The Most Joint Research Center for AI Technology and All Vista Healthcare (AINTU) was established under guidance of the Ministry of Science and Technology (MOST) in Taiwan in 2018. AINTU mainly focuses on two research fields, AI Technology and Biomedicine, and is determined to form an international AI innovation research center to establish academic benchmarks, serve as a bridge between domestic and international professionals, promote industry-academia collaboration, cultivate AI talents, and provide solutions to social problems. Not long after AINTU is established, a Memorandum of Understanding (MOU) has been signed between the center and Stanford University, and several international forums were held in collaboration with the Artificial Intelligence Research Center of National Institute of Advanced Industrial Science and Technology (AIST AIRC) of Japan. And in 2019, with the referral from the MOST and the Ministry of Foreign Affairs (MOFA), AINTU has also served and collaborated with many important international guests to promote the development of AI technology in Taiwan. The guests including: AI delegation of UK, AI delegation of Israel, Finland Trade Center in Taiwan, Senate Committee on Health and Social Policy of Czech, Deputy Minister of Digital Economy Promotion Agency (DEPA) of Thailand, delegation of American Institute in Taiwan (AIT), delegation of Ho Chi Minh City University of Technology, President of University of Waterloo, delegation of Federal Ministry of Education and Research of Germany, delegation of Directorate General of Internal Market, Industry, Entrepreneurship and SMEs of European Commission (DG Grow), delegation of French National Research Agency (ANR), President of the committee for ethical research in digital science in France (CERNA), and Dean of the Faculty of Applied Science & Engineering of University of Toronto.
Meanwhile, our center actively visited global organizations leading in AI research, especially AI in medical and healthcare, such as Harvard Medical School, MIT, MGH/BWH, Stanford University, and Google in US, and Amii. Vector, and Mila in Canada. During 2020, several physical activities with international institutions and cross-strait interschool collaborations have been planned; however, due to the COVID-19 pandemic, most of the events were suspended.
In 2020, there were totally 31 MOST AI innovation programs executing under the AINTU. And the members including 174 professors (as principle investigators or co- principle investigators), 105 PhD students, 581 master’s students, and 11 fulltime researchers.
With the signed MOU and collaboration project agreements, AINTU has been actively bridging the industry and academia, to promote possible collaborations. And the center has also been continuously organizing educational events for students, researchers or AI practitioners, to help them learn deeper about AI technologies. In 2018 and 2019, AINTU has organized more than 10 seminars, 17 AI Meetup events, 11 workshops and refresher courses, 4 summer schools and hands-on courses. More than 3000 participants have joined the events.
With the understanding to the society’s concern on the issues of ethical, law, and regulation generated in the research process of AI technologies, in the past 3 years, AINTU has hosted 6 forums on the topics of legal issues faced during the research of technologies, and has also established the team of AI-ethical, legal, and social impact (AI-ELSI) at the beginning of foundation to provide possible solutions for the issues.
▎學術卓越、樹立標竿
Establishing Academic Benchmarks
臺大AI中心轄下計畫團隊均具有擁有雄厚研究實力，研究成果豐碩亮眼，於2020年1月至12月期間，共發表191篇國際期刊論文、13篇國內期刊論文、367篇國際研討會論文、並取得6件國外專利，1件國內專利、申請7件國內專利、1項國內商標註冊獲准及3項美國臨時專利申請，取得TFDA通過執行先導臨床試驗1件。舉辦了9場國際級競賽，包括亞洲區腦腫瘤切割競賽及急重症智慧醫療數據松比賽。同時，中心也建置Taitk Server 網站 (taitk.org)，將科技部AI創新專案中的計畫衍生的資料集、工具集，以及AI模型統整釋出，三年來已累積59項，協助擴大專案成果效益。
The top-notch domestic research teams gathered within AINTU have published numerous excellent research results. From January to December in 2020, the teams have published 191 papers on international journals, 13 papers on domestic journals, and 367 papers on international conferences; obtained 6 foreign patents, a domestic patent, a domestic trademark, and a permission from TFDA to execute pilot clinical trial; applied 7 domestic patents, and 3 provisional applications of USPTO.
And the teams have also hosted 9 international competitions, including the Asia Cup Brain Tumor Segmentation Challenge and Taiwan Smart Emergency and Critical Care Data Science Conference, Workshop, and Datathon.
AINTU has built a Taitk Server (taitk.org) to aggregate 59 items of resources, including datasets, toolkits, and AI models, of the projects among MOST sponsored AI innovation program.
In 2020, AINTU has announced several significant achievements: on April 2021, Prof. Hong-Yuan Liao has published Yolov4, which is the fastest Object Detection technology on earth, and was listed the first and second place on international competitions. The team has made the source code public, which has been cited more than 300 times in the first half year, and is definitely a great contribution to the research community; Prof. Winston Hsu has also announced the explainable AI module – xCos in May, 2020. The technology not only preserves high accuracy of face recognition, but also provides explanations on the inferenced results. The technology can let users understand about how AI makes decisions, and helps to increase human’s trust in AI technologies. On the side of biomedicine, the project team of Prof. Fei-Pei Lai, co-PI of the center, has been invited by the MOST to present the “AECOPD System for Precision Medicine” on a press conference. The system can offer COPD patients precise healthcare services and remind them for early treatment by a smart wearable device, including to control their body state immediately by a long-term continuous evaluation and to predict possible seizures in future 7 days. It is now implemented in National Taiwan University Hospital (NTUH) to support clinical decisions.
▎迎向國際、接軌世界
Bridging to International Professionals
中心自成立以來，積極尋求國際研究夥伴，以提升臺灣AI技術及生技醫療領域在國際間之能見度。為達成此一目標，中心已與美國、加拿大、歐洲、亞洲多國知名學術機構建立深化之合作關係。在美、加方面的合作，中心與美國史丹佛大學於2018年簽署合作備忘錄，並派駐研究人員於史丹佛大學進行移地研究，研究課題包含運用電子病歷進行心律不整、急重症等疾病預測；於2019年與加拿大多倫多大學簽署合作備忘錄，同年10月也與美國哈佛大學醫學院關係研究機構 (Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life Inc.) 簽署合作備忘錄，並於2020年初，雙方就人工智慧在基因體學、生技及醫療應用、臨床研究、老年與老年病學研究等議題申請國際合作研究計畫。
▲ 中心陳信希主任擔任 FinNLP 2019 最佳論文獎頒獎人
Prof. Hsin-Hsi Chen, director of AINTU, served as presenter of Best Paper Award of FinNLP 2019
Since establishment, in order to raise the prominence of domestic AI technology and biomedicine fields on the world stage, AINTU has been actively searching for international research partners. For instance, AINTU has built partnerships with several prestigious foreign institutes in the US, Canada, Europe, and Asia.
