IDS Distinguished Speaker Series #1: On the Principles of Parsimony and Self-Consistency: Structured Compressive Closed-Loop Transcription

IDS Distinguished Speaker Series #1
Professor Yi Ma

Host: HKU Musketeers Foundation Institute of Data Science
Co-Host: Department of Computer Science, HKU
Title: On the Principles of Parsimony and Self-Consistency: Structured Compressive Closed-Loop Transcription
Speaker: Professor Yi Ma, Electrical Engineering and Computer Sciences, University of California, Berkeley
Moderator: Professor TW Lam, Head of the Department of Computer Science, HKU
Date: Nov 25, 2022
Time: 3:00 – 4:00pm
Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.

Seminar recording:

Abstract

Ten years into the revival of deep networks and artificial intelligence, we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of intelligence in general. We introduce two fundamental principles, Parsimony and Self-consistency, that address two fundamental questions regarding Intelligence: what to learn and how to learn, respectively. We argue that these two principles can be realized in entirely measurable and computable ways for an important family of structures and models, known as a linear discriminative representation (LDR). The two principles naturally lead to an effective and efficient computational framework, known as a compressive closed-loop transcription, that unifies and explains the evolution of modern deep networks and modern practices of artificial intelligence. Within this framework, we will see how fundamental ideas in information theory, control theory, game theory, sparse coding, and optimization are closely integrated in such a closed-loop system, all as necessary ingredients to learn autonomously and correctly. We demonstrate the power of this framework for learning discriminative, generative, and autoencoding models for large-scale real-world visual data, with entirely white-box deep networks, under all settings (supervised, incremental, and unsupervised). We believe that these two principles are the cornerstones for the emergence of intelligence, artificial or natural, and the compressive closed-loop transcription is a universal learning engine that serves as the basic learning units for all autonomous intelligent systems, including the brain.

Related papers can be found at:
https://arxiv.org/abs/2207.04630 and https://www.mdpi.com/1099-4300/24/4/456/htm.

Speaker

Professor Yi Ma
Professor @ Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Yi Ma is a Professor at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research interests include computer vision, high-dimensional data analysis, and intelligent systems. Yi received his Bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two Masters degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000. He has been on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He then joined the faculty of UC Berkeley EECS in 2018. He has published about 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized principal component analysis, and high-dimensional data analysis. He received the NSF Career award in 2004 and the ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM.

Moderator

Professor TW Lam
Head of the Department of Computer Science, HKU

Professor TW Lam joined HKU after receiving his PhD in Computer Science from University of Washington in 1988. He served as an associate dean of the Faculty of Engineering from 2001 to 2006 and is currently the Head of the Computer Science Department and the Executive Director of the HKU-SCF FinTech Academy. His research ranges from theoretical analysis of algorithms to big data analytics for health informatics. He has published over 200 research articles in international conferences and journals, and the most impactful ones come from the engineering work in bioinformatics software. More recently, he has been aspiring to tackle industrial problems with intricate algorithmic techniques and has been awarded a number of ITF grants. In 2014, he together with Professor D Cheung and Dr R Luo has co-founded a startup company to advance the bioinformatics software technologies for analyzing high-throughput DNA sequencing data for clinical applications. Professor Lam loves teaching, especially in inspiring students to learn theoretical subjects. He has received several teaching awards from the Department and the Faculty, as well as a University-level research student supervisor award.

For information, please contact:
Email: idatasci@hku.hk

HKU-IDS Scholar Seminar Series #2: Huge Language Models: Why They Matter, How They Work, And When They Fail

HKU Musketeers Foundation Institute of Data Science (HKU-IDS) proudly presents the second event under the “HKU-IDS Scholar Seminar Series” which will run through the year of 2022-23.  The seminar series features the HKU-IDS new recruits under HKU-100 Professoriate Recruitment Campaign forming the backbone for the Institute’s cross-disciplinary collaboration. These young talents are co-hosted by IDS and different Departments including Computer Science, Electrical and Electronic Engineering, Architecture, Pharmacy and Pharmacology, Industrial and Manufacturing Systems Engineering and Mathematics.

