自1969年IJCAI创办以来,还没有华人学者荣获这一奖项,杨强教授是第一位。国际人工智能联合会议(International Joint Conference on Artificial Intelligence,简称为「IJCAI」)是人工智能领域中最主要的学术会议之一,其设立了卓越研究奖、计算机与思想奖、Donald E. Walker 杰出服务等奖项,主要授予那些在人工智能领域做出非凡贡献的科学家们。IJCAI Donald E. Walker 杰出服务奖项由 IJCAI 于 1979 年设立,旨在表彰 AI 领域的资深科学家在其职业生涯中对该领域的贡献和服务。以前的获奖者有:美国经济学家伯纳德·梅尔策(Bernard Meltzer)、人工智能领域的先驱亚瑟·塞缪尔(Arthur Samuel)、面部识别技术的先驱之一伍德罗·布莱索(Woodrow Bledsoe)等等。
![香港科技大学杨强教授获国际人工智能联合会(IJCAI)2023杰出服务奖](data:image/jpeg;base64,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)
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杨强生于1961 年 12 月,高中毕业于清华大学附属中学。1982年毕业于北京大学天体物理学专业,获理学学士学位,毕业后通过CUSPEA项目赴美国留学。他于1982年赴马里兰大学深造,1985年获得天体物理学硕士后转学人工智能专业,获得硕士及博士学位。1989年至2012年,杨强先后于滑铁卢大学和西蒙菲莎大学以及香港科技大学任副教授以及教授职位。2012年,杨强将主要精力转向人工智能和大数据的产业化研究,在华为诺亚方舟实验室担任创始主任。2015至2018年,在香港科技大学计算机科学与工程系任系主任。在2013年7月举行的第27届先进人工智能(AAAI)大会上,杨教授被推选为AAAI 院士,是第一位获此殊荣的华人。2017年,杨强当选2019国际人工智能联合会(IJCAI)理事会主席,是IJCAI首次由华人科学家担任IJCAI理事会主席身份。2021,杨强担任国际人工智能大会 AAAI 大会主席,此次任职是AAAI大会历史上第二位大会主席,再次成为首次担任主席的华人。除了多个「首位华人」,杨强的学术成就也十分耀眼,是人工智能的重要领域“迁移学习”和“联邦学习”的领军人物。他是IEEE Fellow(2009)、IAPR Fellow(2012)、AAAS Fellow(2012)、AAAI Fellow(2013)、ACM Fellow(2017)、CAE和RSC Fellow(加拿大工程院和皇家学院院士,2021)。在人工智能和数据挖掘领域,他已经发表了400多篇论文,是人工智能和数据挖掘顶级会议,比如IJCAI、ACL、SDM、 WSDM 、 KDD、CIKM、 AAAI的常客。在谷歌学术,杨强的被引数超过了 10 万,h-index 超过了 130。
![香港科技大学杨强教授获国际人工智能联合会(IJCAI)2023杰出服务奖](data:image/jpeg;base64,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)
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杨强主要研究方向为人工智能,包括联邦学习、迁移学习、智能规划。其中最为大众熟知的是迁移学习、联邦学习等在两个或两个以上的领域之间进行的机器学习方法。
所谓迁移学习,要素是发现两个领域之间的共性。在机器学习里便是让机器学会发现共同特征的能力。用一个浅显的例子来说明便是,比方说你建了一个荐书网站又建了一个推荐食品的网站,那么我们可以拿荐书网站模型迁移到新场景下应用。如果不用迁移学习,就得等很长时间获得足够多的数据来做智能推荐。机器学会迁移学习,便能快速高效的进行智能推荐。而联邦学习,其基本思想是建立一个共有模型,各个参与者的身份和地位相同,通过加密机制下的参数交换方式,实现不同企业、不同部门所拥有的数据不交换、不移动。在不违反数据隐私保护法律法规的前提下,模型利用全量数据进行训练和模型优化,从而得到最优模型结果。无论是迁移学习,还是联邦学习,现在都已经成为人工智能/机器学习的前沿技术,成为这些领域研究者致力的方向之一。杨强领导迁移学习和联邦学习研究领域,在实践中解决小数据、数据孤岛、用户隐私和数据安全问题;为全球数百万用户和企业开发了用于普惠金融、健康和电子商务应用程序的开源产业级系统。他的研究对大规模人工智能和数据挖掘工程系统和应用、新标准、开源软件产生了较大的影响,并提升了社会的知识水平。他带领微众银行开源了全球首个工业级联邦学习技术框架。杨强是国际领先的大型人工智能和数据挖掘解决方案工程实践专家。