In the US and Canada, since the fulfilment of MOU in 2018, AINTU has stationed a visiting research member in Stanford University, and has been collaborating on research projects ever since. The topics of research include using digital Electronic Medical Records for Arrhythmia and critical patient prediction. In 2019, AINTU has built partnerships with institutes including University of Toronto and the Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life Inc. And the partnership with Marcus Institute for Aging Research has led to application for international collaboration projects on biogenetics, biotechnology, clinical research, and gerontology in 2020.
In Asia, AINTU has built partnerships with the prestigious institutions in Japan, including the AIST AIRC, RIKEN Center for Advanced Intelligence Project (RIKEN AIP), the Research Institute of Electrical Communication of Tohoku University (RIEC), and Kyoto University. AINTU and AIST AIRC have co-organized a “FinNLP” competition in a top-tier conference, the IJCAI-PRICAI 2020. There are 17 papers accepted and published. Furthermore, AINTU and AIRC have co-organized a “FinNum2” competition in NTCIR-15 in 2020, which has enrolled by 13 teams from 7 countries and 15 institutes. AINTU have also co-organized a “SHINRA2020-ML” competition in NTCIR-15 with RIKEN AIP. “When AI Meets Human Science”, a symposium focused on interdisciplinary AI and human studies, was co-hosted with the RIEC in order to promote collaboration and communication between domestic and international practitioners in the fields of AI technology and humanities and social sciences. An online and onsite hybrid symposium called “AI & Smart Medicine for Digital Health” has also been co-hosted with Kyoto University in December 2020 to promote the research achievements and applications of the domestic medical AI field. With the dedication in the past years, AINTU has also been granted the right to host ACM SIGIR 2023, which is the first time for Taiwan to host the top-tier conference in the field of information retrieval.
▲ 中心於 2020 年 12 月偕同台大醫院及轄下 8 個生技醫療領域團隊於「醫療科技展」中展出
AINTU has participated in the “Healthcare+ Expo” in December 2020 with the teams and NTUH.
In order to achieve commercialization and promote industry-academia collaboration, AINTU has continuously been working on building partnerships with domestic and international corporations. Until 2020, AINTU has signed MOU and Non-disclosure agreement (NDA) with numerous corporations and institutes, and provide resources for project team spin-off. Here is a good example, from July 2019 to June 2020, the project team of Prof. Yu-Chuan Li was granted to execute Taiwan Reputed University Startups to Taiwan Unicorns of MOST Program (MOST TRUST-U Programs) which titled “AI – enhanced Safety of Prescription and clinical application.” The team has established a start-up named “AESOP Technology, Inc.” and released “MedGuard”, a system powered by AI engines to detect potential errors in prescriptions and provide optimal recommendations. Besides, it is notable that the license fee from the AINTU project teams reached over 60 million NTD in 2020, showing the strong value creation through technology transfer from academia to industry.
▲ 中心與中國信託簽訂產學合作計畫
AINTU has signed a collaboration project with CTBC Bank Co., Ltd.
To further promote the project teams and bridging them with the industry, AINTU has participated in the “Healthcare+ Expo” in December 2020 with the teams and NTUH. During the 4 days exhibition, more than 1,100 potential collaboration discussions took places, and this surely can help project teams’ business break-through. As one of the exhibitors, Apollo Medical Optics, Ltd., the company co-founded by Prof. Sheng-Lung Huang and develops OCT-based devices for dermatology, thought that the exhibition has brought more potential partners (representatives of hospitals or clinics, and doctors) than expected, which is very helpful for them to promote their products in the future.
▲中心偕同王偉仲教授與健保署,與 NVIDIA 合作參與全球 COVID-19 聯邦學習計畫。
AINTU has joined the EXAM initiative of NVIDIA with the National Health Insurance Administration of Taiwan and the project team of Prof. Wei-
Chung Wang.
AINTU has signed a collaboration project with CTBC Bank Co., Ltd. in 2020. The project was mainly focused on using natural language process technology to analyze dialogues and to understand customers’ intention. The technology has got CTBC’s approval and will be applied in real bank services in next year.
On the aspect of collaboration with global industry, the center has joined the EXAM (EMR CXR AI Model) initiative of NVIDIA with the project team of Prof. Wei-Chung Wang. With the help of federated learning (FL), medical practitioners can acquire prediction for the level of oxygen required by incoming SARS-CoV-2 patients. The FL model has been released on NVIDIA GPU Cloud and the result has also been submitted to an international journal. The partnership has led to a collaboration between the center, National Health Insurance Administration of Ministry of Health and Welfare in Taiwan, and NVIDIA, which is certainly a great example of an international tripartite collaboration.
▲Capstone 計畫團隊於「2020 醫療科技展」中展出
Teams of the Capstone project has exhibited in the “2020 Healthcare+ Expo”
We all know that the application field and high-quality data are critical for developing useful AI technologies. On December 2019, AINTU hosted a seminar on AI technologies in NTUH, and introduced the hospital information system, the process and pitfalls of applying AI technologies, medical image analysis, and suggestions on the steps of building the domain and how to integrate external interdisciplinary resources. AINTU has also worked closely with the Center of Intelligent Healthcare of NTUH in 2020. The centers have collaborated to co-host 2 internal Smart-Healthcare seminars, develop a procedure for NTUH’s Software as Medical Device trial, and promote field applications of AI technologies in NTUH.
On March 2020, AINTU submitted a Capstone project proposal, titled “Intelligent Emergency Treatment: The Comprehensive Strategy to Resolve Overcrowding Patient Flow in Emergency Department with Artificial Intelligence Technology,” which was approved by MOST and started to execute by the center since then. This three-year project has targeted the Department of Emergency Medicine of NTUH as the experimental field, and planned to introduce AI models to each stage of patient flow.
