Mingyue (Ming) TangPh.D. StudentDepartment of Computer Science University of Illinois Urbana-Champaign (UIUC) Email: mt55 [at] illinois [dot] edu
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Bio
My name is Mingyue (Ming) Tang, I am a Ph.D. student in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC).
Working with Prof. Elahé Soltanaghai on wireless and IoT problems with machine learning approches.
I was a M.Eng. (originally a Ph.D.) student at the University of Virginia (UVa) Link Lab .
I am passionate about exploring the potential of technology to improve our daily lives.
My research interests include Mobile Sensing, Mobile Computing, Signal Processing and Internet of Things, Data Mining, Pervasive Computing, and Healthcare Systems.
Through my work, I aim to create innovative wireless/mobile sensing solutions that can address real-world problems and make a positive impact on society.
I believe that technology has the power to revolutionize the way we live and work, and I am excited to share my research and ideas with you!
Previously, I cooperated with Prof. Laura Barnes (UVa), Prof. Ang Li (UMD), Prof. Mehdi Boukhechba (UVa, now at Janssen R&D), Prof. Carl Yang (Emory),
Prof. Pan Li (GaTech), Prof. José Luis Ambite (USC ISI), Prof. Tiffany Tang (WKU), and Prof. Pinata Winoto(WKU)
Main Research Interests
Ming's research interests are Wireless Sensing, Mobile Sensing, Intelligent Internet of Things (IoT) System, and all the above things + Healthcare.
- Mobile Sensing
- Artificial intelligence in Internet of Things (AIoT)
- Ubiquitous Computing in Healthcare
News
| [05/2023] | Transfer to UIUC. Graduated again, glad to received my second master degree in Systems and Information engineering at UVa! |
| [04/2023] | Our fluid overload detection paper accepted by CHIL 2023 has been selected for an oral presentation (13.3%)! |
| [04/2023] | One paper was accepted by EMBC 2023 on personalized state anxiety detection using linguistic indicators! |
| [04/2023] | One paper was accepted by CHIL 2023 on fluid overload detection in ESKD patients! |
| [03/2023] | I decided to choose University of Illinois Urbana-Champaign (UIUC) as my next stop to continue my Ph.D. journey. |
| [01/2023] | I started my internship at Abbott as Scientist I. |
Selected Publications
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Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and Machine Learning Pipeline |
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SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder |
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PFed-LDP: A Personalized Federated Differential Privacy framework for IoT sensing |
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Mobile Sensing in the COVID-19 Era |
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Dynamic Network Anomaly Modeling of Cell-Phone Call Detail Records for Infectious Disease Surveillance |
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GNNs in IoT: A Survey |
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Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction |
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Using Ubiquitous Mobile Sensing and Temporal Sensor-Relation GNN to Predict Fluid Intake of End Stage Kidney Patients |
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A Smartwatch Based System for Monitoring Fluid Consumption of End-Stage Kidney Patients |
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Semi-supervised Graph Instance Transformer for Mental Health Inference |
Experience
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Abbott, TX, US Scientist I, Spring 2023 Mentor: Mingming Zhang |
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University of Virginia, Data Science School VA, US Teaching Assistant, Spring 2021 - Present |
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Novartis, Inc., NJ, US Data Strategy Team, Summer 2020 |
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University of Southern California, Information Science Institute (ISI) , Marina Del Rey, US Research Assistant, Fall 2019 & Fall 2020 Mentors: José Luis Ambite , Pedro Szekely |
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Selected Projects
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Federated Learning on IoT data Optimized the accuracy of collaborative training data from IoT edge devices while preserving privacy. |
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SIMS - Social Interactions Monitoring Study Monitoring social state anxiety with wearable sensors and webcams. |
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FluiSense Using multi-modal mobile sensing for better fluid control for end stage kidney disease (ESKD) Patients. Conducted a 4-week study and collected time-series data with on-body physiological and behavioral sensors (e.g., PPG, IMU) from ESKD patients.
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Graph Unsupervised Representation Learning A new unsupervised way of graph learning, addressed existing limitations in graph autoencoder, graph structure learning, and infomax-based methods.
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Service
Teaching
© Mingyue (Ming) Tang