08:45
REGISTRATION
09:15
WELCOME
Nick White - Independent
Nick graduated with a BS and MS in Electrical Engineering from Stanford University where he focused in Signal Processing, Optimization and Control especially as they apply to the fields of Neural Networks and Artificial Intelligence. Upon graduation, he taught and researched artificial intelligence and applied mathematics at Stanford. Nick served at the AI Specialist for Hong Kong-based AI incubator Zeroth.AI where he coached teams from 5 continents across diverse industries including finance, agriculture, robotics and blockchain. Nick leverages his deep technical expertise and knowledge of the AI industry to identify business opportunities in emerging technologies and to guide investment decisions. Nick most recently branched into the blockchain space as a co-founder of Holon Partners, a Crypto-focused advisory and investment firm.
AN INTRODUCTION TO DL PROJECTS & METHODS
09:30
Victor Lam - Deputy Govt Chief Information Officer - The Government of the HKSAR
Big Data and Artificial Intelligence - the Digital Frontier for Building Hong Kong as a Smart City
Victor Lam - The Government of the HKSAR
Big Data and Artificial Intelligence – the Digital Frontier for Building Hong Kong as a Smart City
The Government of the Hong Kong Special Administrative Region released the Smart City Blueprint for Hong Kong in December 2017, with the vision to build Hong Kong into a world-class smart city. The Blueprint maps out development plans for the next five years to enhance the effectiveness of city management, improve people's quality of living, and increase Hong Kong's attractiveness and sustainability by making use of innovation and technology. Big data, machine learning and artificial intelligence all play an important role to support the vision. In this session, Mr. Lam will share with the audience the measures the Government are taking to facilitate the development of big data analytics and artificial intelligence in the Government and the industry.
Mr Victor Lam joined the Hong Kong Government as an Analyst/Programmer in August 1985, immediately after his graduation from the University of Hong Kong, majoring in Computer Studies.
Being an information technology (IT) professional, Mr Lam has provided IT service to various government departments, including Housing Department, Customs and Excise Department, the former Trade Department and the former Information Technology Services Department. He was the project manager of the Air Cargo Clearance System in preparation for the opening of the Hong Kong International Airport at Chek Lap Kok in 1997-98. In 1998-2001, he completed a review on the “.hk” domain name administration regime and facilitated the establishment of the Hong Kong Internet Registration Corporation. He assumed the position of Chief Systems Manager (Human Resources) of the Office of the Government Chief Information Officer in 2002-05 to take forward a series of cultural and organisational change programmes of the Office. He took up the Assistant Government Chief Information Officer position in 2006 to help government departments transform their businesses through IT. Currently he is the Deputy Government Chief Information Officer.
09:55
Brian Cheung - PhD Student - UC Berkeley/Google Brain
Beyond Gradients: Learning Algorithms without clear objectives
Brian Cheung - UC Berkeley/Google Brain
Beyond Gradients: Learning Algorithms without clear objectives
Brian Cheung is a PhD Student at UC Berkeley working with Professor Bruno Olshausen at the Redwood Center for Theoretical Neuroscience. His research interests lie at the intersection between machine learning and neuroscience. Drawing inspiration from these fields, he hopes to create systems which can solve complex vision tasks using attention and memory.
10:20
Hamed Valizadegan - Senior ML Scientist - NASA Ames Research Center
Machine Learning for Space Projects: Example Engineering and Science Case Studies
Hamed Valizadegan - NASA Ames Research Center
Machine learning For Space Projects: Example Engineering and Science Case Studies
The success of the space projects depends much on our ability to understand and analyze their collected science and engineering data. In the science domain, the amount of collected data is so very large that requires building automatic tools to make sense of them. In this domain, often the data is not annotated well and/or there is not enough representative features for effective model construction. In the engineering domain, there are components that are designed and built for the first time and there is a limited domain knowledge available for them. This makes the hand-coded physics-based models less accessible and models that can utilize data for prediction more desirable. However, often there is a small number of working and failed units to learn from.
In this talk, I provide examples of both cases and show how machine learning can help in these imperfect scenarios. First, I use our efforts in life time prediction of Fine Guidance Sensors of Hubble Space Telescope as an example to demonstrate how little domain knowledge can help us develop effective machine learning models when the data availability is scarce. And then, I demonstrate our experience of using machine learning to classify the transit-like signals of Kepler spacecraft when annotation is imperfect and data features are not representative.