The innovative emergency room (ER) system powered by AI technologies can assist medical practitioners on precision ER triage, analyses of medical record, immediate identification of critical condition, clinical decision support for ED discharge, and prognosis prediction for in-hospital cardiac arrest. An intelligent edge device is also equipped in the domain to consolidate the medical systems and assist medical practitioners to take immediate action on emergent situations, to provide a better and more comfortable ER environment.
除此之外，中心也透過辦理學術活動的方式培養人才，並以不同面向推廣AI知識。首先，針對初接觸人工智慧的大學生與研究生，於每年八月舉辦「AI Summer School」人工智慧技術夏季學習營，邀請多位知名教授，進行為期3~4天的課程與實作練習。2020年為第三屆，參加人數從第一年的86人成長到225人。為使人才培育能更向下扎根，中心於2020年7月與王偉仲教授合作，針對高中生規劃一系列Medical AI School之暑期營，共計216名高中生參與本次暑期營，透過實體課程搭配線上課程，並設計兩個實際醫療影像專案，編列專屬於高中生之Medical AI教材，對高中生相關Medical AI基礎教育貢獻心力。
針對研究人員與學者，中心著力於鏈結國際AI研究單位促進交流與合作。中心與Google連續三年共同舉辦『Google AI Bootcamp創新研究營』，參與人數從2018年的150人成長到2020年的550人。每年皆邀請十餘位Google Brain及Google Research學者及本中心的AI學者就最新AI研究成果進行分享交流。全幅健康照護子中心也透過跨中心/學術單位合作(包括成大AI中心、臺大生醫電資所、臺大應用數學所、臺大醫院、中研院等)，合辦多場專題演講、內訓課程、Medical AI Meetup等，在2018~2020年間已有逾2000人次參與。透過定期舉辦訓練課程，培育更多優秀的研究人員，讓臺灣在AI應用於生醫照護的成果與能量能持續發展與成長。
▲ 中心主辦科技部 AI 創新研究專案 – 跨域觀摩交流暨成果發表會
AINTU has hosted the “Exhibition for AI Innovation Research Center Program of the MOST”.
The foundation of developing scientific researches is to cultivate research talents. In 2020, the project teams within AINTU have gathered 105 domestic and foreign PhD students, 581 master’s students, 82 undergraduate students, 2 international students for the project. And to promote the communication between domestic AI research teams, the four AI centers under guidance of the MOST have been taking turns to host interdisciplinary research exhibitions in each quarter. The events were composed of poster sessions and live demo by the project teams within each center, and keynotes provided by industrial professionals. On November 2020, AINTU has hosted a research exhibition including all 72 project teams within the four AI centers. The event not only promoted the connection between domestic researchers but also let the society understand more about researches of the centers.
Besides cultivating research talents within the project teams, AINTU is also dedicating in promoting knowledge of AI and sharing new perspectives by hosting educational events. First of all, targeting the beginners, undergraduate and graduate students, AINTU has organized “AI Summer School” in August in the past 3 years, and invited prestigious AI professionals to conduct the lectures. 2020 is the third year of the summer school, and the number of participants has increased from 86 (the 1st year) to 225 (the 3rd year). Besides, in order to nurture talents and build the base, the center has collaborated with the team of Prof. Wei-Chung Wang to conduct a Medical AI School for high school students, which was composed of live and online lectures. The materials used in lectures were specially designed for high school students. And the team has gathered and compiled the data used in the event and submitted a paper to an international journal in hope of making contribution to the basic education of medical AI.
▲ 人才培育活動:Medical AI Meetup Educational Events : Medical AI Meetup
For AI researchers and faculties, AINTU has focused on creating opportunities for our teams to communicate and to explore potential collaborations with top-tier international institutes. AINTU and Google have been co-organizing the “Google AI Bootcamp” for successive 3 years. The number of participants increased from 150 (2018) to 550 (2020). The camp not only provided lectures by professionals from Google Brain, Google Research, and the AINTU, but also served as a platform for researchers to connect and collaborate. The All Vista Healthcare sub-center of AINTU (MAHC) has also co-hosted lectures, forums and Medical AI Meetups with several research centers and institutes including: MOST Artificial Intelligence Biomedical Research Center, Graduate Institute of Biomedical Electronics and Bioinformatics of NTU, The Institute of Applied Mathematical Sciences of NTU, NTUH, and Academia Sinica. It is notable that the events have attracted more than 2,000 participants in the past 3 years. By conducting the educational events, the center hopes to cultivate more and more research talents and promote the research energy and results of AI application on medical and healthcare in Taiwan.
We understand that strong AI talents are the key to competitiveness. AINTU was dedicated to attract talents worldwide and to make Taiwan an AI talent hub of the Asia-Pacific region. With the goal in mind, AINTU obtained the right to host Machine Learning Summer School (MLSS 2021 Taipei) in 2021. More than 20 renowned international scholars and professionals are invited to give lectures to thousands of students from all over the world.
而自前(2019)年年底起，COVID-19的散播持續對全球社會的各個面向造成重大的影響，而臺灣不論是政府、民間或是產學研醫各界都在各自的崗位上齊心協力抗疫，為了盡一份心力，也讓世界看見臺灣的努力，中心於2020年舉辦 “TAIWAN is Helping：全方位AI x防疫論壇” 三場系列論壇，結合國內外產官學研醫專家包含前副總統陳建仁等超過30位意見領袖參與擔任講者與談。系列影片在會後也受到熱烈迴響，顯見社會大眾對於此議題的關注，也展現本次系列活動的重要性。
Another important dedication of the AINTU is to provide solutions to social problems, and to execute research and guide communications on the issues of ethics, law and regulations of the society. Since establishment, the center has hosted forums on the topics of legal issues faced during the research of technologies. On October 2020, AINTU has co-hosted a forum with the project team of Prof. Weijing Wang, and invited 8 prestigious professionals to share their points from the aspects of the framework, law, ethics, psychiatry, and science. The speakers have also shared examples of difficulties faced in practice, and provided solutions from other countries as possible guidance.
The MAHC has also established the team of AI-ethical, legal, and social impact (AI-ELSI) at the beginning of foundation. The main mission of the team is to support the possible legal and ethical issues generated during execution of the projects within the center. The team has not only conducted several forums and lectures to improve the communication between professionals in separated fields, but has also been continuously working on related studies to provide resource for supporting the establishment of domestic regulations and management systems.