Title: Huge Language Models: Why They Matter, How They Work, And When They Fail
Speaker: Dr. Tao Yu, the HKU Musketeers Foundation Institute of Data Science and Department of Computer Science, HKU
Moderator: Professor Reynold Cheng, Associate Director of the HKU Musketeers Foundation Institute of Data Science; Professor of Department of Computer Science, HKU
Date: November 17, 2022
Time: 4:00 – 5:00pm

Seminar recording:

Abstract

Recently, we have seen dramatic advances in natural language processing (NLP) driven by huge pre-trained language models such as GPT-3 and DALLE-2. Instead of building many small task-specific models, there is a movement to create and use these more all-purpose huge language models for many NLP applications. The most intriguing finding is that these models employ a new learning paradigm: in-context learning, where they learn to do a downstream task simply by conditioning on a prompt consisting of a few input-output examples without any parameter updates. In this seminar, the speaker will provide a short overview of these language models, discuss their recent progress, why they matter, how they work, and when they fail.

Speaker

Dr Tao YU
Assistant Professor @ Musketeers Foundation Institute of Data Science & Department of Computer Science, HKU 

Tao Yu is an Assistant Professor in the Department of Computer Science, The University of Hong Kong. He is also a Postdoctoral Research Fellow in the Department of Computer Science and Engineering at University of Washington and a co-director of the NLP group at The University of Hong Kong. His research interest is in Natural Language Processing and Deep Learning, with a focus on designing and building conversational natural language interfaces that can help humans explore and reason over data in any application (e.g., relational databases and mobile apps) in a robust and trusted manner. He has published and served in the program committee at ACL, EMNLP, ICLR, NAACL, etc. He co-organized the Interactive and Executable Semantic Parsing workshop at EMNLP 2020, and was recently awarded the “Fall 2021 Amazon Research Award”. For more details, please browse this page.

Dr. Yu is currently involved in a research project titled “Democratizing data science via conversational executable natural language understanding: building AI collaborators for everyone including laypeople via a natural language interface to coding, databases, and apps.”

Moderator

Professor Reynold CHENG
Associate Director @ the Musketeers Foundation Institute of Data Science; Professor @ Department of Computer Science, HKU

Reynold Cheng is a Professor of the Department of Computer Science in the University of Hong Kong (HKU). His research interests are in data science, big graph analytics and uncertain data management. He was the Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. He received his BEng (Computer Engineering) in 1998, and MPhil (Computer Science and Information Systems) in 2000 from HKU. He then obtained his MSc and PhD degrees from Department of Computer Science of Purdue University in 2003 and 2005.

Prof. Cheng has received numerous academic awards, and he is also the Associate Director of the Musketeers Foundation Institute of Data Science. For full biography of Prof. Cheng, please browse: https://datascience.hku.hk/reynold-cheng/

For information, please contact:
Email: idatasci@hku.hk

The New IDS Premises Welcomes the Three Musketeers – A Moment for Delight and Progress

The New IDS Premises Welcomes the Three Musketeers - A Moment for Delight and Progress!

Official Launch of the IDS Office (30 September 2022)

The new office premises of the HKU Musketeers Foundation Institute of Data Science at Graduate House, received its first batch of guests – our benefactors from the Musketeers Education and Culture Charitable Foundation, Mr Stanley Chu, Dr Lawrence Fung, and Mr Leong Ka-chai, on September 30, 2022.

Furnished with a modern taste for a research space and equipped with a wealth of advanced audio-visual facilities, the three Musketeers toured in delight at the newly renovated IDS office, with the company of Professor Max Shen, Director of the IDS, Vice-President and Pro-Vice-Chancellor (Research), IDS scholars and Steering Committee members. The eventful day was kick-started with Professor Shen’s sharing of the accomplishment of the Institute since its dedication ceremony on January 5, 2021. With the support of the University management and stakeholders, IDS has gone far and continued to grow stronger and bigger. Young researchers recruited under the Data Science Cluster in the HKU-100 Recruitment Campaign, who were also present that day, impressed the three Musketeers with their aspirations of building a world-class data science research profile for IDS at the meeting that day.

Virtual Tour at the IDS Office Now!

