Modar is a tech entrepreneur and expert in Artificially Intelligent vision technologies, Deep Learning and Ambient Intelligence. He is currently founder and CEO at Eyeris, the worldwide leading Deep Learning-based emotion recognition software. The company’s flagship product EmoVu reads facial micro-expressions in real-time and uses Convolutional Neural Networks (CNN's) as a Deep Learning architecture to train and deploy its algorithm into a myriad of today’s commercial applications. Modar combines a decade of experience between Human Machine Interaction (HMI) and Audience Behavior Measurement. He is a frequent speaker and keynoter on “Ambient Intelligence” as the next frontier in AI, a winner of several technology and innovation awards and has been featured in the Wall Street Journal, the Huffington Post, Bloomberg and many other major publications for his work.

Gil serves as the head of Lyft's Machine Learning Platform. Previously he was co-founder of Octarine, a security startup, and VP Product of Reflektion, an e-commerce personalization company, and AppDirect, the largest B2B app marketplace. Gil also spent a few years in product positions at Google in the Ads group, where he helped integrate YouTube and DoubleClick after their acquisition.

Melanie Beck is a senior data scientist at Seagate Technology in Bloomington, MN. After earning her Ph.D. in astrophysics from the University of Minnesota, Melanie leveraged her research skills in deep learning to solve image classification and time series forecasting challenges. At Seagate, Melanie develops end to end deep learning projects for a variety of problems from identifying product defects to predicting faults in production equipment.

Dr. Chokor has developed and integrated emerging IIoT data-driven solutions in multiple industries. As a staff data scientist at Seagate Technology, Abbas unlocks the value of industrial and manufacturing data through leading edge science and mentors uprising data scientists. Before Seagate, Abbas was a lead data scientist on multiple predictive maintenance projects within First Solar, Siemens, and Schlumberger, for their complex assets, including solar panels, trains, and Oil & Gas surface machines. Abbas holds a Ph.D. from Arizona State University in Tempe, Arizona and has co-authored dozens of research publications. His research interests include developing state-of-the-art edge solutions in the field of predictive maintenance and online learning.

Pranjal helped set up Cisco Innovation Labs after dropping out of his PhD and handles Machine Learning/Product there. He also serves as an Entrepreneur-in-Residence at Vonzos Partners. Previously he conducted ML research at Adobe Research, IBM Research, University of Alberta, Purdue University and Northwestern University. He attended Stanford GSB in the Ignite program and is an On Deck Fellow.

Matthew Dixon is an Assistant Professor of Finance and Statistics at the Illinois Institute of Technology. His research in computational methods for finance is funded by Intel. Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modeling. He holds a Ph.D. in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis respectively. He has published over 20 peer reviewed publications on machine learning and financial modeling, has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert, and is a frequently invited speaker in Silicon Valley and on Wall Street. He has published R packages, served as a Google Summer of Code mentor and is the co-founder of the Thalesians.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, and Washington DC. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia.

Rezsa is currently the Director of Machine Learning at Vevo. He is a Machine Learning leader and innovator with a background in computational engineering, data science, and AI. His ML experience spans across diverse applications, from developing deep learning recommender systems in the entertainment industry to oil well blowout risk prediction in the oil & gas industry.

Dr. Garberson develops machine learning algorithms to screen for childhood cognitive conditions such as autism at Cognoa. This development work ranges from analyses to identify and compensate for defects in data that will be used in machine learning to development and optimization of the algorithms to analyzing clinical study results to validate the performance of the algorithms in real life. 

Prior to joining Cognoa, Dr. Garberson spent two years developing machine learning algorithms for energy and consumer comfort optimization in smart thermostats at a startup called EcoFactor. Before that he ran analyses on petabytes of data collected at particle colliders, including the Large Hadron Collider in Switzerland, to search for statistically significant evidence of new fundamental particles and to perform more precise measurements of the properties of existing particles. 

Luba Gloukhova leads and executes advanced machine learning projects for high tech firms and major research universities in Silicon Valley. She also preaches what she practices, serving as the founding chair of Deep Learning World – the premier conference covering the commercial deployment of deep learning – and delivering highly-rated talks at many other events as well. Luba previously supported Stanford faculty as an internal consultant at the university's Graduate School of Business, conceiving and generating innovative solutions to accelerate research.

