[행사/세미나] 인공지능대학원 전문가 초청 세미나 개최 안내 (Dr. Marsal Gavalda, Director of Machine Learning, Commerce Platform, 3/12(금) 10:00)
- 인공지능학과
- 조회수26457
- 2021-03-03
*참석 확인을 위해 참여 시 학과명(학번) +이름(인공지능학과20212021 홍길동) 으로 접속하여 주시기 바라며, 세미나 참석 활동은 추후 연구수당 평가 기준으로 활용될 수 있습니다.
ㅇ 일시 : 3/12(금) 10:00 ~ 12:00
ㅇ 진행 : Webex활용
- 미팅번호 : 184 261 7233
- 비밀번호 : 0312
- 미팅링크 https://skku-ict.webex.com/skku-ict/j.php?MTID=m2def62e0efd11c4b77e90094989642ef
ㅇ Speaker : Dr. Marsal Gavalda
Director of Machine Learning, Commerce Platform, Square
ㅇTitle : Developing ML-driven customer-facing product features
발표 이후 연사분인 Dr. Marsal Gavaldà와 1:1 미팅 시간을마련하였습니다.
미국 회사의 현업에 계시는 Dr. Marsal Gavaldà에게 본인 연구/커리어와 관련한 답변을 얻으실 수 있으실 것입니다.
참여를 원하는 사람은 3월 10일까지 아래 주소에 이름과 이메일 주소를 남겨주시기를 바랍니다.
https://forms.gle/3fjZ4UD9jhc2a6MBA
ㅇAbstract
As Machine Learning becomes a core component of any forward-looking company, how can we weave ML-driven functionality into the products and services we offer? This talk will explain the methodology
we follow at Square when developing ML-driven customer-facing product features, a process based on paying close attention to four key and interdependent aspects, namely: Design, Modeling, Engineering, and Analytics.
Design is concerned about the usefulness and remarkability of the feature, and thus cares about the overall functionality, ease of use, and aesthetics of the experience.
Modeling is concerned about the accuracy of the ML model, and thus cares about the training data, the features and performance of the model, and —crucially for a customer-facing product— how the application behaves in the face of the mistakes the model will inevitably make (false positives, false negatives, lack of predictions above a certain confidence).
Engineering in turn is concerned about running the ML model at scale, and thus cares about the latency, throughput, and robustness of the inferencing service.
Finally, Analytics is concerned about the adoption of the feature, and thus cares about the instrumentation to capture detailed usage, the definition of success metrics and dashboards, and the collection of feedback in a manner that the ML model can learn from, and thus keep improving over time.
When all these aspects align, we can create remarkable ML-powered experiences that delight our customers.
ㅇBio
Marsal Gavalda is a senior R&D executive with deep expertise in speech, language, and machine learning (ML) technologies. Marsal currently leads the Commerce ML team at Square and develops ML-driven seller personalization and commerce intelligence features that serve Square's overarching purpose of economic empowerment. Previously, Marsal headed the Machine Intelligence team at the social media platform Yik Yak and also served as the Director of Research at MindMeld (acquired by Cisco).
Marsal holds a PhD in Language Technologies and an MS in Computational Linguistics, both from Carnegie Mellon University, and a BS in Computer Science from BarcelonaTech.
He is the author of over thirty technical and literary publications, thirteen issued patents, and is fluent in six languages. Every summer Marsal organizes a science and humanities summit in his hometown of Barcelona on topics as diverse as machine translation, music, or the neuroscience of free will.