Lane, Nicholas D.

A survey of mobile phone sensing

Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine

Mobile Crowdsensing and CrowdsourcingContext-Aware Activity Recognition Systems

Cited by 2,292

Flower: A Friendly Federated Learning Research Framework

Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Li, K. H., Parcollet, T., de Gusmão, P. P. B., et al. (2020) · arXiv

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 622

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

Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016) · Perspectives on Psychological Science

Human Mobility and Location-Based AnalysisMobile Crowdsensing and Crowdsourcing

Cited by 515

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., & Kawsar, F. (2016) · 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 450

MoodScope: building a mood sensor from smartphone usage patterns

LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013) · Proceeding of the 11th annual international conference on Mobile systems, applications, and services

Context-Aware Activity Recognition SystemsEmotion and Mood Recognition

Cited by 444

BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing

Lane, N., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A., Berke, E., Choudhury, T., & Campbell, A. (2011) · Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare

Mobile Health and mHealth ApplicationsDigital Mental Health Interventions

Cited by 409

Unobtrusive Sleep Monitoring using Smartphones

Chen, Z., Lin, M., Chen, F., Lane, N., Cardone, G., Wang, R., Li, T., Chen, Y., Choudhury, T., & Cambell, A. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques

Context-Aware Activity Recognition SystemsSleep and related disorders

Cited by 300

Can Deep Learning Revolutionize Mobile Sensing?

Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 282

Multimodal Deep Learning for Activity and Context Recognition

Radu, V., Tong, C., Bhattacharya, S., Lane, N. D., Mascolo, C., Marina, M. K., & Kawsar, F. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 281

Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables

Bhattacharya, S., & Lane, N. D. (2016) · Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems CD-ROM

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 236

Federated benchmarking of medical artificial intelligence with MedPerf

Karargyris, A., Umeton, R., Sheller, M. J., Aristizabal, A., George, J., Wuest, A., Pati, S., Kassem, H., Zenk, M., Baid, U., et al. (2023) · Nature Machine Intelligence

Artificial Intelligence in Healthcare and EducationPrivacy-Preserving Technologies in Data

Cited by 188

Enabling large-scale human activity inference on smartphones using community similarity networks (csn)

Lane, N. D., Xu, Y., Lu, H., Hu, S., Choudhury, T., Campbell, A. T., & Zhao, F. (2011) · Proceedings of the 13th international conference on Ubiquitous computing

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 171

IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition

Kwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Plötz, T. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Context-Aware Activity Recognition SystemsHuman Pose and Action Recognition

Cited by 152

Towards multimodal deep learning for activity recognition on mobile devices

Radu, V., Lane, N. D., Bhattacharya, S., Mascolo, C., Marina, M. K., & Kawsar, F. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 147

DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware

Mathur, A., Lane, N. D., Bhattacharya, S., Boran, A., Forlivesi, C., & Kawsar, F. (2017) · Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 128

μNAS: Constrained Neural Architecture Search for Microcontrollers

Liberis, E., Dudziak, Ł., & Lane, N. D. (2021) · Proceedings of the 1st Workshop on Machine Learning and Systems

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 90

The Final Frontier: Deep Learning in Space

Kothari, V., Liberis, E., & Lane, N. D. (2020) · Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications

Advanced Neural Network ApplicationsSpace Technology and Applications

Cited by 86

Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices

Mathur, A., Zhang, T., Bhattacharya, S., Velickovic, P., Joffe, L., Lane, N. D., Kawsar, F., & Lio, P. (2018) · 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

Context-Aware Activity Recognition SystemsMobile Crowdsensing and Crowdsourcing

Cited by 70

Low-resource multi-task audio sensing for mobile and embedded devices via shared deep neural network representations

Georgiev, P., Bhattacharya, S., Lane, N. D., & Mascolo, C. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

Music and Audio ProcessingSpeech Recognition and Synthesis

Cited by 67

Adaptable butterfly accelerator for attention-based NNs via hardware and algorithm co-design

Fan, H., Chau, T., Venieris, S. I., Lee, R., Kouris, A., Luk, W., Lane, N. D., & Abdelfattah, M. S. (2022) · 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 66

LEO: scheduling sensor inference algorithms across heterogeneous mobile processors and network resources

Georgiev, P., Lane, N. D., Rachuri, K. K., & Mascolo, C. (2016) · Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking

IoT and Edge/Fog ComputingMobile and Web Applications

Cited by 58

Predicting Patient Outcomes with Graph Representation Learning

Tong, C., Rocheteau, E., Veličković, P., Lane, N., & Liò, P. (2022) · Studies in Computational Intelligence

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 38

On-device Federated Learning with Flower

Mathur, A., Beutel, D. J., de Gusmão, P. P. B., Fernandez-Marques, J., Topal, T., Qiu, X., Parcollet, T., Gao, Y., & Lane, N. D. (2021) · arXiv

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 22

Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach

Hu, B., Guan, Y., Gao, Y., Long, Y., Lane, N., & Ploetz, T. (2018) · arXiv

Gait Recognition and AnalysisBiometric Identification and Security

Cited by 16