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D-Index & Metrics

Computer Science

D-Index
65
Citations
22548
World Ranking
2419
National Ranking
135

Overview

Nicholas D. Lane is affiliated with the University of Cambridge in the United Kingdom and has a research portfolio focused primarily on computer science, with an emphasis on artificial intelligence and its related subfields.

Their recent scholarly work includes several notable publications: IMUTube (2020), published in the Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies; Flower: A Friendly Federated Learning Research Framework (2020), available through arXiv (Cornell University); Federated benchmarking of medical artificial intelligence with MedPerf (2023) in Nature Machine Intelligence; Zero-Cost Proxies for Lightweight NAS (2021), also on arXiv; and FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout (2021), again on arXiv.

Their research is often conducted in collaboration with a network of frequent co-authors, including Stylianos I. Venieris, Xinchi Qiu, Javier Fernández-Marqués, Titouan Parcollet, and Royson Lee.

The scientist's publication record reveals a strong presence in prominent venues such as arXiv (Cornell University), Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, GetMobile Mobile Computing and Communications, Proceedings of the AAAI Conference on Artificial Intelligence, and Computer.

Nicholas D. Lane's main fields of study encompass computer science predominantly, with subfields including artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, signal processing, and computer networks and communications.

  • Advanced Neural Network Applications
  • Privacy-Preserving Technologies in Data
  • Domain Adaptation and Few-Shot Learning
  • Context-Aware Activity Recognition Systems
  • Anomaly Detection Techniques and Applications
  • Stochastic Gradient Optimization Techniques
  • Human Pose and Action Recognition

The academic output and collaborations suggest a focus on federated learning, neural network design and optimization, privacy technologies, and activity recognition within ubiquitous computing environments.

Best Publications

  • A survey of mobile phone sensing

    Nicholas D Lane;Emiliano Miluzzo;Hong Lu;Daniel Peebles

  • Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

    Emiliano Miluzzo;Nicholas D. Lane;Kristóf Fodor;Ronald Peterson

  • SoundSense: scalable sound sensing for people-centric applications on mobile phones

    Hong Lu;Wei Pan;Nicholas D. Lane;Tanzeem Choudhury

  • BikeNet: A mobile sensing system for cyclist experience mapping

    Shane B. Eisenman;Emiliano Miluzzo;Nicholas D. Lane;Ronald A. Peterson

  • The Rise of People-Centric Sensing

    A.T. Campbell;S.B. Eisenman;N.D. Lane;E. Miluzzo

  • The Jigsaw continuous sensing engine for mobile phone applications

    Hong Lu;Jun Yang;Zhigang Liu;Nicholas D. Lane

  • People-centric urban sensing

    Andrew T. Campbell;Shane B. Eisenman;Nicholas D. Lane;Emiliano Miluzzo

  • MoodScope: building a mood sensor from smartphone usage patterns

    Robert LiKamWa;Yunxin Liu;Nicholas D. Lane;Lin Zhong

  • Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges.

    Gabriella M. Harari;Nicholas D. Lane;Rui Wang;Benjamin S. Crosier

  • Bewell: A smartphone application to monitor, model and promote wellbeing

    Nicholas Lane;Mashfiqui Mohammod;Mu Lin;Xiaochao Yang

  • The BikeNet mobile sensing system for cyclist experience mapping

    S. B. Eisenman;E. Miluzzo;N. D. Lane;R. A. Peterson

  • DeepX: a software accelerator for low-power deep learning inference on mobile devices

    Nicholas D. Lane;Sourav Bhattacharya;Petko Georgiev;Claudio Forlivesi

  • Urban sensing systems: opportunistic or participatory?

    Nicholas D. Lane;Shane B. Eisenman;Mirco Musolesi;Emiliano Miluzzo

  • DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning

    Nicholas D. Lane;Petko Georgiev;Lorena Qendro

  • Unobtrusive sleep monitoring using smartphones

    Zhenyu Chen;Mu Lin;Fanglin Chen;Nicholas D. Lane

  • Can Deep Learning Revolutionize Mobile Sensing

    Nicholas D. Lane;Petko Georgiev

  • Automatically characterizing places with opportunistic crowdsensing using smartphones

    Yohan Chon;Nicholas D. Lane;Fan Li;Hojung Cha

  • CenceMe: injecting sensing presence into social networking applications

    Emiliano Miluzzo;Nicholas D. Lane;Shane B. Eisenman;Andrew T. Campbell

  • CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones

    Chuang-Wen You;Nicholas D. Lane;Fanglin Chen;Rui Wang

  • Multimodal Deep Learning for Activity and Context Recognition

    Valentin Radu;Catherine Tong;Sourav Bhattacharya;Nicholas D. Lane

  • An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices

    Nicholas D. Lane;Sourav Bhattacharya;Petko Georgiev;Claudio Forlivesi

Frequent Co-Authors

Andrew T. Campbell
Andrew T. Campbell Dartmouth College
Feng Zhao
Feng Zhao Microsoft (United States)
Tanzeem Choudhury
Tanzeem Choudhury Cornell University
Ranveer Chandra
Ranveer Chandra Microsoft (United States)
Mirco Musolesi
Mirco Musolesi University College London
Thomas Moscibroda
Thomas Moscibroda Microsoft (United States)
Hojung Cha
Hojung Cha Yonsei University
Suman Nath
Suman Nath Microsoft (United States)
Lorenzo Torresani
Lorenzo Torresani Facebook (United States)
Cecilia Mascolo
Cecilia Mascolo University of Cambridge

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