Zulfiqar earned his Masters degree in Computer Science from University of Washington, with his research primarily focused on Deep Learning based solutions in the cyber security industry. He collaborated with Infoblox, an IT automation and security firm, during his graduate degree to work in the Explainable AI domain. His research project titled “Interpretation of Deep Learning based Domain Generation Algorithms classifiers” focused on visualizing the feature extraction process of trained DGA classifier models currently deployed by Infoblox to provide a detailed analysis and better interpretability of neural network models utilized as DGA classifiers. During his summer internship with REI Systems, he worked on a Computer Vision based solution to assess and evaluate the damage in areas affected by natural disasters from satellite imagery. Zulfiqar's research interests include XAI, Natural Language Processing, Recommendation Systems and Computer Vision.

Anthony is a lead software engineer at Genesys where he is working on several AI and ML projects related to customer experience. He has over 20 years experience in designing and building scalable software. Anthony holds a Ph.D. degree in Electrical Engineering with specialization in Intelligent Robotics Software and has worked on various problems in the areas of human-AI interaction and predictive analytics for SaaS business optimization.

Piyush leads AI Product Management at Nauto. In his role, he is responsible for building the next wave of AI-based features that enhance road safety. He focuses on exploring novel research ideas in the field of computer vision and vehicle safety and productizing them to make roads safer for drivers and pedestrians. 

In his previous roles, Piyush led the development of NLP platforms at Adobe and SAP. He also helps startups in the field of AI with product/market fit, strategy, and user experience.

Geeta Chauhan is leading AI Partnerships at Facebook with expertise in building resilient, anti-fragile, large scale distributed platforms for startups and Fortune 500s.

She is winner of Women in IT – Silicon Valley – CTO of the year 2019, an ACM Distinguished Speaker and thought leader on topics ranging from Ethics in AI, Deep Learning, Blockchain, IoT. She is passionate about promoting use of AI for Good.

Justin Chien is a Sr. Data Scientist at 6sense, a marketing technology startup in San Francisco. He went from studying biology & music at Boston College to mastering in epidemiology at UCLA where he discovered predictive modeling. Combining this statistical “magic” with his passion in cutting-edge tech, Justin is now trying to combine the two into new applications. His free time is spent on a never-ending quest to try new cuisines with his wife and building mechanical keyboards.

Ilke Demir earned her Ph.D. in Computer Science from Purdue University, focusing on 3D vision approaches for generative models, urban reconstruction and modeling, and computational geometry for synthesis and fabrication. Afterwards, she joined Facebook as a Postdoctoral Research Scientist working with Ramesh Raskar from MIT. Her research included human behavior analysis and deep learning approaches in virtual reality, geospatial machine learning, and 3D reconstruction at scale. In addition to her publications in top-tier venues (SIGGRAPH, ICCV, CVPR), she has organized workshops, competitions, and courses in the intersection of deep learning and computer vision. She has received several awards and honors such as Jack Dangermond Award, Bilsland Dissertation Fellowship, Industry Distinguished Lecturer, and GHC Fellow, in addition to her best paper/poster/reviewer awards. Currently she is a Senior Research Scientist at Intel, leading the computer vision and deep learning research in the world's largest volumetric capture stage.

Jason Gauci leads the Applied Reinforcement Learning team @ Facebook AI. Jason has 13 years of experience building machine learning systems at Facebook AI, Apple, Google Research, and Lockheed Martin Applied Research, and has a PhD in computer science from UCF with a focus on Neuroevolution.

Jen Gennai leads Google’s Responsible Innovation team which is responsible for operationalizing Google’s AI Principles, ensuring that Google’s products have fair and ethical outcomes on individual users and the world. Her team works with product and engineering, leveraging a multidisciplinary group of experts in ethics, human rights, user research, racial justice and gender equity to validate that products and outputs align with our commitments to fairness, privacy, safety, societal benefit and more. Before she co-authored the AI Principles and founded Responsible Innovation, Jen worked on machine learning fairness and founded the Ethical ML team in Trust & Safety.

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.

Vishal (_‘Vish’_) Hawa is Principal Data Scientist at The Vanguard Group. Vish has over 15 years of experience in Retail and Financial services industry and works closely with Marketing Managers , financial plan designers through propensity, life-time valuations and financial modeling. Vish works on creating data-products in machine intelligence space that utilize consumer attributes to generate consumer insights. Vish is thought leader and has spoken at many regional and national conferences in applied machine learning with the idea of promoting data driven decisions and harnessing actionable insights. Vish has executive management from Wharton school of business, post-graduation degrees in Information sciences, Statistics and Computer engineering from Indian Statistical Institute and Bachelors in Engineering from National Institute of Technology-India.

At Standard Cognition, Sean works on research and engineering of machine learning systems that ship deep neural networks to production with reduced generalization error. He has a background in machine learning, information theory, and complex systems. Previously, he was a machine learning engineer at Explorer.ai building maps for self-driving cars and before that he worked in machine learning research as a graduate student at the University of Arizona.