Dr Lawrence Fung (right), Professor Max Shen, Director of the IDS (second to the right), Mr Leong Ka-chai and Mr Stanley Chu (second and third to the left), and Professor Reynold Cheng, Associate Director of the IDS (left), officiated the launch of the IDS premises in a delightful manner.

As Professor Shen has stated, the IDS positions itself to be a place for “facilitating cross-disciplinary research” which can then help make HKU one of the most diverse and outstanding hubs for having different people collaborate together. It echoes the well wishes Dr Lawrence Fung has expressed to us at the event, that the three Musketeers “always hope HKU to be the best, and IDS to be the best” as a research institute. The meeting concluded with the three Musketeers leaving us a food for thought, to which the work of IDS shall gear, that artificial intelligence, being as indispensable as it is in aiding decision-making, will not only become “Explainable AI”, but also “Applicable AI” to the greater crowds.

Professor Max Shen leading the three Musketeers in the hallway into the IDS new premises.
Professor Reynold Cheng and Professor Michael Ng, Associate Directors of the IDS, paying attention to the progress update.
Sharing from the Director of IDS.
Fruitful exchanges between the three Musketeers, IDS Steering Committee members, and IDS scholars.
Our young IDS talents as the most valuable asset of the Institute.
Mr Stanley Chu, one of the three Musketeers.
IDS scholar, Dr Chao Huang from Computer Science.
IDS scholar, Dr Man-chung Yue from IMSE.
Say cheers! – Group photo of all the participants at the official launch of the IDS premises.
Group photo of IDS scholars and postgraduate students.

HKU-IDS Scholar Dr Wenjie Huang received the 2022 NSFC Young Scientists Fund!

HKU-IDS Scholar Dr Wenjie Huang received the 2022 NSFC Young Scientists Fund! 黃文傑博士於2022年度中國國家自然科學基金「青年科學基金項目」中獲獎！

HKU-IDS scholar, Dr Wenjie Huang, Research Assistant Professor in the Department of Industrial and Manufacturing Systems Engineering, has been awarded the 2022 Young Scientists Fund under the National Natural Science Foundation for his project “Risk-aware accelerated and variance-reduced reinforcement learning with application in portfolio optimization”. This year, HKU has a total of 16 awardees to this fund, covering a number of STEM-related disciplines.  For more information about Dr Huang, please browse: https://datascience.hku.hk/people/wenjie-huang/

NSFC Young Scientists Fund (YSF) renders support to young academics and encourages them to focus on self-chosen area for basic research. It is the first time for the YSF to accept direct applications from academics affiliated with institutions in Hong Kong and Macau. Let’s extend our greatest congratulations to Dr Huang for his achievement!

HKU-IDS Scholar、工業及製造系統工程學系研究助理教授黃文傑博士於2022年度中國國家自然科學基金「青年科學基金項目」中獲獎！是次獲獎項目，名為「加速和方差缩减的风险控制强化学习算法与投资组合优化的应用」。本年度港大共有16位年輕科學家獲選，涵蓋多個理工科技學系。有關黃博士的個人簡歷，請瀏覽以下網站： https://datascience.hku.hk/people/wenjie-huang/

中國國家自然科學基金「青年科學基金項目」旨在支持青年科學家，於自選的研究方向上開展基礎研究工作。這是基金委員會首次接納來自港澳地區學府青年科學家的申請，在此再次恭喜黃博士獲獎！ 

HKU-IDS scholar Dr Tao Yu received the Fall 2021 Amazon Research Award!

HKU-IDS scholar Dr Tao Yu received the Fall 2021 Amazon Research Award!

We are pleased to announce that our IDS scholar, Dr Tao Yu, Assistant Professor, HKU-IDS and Department of Computer Science, has been awarded the Fall 2021 Amazon Research Award!

Dr Yu’s award-winning research proposal is titled “Scalable Conversational Structured Knowledge Grounding with a Unified Language Model”, under the fall 2021 AWS AI call for proposals. Dr Yu’s research interests cover Natural Language Processing and Artificial Intelligence. For more information about Dr Yu, please browse: https://datascience.hku.hk/people/tao-yu/

The award accords recognition to outstanding research topics across various disciplines for which the quality of scientific content, creativity, potential to impact both the research community and society, theoretical advances, practical applications, and such were reviewed. Awardees represent more than 30 universities in eight countries this year. Full list of the award recipients can be found here.

