Mountain View, CaliforniaSilicon Valley Innovation Expert
Japan Innovation Consultant
Limited Partner and Supermentor at 500 Startups (VC), General Partner at Phase4 LLC (VC), Chief Technology Officer (CTO) and Chief Product Officer (CPO)
James is founder of The CxO Lab, a Silicon Valley-based consultancy which helps global companies participate in the “digital innovation stack” (digital technology / R&D, startups, startup ecosystems, incubators and accelerators, partnerships, investment, and M&A).
He spent 30 years as an engineer, engineering leader, and executive at companies including Xerox Parc, Cisco, AOL, Netscape, HP, IBM, and more. He is an active mentor in the startup community, mentoring hundreds of companies..
The CxO Lab specializes in creating successful connection between Japanese companies and Silicon Valley. James is experienced with the particular challenges that Japanese companies face when working with Silicon Valley, or when using Silicon Valley methods.
Using our expertise and network of experts, we are your guide and portfolio of resources at your side as you move forward in your innovation planning and execution. We offer advising, coaching, training, hands-on projects, Silicon Valley information, analysis, and direct connection across the all options in the Digital Innovation Stack.
“The talent, intelligence, and good intentions on both sides of the Pacific are a powerful combination. However there are misalignments that exist and must be identified and addressed for successful collaborations.
For the past decade we have been working successfully in Japan, helping companies bridge the divide, incorporate the best elements of Silicon Valley style and techniques, and participate in meaningful and productive business activities in Silicon Valley.”
For more than 10 years we’ve successfully collaborated with Japanese Companies, including Recruit Holdings, DOTS Shibuya, Docomo Innovation Village, Mistletoe, B Dash Ventures, Kashiwa-no-ha Smart City / Mitsui Fudosan, Digital Garage, and others. Our projects included:
Projects: Incubation program Assessment – Strategy – Restructuring – ongoing Advising and Mentorship
Media Technology Labs was Recruit’s internal business creation and incubation group. It ran New Ring, a monthly idea pitch competition, incubates new businesses, and spins off new lines of business.
Recruit had built the program in an attempt to copy SIlicon-Valley style, however after several years it was still failing to produce consistent results. CxO Lab was retained to find the core problems and help rehabilitate the program.
Santa Clara, California Leadership & Communication Development Expert
With a career spanning 20+ years, Yoon has worked in companies such as Sun Microsystems, PayPal, Rosetta Stone, Visa, Symphony, and Veritas doing the full gamut of project, program, and agile software management. His background in agile methodology, program management, and behavioral development have made him a valued advisor to many leaders at his companies. His leadership has often acted as a multiplier effect in company transformations and was part of the executive team that led Symphony Communication to unicorn status! As an experienced coach and mentor for leaders in organizations, Yoon consults in up-leveling individuals and teams using methods in Design-thinking, Lean principles, and Agile methodologies to bring out the best in organizations. He is a master storyteller, certified Gallup StrengthsFinder Coach, and certified MBTI Practitioner. He is an active speaker and instructor in the areas of influence, power, and organizational behavior development. Yoon currently leads program management and operations for the Customer Experience & Design Organization at Veritas.
Palo Alto, California ML Engineer, ML Expert
Ken’s main interests are machine learning (ML) and natural language understanding. He has 15 years of combined experience in graduate school and industry, having obtained a M.Sc. degree from the University of Alberta (Canada), and nearly completing a Ph.D. before deciding to leave academia to join an early-stage startup company. Ken has since worked for several different startups, including Solvvy, AppZen, and most recently Intellipse. He enjoys the challenge of building AI-driven applications that can understand human language and make predictions that measurably impact business goals.
About Ken’s Current Job
Intellipse’s vision is to revolutionize the way companies communicate with users who visit their website. Ken is the lead ML engineer responsible for creating algorithms that (a) understand user behavior, and (b) recommend actions, such as displaying an informative message, in order to help the user achieve his/her intent. Our system learns from hundreds of thousands of historical user sessions, containing millions of events, and automatically finds patterns of behavior that are correlated with the company’s key performance metrics. The learned model is then employed in real-time to classify users as they browse the website, so that the most useful actions are recommended for each individual.
Question answering is an active area of research in ML and natural language understanding. The goal is to return the correct answer to a question submitted by a user. For example, given the question “Which country is also a continent?”, the correct answer is “Australia”. Open-domain systems attempt to answer questions on any topic. A famous example is the IBM Watson system that defeated two human champions on the trivia game show “Jeopardy”.
In business applications, a question answering system is typically closed-domain, which means that the system is only expected to answer questions related to a particular company’s product or service. I worked on a closed-domain system at Solvvy that was trained to answer customer support questions for our clients. These ranged from common questions such as “What is your return policy?”, to more specific questions such as “The app is not syncing with my smart watch. How do I fix it?”.
In this presentation, I will review some fundamental ML concepts, then explain some common ways of representing text data for ML, namely, TF-IDF and Word2Vec vectors. I will then illustrate how to train a simple question answering system using two different machine learning algorithms, including a neural network (deep learning) approach. I will also discuss the concept of transfer learning, which involves training on other data sources in order to improve predictive performance.
