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Nitesh V. Chawla - IEEE Xplore Author Profile
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Nitesh V. Chawla

Also published under: N. V. Chawla, Nitesh Chawla, N. Chawla

Publications
96
Citations
2,409
Publications by Year
19982024
Co-Authors:
Show All Co-Authors (181)
Image of author Nitesh V. Chawla

Nitesh V. Chawla

Also published under: N. V. Chawla, Nitesh Chawla, N. Chawla

Affiliation

Dept. Computer Science and Engineering
University of Notre Dame
Notre Dame, USA

Publication Topics

Biography

Nitesh V. Chawla is currently a Frank M. Freimann Professor of computer science and engineering with the University of Notre Dame, Notre Dame, IN, USA. He is the Founding Director of the Lucy Family Institute for Data and Society. His research interests include making fundamental advances in artificial intelligence, data science, and network science. He received the Ph.D. degree in 2002 at the University of South Florida, USA. He is a Senior Member of the IEEE. Contact him at nchawla@nd.edu.(Based on document published on 1 November 2021).
Publications
96
Citations
2,409
Publications by Year
19982024
Co-Authors:
Show All Co-Authors (181)
Author's Published Works

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Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data

Damien A. Dablain;Nitesh V. Chawla

2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Real-world data is often imbalanced, such that the number of training instances varies by class. Data augmentation (DA) of under-represented classes is commonly used to improve model generalization in the face of class imbalance. Despite its ubiquity, the impact of data augmentation on machine learning (ML) models is not clearly understood. Here, we undertake a holistic examination of the effect o...Show More

Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data

Damien A. Dablain;Nitesh V. Chawla

Year: 2024 | Conference Paper |

SMOTE for gene regulatory network sampling

Gonzalo A. Ruz;Nitesh V. Chawla

2024 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Year: 2024 | Conference Paper |
In many cases, the search for synthetic functional networks of a gene regulatory network model can be a difficult and time-consuming task. In this paper, we present a method that uses the popular SMOTE algorithm to sample synthetic functional networks in order to boost the results obtained by an evolutionary computation framework in a previous stage. We consider threshold Boolean networks for gene...Show More

SMOTE for gene regulatory network sampling

Gonzalo A. Ruz;Nitesh V. Chawla

Year: 2024 | Conference Paper |

Representing Outcome-Driven Higher-Order Dependencies in Graphs of Disease Trajectories

Steven J. Krieg;Nitesh V. Chawla;Keith Feldman

2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)
Year: 2024 | Conference Paper |
The widespread application of machine learning techniques to biomedical data has produced many new insights into disease progression and improving clinical care. Inspired by the flexibility and interpretability of graphs (networks), as well as the potency of sequence models like transformers and higher-order networks (HONs), we propose a method that identifies combinations of risk factors for a gi...Show More

Representing Outcome-Driven Higher-Order Dependencies in Graphs of Disease Trajectories

Steven J. Krieg;Nitesh V. Chawla;Keith Feldman

Year: 2024 | Conference Paper |

Efficient Augmentation for Imbalanced Deep Learning

Damien A. Dablain;Colin Bellinger;Bartosz Krawczyk;Nitesh V. Chawla

2023 IEEE 39th International Conference on Data Engineering (ICDE)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Deep learning models may not effectively generalize across under-represented or minority classes. We empirically study a convolutional neural network’s (CNN) internal representation of imbalanced image data and measure the generalization gap between a model’s feature embeddings in the training and test sets, showing that the gap is wider for minority classes. This insight enables us to design an e...Show More

Efficient Augmentation for Imbalanced Deep Learning

Damien A. Dablain;Colin Bellinger;Bartosz Krawczyk;Nitesh V. Chawla

Year: 2023 | Conference Paper |

Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows

Yihong Ma;Md Nafee Al Islam;Jane Cleland-Huang;Nitesh V. Chawla

IEEE Intelligent Systems
Year: 2023 | Volume: 38, Issue: 2 | Magazine Article |
Cited by: Papers (5)
With the increasing deployment of small unmanned aerial systems (sUASs) on various tasks, it becomes crucial to analyze and detect anomalies from their flight logs. To support research in this area, we curate Drone Log Anomaly (DLA), the first real-world time series anomaly detection dataset in the domain of sUASs, which contains 41 sUAS flight logs annotated with various types of anomalies. As an...Show More

Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows

Yihong Ma;Md Nafee Al Islam;Jane Cleland-Huang;Nitesh V. Chawla

Year: 2023 | Volume: 38, Issue: 2 | Magazine Article |

Deep Multimodal Complementarity Learning

Daheng Wang;Tong Zhao;Wenhao Yu;Nitesh V. Chawla;Meng Jiang

IEEE Transactions on Neural Networks and Learning Systems
Year: 2023 | Volume: 34, Issue: 12 | Journal Article |
Cited by: Papers (11)
Complementarity plays a significant role in the synergistic effect created by different components of a complex data object. Complementarity learning on multimodal data has fundamental challenges of representation learning because the complementarity exists along with multiple modalities and one or multiple items of each modality. Also, an appropriate metric is needed for measuring the complementa...Show More

Deep Multimodal Complementarity Learning

Daheng Wang;Tong Zhao;Wenhao Yu;Nitesh V. Chawla;Meng Jiang

Year: 2023 | Volume: 34, Issue: 12 | Journal Article |

SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance

Zhichun Guo;Jun Tao;Siming Chen;Nitesh V. Chawla;Chaoli Wang

IEEE Transactions on Visualization and Computer Graphics
Year: 2023 | Volume: 29, Issue: 8 | Journal Article |
Cited by: Papers (3)
Comprehensively evaluating and comparing researchers’ academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this article, we present an interactive visualization framework, SD$^{2}$2, to enable flexible data partition and composition to sup...Show More

SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance

Zhichun Guo;Jun Tao;Siming Chen;Nitesh V. Chawla;Chaoli Wang

Year: 2023 | Volume: 29, Issue: 8 | Journal Article |

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

Damien Dablain;Bartosz Krawczyk;Nitesh V. Chawla

IEEE Transactions on Neural Networks and Learning Systems
Year: 2023 | Volume: 34, Issue: 9 | Journal Article |
Cited by: Papers (176)
Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have further magnified the importance of the imbalanced data problem, especially when learning from images. Therefore, there is a need for an oversampling method that is specifically tailored to deep learning models, can work o...Show More

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

Damien Dablain;Bartosz Krawczyk;Nitesh V. Chawla

Year: 2023 | Volume: 34, Issue: 9 | Journal Article |

Modeling Co-Evolution of Attributed and Structural Information in Graph Sequence

Daheng Wang;Zhihan Zhang;Yihong Ma;Tong Zhao;Tianwen Jiang;Nitesh V. Chawla;Meng Jiang

IEEE Transactions on Knowledge and Data Engineering
Year: 2023 | Volume: 35, Issue: 2 | Journal Article |
Cited by: Papers (6)
Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the nodes. We find that real dynamic attributed graphs exhibit complex phenomenon of co-evolution between node attributes and graph structure. Learning node embeddings for forecasting change of node attributes and evolution of...Show More

Modeling Co-Evolution of Attributed and Structural Information in Graph Sequence

Daheng Wang;Zhihan Zhang;Yihong Ma;Tong Zhao;Tianwen Jiang;Nitesh V. Chawla;Meng Jiang

Year: 2023 | Volume: 35, Issue: 2 | Journal Article |

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

Md Nafee Al Islam;Yihong Ma;Pedro Alarcon;Nitesh Chawla;Jane Cleland-Huang

2022 IEEE 30th International Requirements Engineering Conference (RE)
Year: 2022 | Conference Paper |
Cited by: Papers (10)
CyberPhysical systems (CPS) must be closely monitored to identify and potentially mitigate emergent problems that arise during their routine operations. However, the multivariate time-series data which they typically produce can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, ...Show More

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

Md Nafee Al Islam;Yihong Ma;Pedro Alarcon;Nitesh Chawla;Jane Cleland-Huang

Year: 2022 | Conference Paper |

Dynamic Attributed Graph Prediction with Conditional Normalizing Flows

Daheng Wang;Tong Zhao;Nitesh V. Chawla;Meng Jiang

2021 IEEE International Conference on Data Mining (ICDM)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Graph representation learning aims at preserving structural and attributed information in latent representations. It has been studied mostly in the setting of static graph. In this work, we propose a novel approach for representation learning over dynamic attributed graph using the tool of normalizing flows for exact density estimation. Our approach has three components: (1) a time-aware graph neu...Show More