Holder of a PhD in computer science with focus on machine learning and data mining from Michigan State University, Hamed Valizadegan joined NASA Ames Research Center (UARC) as a machine learning research scientist in 2013. At Ames, he has been involved with multiple projects including Hubble Space Telescope, Kepler mission and ASRS aviation safety reports! Before joining NASA Ames, he spent three years at University of Pittsburgh conducting research in Medical Informatics. He has published in prestigious venues such as International Conference on Neural Information Processing Systems (NIPS), ACM SIGKDD conference on Knowledge discovery and Data Mining (KDD), and Artificial Intelligence and Statistics (AISTATS).
10:45
COFFEE
NLP & SEARCH
11:30
Alan Lu - Director of Engineering & Applied Science - eBay
Deep Learning in eCommerce Text Search at eBay
Alan Lu - eBay
Deep Learning in eCommerce Text Search at eBay
In recent years, deep learning models became state-of-the-art in areas like speech recognition, computer vision, and more recently natural language processing. These models learn tasks through learning data representation rather than relying on heavy-handed feature engineering. eBay, with more than 1 billion listing items from 150 million sellers across the globe, stands to benefit immensely from such land-shifting scientific and technological advances. Search at eBay is one example. I will talk about how we apply deep learning to two search tasks. First, how we use query categorization to narrow searches to relevant subsets of item listings, greatly boosting search relevance. Second, how we use click prediction to improve search ranking.
11:55
Johnson Poh - Head of Data Science - DBS Bank
Speech Enabled NLP for Event Extraction
Johnson Poh - DBS Bank
Speech Enabled NLP for Event Extraction
It is often challenging to make sense of the information hidden within large volumes of unstructured data and speech. How do we seamlessly classify and extract key ideas with automation? In this presentation, we explore the open source tools, algorithms and services that may come in handy for the design of a reference architecture to sieve out underlying insights from texture descriptions. More specifically, we discuss how we can assemble a suite of text mining and probabilistic graphical processes into an automated pipeline, built on top of a modular framework to surface embedded topics of interest.
Johnson is currently Head Data Science for Big Data Analytics Center of Excellence at DBS Bank. He holds an adjunct faculty appointment at SMU School of Information Systems where his core focus areas include applied statistical computing, machine learning as well as big data tools and techniques. An avid programmer and data enthusiast, Johnson enjoys developing apps and data products. Most recently, he was awarded first prize in Singapore’s largest coding competition, Hackathon@SG 2015 as well as the CapitaLand Data Challenge 2016. Johnson completed his bachelor’s degree at University of California, Berkeley, majoring in the subjects of Pure Mathematics, Statistics and Economics. He received his postgraduate degree in Statistics at Yale University.
Guan Wang - Swiss Re
Annotation Tools & Beyond
Guan will briefly talk about a recent open source project he has been working on called Chinese Annotator that uses active learning for generating labeling data for Chinese natural language processing tasks, and experiences generated around and beyond the project.
Guan works as Data Scientist at Swiss Re. His job involves using machine learning and data mining techniques for automation and augmented intelligence. Previously Guan was working on data mining applications at Lenovo, and machine vision projects at ASTRI. Guan was also doing research work on genetic algorithms and complex network analysis during his study.
12:35
LUNCH
13:45
Masashi Sugiyama - Director - RIKEN Center for Advanced Intelligence Project
Machine Learning from Weak Supervision
Masashi Sugiyama - RIKEN Center for Advanced Intelligence Project
Machine Learning from Weak Supervision
Machine learning with big data is making a great success. However, there are various application domains that prohibit the use of massive labeled data. In this talk, I will introduce our recent advances in classification from weak supervision, including classification from two sets of unlabeled data, classification from positive and unlabeled data, a novel approach to semi-supervised classification, and classification from complementary labels.
Masashi Sugiyama received the PhD degree in Computer Science from Tokyo Institute of Technology, Japan in 2001. He has been Professor at the University of Tokyo since 2014 and he has concurrently served as Director of RIKEN Center for Advanced Intelligence Project since 2016. His research interests include theories and algorithms of machine learning (such as covariate shift adaptation, density ratio estimation, and reinforcement learning) and their applications to real-world problems.
RECOMMENDATIONS
14:10
Icarus So - Data Scientist - TVB
Apply Reinforcement Learning to Programme Recommendation
Icarus So - TVB
Apply Reinforcement Learning to Programme Recommendation
Many people think reinforcement learning is too advanced and too academic which cannot apply in real business cases. The fact is just an opposite and very easy to be adopted gradually. In the presentation, Icarus will share his experience how to apply it in program recommendation from scratch to deep reinforcement learning. Traditional recommendation algorithms are collaborative filtering and content filtering. Icarus will explain how to encode them in a deep reinforcement network and add another important feature set, impression to the neural network. Difficulties such as avoiding local maximums and tricks on how to apply on startup will also be shared.
