PLANNED TALKS

Learn how to automate your systems, how to build chat bots and the future of deep learning. Explore the applications of machine learning, NLP, and computer vision transferring Neural Network know-how from academia to architects

WORLD'S BIGGEST AI ONLINE
CONFERENCE FOR DEVELOPERS

AI WITH THE BEST

100 SPEAKERS - 2 DAYS - 4 TRACKS
14-15 October 2017
Location: Online
Access platform

4 TRACKS

Machine Learning AI Online With The Best Conference

1

MACHINE LEARNING RESEARCH
Machine and Deep Learning Fundamentals, implementation and new modelling strategies for your frameworks straight from the labs.
Chatbot online developer AI conference

2

DEEP LEARNING,
NLP & CHATBOTS
Detection, tracking & integration tips for robots, drones & autonomous vehicles. Leverage conversational architecture with bot-builders and a slick UX.
Computer Vision online developer AI conference

3

APPLIED AI: STARTUPS, INDUSTRY & SOCIETY
Discover the applications of AI from startups and industry, what it takes to manage an AI company and the impacts on society with this technology.
Platform and Programme Demos online developer AI conference

4

DEMOS & TUTORIALS
Be the first to discover the algorithms, APIs, platforms and tools enabling AI tech with these hands-on sessions, demos and workshops.

FEATURED SPEAKERS

Ian Goodfellow
Research Scientist at Google Brain
Ian Goodfellow is a staff research scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning (www.deeplearningbook.org) and the inventor of generative adversarial networks. He is generally interested in all things deep learning, and usually focuses on generative models, machine learning security, and differential privacy.