In hope of making positive impacts on the society, and let the world see Taiwan’s efforts made to combat the SARS-CoV-2 virus, the center has conducted a series of “TAIWAN is Helping: AI x Epidemic Prevention Online Forum”. The 3 forums were composed of talks and panels given by more than 30 key opinion leaders from the industry, government and academia, including Dr. Chien-Jen Chen, the former Vice President of Taiwan. The videos of the series have received many attentions and positive responses after the events, which shows the society’s concern on the issue, and the importance of conducting the event.
▲ “TAIWAN is Helping:全方位 AI x 防疫論壇 ”
“TAIWAN is Helping: AI x Epidemic Prevention Online Forum”
Titouan Parcollet is an associate professor in computer science at the Laboratoire Informatique d’Avignon (LIA), from Avignon University (FR) and a visiting scholar at the Cambridge Machine Learning Systems Lab from the University of Cambridge (UK). Previously, he was a senior research associate at the University of Oxford (UK) within the Oxford Machine Learning Systems group. He received his PhD in computer science from the University of Avignon (France) and in partnership with Orkis focusing on quaternion neural networks, automatic speech recognition, and representation learning. His current work involves efficient speech recognition, federated learning and self-supervised learning. He is also currently collaborating with the university of Montréal (Mila, QC, Canada) on the SpeechBrain project.
Mirco Ravanelli
Post-doc Researcher
MILA, Université de Montréal
Mirco Ravanelli is currently a postdoc researcher at Mila (Université de Montréal) working under the supervision of Prof. Yoshua Bengio. His main research interests are deep learning, speech recognition, far-field speech recognition, cooperative learning, and self-supervised learning. He is the author or co-author of more than 50 papers on these research topics. He received his PhD (with cum laude distinction) from the University of Trento in December 2017. Mirco is an active member of the speech and machine learning communities. He is founder and leader of the SpeechBrain project.
Shinji Watanabe
Associate Professor
Carnegie Mellon University
Shinji Watanabe is an Associate Professor at Carnegie Mellon University, Pittsburgh, PA. He received his B.S., M.S., and Ph.D. (Dr. Eng.) degrees from Waseda University, Tokyo, Japan. He was a research scientist at NTT Communication Science Laboratories, Kyoto, Japan, from 2001 to 2011, a visiting scholar in Georgia institute of technology, Atlanta, GA in 2009, and a senior principal research scientist at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA USA from 2012 to 2017. Prior to the move to Carnegie Mellon University, he was an associate research professor at Johns Hopkins University, Baltimore, MD USA from 2017 to 2020. His research interests include automatic speech recognition, speech enhancement, spoken language understanding, and machine learning for speech and language processing. He has been published more than 200 papers in peer-reviewed journals and conferences and received several awards, including the best paper award from the IEEE ASRU in 2019. He served as an Associate Editor of the IEEE Transactions on Audio Speech and Language Processing. He was/has been a member of several technical committees, including the APSIPA Speech, Language, and Audio Technical Committee (SLA), IEEE Signal Processing Society Speech and Language Technical Committee (SLTC), and Machine Learning for Signal Processing Technical Committee (MLSP).
Michael Bronstein
Professor
Imperial College London
Karteek Alahari
Permanent Research Scientist
Inria Grenoble Rhône-Alpes Center
Karteek Alahari is a senior researcher (known as chargé de recherche in France, which is equivalent to a tenured associate professor) at Inria. He is based in the Thoth research team at the Inria Grenoble – Rhône-Alpes center. He was previously a postdoctoral fellow in the Inria WILLOW team at the Department of Computer Science in ENS (École Normale Supérieure), after completing his PhD in 2010 in the UK. His current research focuses on addressing the visual understanding problem in the context of large-scale datasets. In particular, he works on learning robust and effective visual representations, when only partially-supervised data is available. This includes frameworks such as incremental learning, weakly-supervised learning, adversarial training, etc. Dr. Alahari’s research has been funded by a Google research award, the French national research agency, and other industrial grants, including Facebook, NaverLabs Europe, Valeo.
Prateek Mittal
Professor
Princeton University
Sijia Liu
Assistant Professor
Michigan State University
Sijia Liu is currently an Assistant Professor at the Computer Science & Engineering Department of Michigan State University. He received the Ph.D. degree (with All-University Doctoral Prize) in Electrical and Computer Engineering from Syracuse University, NY, USA, in 2016. He was a Postdoctoral Research Fellow at the University of Michigan, Ann Arbor, in 2016-2017, and a Research Staff Member at the MIT-IBM Watson AI Lab in 2018-2020. His research spans the areas of machine learning, optimization, computer vision, signal processing and computational biology, with a focus on developing learning algorithms and theory for scalable and trustworthy artificial intelligence (AI). He received the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). His work has been published at top-tier AI conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AISTATS, and AAAI.
Soheil Feizi
Assistant Professor
University of Maryland, College Park (UMD)
Soheil Feizi is an assistant professor in the Computer Science Department at University of Maryland, College Park. Before joining UMD, he was a post-doctoral research scholar at Stanford University. He received his Ph.D. from Massachusetts Institute of Technology (MIT). He has received the NSF CAREER award in 2020 and the Simons-Berkeley Research Fellowship on deep learning foundations in 2019. He is the 2020 recipient of the AWS Machine Learning Research award, and the 2019 recipients of the IBM faculty award as well as the Qualcomm faculty award. He is the recipient of teaching award in Fall 2018 and Spring 2019 in the CS department at UMD. His work has received the best paper award of IEEE Transactions on Network Science and Engineering, over a three-year period of 2017-2019. He received the Ernst Guillemin award for his M.Sc. thesis, as well as the Jacobs Presidential Fellowship and the EECS Great Educators Fellowship at MIT.
Srinivasan Arunachalam
Research Staff Member
IBM T. J. Watson Research Center
I am a Research Staff Member at IBM T. J. Watson Research Center.
Prior to this, I was a Postdoctoral Researcher at the Center for Theoretical Physics, MIT. I received my Ph.D. in 2018 from Centrum Wiskune & Informatica and QuSoft, Amsterdam, Netherlands, supervised by Ronald de Wolf. Before that I finished my M.Math in Mathematics from University of Waterloo and Institute of Quantum computing, Canada in 2014, supervised by Michele Mosca.