TVB is the largest television company in Hong Kong and its service spreads to Southeast Asia. It has more than million of users watching every day. Icarus is a Data Scientist in TVB. He designs the workflow and drives different data science projects which include program recommendation for MyTvSuper and BigBigChannel. Previously, he worked in Jobable, a startup matching candidates and job posts. He worked closely with the marketing team and help raise funding for series A with collaborated analysis blogs and deep learning models.
GANs & CNNs
14:35
Mohammad Yousuf Hussain - Senior Technology and Innovation Specialist - HSBC
Application of GANs in Algorithmic Trading and Aggregation of Low Alpha Strategies
Mohammad Yousuf Hussain - HSBC
Application of Generative Adversarial Networks (GANs) in Algorithmic Trading and Aggregation of Low-Alpha Strategies
Machine learning is attracting a lot of attention recently due to the promising capabilities of adapting, learning and even self-teaching algorithms. Application of machine learning in algorithmic trading offers a variety of opportunities to extract greater alpha and improve order execution. In this session, I am going to present the application of Generative Adversarial Networks (GANs) in algorithmic trading and share insights on aggregation of strategies.I would highlight the key functions of a strategy aggregator; categorisation of strategies, classification of market behaviour and application of ensemble learning. I would then briefly discuss about the design and development of controls for the aggregator framework that allows ease in monitoring and optimisation of the solution.
Mohammad Yousuf Hussain, CFA is a Senior Technology and Innovation Specialist at HSBC. Working in the Applied Innovation and Strategic Investments team, he has designed and delivered a number of artificial intelligence based electronic trading solutions. Previously, he was a Senior Consultant at GreySpark Partners where he delivered projects for UBS, HSBC, Mizuho Securities, Nomura-Instinet, Interactive Brokers and SFC. He developed expertise in assessing trading algorithms by investigating the market abuse incidents for the regulator.
15:00
COFFEE
Michael Natusch - Prudential
Horses for Courses: Deep Learning Beyond Niche Applications
Machine learning in general and deep learning in particular are driving major advances for a wide range of specific finance use cases. This talk will outline how enterprise-wide learning loops will extend these point success to a coherent AI strategy and also show what other elements are required for success, using real-world examples at Prudential plc.
Michael is the Global Head of AI in the Group Digital team of Prudential plc. He joined Prudential last year from Silicon Valley based Pivotal Labs where he led the Data Science team. His experience lies in the application of artificial intelligence methods to large-scale, multi-structured data sets, in particular neural network based deep learning techniques. Michael previously founded and sold a ‘Silicon Roundabout’ based startup and prior to that was a partner at a major consulting firm. Michael holds a PhD in theoretical physics from the University of Cambridge and is a Fellow of the Royal Statistical Society.
16:00
Joni Zhong - Research Scientist - National Institute of Advanced Industrial Science and Technology, Japan
Crossmodal Understanding and Prediction for Cognitive Robots
Joni Zhong - National Institute of Advanced Industrial Science and Technology, Japan
Cross-modal Understanding and Prediction for Cognitive Robots
Our brain works as a predictive machine, which works constantly as an active inference and keeps utilizing both active motor action and perception to minimize the prediction error. At the first part of the talk, I will briefly introduce this hypothesis in neuroscience and psychology and proposed a hierarchical predictive cognitive model for robots. At the second part, I will present how to apply this cognitive model to crossmodal understanding and prediction for robots using the state-of-the-art machine learning algorithms. A few robot video demonstrations based on these methods will also be shown.
Junpei “Joni” Zhong is currently a research scientist at National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. He received BEng from the South China University of Technology in 2006, M.Phil from the Hong Kong Polytechnic University in 2010 and Ph.D. ("with great distinction") from the University of Hamburg in 2015. From 2014 to 2018, he has been participating in a few projects in Germany, UK, and Japan. His research interests are machine intelligence, machine learning, cognitive robotics and assistive robotics. He has been awarded the EU Marie-Curie Fellowship from 2010 to 2013. He is also a founding member of the organization "Consciousness Research Network" and a Guest Editor of journal "Complexity" and "Interaction Studies".
AI & REGULATION CHALLENGES
16:20
PANEL SESSION: Who Should Be Responsible for Regulating AI?
Michael Natusch - Prudential
Horses for Courses: Deep Learning Beyond Niche Applications
Machine learning in general and deep learning in particular are driving major advances for a wide range of specific finance use cases. This talk will outline how enterprise-wide learning loops will extend these point success to a coherent AI strategy and also show what other elements are required for success, using real-world examples at Prudential plc.
