Bio

I work as a Principal Research Scientist and Tech Lead for Machine Learning at Nokia Bell Labs in Cambridge. I am also a Visiting Industry Fellow at the University of Cambridge and serve on the Editorial Board of ACM IMWUT journal as an Associate Editor. From 2019-2021, I was a member of the ACM Future of Computing Academy.

My research interests are broadly in machine learning and mobile systems. Currently, I am working on projects involving Federated Learning, Self-Supervised Learning, On-Device ML, and Algorithmic Fairness — all in the context of mobile and embedded devices. In addition, I am also interested in exploring the design of novel ML-driven sensory systems and applications. Previously, I have done research in the areas of Ubiquitous Computing, Human-Computer Interaction, and ICT4D. I am a recipient of the Wolfond Fellowship at the University of Toronto, the mBillionth Award South Asia, and two Best Paper Honorable Mention Awards. My research has been covered by several media organizations including the New Yorker, Financial Times, Livemint and Canadian Broadcasting Corporation.

I hold a Ph.D. in Machine Learning from the University College London where I worked under the supervision of Dr. Nic Lane and Prof. Nadia Berthouze. I also hold a Masters in Computer Science from the University of Toronto and a B.Tech. from DA-IICT where I was awarded the President's Gold Medal.

Federated Learning

Self-Supervised Learning

Mobile Sensing and Systems

Domain Adaptation

News

  • July 2022: We proposed FLAME, a technique for federated learning in multi-device settings. Paper accepted to ACM IMWUT.
  • May 2022: We released the source code for Orchestra. Please check the GitHub repo.
  • May 2022: Our work Orchestra has been accepted to ICML 2022. Orchestra is one of the first approaches for unsupervised FL in cross-device settings.
  • January 2022: We proposed a new technique for collaborative self-supervised learning (ColloSSL). Paper accepted to ACM IMWUT.
  • January 2022: Paper accepted to ICASSP 2022 on CycleGAN-based generation of personalized therapeutic music to help with sleep disorders.
  • December 2021: We proposed a new framework for extending unsupervised domain adaptation to distributed, multi-domain settings. Paper accepted to IEEE Transactions on Parallel And Distributed Systems.
  • November 2021: I am co-chairing the Industry Track for IEEE Percom 2022. Please consider submitting a paper.
  • October 2021: I am serving on the Program Committees of ACM/IEEE IPSN 2022 and ACM CHI 2022.
  • September 2021: Two papers on federated learning and embedded ML fairness accepted to AIChallengeIoT at ACM Sensys 2021.
  • July 2021: Our paper on extending self-supervised learning to multi-devices settings was accepted to Self-Supervised Learning for Reasoning and Perception at ICML 2021.
  • May 2021: Two papers on detecting chronic pain by modeling on-body sensor data with Graph Neural Networks accepted to ACM IMWUT and ACM Transactions on Computing for Healthcare.
  • March 2021: Our paper on deploying Federated Learning on Android devices was accepted to the On-Device Intelligence Workshop at MLSys 2021.
  • December 2020: Our paper on quantifying the carbon footprint of FL was accepted to the Tackling Climate Change with Machine Learning workshop at NeurIPS 2020.
  • September 2020: We released Flower, an open-source Federated Learning framework, to accelerate FL research and deployments.
  • September 2020: We released LibriAdapt, a large-scale speech dataset to facilitate research in domain adaptation.

Publications

[P57] Lubana E.S., Tang C.I., Kawsar F., Dick R., Mathur A.. "Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering". ICML 2022 (Spotlight Talk)

[P56] Cho H., Mathur A., Kawsar F. "FLAME: Federated Learning Across Multi-device Environments". ACM IMWUT, 2022.

[P55] Jain Y., Tang C.I., Min C., Kawsar F., and Mathur A. "ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition". ACM IMWUT, 2022.

[P54] Gan S., Mathur A., Isopoussu A., Kawsar F., Berthouze N., Lane N.D. "FRuDA: Framework for Distributed Adversarial Domain Adaptation". IEEE Transactions on Parallel And Distributed Systems, 2022.

[P53] Toussaint W., Mathur A., Ding A., Kawsar F. "Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning". AIChallengeIoT, ACM SenSys 2021.

[P52] Cho H., Mathur A., Kawsar F. "Device or User: Rethinking Federated Learning in Personal-Scale Multi-Device Environments". AIChallengeIoT, ACM SenSys 2021.

[P51] Franklin M., Lagnado D., Min C., Mathur A., Kawsar F. "Designing Memory Aids for Dementia Patients using Earables". EarComp 2021.

[P50] Wang C., Olugbade T., Mathur A., Williams A., Lane N.D., Berthouze N. "Chronic Pain Protective Behavior Detection with Deep Learning". ACM Transactions on Computing for Healthcare 2021.

