{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:10:10Z","timestamp":1750198210381,"version":"3.41.0"},"reference-count":3,"publisher":"Association for Computing Machinery (ACM)","issue":"April","license":[{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Ubiquity"],"published-print":{"date-parts":[[2020,4,30]]},"abstract":"<jats:p>Ubiquity's senior editor Dr. Bushra Anjum sits with Akhil Mathur, a principal research scientist at Nokia Bell Labs in Cambridge (United Kingdom). Both discuss improving the robustness of AI models deployed on mobile and wearable devices to minimize the accuracy degradation as these devices are used in new domains.<\/jats:p>","DOI":"10.1145\/3397260","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T19:29:59Z","timestamp":1588620599000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A conversation with Akhil Mathur"],"prefix":"10.1145","volume":"2020","author":[{"given":"Bushra","family":"Anjum","sequence":"first","affiliation":[{"name":"Amazon, San Luis Obispo, CA"}]}],"member":"320","published-online":{"date-parts":[[2020,5,3]]},"reference":[{"volume-title":"Exploring the interplay between community media and mobile web in developing regions. ACM MobileHCI","year":"2013","author":"Mathur A.","key":"e_1_2_2_1_1"},{"volume-title":"Unsupervised domain adaptation for inertial sensing models. To appear in ACM IMWUT","year":"2020","author":"Chang Y.","key":"e_1_2_2_2_1"},{"volume-title":"ACM IPSN","year":"2019","author":"Mathur A.","key":"e_1_2_2_3_1"}],"container-title":["Ubiquity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3397260","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3397260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:37Z","timestamp":1750195897000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3397260"}},"subtitle":["toward the vision of equitable AI"],"short-title":[],"issued":{"date-parts":[[2020,4,30]]},"references-count":3,"journal-issue":{"issue":"April","published-print":{"date-parts":[[2020,4,30]]}},"alternative-id":["10.1145\/3397260"],"URL":"https:\/\/doi.org\/10.1145\/3397260","relation":{},"ISSN":["1530-2180"],"issn-type":[{"type":"electronic","value":"1530-2180"}],"subject":[],"published":{"date-parts":[[2020,4,30]]},"assertion":[{"value":"2020-05-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}