Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tia Gao
dblp:45/301
· DBLP profile ↗
9ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 7 · 1 first-authorSystems, architecture and hardware · 1Computer networks · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Internet of things and sensor networks · 50% Wireless networking · 50% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless networking
qos |
0.1 | 1 | 2008 | MEDiSN: medical emergency detection in sensor networks · SenSys 2008 |
Internet of things and sensor networks
wireless sensor network |
0.1 | 1 | 2008 | MEDiSN: medical emergency detection in sensor networks · SenSys 2008 |
Medical and health informatics › clinical monitoring
patient monitoring |
0.0 | 1 | 2008 | MEDiSN: medical emergency detection in sensor networks · SenSys 2008 |
Methods — techniques the papers use, named apart from their topics
encryption · 0.2data compression · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | MEDiSN: Medical emergency detection in sensor networksabstractStaff shortages and an increasingly aging population are straining the ability of emergency departments to provide high quality care. At the same time, there is a growing concern about hospitals' ability to provide effective care during disaster events. For these reasons, tools that automate patient monitoring have the potential to greatly improve efficiency and quality of health care. Towards this goal, we have developed MEDiSN , a wireless sensor network for monitoring patients' physiological data in hospitals and during disaster events. MEDiSN comprises Physiological Monitors (PMs), which are custom-built, patient-worn motes that sample, encrypt, and sign physiological data and Relay Points (RPs) that self-organize into a multi-hop wireless backbone for carrying physiological data. Moreover, MEDiSN includes a back-end server that persistently stores medical data and presents them to authenticated GUI clients. The combination of MEDiSN's two-tier architecture and optimized rate control protocols allows it to address the compound challenge of reliably delivering large volumes of data while meeting the application's QoS requirements. Results from extensive simulations, testbed experiments, and multiple pilot hospital deployments show that MEDiSN can scale from tens to at least five hundred PMs, effectively protect application packets from congestive and corruptive losses, and deliver medically actionable data. JeongGil Ko, Jong Hyun Lim, Yin Chen 0002, Rvazvan Musvaloiu-E, Andreas Terzis, Gerald M. Masson, Tia Gao, Walt Destler, Leo Selavo, Richard P. Dutton |
ACM Trans. Embed. Comput. Syst. | 7 |
| 2008 | MEDiSN: medical emergency detection in sensor networksabstractStaff shortages and an increasingly aging population are straining the ability of emergency departments to provide high-quality care. Moreover, there is a growing concern about the ability of hospitals to provide effective care during disaster events. Tools that automate patient monitoring would greatly improve efficiency, quality of care, and the volume of patients treated. Towards this goal, we have developed MEDiSN, a wireless sensor network for monitoring patients' vital signs in hospitals and disaster events. MEDiSN consists of Patient Monitors which are custom-built, patient-worn motes that sample, compress and secure medical data, and Relay Points that form a static multi-hop wireless backbone for carrying patient data. Moreover, MEDiSN includes a back-end server that persistently stores medical data and presents them to multiple GUI clients. MEDiSN's heterogeneous architecture enables it to address the compound challenge of reliably delivering large volumes of data while meeting the application's QoS requirements. JeongGil Ko, Razvan Musaloiu-Elefteri, Jong Hyun Lim, Yin Chen 0002, Andreas Terzis, Tia Gao, Walt Destler, Leo Selavo |
SenSys | 6 |
| 2006 | Pervasive Patient Tracking for Mass Casualty Incident Response
Alexander M. Alm, Tia Gao, David White 0001 |
AMIA | 2 |
| 2006 | Information Collection and Dissemination: Toward a portable, real-time information sharing platform for emergency response
David Crawford, Tia Gao, David White 0001 |
AMIA | 2 |
| 2006 | Iterative User-Centered Design of a Next Generation Patient Monitoring System for Emergency Medical Response
Tia Gao, Matthew I. Kim, David White 0001, Alexander M. Alm |
AMIA | 1 |
| 2006 | Service-Oriented Architecture for Disaster Response: Integration of AID-N, MICHAELS, WISER, and ESSENCE
Logan Hauenstein, Tia Gao, David White 0001 |
AMIA | 2 |
| 2006 | Paramedic Operations Specification as a Launching Point for Mass Casualty Incident-Targeted Engineering Design
Azmat Husain, Jonathan H. Sharp, Tia Gao, David White 0001 |
AMIA | 3 |
| 2006 | The Design of a Decentralized Electronic Triage System
Tammara Massey, Tia Gao, Matt Welsh, Jonathan H. Sharp, Majid Sarrafzadeh |
AMIA | 2 |
| 2006 | An Emergency Response UAV Surveillance System
Pedro A. Rodriguez, William J. Geckle, Jeffrey D. Barton, John Samsundar, Tia Gao, Myron Z. Brown, Sean R. Martin |
AMIA | 5 |