Huge congratulations to Dr Yu!

Post-doctoral Fellow in Complex Networks/Urban Science

Careers at IDS

Post-doctoral Fellow in Complex Networks/Urban Science

Position  

Applications are invited for the appointment as a Post-doctoral Fellow in the HKU Musketeers Foundation Institute of Data Science (HKU-IDS) and the Department of Urban Planning and Design (Job Ref.: 515650), to commence as soon as possible starting Fall 2022 for at least one year, with the possibility of renewal. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.

Qualification  

  • Applicants should have a research background in complex networks, complex systems, urban data science, Bayesian inference, or statistical/theoretical/computational physics. Applicants with experience in complex networks are preferred, while individual research interests will also be taken into consideration.
  • Applicants should possess a Ph.D. degree in Data Science, Physics, Urban Planning, Computer Science, Math, Statistics, or related disciplines.
  • Applicants with extensive research experience and strong skills in mathematical and computational modelling are welcome to apply. Proficiency in coding languages such as Python, R, or C++ is preferred.

Application

If interested, applicants may email their application package including an up-to-date C.V., a cover letter, and at least one representative work to Dr. Alec Kirkley (akirkley@hku.hk). Two references will be requested later on. Any enquiry can be sent to the same email address.

Apply Now

About the mentor

The appointee will be working under the supervision of Dr. Alec Kirkley, an Assistant Professor jointly appointed in the Institute of Data Science and Department of Urban Planning and Design at HKU. He obtained his PhD in physics at the University of Michigan, working with Mark Newman on complex networks and statistical physics, and did his undergraduate studies at the University of Rochester, beginning his studies in complex systems with Gourab Ghoshal. His research focuses on the theory of complex networks and the statistical physics of urban systems, with specific interests involving the characterization of structure in networks with metadata, the development of analysis methods and algorithms for statistical inference with network data, the structure and dynamics of human mobility, and the spatial manifestation of socioeconomic inequality. His research involves a balance of mathematical theory, computer simulation, and analysis of empirical data. His overarching goal is to develop physics-inspired mathematical and computational methods to aid in the understanding and modeling of complex networks and urban systems. More info can be found at his personal website, https://aleckirkley.com/.

HKU Global Professoriate Recruitment Campaign

Careers at IDS

HKU Global Professoriate Recruitment Campaign

We’ve been targeting data science recruits since the launch of the HKU-100 Global Recruitment Exercise in 2021, and we’re expecting to increase the number of new hires in the coming years for the data science cluster.  The University is determined to dedicate its efforts to developing this relatively new but potentially highly rewarding area, delivering world-class data science research. With the injection of funding from both the University through its global recruitment campaign and the Musketeers Foundation through its generous donation, we believe the new Institute is set for success in its research and educational work.

HKU Global Professoriate Recruitment Campaign

The University of Hong Kong (HKU) is recruiting 30 outstanding academics in data science and/or big data related fields with potential for scientific and scholarly breakthroughs to join its newly established Institute of Data Science (HKU-IDS) as assistant, associate, or full professors, on tenure-track or with direct tenure.

To achieve strategic cluster recruitment, applications are invited for positions in the two major themes of data science:

  • Core: fundamentals in machine learning and artificial intelligence, big data systems, data security and privacy, natural language processing, Bayesian learning, casual inference, decision making under uncertainty, interface between optimization and machine learning
  • Applied: the use of data science in computational social science, bioinformatics, medicines, Fintech, visions and robotics, and smart cities

We are seeking dedicated and creative scholars to help build up HKU-IDS through active research and strong commitment to teaching and mentoring of students. We welcome outstanding candidates from diverse disciplines in computer science, statistics, optimization, applied mathematics, social science, bioinformatics, business, and other related areas to apply.

Each successful candidate will be jointly appointed in HKU-IDS and the academic department at HKU best aligned with the candidate’s work.