Michael is the Global Head of AI in the Group Digital team of Prudential plc. He joined Prudential last year from Silicon Valley based Pivotal Labs where he led the Data Science team. His experience lies in the application of artificial intelligence methods to large-scale, multi-structured data sets, in particular neural network based deep learning techniques. Michael previously founded and sold a ‘Silicon Roundabout’ based startup and prior to that was a partner at a major consulting firm. Michael holds a PhD in theoretical physics from the University of Cambridge and is a Fellow of the Royal Statistical Society.
Eric Yeung - Smart City Consortium
Eric Yeung is currently the Executive Director of Skyzer VC Group and he is also the founder of Skyzer Group. Eric completed his undergraduate study in the Department of Computer Science & Engineer of Chinese University of Hong Kong. He also obtained the Master Degrees of Business Administration (MBA) and the Science in E-Commerce.
Eric plays an active role in IT industry as well. He is the president of Smart City Consortium, the chairman of Hong Kong Software Industry Association, the vice chairman of the Internet Professional Association and the vice president of E-Sports Association Hong Kong. Devoting himself to various types of community service, Eric endeavors to promote technology innovation and encourage the youth to contribute to the society.
Juergen Rahmel - HSBC Germany
Dr. Juergen Rahmel received his PhD (Artificial Intelligence) in Germany for research which covered Artificial Intelligence and Machine Learning in the areas of Case-based diagnosis, connectionist learning with Neural Networks as well as topological and hierarchical interpretation of Neural Networks. The results of his research were successfully applied in the fields of medical diagnosis (nerve lesions, tumor grading) as well as technical diagnosis (fault detection).
Dr. Rahmel has extensive experience in the international financial sector, in roles as Director, Head of IT and Programme Manager of global initiatives for multinational banks. He served as the Head of Artificial Intelligence in the Applied Innovation Team of HSBC as well as Head of Innovation Quality management for all areas of FinTech technologies. He currently is Chief Digital Officer of HSBC Germany.
Kristi L.Swartz - Bryan Cave
As Bryan Cave’s Hong Kong office managing partner, Kristi Swartz concentrates her practice in corporate finance, focusing on SFC investigations and fintech matters such as robo-advisory services, peer-to-peer lending and alternative payments. Kristi has practiced in Hong Kong for over 20 years, and works with emerging growth companies through to listed multinational corporations in Hong Kong, China, Asia and Europe across a broad range of industries. Recognised as a leader in the financial technology and regulatory fields, Kristi has spoken on panels providing insight for companies to remain compliant and was named one of the “Women in Finance” by Let’s Talk Payments. Kristi is a front-runner in bringing new concepts to the market, and is currently advising on the regulatory requirements for ICOs and the implementation of equity crowdfunding for professional investors.
17:00
CONVERSATION AND DRINKS
08:45
DOORS OPEN
Tak Lo - Zeroth.AI
Tak Lo is creating startup leaders in Asia. He is a Partner at Zeroth, an AI-focused early stage funding program. Previously, he sourced, invested, and mentored companies in the $3m Techstars London seed fund. He has worked with over 50 companies from the Asian, European, and NYC startup communities angel investing, founding, and mentoring startups. Tak started his career in the US Army. He graduated from the London Busi ness School and the University of Chicago.
Nick White - Independent
Nick graduated with a BS and MS in Electrical Engineering from Stanford University where he focused in Signal Processing, Optimization and Control especially as they apply to the fields of Neural Networks and Artificial Intelligence. Upon graduation, he taught and researched artificial intelligence and applied mathematics at Stanford. Nick served at the AI Specialist for Hong Kong-based AI incubator Zeroth.AI where he coached teams from 5 continents across diverse industries including finance, agriculture, robotics and blockchain. Nick leverages his deep technical expertise and knowledge of the AI industry to identify business opportunities in emerging technologies and to guide investment decisions. Nick most recently branched into the blockchain space as a co-founder of Holon Partners, a Crypto-focused advisory and investment firm.
STARTUP SESSION
Willie Lee - imago.ai
Linguistics AI Training Service
We aim to make life easier with smarter and friendlier software. As a company with international staff and background, we build Artificial Intelligence training platform to get the robots smarter and improve faster.
09:35
Michal Szczecinski - Head of Analytics and Data Science - GoGoVan
Applying Machine Learning in On-Demand Logistics
Michal Szczecinski - GoGoVan
Applying Machine Learning in On-Demand Logistics
How we build data services that enable improvement of core aspects of our business.
Michal is Head of Analytics and Data Science at GOGOVAN. Previously he worked in data teams of PwC, NaturalMotion and Zynga. He was Founder of Artificial Intelligence Oxford and Quantified Self Oxford meetup groups.