Shang-Wen (Daniel) Li
Senior Applied Scientist
Amazon AI
Shang-Wen (Daniel) Li is an Engineering and Science Manager at Facebook AI. His research focuses on natural language and speech understanding, conversational AI, meta learning, and auto ML. He led a team at AWS AI on building conversation AI technology for call center analytics and chat bot authoring. He also worked at Amazon Alexa and Apple Siri for implementing their conversation assistants. He earned his PhD from MIT CSAIL with topics on natural language understanding and its application to online education. He co-organized the workshop of “Self-Supervised Learning for Speech and Audio Processing” at NeurIPS (2020) and the workshop of “Meta Learning and Its Applications to Natural Language Processing” at ACL (2021).
Thang Vu
Prof. Dr.
University of Stuttgart
Thang Vu received his Diploma (2009) and PhD (2014) degrees in computer science from Karlsruhe Institute of Technology, Germany. From 2014 to 2015, he worked at Nuance Communications as a senior research scientist and at Ludwig-Maximilian University Munich as an acting professor in computational linguistics. In 2015, he was appointed assistant professor at University of Stuttgart, Germany. Since 2018, he has been a full professor at the Institute for Natural Language Processing in Stuttgart. His main research interests are natural language processing (esp. speech, natural language understanding and dialog systems) and machine learning (esp. deep learning) for low-resource settings.
Song Han
Assistant Professor
Massachusetts Institute of Technology
Song Han is an assistant professor at MIT’s EECS. He received his PhD degree from Stanford University. His research focuses on efficient deep learning computing. He proposed “deep compression” technique that can reduce neural network size by an order of magnitude without losing accuracy, and the hardware implementation “efficient inference engine” that first exploited pruning and weight sparsity in deep learning accelerators. His team’s work on hardware-aware neural architecture search that bring deep learning to IoT devices was highlighted by MIT News, Wired, Qualcomm News, VentureBeat, IEEE Spectrum, integrated in PyTorch and AutoGluon, and received many low-power computer vision contest awards in flagship AI conferences (CVPR’19, ICCV’19 and NeurIPS’19). Song received Best Paper awards at ICLR’16 and FPGA’17, Amazon Machine Learning Research Award, SONY Faculty Award, Facebook Faculty Award, NVIDIA Academic Partnership Award. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution on “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on lowpower mobile devices.” Song received the NSF CAREER Award for “efficient algorithms and hardware for accelerated machine learning” and the IEEE “AIs 10 to Watch: The Future of AI” award.
Hung-Yi Lee
Associate Professor
National Taiwan University
Hung-yi Lee received the M.S. and Ph.D. degrees from National Taiwan University (NTU), Taipei, Taiwan, in 2010 and 2012, respectively. From September 2012 to August 2013, he was a postdoctoral fellow in Research Center for Information Technology Innovation, Academia Sinica. From September 2013 to July 2014, he was a visiting scientist at the Spoken Language Systems Group of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
John Shawe-Taylor
Professor
University College London
John Shawe-Taylor is professor of Computational Statistics and Machine Learning at University College London. He has helped to drive a fundamental rebirth in the field of machine learning, with applications in novel domains including computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 250 papers and two books that have together attracted over 80000 citations.
He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.
He was appointed UNESCO Chair of Artificial Intelligence in November 2018 and is the leading trustee of the UK Charity, Knowledge 4 All Foundation, promoting open education and helping to establish a network of AI researchers and practitioners in sub-Saharan Africa. He is the Director of the International Research Center on Artificial Intelligence established under the Auspices of UNESCO in Ljubljana, Slovenia.
Been Kim
Staff Research Scientist
Google Brain
Been Kim is a staff research scientist at Google Brain. Her research focuses on improving interpretability in machine learning by building interpretability methods for already-trained models or building inherently interpretable models. She gave a talk at the G20 meeting in Argentina in 2019. Her work TCAV received UNESCO Netexplo award, was featured at Google I/O 19′ and in Brian Christian’s book on “The Alignment Problem”. Been has given keynote at ECML 2020, tutorials on interpretability at ICML, University of Toronto, CVPR and at Lawrence Berkeley National Laboratory. She was a co-workshop Chair ICLR 2019, and has been an area chair/senior area chair at conferences including NeurIPS, ICML, ICLR, and AISTATS. She received her PhD. from MIT.
Philipp Krähenbühl
Assistant Professor
University of Texas at Austin
Philipp is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. He received his PhD in 2014 from the CS Department at Stanford University and then spent two wonderful years as a PostDoc at UC Berkeley. His research interests lie in Computer Vision, Machine learning and Computer Graphics. He is particularly interested in deep learning, image, video, and scene understanding.
Cho-Jui Hsieh
Assistant Professor
University of California, Los Angeles
Cho-Jui Hsieh is an assistant professor in UCLA Computer Science Department. He obtained his Ph.D. from the University of Texas at Austin in 2015 (advisor: Inderjit S. Dhillon). His work mainly focuses on improving the efficiency and robustness of machine learning systems and he has contributed to several widely used machine learning packages. He is the recipient of NSF Career Award, Samsung AI Researcher of the Year, and Google Research Scholar Award. His work has been recognized by several best/outstanding paper awards in ICLR, KDD, ICDM, ICPP and SC.
Ming-Wei Chang
Research Scientist
Google Research
Ming-Wei Chang is currently a Research Scientist at Google Research, working on machine learning and natural language processing problems. He is interested in developing fundamental techniques that can bring new insights to the field and enable new applications. He has published many papers on representation learning, question answering, entity linking and semantic parsing. Among them, BERT, a framework for pre-training deep bidirectional representations from unlabeled text, probably received the most attention. BERT achieved state-of-the-art results for 11 NLP tasks at the time of the publication. Recently he helped co-write the Deep learning for NLP chapter in the fourth edition of the Artificial Intelligence: A Modern Approach. His research has won many awards including 2019 NAACL best paper, 2015 ACL outstanding paper and 2019 ACL best paper candidate.
