoring outlier frame ...Show More

Key Point-Based Driver Activity Recognition

Arpita Vats;David C. Anastasiu

Year: 2022 | Conference Paper |

The 5th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Xiaodong Yang;Yue Yao;Liang Zheng;Pranamesh Chakraborty;Anuj Sharma;Qi Feng;Vitaly Ablavsky;Stan Sclaroff

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (73)
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Transportation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countrie...Show More

The 5th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Xiaodong Yang;Yue Yao;Liang Zheng;Pranamesh Chakraborty;Anuj Sharma;Qi Feng;Vitaly Ablavsky;Stan Sclaroff

Year: 2021 | Conference Paper |

The 4th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Xiaodong Yang;Liang Zheng;Anuj Sharma;Rama Chellappa;Pranamesh Chakraborty

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (79)
The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors, where computer vision and deep learning have shown promise in achieving large-scale practical deployment. The 4th annual edition of the AI City Challenge ha...Show More

The 4th AI City Challenge

Milind Naphade;Shuo Wang;David C. Anastasiu;Zheng Tang;Ming-Ching Chang;Xiaodong Yang;Liang Zheng;Anuj Sharma;Rama Chellappa;Pranamesh Chakraborty

Year: 2020 | Conference Paper |

CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

Zheng Tang;Milind Naphade;Ming-Yu Liu;Xiaodong Yang;Stan Birchfield;Shuo Wang;Ratnesh Kumar;David Anastasiu;Jenq-Neng Hwang

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (275)
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the bes...Show More

CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

Zheng Tang;Milind Naphade;Ming-Yu Liu;Xiaodong Yang;Stan Birchfield;Shuo Wang;Ratnesh Kumar;David Anastasiu;Jenq-Neng Hwang

Year: 2019 | Conference Paper |

Stock Price Prediction Using News Sentiment Analysis

Saloni Mohan;Sahitya Mullapudi;Sudheer Sammeta;Parag Vijayvergia;David C. Anastasiu

2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)
Year: 2019 | Conference Paper |
Cited by: Papers (120)
Predicting stock market prices has been a topic of interest among both analysts and researchers for a long time. Stock prices are hard to predict because of their high volatile nature which depends on diverse political and economic factors, change of leadership, investor sentiment, and many other factors. Predicting stock prices based on either historical data or textual information alone has prov...Show More

Stock Price Prediction Using News Sentiment Analysis

Saloni Mohan;Sahitya Mullapudi;Sudheer Sammeta;Parag Vijayvergia;David C. Anastasiu

Year: 2019 | Conference Paper |

Study Area Recommendation via Network Log Analytics

Anupama Upadhayula;Avinash Ravilla;Ishwarya Varadarajan;Sowmya Viswanathan;David C. Anastasiu

2019 7th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud)
Year: 2019 | Conference Paper |
With the current surge in the number of devices connected to the Internet all over the world, usage of network bandwidth has also seen a huge increase. This has led to network traffic congestion and slowdowns. It is increasingly important to anticipate future network usage, which would help prepare and provide adequate infrastructure or service support. In this paper, we propose a model that uses ...Show More

Study Area Recommendation via Network Log Analytics

Anupama Upadhayula;Avinash Ravilla;Ishwarya Varadarajan;Sowmya Viswanathan;David C. Anastasiu

Year: 2019 | Conference Paper |

Effective Vehicle Tracking Algorithm for Smart Traffic Networks

Shuai Hua;David C. Anastasiu

2019 IEEE International Conference on Service-Oriented System Engineering (SOSE)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
A smart traffic analysis system could be used to reduce congestion, prevent accidents, as well as to control traffic flow. Such a system would need to make use of many technologies, such as computer networking, communication, image processing, object detection, and tracking. In this paper, we introduce an efficient vehicle tracking algorithm which could be used to help create a smart city traffic ...Show More

Effective Vehicle Tracking Algorithm for Smart Traffic Networks

Shuai Hua;David C. Anastasiu

Year: 2019 | Conference Paper |

Improving Student Motivation through Competitive Active Learning

Manika Kapoor;Shuai Hua;David C. Anastasiu

2018 IEEE Frontiers in Education Conference (FIE)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
This Research Work in Progress paper introduces the Competitive Learning Platform (CLP), an online tool that provides automatic partial performance feedback to students or groups of students on individual or collaborative assignments. CLP motivates students to think outside-the-box and come up with novel solutions that can lead to improved assignment results before the assignment deadline. We deve...Show More

Improving Student Motivation through Competitive Active Learning

Manika Kapoor;Shuai Hua;David C. Anastasiu

Year: 2018 | Conference Paper |

The 2018 NVIDIA AI City Challenge

Milind Naphade;Ming-Ching Chang;Anuj Sharma;David C. Anastasiu;Vamsi Jagarlamudi;Pranamesh Chakraborty;Tingting Huang;Shuo Wang;Ming-Yu Liu;Rama Chellappa;Jenq-Neng Hwang;Siwei Lyu