09:55
Ray Horan - CEO - Emotics
Applications of Facial Micro-expression Analysis to Finance
Ray Horan - Emotics
Applications of Facial Micro-expression Analysis to Finance
A look at two uses cases where micro-expression analysis can be used in the world of finance. The first will deal with adherence to compliance training and the second involves providing alternative data to assist investment strategies.
Ray is CEO of Emotics, a Hong-Kong based RegTech company. Having started his career in accounting, Ray has held senior roles, and been on the founding team, of enterprise software solutions in business process automation, business intelligence, marketing
Ken Yeung - Clare.AI
Multilingual text processing (NLP)
We differentiate in 3 ways, guided by our vision to have reliable and natural conversation at scale: 1. Extensibility and Ability to Deploy On-Premise: Since our system is API-based, we will be able to extend to other social media platforms (Facebook Messenger, WeChat, Line, etc.) and voice. It will be controlled using the same backend. 2. Cross Line of Businesses: Other than retail banking, we can also repurpose and service insurance, asset managers, etc. 3. Cross Language: We currently offer 4 different language modules – Mandarin, Cantonese, English and Japanese. We will be able to rapidly deploy to other language in Asia by replicating our deployment model.
Prior to Clare.AI, he was principal developer of the Algorithmic Trading team of Saxo Bank. He spent 10 years in investment banking IT experience. He worked in various roles in Saxo Bank developing award winning, global multi-asset trading platform handling millions of price updates and trades everyday using proprietary machine learning algorithms.
Darko Matovski - causaLens
AI in Investing
Measurements about economic activity are primarily represented in the form of time-series data. This is always the case for real-time data generated from connected devices. Time-series data is unique due to the presence of temporal characteristics. Building models using this type of data is difficult and requires specialised expertise while at the same time there are many algorithms available for analysis and modelling. Connected devices generate time series, i.e. data that shows how a particular variable or measurement changes over time. For the purposes of building an economic model, data of any kind (text, images, voice, video etc.) ultimately gets transformed into the form of time series. However, there are few tools to effectively analyse this sort of data using next-generation technology like machine learning and AI. Until now, economic researchers have used structural models and econometric models developed in the previous century. The performance of these models has been underwhelming and plagued with lack of robustness and small sample sizes. Modern algorithms, such as machine learning and new sources of data stand to completely change the face of economic modeling and forecasting. Yet, the adoption of modern AI models in economic research has been slow, primarily due to lack of technology and talent. causaLens has developed the world's first AI-driven virtual data scientist, capable of understanding large scale time series data with minimal human effort.
Dr. Darko Matovski is the CEO of causaLens. The company provides automated machine learning solution for time-series predictions and serves prominent organisations including hedge funds and asset managers. Darko has also worked for cutting edge hedge funds and research institutions. For example, the National Physical Laboratory in London (where Alan Turing worked) and Man Group in London. Darko has a PhD in Machine Learning and an MBA.
10:55
COFFEE
AI APPLICATIONS: FINANCE
11:30
Subin Liengpunsakul - Deputy Director - Bank of Thailand
Machine Learning in the Context of Central Banks and Policy Analysis
Subin Liengpunsakul - Bank of Thailand
Machine learning in the Context of Central Banks and Policy Analysis
In recent years, central banks have made progress in integrating (big) data analytics into their policy analysis and decision making. As part of its three-year (2017-2019) strategic plan, the BOT has increased its usage of granular data as well as enhanced its analytics capability. The BOT has also set up a new Data Analytics Unit, which works closely with various departments to extract insights from data, deepening the understanding of the economy and financial system. These initiatives aim at promoting evidence-based policy making and improving work efficiency at the BOT. In addition to surveying machine learning in the context of central banks, this session also presents several analytics use-cases at the BOT.
Mr. Liengpunsakul is Deputy Director of Data Analytics Unit at the Bank of Thailand. Set up in 2017 to enhance the Bank’s analytics capability, the Unit works closely with various departments to extract insights from data, promoting evidence-based policy making and improving work efficiency at the Bank. Prior to his current position, he has worked in various areas of the Bank, including Enterprise Risk Management Department and Statistics and Information System Department.
Mr. Liengpunsakul received an MPhil in Finance from the University of Cambridge (King’s College), and an MSc in Operations Research from Stanford University.
Chinnawat Devahastin Na Ayudhya - Bank of Thailand
Machine learning in the Context of Central Banks and Policy Analysis
In recent years, central banks have made progress in integrating (big) data analytics into their policy analysis and decision making. As part of its three-year (2017-2019) strategic plan, the BOT has increased its usage of granular data as well as enhanced its analytics capability. The BOT has also set up a new Data Analytics Unit, which works closely with various departments to extract insights from data, deepening the understanding of the economy and financial system. These initiatives aim at promoting evidence-based policy making and improving work efficiency at the BOT. In addition to surveying machine learning in the context of central banks, this session also presents several analytics use-cases at the BOT.
