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Andrea Pasqua is a data science manager at Uber, where he leads the time series forecasting and anomaly detection teams. Previously, Andrea was director of data science at Radius Intelligence, a company spearheading the use of machine learning in the marketing space; a financial analyst at MSCI, a leading company in the field of risk analysis; and a postdoctoral fellow in biophysics at UC Berkeley. He holds a PhD in physics from UC Berkeley.

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Camille Eddy began working on advanced robotics during an internship at HP Inc’s headquarters in Palo Alto, California as a Machine Learning Intern. She continued to intern at X, formerly Google X, in their robotics initiative and also began working with NVIDIA on their autonomous car project. She is currently studying for a Bachelor's degree in Mechanical Engineering. Camille speaks nationally on inclusion in the tech community and regularly writes on the topic. She also enjoys volunteering for STEM advocacy organizations for young girls and students.

It is essential to consider how our products are designed. We will dive deep into several use cases of how the tech industry uses data sets and how often the same data set may be across the tech industry causing bias issues to propagate. Another important aspect of the design is our bias blindspots as human beings and the concept of levers or tools to unveil this bias and create meaningful suggestions on how to seek out clarity and inclusion. Enter explainable AI or fairness AI tools that produce suggestions for analyzing projects. The audience members who will benefit the most from this talk are those with some familiarity with AI tools and general knowledge of common data sets in use today. 

Key takeaways: 
- How AI has been used in personal data
- How data sets for popular applications have been created
- How often data sets are used across disciplines 
- Todays out of the box applications by Google and Microsoft
- Authors and grassroots organizations 
   

 

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Carl Osipov is a staff program manager focused on helping Google’s customers and business partners get trained and certified to run machine learning and data analytics workloads on Google Cloud. Carl has more than 17 years of experience in the IT industry and has held leadership roles for programs and projects in the areas of big data, cloud computing, service-oriented architecture, machine learning, and computational natural language processing at some of the world’s leading technology companies across the United States and Europe. Carl has written over 20 articles for professional, trade, and academic journals and holds six patents from the USPTO. He has received three corporate awards from IBM for his innovative work. You can find out more about Carl on his blog.

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Cibele is a  Senior Machine Learning Engineer at Twitter Cortex, where she helps to build Twitter’s deep learning platform. Prior to working at Twitter, Cibele worked at Apple as a Data Scientist and Systems Design Engineer; and at Analog Devices as Product Applications Engineer . At Analog Devices, she worked on building machine learning algorithms that use smartphone sensors to understand a person’s behavior. Cibele obtained her B.S. from Stanford University in Electrical Engineering and Physics and her M.S. from the California Institute of Technology in Electrical Engineering with an emphasis in Computer Vision and Machine Learning.

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Gergely Debreczeni graduated as physicist, and holds a PhD degree in particle physics. He has spent 15+ years in participating and leading fundamental particle and gravitational physics research. As the Computing Coordinator of the LIGO – Virgo experiment he took part in the discovery of the Gravitational Waves. Specialized in measurement data processing, high-performance computing and AI technologies. He joined the  Kishonti company at 2013 and was one of the first member of AImoitve established in 2015. In its early days he conducted the Computer Vision Research Group and now acts as the Chief Scientist of the company.

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Gergely is the professional leader of the AI Coalition of Hungary that has the goal to create the AI strategy for the Hungarian government. He is also teaching Artificial intelligence for business professionals at the Frankfurt School of Finance and Management. Originally an economist, Gergely founded a startup that was developing artificial intelligence and later a consultancy called AI Partners, who is focusing on helping corporations to live with the opportunities that AI brings to businesses.

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Ignacio Más acts as Senior Expert and Head of Technology Innovation in OSS in Ericsson Business Unit Digital Systems. Previously he has been one of the top System Architects in Ericsson Group Function Technology holding the tittle of Senior Expert in Programmable Network Architecture. He obtained a PhD in Telecommunications from the Royal Institute of Technology, (KTH, Sweden) and a Master of Science from both KTH and the Telecommunications Engineering school of 'Universidad Politécnica de Madrid'. Ignacio joined Ericsson in 2005 and started working in IETF standardization, IPTV and messaging architectures and media related activities inside Ericsson Research. When he joined Group Function Technology he became the CTO office expert in media, SDN and cloud networking related issues. In his role as Senior Expert in Ericsson he now leads the company in the evolution to a network architecture focused on flexibility and programmability with emphasis on SDN and NFV technical paradigm implications. In his current position he as well leads the overall evolution of network management and control (OSS) towards a model-based horizontal management and orchestration paradigm.