Dynamic Attributed Graph Prediction with Conditional Normalizing Flows

Daheng Wang;Tong Zhao;Nitesh V. Chawla;Meng Jiang

Year: 2021 | Conference Paper |

Predicting Job Performance Using Mobile Sensing

Shayan Mirjafari;Hessam Bagherinezhad;Subigya Nepal;Gonzalo J. Martinez;Koustuv Saha;Mikio Obuchi;Pino G. Audia;Nitesh V. Chawla;Anind K. Dey;Aaron Striegel;Andrew T. Campbell

IEEE Pervasive Computing
Year: 2021 | Volume: 20, Issue: 4 | Magazine Article |
Cited by: Papers (3)
We hypothesize that behavioral patterns of people are reflected in how they interact with their mobile devices and that continuous sensor data passively collected from their phones and wearables can infer their job performance. Specifically, we study day-today job performance (improvement, no change, decline) of N=298 information workers using mobile sensing data and offer data-driven insights int...Show More

Predicting Job Performance Using Mobile Sensing

Shayan Mirjafari;Hessam Bagherinezhad;Subigya Nepal;Gonzalo J. Martinez;Koustuv Saha;Mikio Obuchi;Pino G. Audia;Nitesh V. Chawla;Anind K. Dey;Aaron Striegel;Andrew T. Campbell

Year: 2021 | Volume: 20, Issue: 4 | Magazine Article |

motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data

Suwen Lin;Xian Wu;Nitesh V. Chawla

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)
Year: 2021 | Conference Paper |
The proliferation of wearable sensors allows for the continuous collection of temporal characterization of an individual's physical activity and physiological data. This is enabling an unprecedented opportunity to delve into a deeper analysis of the underlying patterns of such temporal data and to infer attributes associated with health, behaviors, and well-being. However, there remain several cha...Show More

motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data

Suwen Lin;Xian Wu;Nitesh V. Chawla

Year: 2021 | Conference Paper |

Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels

Suwen Lin;Louis Faust;Nitesh V. Chawla

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The prevalence of mobile sensors makes it possible for researchers to collect and analyze the pervasive sensed human activity data with machine learning tools. These analyses and applications heavily rely on the self-report survey data that reflect human physical and psychological behaviors. However, many factors can affect the reliability of such self-report surveys, such as participants' trusty ...Show More

Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels

Suwen Lin;Louis Faust;Nitesh V. Chawla

Year: 2021 | Conference Paper |

Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being

Pablo Robles-Granda;Suwen Lin;Xian Wu;Gonzalo J. Martinez;Stephen M. Mattingly;Edward Moskal;Aaron Striegel;Nitesh V. Chawla;Sidney D'Mello;Julie Gregg;Kari Nies;Gloria Mark;Ted Grover;Andrew T. Campbell;Shayan Mirjafari;Koustuv Saha;Munmun De Choudhury;Anind K. Dey

IEEE Computational Intelligence Magazine
Year: 2021 | Volume: 16, Issue: 2 | Magazine Article |
Cited by: Papers (13)
Assessment of individuals' job performance, personalized health and psychometric measures are domains where data-driven ubiquitous computing will have a profound impact in the near future. Existing work in these domains focus on techniques that use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits to assess well-being and cognitive attributes of individuals. H...Show More

Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being

Pablo Robles-Granda;Suwen Lin;Xian Wu;Gonzalo J. Martinez;Stephen M. Mattingly;Edward Moskal;Aaron Striegel;Nitesh V. Chawla;Sidney D'Mello;Julie Gregg;Kari Nies;Gloria Mark;Ted Grover;Andrew T. Campbell;Shayan Mirjafari;Koustuv Saha;Munmun De Choudhury;Anind K. Dey

Year: 2021 | Volume: 16, Issue: 2 | Magazine Article |

Biomedical Knowledge Graphs Construction From Conditional Statements

Tianwen Jiang;Qingkai Zeng;Tong Zhao;Bing Qin;Ting Liu;Nitesh V. Chawla;Meng Jiang