Apply Now

Enquiries for recruitment details can be sent to: datascience@hku.hk

HKU-IDS Scholar Seminar Series #1: Complex network inference: Efficient algorithms and insights for urban spatial segregation

HKU Musketeers Foundation Institute of Data Science (HKU-IDS) proudly presents the “HKU-IDS Scholar Seminar Series” which will run through the year of 2022-23.  The seminar series will feature the HKU-IDS new recruits under HKU-100 Professoriate Recruitment Campaign forming the backbone for the Institute’s cross-disciplinary collaboration. These young talents are co-hosted by IDS and different Departments including Computer Science, Electrical and Electronic Engineering, Architecture, Pharmacy and Pharmacology, Industrial and Manufacturing Systems Engineering and Mathematics.

Title: Complex network inference: Efficient algorithms and insights for urban spatial segregation
Speaker: Dr. Alec Kirkley, the Musketeers Foundation Institute of Data Science and Department of Urban Planning and Design, HKU
Moderator: Professor Anthony Yeh, Chair Professor, Department of Urban Planning and Design, HKU
Date: August 19, 2022
Time: 10:00 – 11:00am

View the Recording Now

Abstract

In this seminar, the speaker will give an overview of two recent projects he has been involved in, during which new efficient statistical inference algorithms for complex network data are developed.  A belief propagation algorithm for computing one-point marginals and other quantities of interest in probabilistic graphical models on networks with short loops will first be described. This algorithm provides a significant accuracy improvement over standard belief propagation for graphical models over highly clustered networks, which are ubiquitous in complex systems research, and runs in only a fraction of the time it takes to run standard Monte Carlo sampling. The speaker will demonstrate its capabilities using the Ising model from statistical physics as an example system. He will then move on to discuss a fast parameter-free algorithm to perform regionalization, spatially contiguous clustering, over areal units with distributional metadata such as those sampled for census analysis. The problem of regionalization is viewed as one of data compression, optimizing an objective derived using purely combinatorial arguments and the minimum description length principle. The speaker will further demonstrate how the method is capable of recovering planted spatial clusters in noisy synthetic data, and that it can meaningfully coarse-grain real demographic data to provide new insights about urban spatial segregation. Using the description length formulation, it is found that spatial ethnoracial data in metropolitan areas across the U.S. has become more difficult to compress over the period from 1980 to 2010, reflecting the rising complexity of urban segregation patterns of these metros. Increasing ethnoracial diversity at small spatial scales within these metros is identified as a major contributor to this lower data compressibility, while changes in large scale ethnoracial clustering and population are not significant factors.

Speaker

Dr Alec KIRKLEY
Assistant Professor @ Musketeers Foundation Institute of Data Science & Department of Urban Planning and Design, HKU
Alec Kirkley is an Assistant Professor jointly appointed in the Institute of Data Science and Department of Urban Planning and Design at HKU. He obtained his PhD in physics at the University of Michigan and did his undergraduate studies at the University of Rochester. His research focuses on the theory of complex networks and the statistical physics of urban systems, with specific interests involving the characterization of structure in networks with metadata, the development of analysis methods and algorithms for statistical inference with network data, the structure and dynamics of human mobility, and the spatial manifestation of socioeconomic inequality. His research involves a balance of mathematical theory, computer simulation, and analysis of empirical data. His overarching goal is to develop physics-inspired mathematical and computational methods to aid in the understanding and modeling of complex networks and urban systems.

Moderator

Professor Anthony YEH
Chair Professor (Urban Planning and Geographic Information Systems) @ Department of Urban Planning and Design, HKU

Prof Anthony Yeh is currently the Chan To Haan Professor in Urban Planning and Design and Chair Professor in Urban Planning and Geographic Information Systems of the Department of Urban Planning and Design and Director of the Geographic Information Systems (GIS) Research Centre of The University of Hong Kong. His main areas of specialization are the applications of geographic information systems in urban and regional planning and smart cities and urban planning and development in Hong Kong, China, and SE Asia. He has also been serving in multiple positions in regional and local statutory organisations, as well as the Hong Kong SAR Government. For full biography of Professor Yeh, please browse his website here.