As part of Data Analytics Unit, Mr. Chinnawat Devahastin Na Ayudhya is a data scientist at the Bank of Thailand. He graduated with First Class Honors from Chulalongkorn University in Computer Engineering. Pursuing a specialism course in Artificial Intelligence, Mr. Devahastin Na Ayudhya received an MSc degree with Distinction from Imperial College London in 2015. He works closely with business people to deliver intriguing insights and economic indicators from micro data using Python, R, Hadoop, Tableau, Gephi, and whatever it takes to convey meaningful results to users.
11:50
Martin Qiao - Lead Architect for Machine Learning Automation - HSBC
Banking operation automation with A.I. techniques
Martin Qiao - HSBC
Banking operation automation with AI techniques
Many financial service companies are at their early stage to embrace Artificial Intelligence into their daily operation to improve efficiency and reduce human intervention. During this journey the most common struggle is to swing between build a home-brew solution versus buy a vendor solution. In my presentation I would like to share my experience in introducing Artificial Intelligence applications into banking operations, and trying to share my views on when to choose vendor products and how to assess them, and when to develop internal solutions and how to chain up open sources packages into a industrialized solution. Chatbot and sanction screening will be introduced as use cases in terms of NLP area, and signature verification solutions in terms of computer vision area.
Dr. Martin Qiao is currently working as the Lead Architect for Machine Learning Automation in HSBC. He is holding a PhD degree for Mathematics with research experience on various image-related processing. His working experience is mixed with Data Science for insurance marketing and luxury marketing, NLP and computer vision solution design for banking.
AI APPLICATIONS: ENERGY
12:10
Vidal Fernández - Director Big Data - China Light & Power Holdings
Using Big Data & AI to Optimise Wind Farm Performance
Vidal Fernández - China Light & Power Holdings
Using Big Data & AI to Optimize Wind Farm Performance
Big Data & Streaming Analytics Platform help us to collect real time data from IoT sensors that capture wind data and store in our Data Lake. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions between the wind turbines in a wind farm. Because the wake interference, such greedy control strategy can significantly lower the power productions of the downstream and reduce the overall wind farm power production. Using AI methods, based on data collected, we built and deployed a set of AI algorithms to optimize the overall wind farm performance. Based on every wind turbine optimization and the interactions we optimized the overall wind farm performance.
Dr. Fernandez as Director of Big Data Analytics at CLP Holdings is developing the big data strategy with focus on innovation and emerging technologies. Dr. Fernandez is leading the intellectual process of researching and analyzing new technologies, processes and businesses, gathering and synthesizing information and formulating insightful findings and conclusions to deliver value. He is managing a talented team of data scientists to ensure we are deriving from data the right insights to improve business processes, being more efficient, identifying new business models and reshaping the existing ones. Dr. Fernandez has developed several BIG DATA & Analytics solutions as AaaS, based on cloud, for many blue chip companies in different industries: Banking, Insurance and financial Services, Telecommunications, Retail, Utilities and Oil & Gas,... Customer Insights, Smart Grid Analytics, Customer Experience, Predictive Asset Maintenance, Operational Efficiency and Risk Management, Fraud Detection, Anti Money Laundering, Price Optimization, and many other business challenges are some of the delivered solutions deployed.
AI APPLICATIONS: RECRUITMENT & RETAIL
12:30
Kendra Vant - Principal Data Scientist - Seek Limited
Why Settle? Using Machine Learning to Help People Find New Opportunities
Kendra Vant - Seek Limited
Why Settle? Using Machine Learning to Help People Find New Opportunities
When you search for a job, you want to know you have seen everything available that's right for you. If you're looking for your next team member, you want to find the standout applicants as quickly as possible. With 150 million candidates and 700k hirers across 16 countries, the scale of Seek's search and match problem long since outstripped any possibility of manual curation of opportunities and candidates.
In this talk I will introduce our ongoing work using machine learning to build radically more efficient and effective employment marketplaces. In particular, I will discuss our recent efforts combining natural language processing and Deep Learning to connect people to their dream job.
A firm conviction not to die wondering ‘what if?’ has given Kendra Vant a rich and varied career working with memorable people, companies and universities across New Zealand, Australia, the US and Malaysia. Through it all, her greatest satisfaction has come from working with smart people to solve difficult problems.
After doctoral research in experimental quantum physics at MIT and postdoctoral work in applied quantum computing at Los Alamos National Laboratory, Kendra was serendipitously placed to ride the tsunami of corporate interest in applying machine learning to create personalised experiences in an increasingly connected and digital world.