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (62)
The NVIDIA AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these videos to provide actionable insights. These insights will benefit a wide variety of agencies, from traffic control to ...Show More

The 2018 NVIDIA AI City Challenge

Milind Naphade;Ming-Ching Chang;Anuj Sharma;David C. Anastasiu;Vamsi Jagarlamudi;Pranamesh Chakraborty;Tingting Huang;Shuo Wang;Ming-Yu Liu;Rama Chellappa;Jenq-Neng Hwang;Siwei Lyu

Year: 2018 | Conference Paper |

Vehicle Tracking and Speed Estimation from Traffic Videos

Shuai Hua;Manika Kapoor;David C. Anastasiu

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re-identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation,...Show More

Vehicle Tracking and Speed Estimation from Traffic Videos

Shuai Hua;Manika Kapoor;David C. Anastasiu

Year: 2018 | Conference Paper |

Optimal constrained wireless emergency network antenna placement

Swapnil Gaikwad;David C. Anastasiu

2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Communication is paramount, especially during a natural disaster or other emergency. Even when traditional lines of communication become unavailable, emergency response teams must be able to communicate with each other and the outside world. To facilitate this need, major cities across the United States are deploying wireless emergency networks (WENs) that serve as a secure communication channel b...Show More

Optimal constrained wireless emergency network antenna placement

Swapnil Gaikwad;David C. Anastasiu

Year: 2017 | Conference Paper |

The NVIDIA AI City Challenge

Milind Naphade;David C. Anastasiu;Anuj Sharma;Vamsi Jagrlamudi;Hyeran Jeon;Kaikai Liu;Ming-Ching Chang;Siwei Lyu;Zeyu Gao

2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Year: 2017 | Conference Paper |
Cited by: Papers (46)
Web image analysis has witnessed an AI renaissance. The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. In its first year, this Challenge has focused on traffic video data. While millions of traffic video c...Show More

The NVIDIA AI City Challenge

Milind Naphade;David C. Anastasiu;Anuj Sharma;Vamsi Jagrlamudi;Hyeran Jeon;Kaikai Liu;Ming-Ching Chang;Siwei Lyu;Zeyu Gao

Year: 2017 | Conference Paper |

Fast Parallel Cosine K-Nearest Neighbor Graph Construction

David C. Anastasiu;George Karypis

2016 6th Workshop on Irregular Applications: Architecture and Algorithms (IA3)
Year: 2016 | Conference Paper |
Cited by: Papers (6)
The k-nearest neighbor graph is an important structure in many data mining methods for clustering, advertising, recommender systems, and outlier detection. Constructing the graph requires computing up to n2 similarities for a set of n objects. This has led researchers to seek approximate methods, which find many but not all of the nearest neighbors. In contrast, we leverage shared memory paralleli...Show More

Fast Parallel Cosine K-Nearest Neighbor Graph Construction

David C. Anastasiu;George Karypis

Year: 2016 | Conference Paper |

Efficient Identification of Tanimoto Nearest Neighbors

David C. Anastasiu;George Karypis

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
Tanimoto, or (extended) Jaccard, is an important similarity measure which has seen prominent use in fields such as data mining and chemoinformatics. Many of the existing state-of-the-art methods for market-basket analysis, plagiarism and anomaly detection, compound database search, and ligand-based virtual screening rely heavily on identifying Tanimoto nearest neighbors. Given the rapidly increasi...Show More

Efficient Identification of Tanimoto Nearest Neighbors

David C. Anastasiu;George Karypis

Year: 2016 | Conference Paper |

Understanding computer usage evolution

David C. Anastasiu;Al M. Rashid;Andrea Tagarelli;George Karypis

2015 IEEE 31st International Conference on Data Engineering
Year: 2015 | Conference Paper |
The proliferation of computing devices in recent years has dramatically changed the way people work, play, communicate, and access information. The personal computer (PC) now has to compete with smartphones, tablets, and other devices for tasks it used to be the default device for. Understanding how PC usage evolves over time can help provide the best overall user experience for current customers,...Show More

Understanding computer usage evolution

David C. Anastasiu;Al M. Rashid;Andrea Tagarelli;George Karypis

Year: 2015 | Conference Paper |

L2AP: Fast cosine similarity search with prefix L-2 norm bounds

David C. Anastasiu;George Karypis

2014 IEEE 30th International Conference on Data Engineering
Year: 2014 | Conference Paper |
The All-Pairs similarity search, or self-similarity join problem, finds all pairs of vectors in a high dimensional sparse dataset with a similarity value higher than a given threshold. The problem has been classically solved using a dynamically built inverted index. The search time is reduced by early pruning of candidates using size and value-based bounds on the similarity. In the context of cosi...Show More

L2AP: Fast cosine similarity search with prefix L-2 norm bounds

David C. Anastasiu;George Karypis

Year: 2014 | Conference Paper |

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