Marco Cuturi
Research Scientist
Google Brain
I received my Ph.D. in 11/2005 working at Ecole des Mines de Paris. Before that I graduated from the ENSAE with a master degree from ENS Cachan. I worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 03/2007. Between 04/2007 and 09/2008 I worked in the financial industry. After working at the ORFE department of Princeton University between 02/2009 and 08/2010 as a lecturer, I was at the Graduate School of Informatics of Kyoto University between 09/2010 and 09/2016 as an associate professor (tenured in 11/2013). I have joined ENSAE in 09/2016. I now work there part-time, since 10/2018 when I have joined the Paris office of Google Brain, as a research scientist.
Arthur Gretton
Professor
University College London
Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit, and director of the Centre for Computational Statistics and Machine Learning (CSML) at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University.
Arthur’s recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (exponential family and energy-based models), nonparametric hypothesis testing, survival analysis, causality, and kernel methods.
He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018 and 2021, an Area Chair for ICML in 2011 and 2012, a member of the COLT Program Committee in 2013, and a member of Royal Statistical Society Research Section Committee since January 2020. Arthur was program chair for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of the Machine Learning Summer School 2019 in London (with Marc Deisenroth).
Developing a World-Class AI Facial Recognition Solution – CyberLink FaceMe®
Lecture Abstract
CyberLink’s FaceMe® is a world-leading AI facial recognition solution. In this session, Davie Lee (R&D Vice President of CyberLink) will share the fundamentals of developing facial recognition solutions, such as the interface pipeline, and will share the key industrial use cases and trends of AI facial recognition.
Johnny Tseng
PhD, CSIE, National Taiwan University
CTO, Perfect Corp, 2015 – Now
Senior VP, CyberLink, 1999 -2015
Lecture Title
Transform the Beauty Industry through AI + AR: Perfect Corp’s Innovative Vision into the Digital Era
Lecture Abstract
Perfect Corp. is the world’s leading beauty tech solutions provider transforming the industry by marrying the highest level of augmented reality (AR) and artificial intelligence (AI) technology for a re-imagined consumer shopping experience.
Johnny Tseng (CTO of Perfect Corp.) will share the AI/AR beauty tech solutions and the roadmap with Perfect Corp.’s advance AI technology.
Shou-De Lin
Professor
National Taiwan University
Chief Machine Learning Scientist, Appier
Dr. Shou-De Lin is Appier’s Chief Machine Learning (ML) Scientist since February 2020 with 20+ years of experience in AI,machine learning, data mining and natural language processing. Prior to joining Appier, he served as a full-time professor at the National Taiwan University (NTU) Department of Computer Science and Information Engineering. Dr. Lin is the recipient of several prestigious research awards and brings a mix of both academic and industry expertise to Appier. He has advised more than 50 global companies in the research and application of AI, winning awards from Microsoft, Google and IBM for his work. He led or co-led the NTU team to win 7 ACM KDD Cup championships. He has over 100 publications in top-tier journals and conferences, winning various dissertation awards. After joining Appier, Dr. Lin led the AiDeal team to win the Best Overall AI-based Analytics Solution in the 2020 Artificial Intelligence Breakthrough Awards. Dr. Lin holds a BS-EE degree t from NTU and an MS-EECS degree from the University of Michigan. He also holds an MS degree in Computational Linguistics and a Ph.D. in Computer Science, both from the University of Southern California.
Lecture Title
Machine Learning as a Services: Challenges and Opportunities
Lecture Abstract
Businesses today are dealing with huge amounts of data and the volume is growing faster than ever. At the same time, the competitive landscape is changing rapidly and it’s critical for commercial organizations to make decisions fast. Business success comes from making quick, accurate decisions using the best possible information.
Machine learning (ML) is a vital technology for companies seeking a competitive advantage, as it can process large volumes of data fast that can help businesses more effectively make recommendations to customers, hone manufacturing processes or anticipate changes to a market, for example.
Machine Learning as a Service (MLaaS) is defined in a business context as companies designing and implementing ML models that will provide a continuous and consistent service to customers. This is critical in areas where customer needs and behaviours change rapidly. For example, from 2020, people have changed how they shop, work and socialize as a direct result of the COVID-19 pandemic and businesses have had to shift how they service their customers to meet their needs.
This means that the technology they are using to gather and process data also needs to be flexible and adaptable to new data inputs, allowing businesses to move fast and make the best decisions.
One current challenge of taking ML models to MLaaS has to do with how we currently build ML models and how we teach future ML talent to do it. Most research and development of ML models focuses on building individual models that use a set of training data (with pre-assigned features and labels) to deliver the best performance in predicting the labels of another set of data (normally we call it testing data). However, if we’re looking at real-world businesses trying to meet the ever-evolving needs of real-life customers, the boundary between training and testing data becomes less clear. The testing or prediction data for today can be exploited as the training data to create a better model in the future.
Consequently, the data used for training a model will no doubt be imperfect for several reasons. Besides the fact that real-world data sources can be incomplete or unstructured (such as open answer customer questionnaires), they can come from a biased collection process. For instance, the data to be used for training a recommendation model are normally collected from the feedbacks of another recommender system currently serving online. Thus, the data collected are biased by the online serving model.
Additionally, sometimes the true outcome we really care about is usually the hardest to evaluate. Let’s take digital marketing for ecommerce as an example. The most
Shou-De Lin
Professor
National Taiwan University
Dr. Shou-De Lin is Appier’s Chief Machine Learning (ML) Scientist since February 2020 with 20+ years of experience in AI,machine learning, data mining and natural language processing. Prior to joining Appier, he served as a full-time professor at the National Taiwan University (NTU) Department of Computer Science and Information Engineering. Dr. Lin is the recipient of several prestigious research awards and brings a mix of both academic and industry expertise to Appier. He has advised more than 50 global companies in the research and application of AI, winning awards from Microsoft, Google and IBM for his work. He led or co-led the NTU team to win 7 ACM KDD Cup championships. He has over 100 publications in top-tier journals and conferences, winning various dissertation awards. After joining Appier, Dr. Lin led the AiDeal team to win the Best Overall AI-based Analytics Solution in the 2020 Artificial Intelligence Breakthrough Awards. Dr. Lin holds a BS-EE degree t from NTU and an MS-EECS degree from the University of Michigan. He also holds an MS degree in Computational Linguistics and a Ph.D. in Computer Science, both from the University of Southern California.