Drew Hodun is an ML specialist on the Google Cloud professional services team, where he advises financial, autonomous, and tech customers implementing cutting-edge ML use cases and systems on Google Cloud and in hybrid environments. His work ranges from operationalizing ML to GPU/TPU perf tuning.

Dr. Navid Imani is an Applied Researcher with Shipping Science at eBay Corp. specializing on architecting scalable NLP solutions as well as using operations research for industrial planning and optimization. Prior to joining eBay, Dr. Imani had held multiple industry and academic research positions such as at Microsoft. He holds a Ph.D. in Computing Science from Simon Fraser University and has co-authored over 25 research publications in various areas of Machine Learning, Applied Mathematics and Distributed Computing.

Dr. Krishnan currently works as a software engineer and tech lead in the Facebook AI Infra team. Previously, Dr. Krishnan worked at VMware and Pacific Northwest National Laboratory in the area of High Performance Computing. Dr. Krishnan has authored and co-authored over 40 peer-reviewed conference and journal papers. Dr. Krishnan's research interests include Artificial Intelligence, Deep Learning, Recommendation Systems, Distributed Systems, Parallel Computing, High Performance Computing.

Kumaresan is currently responsible for strategy and roadmap of the Core NLP and Machine Translation products at eBay. Previously he has launched and managed multiple products across eBay's internal cloud stack ranging from developer frameworks to network security.

Patrick Miller is the NYC lead of Google's Enterprise AI team. His team builds scalable, cutting-edge machine learning solutions to internal Google problems. Before Google, Patrick led machine learning at Macmillan, a major trade publisher. He's a core contributor to Cognoma, a cancer genomics ML research tool. Patrick has a Master's in Computer Science from the Georgia Institute of Technology.

In 23 years in the data management industry, I’ve worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.

I’ve 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 and engineering, worked with a lot of different industries, and questioned a lot of assumptions.

Now, I promote understanding of Vertica, distributed data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.

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.

Mohammad is currently a Senior Data & Applied Scientist at Microsoft, and Instructor at Stanford University. He is a former Data Scientist at Apple and previously worked for Samsung, Bosch, General Electric and UCLA Research Labs. He received a PhD in Computer Science from the University of California, Riverside and B.Sc. from University of Tehran. Mohammad is the author of the book, ‘Applications of Mining Massive Time Series Data'. He has also been a keynote speaker at more than 40 Data Summits/Conferences around the globe. 

Shweta is chief product officer at Nauto and oversees product and design for Nauto’s fleet, insurance, and automotive offerings. Prior to Nauto, she was the head of product management for Amazon Web Services, as well as various strategy and product management leadership roles at NetApp and Cisco, where she drove the productizing and launch of Cisco’s IoT platform for smart cities. Shweta has an MS in CS from Penn State University and an MBA from INSEAD, France.

Mohamed Sidahmed, Ph.D., IEEE Senior Member, is subject matter expert in machine learning & AI, data-driven modeling and optimization with both theoretical and applied skills. He is the Machine Learning and AI R&D Manager at Shell’s Data Science CoE, where he is leading a multidisciplinary research group with passion for delivering innovation and excellence. He is deriving the vision for advancement of ML & AI research portfolio and technology development across Subsurface, Production & Operations, and New Energies. He has numerous publications and book chapters in the areas of pattern discovery, deep learning representation, and modeling & reasoning across multiple domains.

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI Applications Summit, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Eric has appeared on Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. Eric and his books have been featured in Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, Fast Company, The Financial Times, Forbes, Fortune, GQ, Harvard Business Review, The Huffington Post, The Los Angeles Times, Luckbox Magazine, MIT Sloan Management Review, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.

Nikolay Sorokin is a data scientist at REI Systems Inc. He is coming from mechanical engineering background which he studied at City College of New York, then completed an IT master's degree program at Towson University. Nikolay is passionate about computer vision applications and knowledge extraction from unstructured text documents.

Shams Zaman is a Principal Data Scientist in Network Capital Management group within Verizon. There, he is leading the efforts for developing predictive and prescriptive analytics to ensure optimum allocation of resources. He is developing network business intelligence by developing forecasting models from multivariate time series data sourced from networking cell sites, customer experience and geospatial data. Prior to joining Verizon, he was a Machine Learning Scientist at AI lab, Philips Healthcare. There he developed predictive models from longitudinal Electronic Health Record data for improving patient care. He also worked with advance pre-trained language models to develop state of the art NLP solutions for clinical concept recognition, concept disambiguation, data de-identification etc.

Le Zhang is a data scientist at Walmart Technology in Plano, TX. He has developed anomaly detection engines and prediction models to solve business problems in fraud detection, demand forecasting, and causation analysis. Le received his Ph.D. in Industrial Engineering from Purdue University and has dozens of research publications in Human Factors. His research interests include machine learning, big data, optimization, UI/UX, and ergonomics.