About speaker:
Andy has 25 years experience in software development. He built his first Artificial Neural Network in 1986, worked on various IT projects related to big data and data analytics, from 2016 switched on data science projects and set up a company MindCraft fully dedicated to creating data-driven solutions to bring new ideas from data insights and reduce staff costs / Андрій має 25-річний досвід розробки програмного забезпечення. у 1986 році побудував свою першу штучну нейронну мережу, працював над різними ІТ-проектами, пов'язаними з великими даними та аналітикою даних, з 2016 року перейшов на проекти з Data Science і створив компанію MindCraft, що проектує системи прийняття рішень на основі даних.

Presentation topic:
Data Imputation using Reverse ML

About speaker:
Founded SAVVI AI to make AI/ML work for all of us.

Previously, cofounder/CEO GPShopper, acquired by Synchrony (NYSE:SYF) in 2017. While at Synchrony, was the SVP Entrepreneur in Residence (EIR) and CPO Fintech/AI. Served on SYF's Investment Committee where I helped the SYF Ventures team evaluate prospective venture investments. Also, served as Synchrony's CPO on emerging D2C Fintech / AI products focused on mobile and machine learning.

Current interests are innovative ML/AI based products, tools, and companies and supporting fellow diverse founders refine their operations, technology, visions, and strategy as they go through their entrepreneurial journey.
Named by Forbes as a TOP TEN CEO disrupting the retail industry through technology.

About speaker:
Alex holds a Ph.D. and has over ten years of academic and industry experience in applied data science and machine learning in chemistry, biotechnology, and physics.

About speaker:
Margarita Sordo is a Medical Informatics researcher and Instructor of Medicine at the Brigham & Women's Hospital, Harvard Medical School.

She holds a MSc in Artificial Intelligence from the University of Edinburgh, UK, and a PhD in Computer Science and Artificial Intelligence from the University of Sussex, UK. She is an elected Fellow of the American Medical Informatics Association. She is also an appointed representative of Mexico to the OECD Global Partnership on Artificial Intelligence (GPAI). In this capacity, she serves as member of the steering committee on Artificial Intelligence for Pandemic Response working group, member of the AI for Drug Discovery working group, and member of the Responsible use of AI group.

Her research interests include the definition and application of extraction, representation, gobernance and responsible of use of data and knowledge in medicine and healthcare; development and application of ontologies, artificial intelligence and machine learning for clinical decision support and knowledge discovery. Additionally, she works on developing complex adaptive systems to model patient characteristics and preferences to evaluate the potential impact determinants of health and healthcare policies on patient's health and well-being.

Previous work includes design and development of knowledge-based systems to support the integration of healthcare applications within the enterprise. Dr. Sordo developed GELLO, an expression language for clinical decision support, which was approved as an international standard by Health Level 7 (HL7), and the American National Standards Institute (ANSI) in 2005. She has also worked on developing AI techniques for Postmarketing Surveillance of medications at a national level. She was the architect and principal implementer of eRecommendations for clinical decision support at a national level, sponsored by the Agency for Healthcare Research and Quality (AHRQ) (2010-2011), to evaluate consistently the impact of preventive diagnostic procedures on patient's health.

About speaker:
Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA, before becoming first an analyst and then a product manager at a big data platform named Qubole. Since then, he has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, and automated machine learning. When Danny's not researching, practicing, or talking about data science, he's usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.

About speaker:
Kashyap has 20 years of experience as an industry analyst, P&L leader, management consultant, and advisor to leading companies and start-ups across US, Europe and Asia-Pac.

Venture Capital / Start-up Advisory:
• Advisor to Digital Alpha (NYC);
• Deep Analysis (Boston);
• Gemba Capital (India).

Books / Writing / Media:
• Author of the bestseller "Practical Artificial Intelligence";
• Columnist: Business Line, Info Today (US), Speech Tech (US); Contributor: Forbes.com;
• Quoted/Published in BBC, Quartz, Economist, ET, Mint, Outlook, Eenadu, TechTarget, etc.

Speaking / Teaching:
• Speaker at AI World Government (Washington DC), Digital Leadership (Denmark), AI World (Boston), AIIM (San Antonio), TEDx, b-schools, colleges, IEEE, and corporate events;
• Creator of online courses on AI, Blockchain, Cloud, Automation, and Emerging Technologies;
• Visiting Faculty, Institute of Directors; Guest Lecturer – IIM-B.

Awards / Recognition:
• Top 10 Global Thought Leader on Artificial Intelligence (Thinkers 360);
• 10 most innovative global AI executives (Analytics Insight);
• Top 10 Global Thought Leader on Digital Transformation (Thinkers 360).