Jason Ma, Ph.D.
Managing Director, Technical Site Lead
Google Taiwan
As the Managing Director, Jason Ma oversees Google Taiwan’s site growth, business management and development, as well as leads multiple R&D projects across the board. Before taking this leadership role at Google Taiwan, Jason was a Platform Technology and Cloud Computing expert in the Platform & Ecosystem business group at Google Mountain View, CA. In his 10 years with Google, Jason has successfully led strategic partnerships with global hardware and software manufacturers and major chip providers to drive various innovations in cloud technology. These efforts have not only contributed to a substantial increase in Chromebook’s share in global education, consumer and enterprise markets, but have also attracted global talents to join Google and its partners in furthering the development of hardware and software technology solutions/services.
Prior to joining Google, Jason served on the Office group at Microsoft Redmond, WA. He represented the company in a project, involving Merck, Dell, Boeing, and the United States Department of Defense, to achieve solutions in unified communications and integrated voice technology. In 2007, Jason was appointed Director of the Microsoft Technology Center in Taiwan. During which time, Jason led the Microsoft Taiwan technology team and worked with Intel and HP to establish a Solution Center in Taiwan to promote Microsoft public cloud, data center, and private cloud technologies, connecting Taiwan’s cloud computing industry with the global market and supply chain.
Before joining Microsoft, Jason was Vice President and Chief Technology Officer at Soma.com. At Soma.com, Jason led the team in designing and launching e-commerce services, and partnered with Merck and WebMD on health consultation services and over the counter/prescription drugs/services. Soma.com was in turn acquired by CVS, the second largest pharmacy chain in the United States, forming CVS.com, where Jason served as Vice President and Chief Technology Officer and provided solutions for digital integration.
Jason graduated from the Department of Electrical Engineering at National Cheng Kung University, subsequent which he moved to the United States to further his graduate studies. In 1993, Jason obtained a Ph.D. in Electrical Engineering from the University of Washington, with a focus in the integration and innovation of power systems and AI Expert Systems. In 1997, Jason joined the National Sun Yat-sen University as an Associate Professor of Electrical Engineering. To date, Jason has published 22 research papers and co-authored 2 books. Due to his outstanding performance, Jason was nominated and listed in Who’s Who in the World in 1998.
Kai-Wei Chang
Assistant Professor
University of California, Los Angeles
Kai-Wei Chang is an assistant professor in the Department of ComputerScience at the University of California Los Angeles (UCLA). His research interests include designing robust machine learning methodsfor large and complex data and building fair, reliable, and accountable language processing technologies for social goodapplications. Dr. Chang has published broadly in natural languageprocessing, machine learning, and artificial intelligence. Hisresearch has been covered by news media such as Wires, NPR, and MITTech Review. His awards include the Sloan Research Fellowship (2021), the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award(2010), and the Okawa Research Grant Award (2018). Dr. Chang obtainedhis Ph.D. from the University of Illinois at Urbana-Champaign in 2015and was a post-doctoral researcher at Microsoft Research in 2016.
Dr. Pin-Yu Chen is a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016. Dr. Chen’s recent research focuses on adversarial machine learning and robustness of neural networks. His long-term research vision is building trustworthy machine learning systems. At IBM Research, he received the honor of IBM Master Inventor and several research accomplishment awards. His research works contribute to IBM open-source libraries including Adversarial Robustness Toolbox (ART 360) and AI Explainability 360 (AIX 360). He has published more than 40 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at IJCAI’21, CVPR(’20,’21), ECCV’20, ICASSP’20, KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning. He received a NeurIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award.
Full version:
Dr. Pin-Yu Chen is currently a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science and M.A. degree in Statistics from the University of Michigan, Ann Arbor, USA, in 2016. He received his M.S. degree in communication engineering from National Taiwan University, Taiwan, in 2011 and B.S. degree in electrical engineering and computer science (undergraduate honors program) from National Chiao Tung University, Taiwan, in 2009.
Dr. Chen’s recent research focuses on adversarial machine learning and robustness of neural networks. His long-term research vision is building trustworthy machine learning systems. He has published more than 40 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at IJCAI’21, CVPR(’20,’21), ECCV’20, ICASSP’20, KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning. His research interest also includes graph and network data analytics and their applications to data mining, machine learning, signal processing, and cyber security. He was the recipient of the Chia-Lun Lo Fellowship from the University of Michigan Ann Arbor. He received a NeurIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award. Dr. Chen is currently on the editorial board of PLOS ONE.
At IBM Research, Dr. Chen has co-invented more than 30 U.S. patents and received the honor of IBM Master Inventor. In 2021, he received an IBM Corporate Technical Award. In 2020, he received an IBM Research special division award for research related to COVID-19. In 2019, he received two Outstanding Research Accomplishments on research in adversarial robustness and trusted AI, and one Research Accomplishment on research in graph learning and analysis.
Chun-Yi Lee
Associate Professor
National Taiwan University
Chun-Yi Lee is an Associate Professor of Computer Science at National Tsing Hua University (NTHU), Hsinchu, Taiwan, and is the supervisor of Elsa Lab. He received the B.S. and M.S. degrees from National Taiwan University, Taipei, Taiwan, in 2003 and 2005, respectively, and the M.A. and Ph.D. degrees from Princeton University, Princeton, NJ, USA, in 2009 and 2013, respectively, all in Electrical Engineering. He joined NTHU as an Assistant Professor at the Department of Computer Science since 2015. Before joining NTHU, he was a senior engineer at Oracle America, Inc., Santa Clara, CA, USA from 2012 to 2015.
