About

Dr. Ashok Agrawala is a Professor of Computer Science at the University of Maryland. He serves as the Director of the Maryland Information and Network Dynamics (MIND) Lab, where his research focuses on information and network dynamics. His professional contact information includes a phone number at 301-405-2525 and an email address at agrawala@cs.umd.edu. Dr. Agrawala's work involves advancing understanding in the field of computer science through leadership of the MIND Lab, contributing to research and development in information systems and network analysis.

Research topics

• Computer Science
• Artificial Intelligence
• Information Retrieval
• Data Mining
• Natural Language Processing
• Computer Security
• Mathematics
• Human–computer interaction
• Software engineering
• Operating system
• Geography
• Data science
• World Wide Web

Selected publications

• Anemoi: Breath Analytics for Ailment Prediction and Recovery Tracking

  Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Temporal Analysis on Topics Using Word2Vec: Insights from Health and Sports News

  Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• A Smart, Energy-Efficient, and Low Cost Portable Device for Continuous Breath Analysis

  2024-10-20

  articleSenior author

  The detection of breathing patterns and inferring one's state (physical, mental, or social well-being) remains a difficult task in the current research community. Moreover, early recognition of abnormal respiratory patterns can aid the clinician in early intervention to prevent further deterioration of the patient's condition [1]. In this work, we present a smart and low-cost portable medical device designed for continuous breath monitoring. Our system leverages advanced sensor technology combined with novel signal processing algorithms to achieve high sensitivity and accuracy in detecting breath patterns.

  PublisherDOI

• Temporal Analysis on Topics Using Word2Vec

  arXiv (Cornell University) · 2022 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Data Mining

  The present study proposes a novel method of trend detection and visualization - more specifically, modeling the change in a topic over time. Where current models used for the identification and visualization of trends only convey the popularity of a singular word based on stochastic counting of usage, the approach in the present study illustrates the popularity and direction that a topic is moving in. The direction in this case is a distinct subtopic within the selected corpus. Such trends are generated by modeling the movement of a topic by using k-means clustering and cosine similarity to group the distances between clusters over time. In a convergent scenario, it can be inferred that the topics as a whole are meshing (tokens between topics, becoming interchangeable). On the contrary, a divergent scenario would imply that each topics' respective tokens would not be found in the same context (the words are increasingly different to each other). The methodology was tested on a group of articles from various media houses present in the 20 Newsgroups dataset.

  PublisherOA PDFDOI

• Better off This Way!: Ubiquitous Accessibility Digital Maps via Smartphone-based Crowdsourcing

  2021 · 4 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Information Retrieval

  Accessibility maps are key to support individuals with disabilities to actively participate in the society. The Americans with Disabilities Act (ADA) defines minimum requirements for roads and other public accommodation spaces to be accessible. Yet, it is sufficient to have one accessible route in a place, and available digital-maps lack accessibility information to help finding that accessible route.In this paper, we present the AccessMap system to automatically extend road-maps with accessibility semantics. It enables indoor and outdoor spaces to be automatically marked as visually-impaired and/or wheel-chaired accessible/inaccessible. AccessMap passively crowdsources measurements from sensors available in the users' smartphones to detect accessibility semantics. It employs a probabilistic framework to build and update the map with the semantics. Evaluation of AccessMap in different countries shows that it can passively detect a wide-range of accessibility semantics with high precision and recall (on average around 89.8% and 86.3% respectively). Furthermore, its probabilistic crowdsourcing framework increases the generated map’s average precision and recall to 98.7% and 99% with as few as seven encounters per semantic.

  PublisherDOI

• SDN: philosophy, technology and software defined software

  CSI Transactions on ICT · 2020

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Software engineering
  PublisherDOI

• Energy Efficient IP-Connectivity with IEEE 802.11 for Home M2M Networks

  The Computer Journal · 2017-02-24 · 6 citations

  articleOpen accessSenior author

  Machine-to-machine communication (M2M) technology enables large-scale device communication and networking, including home devices and appliances. A critical issue for home M2M networks is how to efficiently integrate existing home consumer devices and appliances into an IP-based wireless M2M network with least modifications. Due to its popularity and widespread use in closed spaces, Wi-Fi is a good alternative as a wireless technology to enable M2M networking for home devices. This paper addresses the energy-efficient integration of home appliances into a Wi-Fi-and IP-based home M2M network. Toward this goal, we first propose an integration architecture that requires least modifications to existing components. Then, we propose a novel long-term sleep scheduling algorithm to be applied with the existing 802.11 power save mode. The proposed scheme utilizes the multicast DNS protocol to maintain device and service availability when devices go into deep sleep mode. We prototyped our proposed architecture and algorithm to build a M2M network testbed of home appliances. We performed various experiments on this testbed to evaluate the operation and energy savings of our proposal. We also did simulation experiments for larger scale scenarios. As a result of our test-bed and simulation experiments, we observed significant energy savings compared to alternatives while also ensuring device and service availability.

  PublisherOA PDFDOI

• Towards Ubiquitous Accessibility Digital Maps for Smart Cities

  2017-11-07 · 14 citations

  articleSenior author

  Designing indoor and outdoor spaces to become accessible for people with disabilities is of paramount importance. Accessibility leads to improvements in human rights and business outcomes; due to inclusion of a broader range of the population. For example, adding braille writing to signs and installing ramps allow visually-impaired people and the wheel-chaired to navigate on their own.

  PublisherDOI

• Modeling Users’ Behavior from Large Scale Smartphone Data Collection

  EAI Endorsed Transactions on Context-aware Systems and Applications · 2016-09-12 · 7 citations

  articleOpen accessSenior author

  A large volume of research in ubiquitous systems has been devoted to using data, that has been sensed from users’ smartphones, to infer their current high level context and activities. However, mining users’ diverse longitudinal behavioral patterns, which can enable exciting new context-aware applic

  PublisherOA PDFDOI

• To Sense or not to Sense: An Exploratory Study of Privacy, Trust and other related concerns in Personal Sensing Context-aware Applications

  EAI Endorsed Transactions on Context-aware Systems and Applications · 2016-09-12

  articleOpen accessSenior author

  Due to increasing proliferation of smart devices, many users store a significant proportion of personal data on them. Thus, personal sensing applications that sense a user’s context via his smart device have significant privacy implications. In this paper, we conduct an exploratory study of privacy,

  PublisherOA PDFDOI

Frequent coauthors

• Moustafa Youssef

  31 shared

• Ronald L. Larsen

  25 shared

• Satish K. Tripathi

  18 shared

• Tomlinson G. Rauscher

  Sunset Laboratory (United States)

  15 shared

• Preeti Bhargava

  12 shared

• Dheeraj Sanghi

  JK Lakshmipat University

  11 shared

• Ólafur Guðmundsson

  Uppsala University

  11 shared

• Tamer Nadeem

  11 shared