She has worked in insurance, banking, telecommunications, government, gaming and the airline industry and is currently Principal Data Scientist with Seek Asia Pacific and Americas, applying emerging techniques in natural language processing and deep learning to the problem of finding other people their dream job.
12:50
LUNCH
13:50
Rajan Kumar Upadhyay - Machine Learning Technical Expert - DHL
Improving the Efficiency of a Data Center
Rajan Kumar Upadhyay - DHL
Improving the Efficiency of a Data Center
Rajan Kumar Upadhyay is the Machine Learning Technical Expert at DHL, He holds PGPX from UCLA Anderson(US) & APSM from IIM Calcutta(India). He is specialist technologist and togaf 9 certified enterprise architect. he has more than 16 years of strong IT experience varies from various platforms to various technologies. He owns a consistent track record of increasing market share, organizational potential, profitability, and product value within the global markets. In his current role he is associated with DHL, helping supply chain business transformation using intelligent combinations of Data Science , Machine Learning, Natural language processing & Robotic process automation. His day always starts with thinking something new & rolling out those ideas into concept & then reality.
14:10
Yu-Xi Chau - Data Science Manager - A.S. Watson Group
A Flexible answer to Retail CRM: an Ensemble engine for Customer Management
Yu-Xi Chau - A.S. Watson Group
A Flexible answer to Retail CRM: an Ensemble engine for Customer Management
An ensemble model is built on a Spark architecture that utilizes a range of models, including Gradient Boosted Trees, multi-layer Neural Network, Collaborative Filters and others. We show that by grouping the outputs of individual models and collecting their ensemble results from weaker models, rollout to multiple customer management use cases is extremely efficient and saves costs by 70%, yet preserving model robustness and reduces overfitting.The forever complex and versatile nature of retail requires a highly adaptive model with a robust ability to incorporate complicated and changing business rules. Therefore this paradigm allows us to collect and append past heuristics easily, and allow rapid rollout to multiple channels and business units.
From a very young age, Yu-Xi has always been motivated by the thrill of solving problems. This has led him to earn a doctorate in Complexity Science and subsequently entering the industry to solve business problems with Artificial Intelligence. Since then, He has tackled a range of problems, ranging from Gaming, Election Engineering and Retail. Apart from delivering products to bring business value, he has also built up Data Science Teams, training, recruitment and setting best practices. He is currently heading the Data Science Team in A.S. Watson, the biggest Health and Beauty Retailer in the world.
PLENARY SESSION - both tracks in Diamond Ballroom I
Simon Lee - AXA
What does the future of AI look like?
Simon Lee is the Chief Data Scientist at AXA Hong Kong. He is also a Fellow in CAS and Associate in SOA. His current key responsibility is to enhance the adoption of predictive analytics at AXA. In his career, he managed multiple data sharing agreement among multiple institutions and led to triple digit uplift in financial KPIs. He published multiple papers on machine learnings in academic journals and received the best paper award. The application of the innovation leads to simultaneous improvement of loss ratio, new business acquisition and retention for a major insurer in North America.
Jay Feng - Jobr
Applying Deep Learning to Grow Revenue in the Jobs Business
To survive in the jobs space, Jobr had to innovate by implementing deep learning models into different parts of their system. By utilizing Fasttext and continuous bag of word models, Jobr could tune their relevancy ranking by recommending job titles that were in similar industry categories. Ensemble natural language processing methods were also helpful for creating chatbot AI systems that can direct user's to specific inquiries around job matching techniques. A combination of these applied techniques helped us reach metrics in mobile job search that no mobile app had ever reached before resulting in an acquisition by Monster.com.
Jay is the lead data scientist at Jobr, the largest mobile only job app in the U.S. Acquired by Monster Worldwide in 2016, Jobr is leading the field in recommendation systems and machine learning within job search. On the side Jay likes to look at applying machine learning to sports, housing, or urban datasets.
Anik Dey - EMOS Tech
Anik is the Head of Product at EMOS. He holds BEng and MPhil degrees in Electronic and Computer Engineering from HKUST and is focused on translating his extensive research in deep learning for speech and emotion recognition into creating immersive and engaging human-computer interactive systems. Anik has served as the project lead for Moodbox, the world's first smart speaker employing state-of-the-art emotionally intelligent design to provide a highly customized user experience, and Zara the Supergirl, an interactive dialog system for detecting user's emotion and personality from natural conversation, which debuted at the "Robots in Action" exhibition at the World Economic Forum in 2015.