[P49] Wang C., Gao Y., Mathur A., Williams A., Lane N.D., Berthouze N. "Leveraging activity recognition to enable protective behavior detection in continuous data". ACM IMWUT 2021.

[P48] Lei J., Rahman T., Shafik R., Wheeldon A., Yakovlev A., Granmo O., Kawsar F., and Mathur A.. "Low-Power Audio Keyword Spotting using Tsetlin Machines". Journal of Low Power Electronics and Applications.

[P47] Mathur A., Beutel, D.J., Gusmão P.P.B, Fernandez-Marques J., Topal, T., Qiu, X., Parcollet, T., Gao Y., and Lane, N.D. "On-device Federated Learning with Flower". On-Device Intelligence Workshop at MLSys 2021.

[P46] Qiu X., Parcollet T., Beutel D., Topal T., Mathur A., Lane N.D. "Can Federated Learning Save the Planet?"" Tackling Climate Change with Machine Learning workshop at NeurIPS 2020

[P45] Jain Y., Tang C.I., Min C., Kawsar F., and Mathur A. "Group Supervised Learning: Extending Self-Supervised Learning to Multi-Device Settings". ICML 2021 workshop on Self-Supervised Learning for Reasoning and Perception.

[P44] Mathur A., Berthouze N., Lane N.D. "Unsupervised Domain Adaptation under Label Space Mismatch for Speech Classification". Interspeech 2020.

[P43] Beutel, D.J., Topal, T., Mathur A., Qiu, X., Parcollet, T. and Lane, N.D. "Flower: A Friendly Federated Learning Research Framework". arXiv preprint arXiv:2007.14390.

[P42] Mathur A., Kawsar F., Berthouze N., Lane N.D. "LIBRI-ADAPT: A new speech dataset for unsupervised domain adaptation". IEEE ICASSP 2020.

[P41] Chang Y., Mathur A., Isopoussu A., Song J., Kawsar F. "A Systematic Study of Unsupervised Domain Adaptation for Robust Human-Activity Recognition". ACM IMWUT 2020.

[P40] Mathur A., Isopoussu A., Kawsar F., Berthouze N., Lane N.D. "FlexAdapt: Flexible Cycle-Consistent Adversarial Domain Adaptation". IEEE International Conference on Machine Learning and Applications, 2019.

[P39] Gong T., Ramos A.G.C.P, Bhattacharya S., Mathur A., Kawsar F. "AudioDOS: Real-Time Denial-of-Service Adversarial Attacks on Deep Audio Models". IEEE International Conference on Machine Learning and Applications, 2019.

[P38] Min C., Montanari A., Mathur A., and Kawsar F. "A Closer Look at Quality-Aware Runtime Assessment of Sensing Models in Multi-Device Environments", ACM Sensys 2019.

[P37] Antonini M., Vu T., Min C., Montanari A., Mathur A., and Kawsar F. "Resource Characterisation of Personal-Scale Sensing Models on Edge Accelerators", AIChallengeIoT, ACM SenSys 2019.

[P36] Mathur A., Isopoussu A., Berthouze N., Lane N.D., Kawsar F. "Unsupervised Domain Adaptation for Robust Sensory Systems". CML-IoT, Ubicomp 2019, London, UK.

[P35] Min C., Mathur A., Montanari A., Kawsar F. "An Early Characterisation of Wearing Variability on Motion Signals for Wearables". ISCW 2019, London, UK.

[P34] Wang C., Olugbade T., Mathur A., Williams A., Berthouze N., Lane N.D. "Recurrent Network based Automatic Detection of Chronic Pain Protective Behavior using MoCap and sEMG data". ISCW 2019, London, UK.

[P33] Katayama S., Mathur A., van den Broeck M., Okoshi T., Nakazawa J., Kawsar F. "Situation-Aware Emotion Regulation of Conversational Agents with Kinetic Earables". ACII 2019, Cambridge, UK.

[P32] Mathur A., Isopoussu A., Kawsar F., Berthouze N., Lane N.D. "Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems". IPSN 2019, Montreal, Canada.

[P31] Mathur A., Kawsar F., Berthouze N., Lane N.D. "A Vision for Adaptive and Generalizable Audio-Sensing Systems". To Appear in SocialSense @ IPSN 2019.

[P30] Lee S., Min C., Montanari A., Mathur A. , Chang Y., Song J. and Kawsar F. "Automatic Smile and Frown Recognition with Kinetic Earables". The 10th Augmented Human International Conference (AH 2019), Reims Champagne-Ardenne, France.

[P29] Kawsar F., Min C., Mathur A. , Montanari A. "Earables for Personal-scale Behaviour Analytics", IEEE Pervasive Computing, Volume: 17, Issue: 3, 2018.