Prof. Lee’s research focuses on deep reinforcement learning (DRL), intelligent robotics, computer vision (CV), and parallel computing systems. He has contributed to the discovery and development of key deep learning methodologies for intelligent robotics, such as virtual-to-real training and transferring techniques for robotic policies, real-time acceleration techniques for performing semantic image segmentation, efficient and effective exploration approaches for DRL agents, as well as autonomous navigation strategies. He has published a number of research papers on major artificial intelligence (AI) conferences including NeurIPS, CVPR, IJCAI, AAMAS, ICLR, ICML, ECCV, CoRL,, ICRA IROS, GTC, and more. He has also published several research papers at IEEE Transaction on Very Large Scale Integration Systems (TVLSI) and Design Automation Conference (DAC). He founded Elsa Lab at National Tsing Hua University in 2015, and have led the members from Elsa Lab to win several prestigious awards from a number of worldwide robotics and AI challenges, such as the first place at NVIDIA Embedded Intelligent Robotics Challenge in 2016, the first place of the world at NVIDIA Jetson Robotics Challenge in 2018, the second place from the Person-In-Context (PIC) Challenge at the European Conference on Computer Vision (ECCV) in 2018, and the second place of the world from NVIDIA AI at the Edge Challenge in 2020. Prof. Lee is the recipient of the Ta-You Wu Memorial Award from the Ministry of Science and Technology (MOST) in 2020, which is the most prestigious award in recognition of outstanding achievements in intelligence computing for young researchers.
He has also received several outstanding research awards, distinguished teaching awards, and contribution awards from multiple institutions, such as NVIDIA Deep Learning institute (DLI) The Foundation for the Advancement of Outstanding Scholarship (FAOS), The Chinese Institute of Electrical Engineering (CIEE), Taiwan Semiconductor Industry Association (TSIA), and National Tsing Hua University (NTHU). In addition, he has served as the committee members and reviewers at many international and domestic conferences. His researches are especially impactful for autonomous systems, decision making systems, game engines, and vision-AI based robotic applications.
Prof. Lee is a member of IEEE and ACM. He has served as session chairs and technical program committee several times at ASP-DAC, NoCs, and ISVLSI. He has also served as the paper reviewer of NeurIPS, AAAI, IROS, ICCV, IEEE TPAMI, TVLSI, IEEE TCAD, IEEE ISSCC, and IEEE ASP-DAC. He has been the main organizer of the 3rd, 4th, and 5th Augmented Intelligence and Interaction (AII) Workshops from 2019-2021. He was the co-director of MOST Office for International AI Research Collaboration from 2018-2020.
Henry Kautz
Division Director of CISE/IIS
National Science Foundation
Henry Kautz is serving as Division Director for Information & Intelligent Systems (IIS) at the National Science Foundation where he leads the National AI Research Institutes program. He is a Professor in the Department of Computer Science and was the founding director of the Goergen Institute for Data Science at the University of Rochester. He has been a researcher at AT&T Bell Labs in Murray Hill, NJ, and a full professor at the University of Washington, Seattle. In 2010, he was elected President of the Association for Advancement of Artificial Intelligence (AAAI), and in 2016 was elected Chair of the American Association for the Advancement of Science (AAAS) Section on Information, Computing, and Communication. His interdisciplinary research includes practical algorithms for solving worst-case intractable problems in logical and probabilistic reasoning; models for inferring human behavior from sensor data; pervasive healthcare applications of AI; and social media analytics. In 1989 he received the IJCAI Computers & Thought Award, which recognizes outstanding young scientists in artificial intelligence, and 30 years later received the 2018 ACM-AAAI Allen Newell Award for career contributions that have breadth within computer science and that bridge computer science and other disciplines. At the 2020 AAAI Conference he received both the Distinguished Service Award and the Robert S. Engelmore Memorial Lecture Award.
Lecture Abstract
Each AI summer, times of enthusiasm for the potential of artificial intelligence, has led to enduring scientific insights. Today’s third summer is different because it might not be followed by a winter, and it enables powerful applications for good and bad. The next steps in AI research are tighter symbolic-neuro integration
Lecture Outline
History of AI
First Summer: Irrational Exuberance (1948 – 1966)
Artificial neural networks
Behavior = agent + environment
Declarative knowledge representation
Efficient combinatorial search
First Wing (1967-1977)
Second Summer: Knowledge is Power (1978 – 1987)
Knowledge engineering
Knowledge induction
Second Winter (1988 – 2011)
Third Summer: Deep Learning (2012 – ?)
Hierarchical representation learning
Similarity
Why winter might not return
AI for Bad
Keeping your mind private
Fake friends
Autonomous weapons of environmental war
Future of AI
Kinds of neuro-symbol frameworks
Jason Lee
Assistant Professor
Princeton University
Jason Lee received his Ph.D. at Stanford University, advised by Trevor Hastie and Jonathan Taylor, in 2015. Before joining Princeton, he was a postdoctoral scholar at UC Berkeley with Michael I. Jordan. His research interests are in machine learning, optimization, and statistics. Lately, he has worked on the foundations of deep learning, non-convex optimization, and reinforcement learning.
Csaba Szepesvari
Professor
University of Alberta
Csaba Szepesvari is a Canada CIFAR AI Chair, the team-lead for the “Foundations” team at DeepMind and a Professor of Computing Science at the University of Alberta. He earned his PhD in 1999 from Jozsef Attila University, in Szeged, Hungary. In addition to publishing at journals and conferences, he has (co-)authored three books. Currently, he serves as the action editor of the Journal of Machine Learning Research and Machine Learning and as an associate editor of the Mathematics of Operations Research journal, while also regularly serves in various senior positions on program committees of various machine learning and AI conferences. Dr. Szepesvari’s main interest is to develop new, principled, learning-based approaches to artificial intelligence (AI), as well as to study the limits of such approaches. He is the co-inventor of UCT, a Monte-Carlo tree search algorithm, which inspired much work in AI.
Yuh-Jye Lee
Professor, Department of Applied Mathematics, National Yang Ming Chiao Tung University
Research Fellow, Research Center for Information Technology Innovation, Academia Sinica
Dr. Yuh-Jye Lee received the PhD degree in Computer Science from the University of Wisconsin-Madison in 2001. Now, he is a professor of Department of Applied Mathematics at National Chiao-Tung University. He also serves as a SIG Chair at the NTU IoX Center. His research is primarily rooted in optimization theory and spans a range of areas including network and information security, machine learning, data mining, big data, numerical optimization and operations research. During the last decade, Dr. Lee has developed many learning algorithms in supervised learning, semi-supervised learning and unsupervised learning as well as linear/nonlinear dimension reduction. His recent major research is applying machine learning to information security problems such as network intrusion detection, anomaly detection, malicious URLs detection and legitimate user identification. Currently, he focus on online learning algorithms for dealing with large scale datasets, stream data mining and behavior based anomaly detection for the needs of big data and IoT security problems.