For information, please contact:
Email: idatasci@hku.hk

AI and Big Data Research for Health Improvement Panel Discussion

Panel Discussion

Topic: AI and Big Data Research for Health Improvement Panel Discussion: Future of Big Data Application for Public Health in Hong Kong

2:15pm – 3:00pm
(Moderator: Dr. Esther Chan, Associate Professor, Department of Pharmacology and Pharmacy, HKU)
Session 1: Ethics and compliance framework
– Mr. KP Tsang – Chairman of Rare Disease Hong Kong
– Dr. CS Lau – Dean, LKS Faculty of Medicine, HKU
– Dr. Tony Ko – Chief Executive of Hospital Authority Hong Kong
– Ms. Ada Chung – Privacy Commissioner for Personal Data
– Dr. Calvin Ho – Associate Professor of Law, HKU

3:15pm – 4:00pm
(Moderator: Dr. CL Leung, Associate Professor, Department of Pharmacology and Pharmacy, HKU)
Session 2: Facilitate infrastructure for effective application of big data in Hong Kong and beyond
– Mr. Lawrence Wong – Vice President of The Hong Kong Association of the Pharmaceutical Industry (HKAPI)
– Dr. Brian Chung – Hong Kong Genome Institute
– Dr. Alexander Chiu – Medical Director of AXA Hong Kong & Macau
– Dr. Crystal Lau 
– Mr. Allen Yeung – Former Government representative

Learn more: AI and Big Data Research for Health Improvement Symposium
For information, please contact:
Ms. Nicole Fung
Email: nicfung@hku.hk

AI and Big Data Research for Health Improvement Symposium

This symposium aims to demonstrate the capacity of the HKU-IDS and its potential in using AI and healthcare big data to promote public health, informed policy and strategic planning; and to advance research of new therapies and clinical utilities.

Workshop

A series of pre-recorded workshop will be available to watch online starting from Aug 21.
You are also welcomed to join in a live Q&A session on Aug 29 and Sep 1 to interact with our lecturers!

Topic & Speaker
A) Workshop on AI and Big Data Methodology in Health and Medical Sciences
– Dr. Zhonghua Liu
(Assistant Professor, Department of Biostatistics, Columbia University)
Genome-wide Cross-trait Analysis of COVID-19 with Rheumatoid Arthritis,
Systemic Lupus Erythematosus and Venous Thromboembolism

– Dr. Lequan Yu
(Assistant Professor, Department of Statistics & Actuarial Science,
Faculty of Science, HKU)
AI for Medical Imaging: Applications and Beyond

– Dr. Chao Huang
(Assistant Professor, Department of Computer Science, Faculty of Engineering,
and Institute of Data Science, HKU)
Graph Representation Learning for Healthcare Applications

B) Epidemiology in the Era of Big Data
– Dr Celine Chui
(Assistant Professor, School of Nursing, LKS Faculty of Medicine, HKU)
Alternative study design using Big Data

– Dr Francisco Lai
(Research Assistant Professor, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, HKU)
Cohort and Case-Control Studies using Big Data

– Dr Angel Wong
(Assistant Professor, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine)
Evaluation and interpretation of electronic health record data

C) Bioinformatics / Pharmacogenetics
– Dr CL Cheung
(Associate Professor, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, HKU)
Pharmacogenetics: basic principles and clinical applications

– Dr Ryan Au Yeung
(Assistant Professor, School of Public Health, LKS Faculty of Medicine, HKU)
The value of Mendelian randomization in causal inference

– Dr Clara Tang
(Assistant Professor, Department of Surgery, LKS Faculty of Medicine, HKU)
Genomic medicine and congenital disorders: from bench/computer to bedside

Keynote

Prominent speakers, both local and overseas, will share their expertise in state-of-art AI technology and interdisciplinary collaborative projects in using big data to improve public health.

Showcase

Invited local speakers will showcase their healthcare big data projects associated with HKU, including the latest applications of AI technology, bioinformatics, industry or government funded projects on cardiovascular diseases, osteoporosis, Covid-19 vaccines safety and effectiveness. Our speakers will also share their perspective on healthcare big data collaboration in the Greater Bay Area.