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Jaeseong Jeong is a senior researcher at Ericsson Research, Machine Learning team. His research interests include reinforcement learning, large-scale machine learning, telecom data analytic. He received the Ph.D. degree from Korea Advanced Institute of Science and Technology (KAIST) in 2014. Prior to joining Ericsson, he was with Automatic Control Department, KTH Royal Institute of Technology, Sweden as a postdoctoral researcher. 

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Josh Tobin is a Research Scientist at OpenAI and a PhD student in Computer Science at UC Berkeley working with Professor Pieter Abbeel. Josh's research focuses on applying deep learning to problems in robotic perception and control, with a particular concentration on deep reinforcement learning  generative models, and synthetic data. Prior to Berkeley and OpenAI, Josh was a consultant at McKinsey & Co. in New York. Josh has a BA in Mathematics from Columbia University. 

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Karoly Barnabas Bozo is a Technical Evangelist in Microsoft’s One Commercial Partner organization. Part of Karoly’s responsibility is to ensure that different Artificial Intelligence and Machine learning workloads run on Azure cloud. This role involves keeping demonstrations and workshops in the fields of Microsoft’s Data and ML solutions. Karoly is a true Data enthusiast. He started his career 12 years ago with SPSS and Cross table analysis. Then he spent several years in building solutions on relational databases. He was previously a Technical Solution Professional within Microsoft responsible for Data and BI. He also participated in different Big Data and Machine Learning projects at Morgan Stanley and at Lexmark.

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Dr. Varshney is a principal research staff member and manager with IBM Research AI at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the Learning and Decision Making group. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of statistical signal processing and machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015

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Leonid Kuligin is currently a ML cloud engineer at Google Cloud Professional Services Organization. He has more than 10 years of product engineering experience in leading Russian and German tech companies such as HeadHunter Group, Yandex and others. His focus is complicated backends for b2c products based on machine learning and distributed data processing.

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Mark started his career in Microsoft's student program four years ago as a technology loving geek which hasn't changed a lot since then. Now he helps Independent Software Vendors and System Integrators use Microsoft's cutting-edge services to create industry-leading solutions. Moreover he is working with HoloLens, the world's first holographic computer to help Hungary's largest companies redefine their daily workloads to harness to power of Mixed Reality. He's used to talk on conferences about future technologies - like AI, Mixed Reality and Quantum Computing - and their impact on our everyday lives in the next decades.

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Dr Paris Buttfield-Addison is co-founder of Secret Lab a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was mobile product manager for Meebo, a ground-breaking 'Web 2.0' startup which was acquired by Google. Paris particularly enjoys game design, statistics, the blockchain, machine learning, and human-centered technology research and writes technical books on mobile and game development (more than 20 so far) for O’Reilly Media. He is currently writing 'Practical AI with Swift', 'Head First Swift', and the 'Unity Game Development Cookbook'. He holds a degree in medieval history and a PhD in computing.

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Patrick van der Smagt is director of the open-source Volkswagen Group Machine Learning Research Lab in Munich, focussing on probabilistic deep learning for time series modelling, optimal control, and quantum machine learning. He previously directed a lab as professor for machine learning and biomimetic robotics at the Technical University of Munich while leading the machine learning group at the research institute fortiss, and was the head of bionics and assistive robotics at the DLR Oberpfaffenhofen. Long before that, he did his PhD and MSc at Amsterdam’s universities. Besides publishing numerous papers and patents on machine learning, robotics, and motor control, he has won various awards, including the 2013 Helmholtz-Association Erwin Schrödinger Award, the 2014 King-Sun Fu Memorial Award, the 2013 Harvard Medical School/MGH Martin Research Prize, and various best-paper awards. He is founding chairman of a non-for-profit organisation for Assistive Robotics for tetraplegics and co-founder of various companies.

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Stefan is a machine learner, data scientist, ex-roboticist, and consultant focusing on deep learning and computer vision.

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Zoltan is Principal Spark Instructor at Databricks. He's been delivering Databricks and Spark classes for over 3 years and he is one of the contributors to Databricks' Machine Learning curriculum. Besides working with Databricks, he is CTO of datapao.com and a professor at the Central European University. Earlier he built and later lead the team that managed a petabyte-scale data infrastructure at Prezi.com. Before joining Databricks he worked on RapidMiner's Spark integration projects.

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