Before that, Luba gained industry experience in high frequency trading analysis, catastrophe risk modeling, and marketing analytics. She received her master’s in analytics from the University of San Francisco and two bachelors degrees from Berkeley: applied mathematics and economics. Luba also teaches yoga and enjoys an active lifestyle.

Kate Highnam has a background in Computer Science and Business, focusing on security, embedded devices, and accounting. At the University of Virginia, her thesis was a published industrial research paper containing an attack scenario and repair algorithm for drones deployed on missions with limited ground control contact. After joining Capital One as a Data Engineer, Kate has developed features within an internal DevOps Pipeline and Data Lake governance system. Currently, she builds machine learning models to assist cybersecurity experts and enhance defenses.

Dr. Kelly is currently a Wolfram Technology Consultant, specifically consulting with Wolfram Finance Platform. He has given innumerable seminars on the use of Mathematica in finance. He has used Mathematica and Microsoft Excel to set up financial databases, construct mathematical and statistical models of trading data, write code to simulate newly devised trading strategies, write interface code to communicate with the trading platform to institute automated trading, analyze the PNL of trades, write research reports, evaluate options and futures prices, and implement trading strategies. 

Dr. Kelly was a senior lecturer in mathematics in the School of Quantitative Methods and Mathematical Sciences at the University of Western Sydney (UWS), transferring in 2000 to the position of senior lecturer in finance in the School of Economics and Finance. Dr Kelly has since moved to Chicago, and until 2007 was associate professor of finance at the Stuart Graduate School of Business, Illinois Institute of Technology. In this capacity he taught two streams of courses: financial modeling and computational finance, using Mathematica, Matlab and Excel for master’s and PhD students. Following his teaching career, Dr. Kelly was employed as the research director for DB Trading and Unetich Trading LLC at the Chicago Board of Trade, as well as financial consultant to a number of investment firms and trading companies.

Chris Labbe is a 20+ year veteran of the Hard Drive design industry with jobs in many areas throughout the company and is currently the Managing Technologist of Seagate's Data Science Analytics Development team.

He is now on a grand adventure to help Seagate build a world-class Predictive Analytics organization utilizing a strong background in programming, mathematics, statistics and organizational strategic leadership.

Ricky Loynd is a member of the reinforcement learning research group in the Microsoft Research AI labs in Redmond, Wash. He has 25 years of experience with neural networks, and 11 years of experience in reinforcement learning. Ricky is currently focused on creating deep RL agents with more general, human-like intelligence. As a lead mentor for the Microsoft AI School Advanced Projects course, Ricky has helped teams throughout the company incorporate deep learning systems into their work.

James McCaffrey, who annually leads the full-day deep learning workshop at Machine Learning Week, works for Microsoft Research in Redmond, Wash. James has a PhD in cognitive psychology and computational statistics from the University of Southern California, a BA in psychology, a BA in applied mathematics, and an MS in computer science. James learned to speak to the public while working at Disneyland as a college student, and he can still recite the entire Jungle Cruise ride narration from memory.

Marek Pietrzyk is a Senior Data Engineer at Northwestern Mutual.  Throughout his career, he has held a number of academic and professional positions.  Areas of his experience include market research, survey research, factory automation, software engineering, data warehousing, marketing, business intelligence, analytics, management, and systems engineering.  Marek received his Ph.D. in Mathematics from Wrocław University of Science and Technology in Wrocław, Poland.

Dr. Nalini Polavarapu has been with Monsanto for over ten years, and manages a global team of analytical and IT professionals, specialized in machine learning, operations research and cloud analytics to deliver better products to market faster through data science. Data science activities range from inventing cognitive systems to enhance decision making, predictive and prescriptive analytics to automate/augment decisions, geo-spatial and image analytics to realize precision farming, and analytics to increase operational efficiency.

Nalini also serves as the senior leader on the Data Science Center of Excellence Council partnering with other senior leaders enterprise wide to drive the efforts to realize the vision of transforming into a digital company through data science. Activities include implementing an enterprise wide talent and community development strategy, scalable technology platforms, best practices for development and deployment of analytical solutions and portfolio management.