[P28] Mathur A., Isopoussu A., Kawsar F., Smith R., Lane N.D. and Berthouze N. "On Robustness of Cloud Speech APIs: An Early Characterization". In HASCA Workshop @ Ubicomp 2018.

[P27] Min C., Montanari A., Mathur A., Lee S., and Kawsar F. "Cross-Modal Approach for Conversational Well-being Monitoring with Multi-Sensory Earables", In Computing for WellBeing Workshop @ Ubicomp 2018.

[P26] Min C., Mathur A., Kawsar F. "Exploring Audio and Kinetic Sensing on Earable Devices", In WearSys Workshop @ Mobisys 2018.

[P25] Min C., Mathur A., Kawsar F. "Audio-Kinetic Model for Automatic Dietary Monitoring with Earable Devices", Mobisys 2018.

[P24] Mathur A., Zhang, T., Bhattacharya, S., Veličković, P., Joffe, L., Lane, N. D., Kawsar F, Lió, P. "A Deep Data Augmentation Training Method to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices”. IPSN 2018, Porto, Portugal.

[P23] Mathur A., Lane, N. D., Bhattacharya, S., Boran, A., Forlivesi, C., Kawsar, F. "DeepEye: Resource efficient local execution of multiple deep vision models using wearable commodity hardware". ACM Mobisys 2017.

[P22] Lane, N. D., Bhattacharya, S., Mathur A., Georgiev, P., Forlivesi, C., Kawsar, F. "Squeezing Deep Learning into Mobile and Embedded Devices". IEEE Pervasive Magazine 2017.

[P21] Mathur A., Kalanadhabhatta L.M., Majethia R., Kawsar F. "Moving Beyond Market Research: Demystifying Smartphone User Behavior in India". ACM IMWUT/Ubicomp 2017.

[P20] Lane, N., Bhattacharya, S., Mathur A., Forlivesi, C., Kawsar, F. "Dxtk: Enabling resource-efficient deep learning on mobile and embedded devices with the DeepX toolkit". EAI MobiCase 2016.

[P19] Mashhadi, A., Mathur A., Van den Broeck, M., Vanderhulst, G., Kawsar, F. "Understanding the impact of personal feedback on face-to-face interactions in the workplace". ACM ICMI 2016. (Best Paper Nominee)

[P18] Mathur A., Lane N.D., Kawsar F. "Engagement-aware computing: Modelling user engagement from mobile contexts”. ACM Ubicomp 2016.

[P17] Mathur A., Kawsar F. "The Need to Account for Geographical Diversities in Mobile Data Research”. MobiData @ MobiSys 2016.

[P16] Montanari A., Mashhadi A., Mathur A., Kawsar F. "Understanding the Privacy Design Space for Personal Connected Objects”. ACM BritishHCI 2016.

[P15] Mathur A., Broeck M., Vanderhulst G., Mashhadi A., and Kawsar F. "Tiny Habits in the Giant Enterprise: Understanding the Dynamics of a Quantified Workplace". ACM UbiComp 2015.

[P14] Mathur A., Broeck M., Vanderhulst G., Mashhadi A., and Kawsar F. "Quantified Workplace: Opportunities and Challenges". Physical Analytics Workshop @ MobiSys 2015.

[P13] Mashhadi A., Mathur A., Kawsar F. The Myth of Subtle Notifications. ACM Ubicomp 2014.

[P12] Mathur A., Jaiswal S. Exploring the Interplay between Community Media and Mobile Web in Developing Regions. ACM MobileHCI 2013. (Best Paper Honorable Mention Award)

[P11] Mathur A., Agarwal S., Jaiswal S. Exploring Playback and Recording of Web-based Audio Media on Low-End Feature Phones. ACM DEV 2012.

[P10] Samdaria N., Mathur A., Balakrishnan R. Paying in Kind for Crowdsourcing Work in Developing Regions. Pervasive 2012 (Best Paper Nominee)

[P9] Kumar N., Mathur A., Lal S. Banking 101: Mobile-izing financial inclusion in an emerging India. Bell Labs Technical Journal, Human Sciences and User Experience Edition, 2012

[P8] Mathur A., Majumder A., Datta S., Menon S. LifeView: A Lifelog Visualization Tool for Supporting Sentimental Recall and Sharing. ACM OzCHI 2012

[P7] Mathur A., Ramachandran D., Cutrell E., Balakrishnan R. An Exploratory Study on the Use of Cameraphones and Pico Projectors in Rural India. ACM MobileHCI 2011

[P6] Hutchful D., Mathur A., Joshi A., Cutrell A. Cloze: An Authoring Tool for Teachers with Low Computer Proficiency. IEEE/ACM ICTD '10