Palo Alto, California AI Engineer, AI Expert
Babak is an entrepreneur and computer technologist with main interests in software platforms for Big Data, Artificial Intelligence (AI) and Machine Learning (ML). He has 10 years of combined experience in graduate school and industry, having a PhD in Computer Science from the University of Illinois at Urbana-Champaign (UIUC). before deciding to leave academia to join an early-stage startup company. During his PhD, Babak won Kenichi Miura Award in 2014, which honors graduate students for outstanding accomplishments in High-Performance Computing. After graduation, Babak joined Altiscale, a startup in Silicon Valley working on offering big data services in the cloud, where he led the performance engineering team. Altiscale got acquired by SAP in September of 2016. He then joined Solvvy, a machine learning startup reinventing the customer experience using intelligent Natural Language Processing solutions. In May 2018, Babak helped founding a new startup, Intellipse. He enjoys the challenge of building infrastructure for AI and Big Data applications that can solve real world problems.
About Babak’s Current Job
Intellipse’s vision is to revolutionize the way companies communicate with their users. Babak is leading the whole engineering team responsible for infrastructure, platform, and machine learning. Our system learns from hundreds of thousands of historical user sessions, containing millions of events, and automatically finds patterns of behavior that are correlated with the company’s key performance metrics. The learned model is then employed in real-time to classify users as they browse the website, so that the most useful actions are recommended for each individual.
AI’s potential to change a business is limitless. According to a study at Stanford University, AI market size is projected to reach $60 billion by 2025. However, building AI-enabled software is hard as they involve several components ranging from training data collection to real-time model serving requiring different skills from statistics and data science to API and full stack development.
Thanks to the Kubernetes project, the open-source platform automating the deployment and management of containerized applications, AI and machine learning software can benefit significantly from the scalability and abstractions it provides. AI algorithms must be able to scale to be optimally effective and deep learning algorithms and datasets require a large amount of compute and storage, Kubernetes helps because it is all about scaling based on demand. It also provides a way to deploy AI-enabled workloads over multiple commodity servers across the software pipeline while abstracting away the management overhead. Once the models are trained, serving them in various deployment scenarios, from edge compute to central data centers, poses challenges to non-containerized forms of applications. Again, Kubernetes can provide the necessary flexibility for a distributed rollout of inference agents on a variety of platforms.
Parallel I/O is an essential component of modern High Performance Computing (HPC). Obtaining good I/O performance for a broad range of applications on diverse HPC platforms is a major challenge, in part because of complex inter-dependencies between I/O middleware and hardware. The parallel file system and I/O middleware layers all offer optimization parameters that can, in theory, result in better I/O performance.
Unfortunately, the right combination of parameters is highly dependent on the application, HPC platform, and problem size/concurrency. Scientific application developers do not have the time or expertise to take on the substantial burden of identifying good parameters for each problem configuration. They resort to using system defaults, a choice that frequently results in poor I/O performance. We expect this problem to be compounded on exascale class machines, which will likely have a deeper software stack with hierarchically arranged hardware resources. We present a line of solution to this problem containing an autotuning system for optimizing I/O performance, I/O performance modeling, I/O tuning, I/O kernel generation, and I/O patterns. We demonstrate the value of these solution across platforms, applications, and at scale.
Moraga, CaliforniaAcademic Director of the China EMBA Program and Chevron Professor of Finance at Saint Mary’s College of California.
Tina completed a Bachelor of Arts in Finance at Nanjing University before later completing a Ph.D. in finance from Temple University. Her expertise in finance has led to her appointments as Chevron Professor of Finance and Academic Director of the China EMBA Program at Saint Mary’s College of California.
Tina’s financial expertise covers investment, economics, financial analytics, and financial management among many other areas. The competitive nature of the modern marketplace makes finance an essential component of success. Tina understands the immediate and longer-term effects of finance on business and the impact finance can have on a corporation both at home and abroad. Tina’s presentation provides listeners with a deeper understanding of financial trends illustrated by practical examples. Through her presentation and workshop, you will have the knowledge and skills you need to successfully grow your business.
Glasgow, Scotland Leadership and Organizational Behavior
Elem is an alumnus of the Ohio Dominican University from which she graduated with a Master of Business Administration (MBA). Currently, she is studying in Europe where she is working to complete a Ph.D. in management and digital innovations. As an MBA student, Elem carried out a range of case studies and research projects investigating the behavior of individuals and groups within organizations. Of her many talents, Elem has especially strong communication skills. She communicates effectively with people at all levels and is adept at explaining complex ideas in a straightforward, coherent way that everyone can understand.
Elem’s presentation challenges people to enhance their leadership skills and further their understanding of human behavior in an organizational context. She achieves this by offering engaging insights into a diverse range of topics, including organizational structure, systems and culture, human resource and career management, interpersonal and individual behavior, group and team behavior, diversity and leading organizational change.