Over the course of her career, Nalini held positions of increasing levels of leadership responsibilities managing teams operating across geographies in Americas, EMEA, Asia and Africa, and has successfully established and led an off-shore center in India. She also initiated multiple external partnerships and collaborations with start-ups, large corporations and universities. Being the first data scientist at Monsanto, Nalini played a key role in building a world-class global data science organization which is now at 100+ and growing through hiring and development of data scientists, data science leaders, as well as positions of seniority within the groups.

Nalini is the recipient of highest awards in technology for four consecutive years and a Fellow.

Nalini has a PhD and dual Masters in Computer Science and Bioinformatics from Georgia Institute of Technology in Atlanta, Georgia. Nalini has authored and co-authored several analytical patents, research articles in leading scientific journals and co-authored book chapters on high throughput data analysis and applications.

Domenic Puzio is a Machine Learning Engineer with Koto, the national security division of Kensho Technologies. He graduated from the University of Virginia with degrees in Mathematics and Computer Science. He spends his time building and productionizing machine learning models that use natural language processing and deep learning for problems ranging from document classification to the detection of malware and phishing. He is a contributor to two Apache projects.

David Russell is an analytics professional who currently holds responsibility for running DataRobot's Sales & Operations across the Western US, Canada, and LATAM, markets. David has over 18 year experience focused on successful enterprise deployments in big data, predictive analytics, business intelligence, and high performance data-warehousing solutions. David's core focus is on assisting customers in growing top-line revenue and/or decreasing operations overhead by leveraging data as a strategic asset. Currently, David is helping forward thinking organizations leverage DataRobot's modern, disruptive machine learning platform to create competitive advantages across a wide spectrum of data-drive use cases.

Nitin is a Senior Research Scientist at the AI research group in PayPal Risk Sciences, where he focuses on end-to-end design, development and deployment of AI algorithms for large-scale real-time payments fraud detection. His research involves the next generation of fraud detection capabilities by designing novel fraud problem formulations, utilizing the exhaustive PayPal data assets so as to improve fraud detection accuracy while continuing to enhance the experience of good users. Prior to his current role, he built large-scale machine learning frameworks for stolen identity and stolen financial instruments fraud detection at PayPal, following several years of research & teaching experience in machine learning and mathematical optimization.

Mike serves as Head of Data Science at Uber ATG, UC Berkeley Data Science faculty, and head of Skymind Labs the Machine Learning research lab affiliated with DeepLearning4J.  He has led teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust, Director of Data Sciences for MetaScale/Sears, and CSO for Galvanize where he founded the galvanizeU-UNH accredited Masters of Science in Data Science degree and oversaw the company's transformation from co-working space to Data Science organization.  Mike began his career in academia serving as a mathematics teaching fellow for Columbia University before teaching at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena.

David Walechka is the Decision Sciences Technology Lead at Northwestern Mutual.  He is responsible for team of data scientists and technologists working on complex, large scale machine learning problems.  He pioneered some of the first machine learning models at Northwestern Mutual and implemented the first set of algorithmic underwriting models.  He has 15 years experience working with data and analytics technologies having previously held roles as Senior Information Architect and BI Consultant.

Dr. Eugene Wen is the Vice President for Group Advanced Analytics at Manulife Financial/John Hancock. He is responsible for supporting Group Functions with advanced analytics, establishing a data scientist team, building Centers of Expertise (CoE) in advanced analytics, providing thought leadership, creating governance and policy frameworks for analytics function and providing research and development (R&D) capabilities to businesses across the company.

Prior to his current position at the Manulife Financial/John Hancock, Eugene served as the Vice President and Chief Statistician at the Workplace Safety & Insurance Board (WSIB) and led the national Health Indicators portfolio at the Canadian Institute for Health Information (CIHI). He was trained in both clinical medicine and public health.

Nathan is the Chief Product Officer of Entropix, providing product definition and strategic business planning and market development. Ten years of hardware/software design and innovations in the enterprise HD surveillance market helped Nathan shape the current Entropix software product. He is also the Founder and Chairman of Network Optix, an enterprise video management software company listed as the 7th fastest growing software company in the US. (2016, Inc. 5000). Earlier served as a Director of Sales at Arecont Vision leading the company to record sales.