Schedule

Session 1 – 30 Aug (Tue)
10:00 AM – 10:45 AM
Keynote on Radiology, Medical imaging and biomedical informatics research
10:45 AM – 11:45 AM
Showcase 1

Session 2 – 30 Aug (Tue)
1:30 PM – 2:15 PM
Keynote on Latest development of genetic bioinformatics in Hong Kong
2:15 PM – 3:15 PM
Showcase 2

Session 3 – 30 Aug (Tue)
3:30 PM – 4:15 PM
Keynote on UK’s perspective in big data research and the development of Health Data Research UK
4:15 PM – 5:15 PM
Showcase 3

Session 4 – 31 Aug (Wed)
9:30 AM – 10:15 AM
Keynote on Latest development of International big data collaboration
10:15 AM – 11:15 AM
Showcase 4

Speakers

Dr. Curtis P. Langlotz, MD, PhD
Professor, Radiology & Biomedical Informatics & Director of the Center for Artificial Intelligence in Medicine and Imaging @Stanford University
Dr. Langlotz is Professor of Radiology and Biomedical Informatics and Director of the Center for AIMI Center at Stanford University. Dr. Langlotz’s laboratory investigates the use of deep neural networks and other machine learning technologies to help physicians detect disease and eliminate diagnostic errors. He has led many national and international efforts to improve medical imaging, including the Medical Imaging and Data Resource Center, a U.S. national COVID-19 imaging research repository. As Associate Chair for Information Systems and a Medical Informatics Director for Stanford Health Care, he is responsible for the computer technology that supports the Stanford Radiology practice, including 7 million imaging studies that occupy 0.7 petabytes of storage. He currently serves on the Board of Directors of the RSNA as Liaison for Information Technology. He has founded 3 healthcare IT companies, which was acquired by Nuance Communications in 2016.
Prof. Andrew Morris
Director @Health Data Research UK
Prof. Morris became the inaugural Director of Health Data Research UK in August 2017. He convenes the International COVID 19 Data Alliance supported by the Bill and Melinda Gates Foundation and Minderoo Foundation. He is seconded as Professor of Medicine, and Vice Principal of Data Science at the University of Edinburgh, having taken up position in August 2014. Prior to this Andrew was Dean of Medicine at the University of Dundee. Andrew was Chief Scientist at the Scottish Government Health Directorate(2012-2017) and has served and chaired numerous national and international grant committees and Governmental bodies. His research interests span informatics and chronic diseases. He has published over 350 original papers and was previously Governor of the Health Foundation(2009-2017) and chaired the Informatics Board at UCLPartners, London(2014-2017). In 2007 he co-founded Aridhia Informatics and is a Fellow of the Royal Society of Edinburgh and the Academy of Medical Sciences.
Prof. Pak Sham
Chair Prof, Psychiatric Genomics & Co-director, State Key Laboratory of Brain & Cognitive Sciences @The University of Hong Kong
Prof. Sham studied Medicine at Cambridge and Oxford Universities, and subsequently trained in Psychiatry at the Bethlem Royal and Maudsley Hospitals in the London. In 2000, He was appointed Professor of Psychiatric and Statistical Genetics at the MRC Social, Genetic and Developmental Psychiatry Research Centre at King’s College London. He was the Head of Department of Psychiatry, The University of Hong Kong from 2007 to 2011, and served as the Director of the Centre for Genomic Sciences from 2011 to 2019. Professor Sham has developed new statistical methods for the analysis of genetic data, and applied such methods to study the etiology of psychiatric disorders and other complex disorders.
Patrick Ryan, PHD
Vice President, Observational Health Data Analytics @Janssen Research and Development
Patrick is also an original collaborator in Observational Health Data Sciences and Informatics, interdisciplinary collaborative to create open-source solutions that bring out the value of observational health data through large-scale analytics. He served as a principal investigator of the Observational Medical Outcomes Partnership, which chaired by the Food and Drug Administration, where he led methodological research to assess the appropriate use of observational health care data to identify and evaluate drug safety issues. Patrick received his undergraduate degrees in Computer Science and Operations Research at Cornell University, his Master of Engineering in Operations Research and Industrial Engineering at Cornell and his PhD in Pharmaceutical Outcomes and Policy from University of North Carolina at Chapel Hill. Patrick has worked in various positions within the pharmaceutical industry at Pfizer and GlaxoSmithKline and also in academia at the University of Arizona Arthritis Center.
For information, please contact:
Ms. Nicole Fung
Email: nicfung@hku.hk