Noah Silverman - Helios.ai
Noah Silverman holds a PhD in statistics from UCLA, and has worked with data and technology for over 20 years. The focus of Dr. Silverman’s work is on developing probabilistic models for complex and stochastic system. Dr. Silverman’s specialties include: Artificial Intelligence, Machine Learning, Hierarchical Bayesian Models, classifiers, support vector machines, and textual analysis. He has authored several papers on mathematical modeling of complex data systems with innovative analysis methods. In addition, he has developed successful solutions for a large number of clients in Finance, Digital Advertising, Sports Betting, Crypto-currency, Education, Medical, and other industries. Prior to consulting, he conceptualized and founded several technology firms including webclipping.com and trademarktracker.com. Dr. Silverman currently runs a consulting firm in Hong Kong, Helios.ai, and is also developing an institutional grade cryptocurrency exchange.
Simon Lee - AXA
What does the future of AI look like?
Simon Lee is the Chief Data Scientist at AXA Hong Kong. He is also a Fellow in CAS and Associate in SOA. His current key responsibility is to enhance the adoption of predictive analytics at AXA. In his career, he managed multiple data sharing agreement among multiple institutions and led to triple digit uplift in financial KPIs. He published multiple papers on machine learnings in academic journals and received the best paper award. The application of the innovation leads to simultaneous improvement of loss ratio, new business acquisition and retention for a major insurer in North America.
Nick White - Independent
Nick graduated with a BS and MS in Electrical Engineering from Stanford University where he focused in Signal Processing, Optimization and Control especially as they apply to the fields of Neural Networks and Artificial Intelligence. Upon graduation, he taught and researched artificial intelligence and applied mathematics at Stanford. Nick served at the AI Specialist for Hong Kong-based AI incubator Zeroth.AI where he coached teams from 5 continents across diverse industries including finance, agriculture, robotics and blockchain. Nick leverages his deep technical expertise and knowledge of the AI industry to identify business opportunities in emerging technologies and to guide investment decisions. Nick most recently branched into the blockchain space as a co-founder of Holon Partners, a Crypto-focused advisory and investment firm.
15:10
END OF SUMMIT
13:50
Matt O'Connor - Reboot.ai
Deep Reinforcement Learning For Strategic Customer Engagement
Matt O'Connor - Reboot.ai
Deep Reinforcement Learning For Strategic Customer Engagement
Chatbots are great for handing simple client inquiries and routine interactions, but more sophisticated customer engagement requires complex strategic decisions - how does a company convert a lead to a sale, reactivate a lapsing customer, or manage their brand in a PR crisis? This workshop will explore using Natural Language Processing on a real-world dataset of millions of customer interactions from companies such as Apply, Amazon, and Uber to assess the situation and understand the potential range of actions and responses, then use Deep Reinforcement Learning to automate a strategic focus at every step of customer engagement.
Matt is a former algorithmic trader, full stack developer, and PMI-ACP certified Agile leader with experience leading distributed teams and automating enterprise scale workflows. Dhruv & Matt are the co-founders of Reboot.ai, Hong Kong’s trusted machine learning and AI development and deployment specialists, with clients including multinational corporates. From Agile development of custom algorithmic solutions, to deployment on cloud services, to integration with legacy databases and reporting systems, to custom employee training, Reboot builds AI solutions for business initiatives.
13:50
Dhruv Sahi - Reboot.ai
Deep Reinforcement Learning For Strategic Customer Engagement
Dhruv Sahi - Reboot.ai
Deep Reinforcement Learning For Strategic Customer Engagement
Chatbots are great for handing simple client inquiries and routine interactions, but more sophisticated customer engagement requires complex strategic decisions - how does a company convert a lead to a sale, reactivate a lapsing customer, or manage their brand in a PR crisis? This workshop will explore using Natural Language Processing on a real-world dataset of millions of customer interactions from companies such as Apply, Amazon, and Uber to assess the situation and understand the potential range of actions and responses, then use Deep Reinforcement Learning to automate a strategic focus at every step of customer engagement.
Dhruv is a data scientist and machine learning engineer with experience transforming the analytics department for Grana, a Hong Kong retail startup. Dhruv & Matt are the co-founders of Reboot.ai, Hong Kong’s trusted machine learning and AI development and deployment specialists, with clients including multinational corporates. From Agile development of custom algorithmic solutions, to deployment on cloud services, to integration with legacy databases and reporting systems, to custom employee training, Reboot builds AI solutions for business initiatives.
15:00
Startup - VC Networking Session - BREAKOUT
Networking & Question Time with Leading VCs
Startup - VC Networking Session - BREAKOUT
An opportunity for startups attending the event to ask questions and pitch to world-leading VCs and investors in AI including Horizons Ventures, Vectr Ventures, Zeroth.ai and Radiant Ventures. View the list of VCs attending here.