[P5] Kam M., Mathur A., Kumar A., Canny J. Designing Digital Games for Rural Children: A Study of Traditional Village Games in India. ACM CHI '09 (Best Paper Honorable Mention Award)

[P4] Kam M., Kumar A., Jain S., Mathur A., Canny J. Improving Literacy in Rural India: Cellphone Games in an After-School Program. IEEE/ACM ICTD '09

[P3] Kam M., Agarwal A., Kumar A., Lal S., Mathur A., Tewari A., Canny J. Designing E-Learning Games for Rural Children in India: A Format for Balancing Learning with Fun. ACM DIS '08

[P2] Kam M., Bhagwani S., Kumar A., Lal S., Mathur A., Tewari A., Canny J. The Social Complexities of User-Centered Design in ICTD: Experiences from Four Schools in India's Villages and Slums. IEEE/ACM ICTD '07

[P1] Tewari A., Kumar A., Mathur A., Lal S., Kam M., Canny J. Mobile Games for Learning English in Rural India: Designing Cellphone Games Informed by Traditional Games. 3rd Digital Games Research Association International Conference (DIGRA'07)

Patents

  • A Method to Improve the Efficiency of Video Sensing Systems. {EP-19174348.3}
  • A system for the detection of facial expression on kinetic earables. {EP-19161913.9}
  • A method and system of making AI models robust and generalizable using a domain-adversarial training technique. {EP-18205945.1}
  • Sensor configuration based on other sensor context determination. {EP-19158220.4}
  • A method to improve the robustness of audio sensing models. {US-16/513212}
  • A method for augmenting user interface on earbuds using audio-kinetic model. {EP-18188821.5}
  • Gesture control of a data processing apparatus. {EP-3502835}
  • Method and devices for processing sensor data. {EP-3483729}
  • Method and device for analyzing sensor data. {EP-3401846}
  • A user equipment and method for protection of user privacy in communication networks. {EP-3382981}
  • Providing user access to remote devices. {EP-3310024}
  • Virtual reality interaction. {EP-3299930}
  • A method for predicting the engagement level of a user of a user device, a related engagement prediction device and user device. {EP-3285217}
  • Monitoring of attacks on bluetooth beacon devices. {EP-3148237}
  • User profiling for location based advertising. {EP-3139572}
  • Sharing resources between devices in a wireless network. {ES-2701303}
  • Providing sponsored content based on location and context. {EP-3001705}
  • Streaming playout of media content using interleaved media players. {US-2016088079}
  • System and method of account access notification. {EP-2919178}
  • System and method for providing and controlling visual display useful for remote control of a variety of devices. {IN-2013DE03396}
  • Methods and systems for multimodal interaction. {US-2015363047}

Mentoring

I enjoy mentoring and collaborating with self-motivated students. If you are looking for on-site, paid internship positions at Nokia Bell Labs, please apply to our formal internship program. The applications typically open in December for positions during June-September of next year.

In addition, I also do academic collaborations with students around the world. These are unpaid and informal research collaborations with the aim of providing research exposure to self-motivated students who aspire to pursue a research degree or career. Please feel free to reach out if you are interested in either of these opportunities.

I have been fortunate to work closely with the following interns and student collaborators:

  • Ekdeep Singh Lubana, University of Michigan, 2021
  • Chi Ian Tang, University of Cambridge, 2021
  • Hyunsung Cho, Carnegie Mellon University, 2021
  • Wiebke Toussaint, Delft University of Technology, 2021
  • Yash Jain, Georgia Tech, 2021
  • Jing Yang, ETH Zurich, 2021
  • Shaoduo Gan, ETH Zurich, 2019
  • Shin Katayama, Keio University, 2018
  • Youngjae Chang, KAIST, 2018
  • Seungchul Lee, KAIST, 2018
  • Tianlin Zhang, University of Cambridge, 2017
  • Manasa Kalanadhabhatta, University of Massachusetts, 2017
  • Rahul Majethia, Shiv Nadar University, 2017

Work Experience

Principal Research Scientist, Nokia Bell Labs Feb '11 - Present

I work in the Pervasive Systems department where I lead our team's research on various machine learning topics, such as Federated Learning, Self-Supervised Learning, Algorithmic Fairness, and Domain Adaptation.

Visiting Researcher, Microsoft Research India May '10 - Aug '10

I spent a summer at MSR to complete my Masters thesis research in collaboration with Dr. Edward Cutrell.

Undergraduate Research Assistant, UC Berkeley May '07 - Dec '08

I worked with Dr. Matthew Kam and Prof. John Canny on MILLEE, a project aimed at developing effective technologies to support out-of-classroom education in rural India.

Contact

Nokia Bell Labs, Broers Bulding, 21 JJ Thomson Avenue, Cambridge, United Kingdom