About speaker:
Paige Roberts (@RobertsPaige) has worked as an engineer, trainer, support technician, technical writer, marketer, product manager, and a consultant in the last 24 years. She has built data engineering pipelines and architectures, documented and tested open source analytics implementations, spun up Hadoop clusters, picked the brains of stars in data analytics, worked with different industries, and questioned a lot of assumptions. She's worked for companies like Pervasive, the Bloor Group, Actian, Hortonworks, Syncsort, and Vertica. Now, she promotes understanding of Vertica, distributed data processing, open source, high scale data engineering architecture, and how the analytics revolution is changing the world.

About speaker:
Olivier is the Head of Decision Science at Moov AI, a Canadian company bringing the power of artificial intelligence and machine learning to businesses. He is a data science expert whose field-leading expertise and cutting-edge knowledge of AI and machine learning has led him to support many companies' digital transformations, as well as implementing projects across a variety of different industries.

He is a mentor for Creative Destruction Labs' Artificial Intelligence Stream and helps start-ups succeed in AI. He leads the Moov AI data team and has assisted a range of leading organizations put in place a data culture – such as Pratt & Whitney Canada, L'Oréal and GSoft.

Olivier is the laureate of the prestigious "30 under 30" prize (2019). He is also co-author of a patent for an advanced algorithm that evaluates the credit worthiness of a borrower.

He is also an international expert representing Canada on the ISO Standards for AI systems as well as Editor of the "ISO/IEC TS 5471 – Quality evaluation guidelines for AI Systems" technical specifications.

Presentation topic:
Tips and Fail From AI Projects Delivered to Customers

More about presentation:
Did you know that only 15% of all AI projects developed today are actually used? Developing a functional and useful AI solution is indeed a challenging task.

From real examples of projects deployed in production and learnings from the field, Olivier illustrates the current portrait of AI in business and proposes a new perspective. You'll discover the best practices and the proper steps for adopting AI in your company.

About speaker:
Computer Vision

Presentation topic:
Cascade models in Computer Vision

More about presentation:
Get less false positives in computer vision tasks, like detection, for real time systems (high performance).

About speaker:
ABB Spa and DMG Spa supplier.

About speaker:
As a creative and driven analytics practitioner, I am able to draw on both my business and technical background to add value from strategy to ideation to implementation to sustainment. As a collaborative and non-hierarchical leader, I am passionate about mentorship and building high functioning and engaged teams.

I am a thought leader in the development of AI Centres of Excellence and have established new functions in many major Canadian and international organizations. These experiences inspired my book, Minding the Machines: Building and Leading Data Science and Analytics Teams (Wiley & Sons, 2021). I have presented to tens of thousands of global business leaders and practitioners across several functions and industries through my teaching, speaking, and consulting.

Presentation topic:
Minding the Machines. Building and Leading Data Science and Analytics Teams

About speaker:
I am an Msc Data Science graduate with more than two years of industry and academy-based experience in Data science.

Presentation topic:
Optimization of a Supply Chain by applying targeted Bayesian network learning (TBNL) to a stock allocation model

More about presentation:
Have you ever wondered, how companies can manage their products and tools supplies? For instance, how does the airline company makes sure to keep field stoking location updated with the necessary repairable parts for planes?

The presentation includes: a) formulation of a stock allocation model and this model extension for local repair facility with the central depot support, and b) application of the targeted Bayesian network learning (TBNL) to extract structural insights of how to plan, manage and operate a supply chain cycle in the proposed ecosystem.

About speaker:
Theresa Melvin is a successful AI strategist, futurist and chief data scientist, who develops real-time, data-driven and operationalized AI/ML pipeline solutions predicated on an economical amalgamation of open-source and commercial technologies, as well as hybrid cloud initiatives.

Presentation topic:
The Future of Data Science: Quantum Machine Learning and Legal AI Systems

About speaker:
Aaron Michel's last company, PathSource, was acquired in March 2017. He's a partner with 1984 Ventures and is focused on seed stage investments. Seed investor in Reggora, Fairmarkit, Signos, Kyte, Nickson, Postscript, Deepscribe, ShelfEngine and others. Selected for Venture Capital Journal's 40 Under 40.

Presentation Topic:
Signal & Noise: Evaluating Career Choices In Data Science

More about presentation:
Founder turned VC Aaron Michel dives into the hard questions of what to do with your data science knowledge. Should you start a company? Join one? How do you choose between opportunities? Aaron will share multiple lenses to help you evaluate what should come next in your career path.