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Year: 2021 | Volume: 18, Issue: 3 | Journal Article |
Cited by: Papers (9)
Conditions play an essential role in biomedical statements. However, existing biomedical knowledge graphs (BioKGs) only focus on factual knowledge, organized as a flat relational network of biomedical concepts. These BioKGs ignore the conditions of the facts being valid, which loses essential contexts for knowledge exploration and inference. We consider both facts and their conditions in biomedica...Show More

Biomedical Knowledge Graphs Construction From Conditional Statements

Tianwen Jiang;Qingkai Zeng;Tong Zhao;Bing Qin;Ting Liu;Nitesh V. Chawla;Meng Jiang

Year: 2021 | Volume: 18, Issue: 3 | Journal Article |

Overcoming Data Sparsity in Predicting User Characteristics from Behavior through Graph Embeddings

Munira Syed;Daheng Wang;Meng Jiang;Oliver Conway;Vishal Juneja;Sriram Subramanian;Nitesh V. Chawla

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Year: 2020 | Conference Paper |
Understanding user characteristics such as demographic information is useful for the personalization of online content promoted to users. However, it is difficult to obtain such data for each user visiting the website. Since demographic data for some users can be collected, their behavior can be used to predict the attributes of unknown users. Through online news consumption, we can infer the attr...Show More

Overcoming Data Sparsity in Predicting User Characteristics from Behavior through Graph Embeddings

Munira Syed;Daheng Wang;Meng Jiang;Oliver Conway;Vishal Juneja;Sriram Subramanian;Nitesh V. Chawla

Year: 2020 | Conference Paper |

MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data

Suwen Lin;Louis Faust;Sidney D’Mello;Gonzalo Martinez;Nitesh V. Chawla

2020 IEEE International Conference on Big Data (Big Data)
Year: 2020 | Conference Paper |
Human abnormal physical and psychological behaviors, such as high level of stress, may result in negative impacts on work and life, if not handled efficiently. However, the continuous collection of behavioral data from questionnaires is not feasible, as is often the case for the natural downside of survey data gathering. Thanks to the proliferation of mobile sensors, it brings compelling opportuni...Show More

MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data

Suwen Lin;Louis Faust;Sidney D’Mello;Gonzalo Martinez;Nitesh V. Chawla

Year: 2020 | Conference Paper |

Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter

Steven J. Krieg;Daniel H. Robertson;Meeta P. Pradhan;Nitesh V. Chawla

2020 IEEE International Conference on Healthcare Informatics (ICHI)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
Networks are powerful and flexible structures for modeling relationships in medical and biological systems, but in a traditional first-order network representation, an edge typically expresses a relationship between a single pair of nodes. In order to analyze complex relationships between groups of nodes, researchers rely on combined sets of these pairwise connections, which can misrepresent the t...Show More

Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter

Steven J. Krieg;Daniel H. Robertson;Meeta P. Pradhan;Nitesh V. Chawla

Year: 2020 | Conference Paper |

Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach

Jennifer J. Schnur;Ryan Karl;Angélica García-Martínez;Meng Jiang;Nitesh V. Chawla

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2020 | Conference Paper |
In this paper, we discuss a tensor decomposition method for imputing similarity scores between individual clinical pictures at predefined patient age intervals in order to construct a dynamic similarity network of patients with respect to early childhood anthropomorphic development. The method leverages Canonical Polyadic Decomposition (or PARAFAC) to compute missing Euclidean similarity scores be...Show More

Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach

Jennifer J. Schnur;Ryan Karl;Angélica García-Martínez;Meng Jiang;Nitesh V. Chawla

Year: 2020 | Conference Paper |

Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

Suman Kundu;Tomasz Kajdanowicz;Przemyslaw Kazienko;Nitesh Chawla

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (1)
A high percentage of information that propagates through a social network is sourced from different exogenous sources. E.g., individuals may form their opinions about products based on their own experience or reading a product review, and then share that with their social network. This sharing then diffuses through the network, evolving as a combination of both network and external effects. Beside...Show More

Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

Suman Kundu;Tomasz Kajdanowicz;Przemyslaw Kazienko;Nitesh Chawla

Year: 2020 | Volume: 8 | Journal Article |

Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems

Chao Huang;Dong Wang;Nitesh V. Chawla

IEEE Transactions on Big Data
Year: 2020 | Volume: 6, Issue: 4 | Journal Article |
Cited by: Papers (18)
Social sensing is a new big data application paradigm for Cyber-Physical Systems (CPS), where a group of individuals volunteer (or are recruited) to report measurements or observations about the physical world at scale. A fundamental challenge in social sensing applications lies in discovering the correctness of reported observations and reliability of data sources without prior knowledge on eithe...Show More

Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems

Chao Huang;Dong Wang;Nitesh V. Chawla

Year: 2020 | Volume: 6, Issue: 4 | Journal Article |

CTGA: Graph-based Biomedical Literature Search

Tianwen Jiang;Zhihan Zhang;Tong Zhao;Bing Qin;Ting Liu;Nitesh V. Chawla;Meng Jiang

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
Scientists are relying heavily on biomedical literature search (BLS) engines (e.g., PubMed) to acquire knowledge. Existing BLS systems adopt a “C-A” paradigm that is to design query-document similarity measurement based on words/phrases in the unstructured Content and to develop search Algorithms. In this work, we argue that structures should be extracted and utilized to bridge the gap between tex...Show More

CTGA: Graph-based Biomedical Literature Search

Tianwen Jiang;Zhihan Zhang;Tong Zhao;Bing Qin;Ting Liu;Nitesh V. Chawla;Meng Jiang

Year: 2019 | Conference Paper |

Imputing Missing Social Media Data Stream in Multisensor Studies of Human Behavior

Koustuv Saha;Manikanta D. Reddy;Vedant das Swain;Julie M. Gregg;Ted Grover;Suwen Lin;Gonzalo J. Martinez;Stephen M. Mattingly;Shayan Mirjafari;Raghu Mulukutla;Kari Nies;Pablo Robles-Granda;Anusha Sirigiri;Dong Whi Yoo;Pino Audia;Andrew T. Campbell;Nitesh V. Chawla;Sidney K. D'Mello;Anind K. Dey;Kaifeng Jiang;Qiang Liu;Gloria Mark;Edward Moskal;Aaron Striegel;Munmun de Choudhury

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)
Year: 2019 | Conference Paper |
Cited by: Papers (12)
The ubiquitous use of social media enables researchers to obtain self-recorded longitudinal data of individuals in real-time. Because this data can be collected in an inexpensive and unobtrusive way at scale, social media has been adopted as a “passive sensor” to study human behavior. However, such research is impacted by the lack of homogeneity in the use of social media, and the engineering chal...Show More

Imputing Missing Social Media Data Stream in Multisensor Studies of Human Behavior

Koustuv Saha;Manikanta D. Reddy;Vedant das Swain;Julie M. Gregg;Ted Grover;Suwen Lin;Gonzalo J. Martinez;Stephen M. Mattingly;Shayan Mirjafari;Raghu Mulukutla;Kari Nies;Pablo Robles-Granda;Anusha Sirigiri;Dong Whi Yoo;Pino Audia;Andrew T. Campbell;Nitesh V. Chawla;Sidney K. D'Mello;Anind K. Dey;Kaifeng Jiang;Qiang Liu;Gloria Mark;Edward Moskal;Aaron Striegel;Munmun de Choudhury

Year: 2019 | Conference Paper |

Outside the Hospital Walls: Associations of Value Based Care Metrics and Community Health Factors

Catherine Markley;Keith Feldman;Nitesh V. Chawla

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Year: 2019 | Conference Paper |
As the healthcare industry shifts from traditional fee-for-service payment to value-based care models, the need to accurately quantify and compare the performance of institutions has become an integral component of both policy and research. To date, several notable metrics have been introduced, including the Centers for Medicare and Medicaids Hospital Value Based Purchasing (HVBP) program. However...Show More

Outside the Hospital Walls: Associations of Value Based Care Metrics and Community Health Factors

Catherine Markley;Keith Feldman;Nitesh V. Chawla

Year: 2019 | Conference Paper |

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