SPEAKERS

Ian Goodfellow
Ian Goodfellow
Researcher, Google Brain
Ian Goodfellow is a staff research scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning (www.deeplearningbook.org) and the inventor of generative adversarial networks. He is generally interested in all things deep learning, and usually focuses on generative models, machine learning security, and differential privacy.
Devi Parikh
Devi Parikh
Faculty, Virginia Tech
Devi Parikh is an Assistant Professor in the School of Interactive Computing at Georgia Tech, and a Visiting Researcher at Facebook AI Research (FAIR). From 2013 to 2016, she was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. From 2009 to 2012, she was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with University of Chicago. She has held visiting positions at Cornell University, University of Texas at Austin, Microsoft Research, MIT, and Carnegie Mellon University. She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009 respectively. She received her B.S. in Electrical and Computer Engineering from Rowan University in 2005. Her research interests include computer vision and AI in general and visual recognition problems in particular. Her recent work involves exploring problems at the intersection of vision and language, and leveraging human-machine collaboration for building smarter machines. She has also worked on other topics such as ensemble of classifiers, data fusion, inference in probabilistic models, 3D reassembly, barcode segmentation, computational photography, interactive computer vision, contextual reasoning, hierarchical representations of images, and human-debugging. She is a recipient of an NSF CAREER award, an IJCAI Computers and Thought award, a Sloan Research Fellowship, an Office of Naval Research (ONR) Young Investigator Program (YIP) award, an Army Research Office (ARO) Young Investigator Program (YIP) award, an Allen Distinguished Investigator Award in Artificial Intelligence from the Paul G. Allen Family Foundation, four Google Faculty Research Awards, an Amazon Academic Research Award, an Outstanding New Assistant Professor award from the College of Engineering at Virginia Tech, a Rowan University Medal of Excellence for Alumni Achievement, Rowan University's 40 under 40 recognition, and a Marr Best Paper Prize awarded at the International Conference on Computer Vision (ICCV).
Sid Reddy
Sid Reddy
Prin Applied Scientist, Microsoft
Sid J Reddy is a recognized expert in Natural Language Processing (NLP) and Computational Linguistics who designed, developed and contributed to dozens of NLP systems used in production in a wide array of use-cases and industry verticals (healthcare, business intelligence, life sciences, legal and e-commerce). His research ranged from acquiring lexical resources through distributed word vector representations learned from big data and applying them to improve state of the art in sequential labeling tasks to using functional theories of grammar for association extraction and question-answering. Dr. Reddy graduated with a Bachelors in Computer Science from the Indian Institute of Technology and moved to the US to develop an effective approach to information extraction with limited training data for his PhD. His interest in NLP drove him to develop text-mining infrastructures from scratch at two technology startups and at the Mayo Clinic. More recently, he founded an NLP lab at Northwestern University with funding from a variety of government and private sources. Sid is a patented inventor, sought-after industry speaker and published author with research featured in over 50 peer-reviewed publications and technical conferences. He is a currently a Principal Applied Scientist at Microsoft.
Inmar Givoni
Inmar Givoni
Director ML, Kindred.ai
Inmar Givoni is the Director of Machine Learning at Kindred, where her team develops algorithms for machine intelligence, at the intersection of robotics and AI. Prior to that, she was the VP of Big Data at Kobo, where she led her team in applying machine learning and big data techniques to drive e-commerce, customer satisfaction, CRM, and personalization in the e-pubs and e-readers business. She first joined Kobo in 2013 as a senior research scientist working on content analysis, website optimization, and reading modelling among other things. Prior to that, Inmar was a member of technical staff at Altera (now Intel) where she worked on optimization algorithms for cutting-edge programmable logic devices. Inmar received her PhD (Computer Science) in 2011 from the University of Toronto, specializing in machine learning, and was a visiting scholar at the University of Cambridge. During her graduate studies, she worked at Microsoft Research, applying machine learning approaches for e-commerce optimization for Bing, and for pose-estimation in the Kinect gaming system. She holds a BSc in computer science and computational biology from the Hebrew University in Jerusalem. She is an inventor of several patents and has authored numerous top-tier academic publications in the areas of machine learning, computer vision, and computational biology. She is a regular speaker at big data, analytics, and machine learning events, and is particularly interested in outreach activities for young women, encouraging them to choose technical career paths.
Hugo Larochelle
Hugo Larochelle
Research Scientist, Google
I currently lead the Google Brain group in Montreal. My main area of expertise is deep learning. My previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models. More broadly, I’m interested in applications of deep learning to generative modeling, reinforcement learning, meta-learning, natural language processing and computer vision. Previously, I was Associate Professor at the Université de Sherbrooke (UdeS). I also co-founded Whetlab, which was acquired in 2015 by Twitter, where I then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, I was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. I obtained my Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio. My academic involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017. I’ve also been an area chair for many editions of the NIPS and ICML conferences. Finally, I have a popular online course on deep learning and neural networks, freely accessible on YouTube.
Matthieu Boussard
Matthieu Boussard
R&D Engineer, Craft AI
Ruslan Salakhutdinov
Ruslan Salakhutdinov
Director AI Research, Apple
Ruslan Salakhutdinov is a UPMC professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. He received his PhD in machine learning from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Department of Computer Science and Department of Statistics. In February of 2016, he moved to Carnegie Mellon University as an Associate Professor. Ruslan's primary interests lie in deep learning, machine learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Connaught New Researcher Award, Google Faculty Award, Nvidia's Pioneers of AI award, and is a Senior Fellow of the Canadian Institute for Advanced Research.
Natalia Konstantinova
Natalia Konstantinova
Lead Soft Eng, First Utility
Natalia Konstantinova is a great enthusiast with over 10 years' experience in the application of Natural Language Processing, Artificial Intelligence, IT and machine learning technologies to real world problems. She got her PhD from the University of Wolverhampton and worked in various fields such as machine translation, ontologies, information extraction and currently dialogue systems and chat bots. She is currently a lead software engineer in the R&D department of First Utility, the UK's largest independent energy provider, supplying gas and electricity to around one million UK homes. Natalia is still participating in academia by serving as an Associate editor of the Cambridge Journal of Natural Language Engineering, collaborating over research papers and organising conferences and workshops. She is a strong believer that modern technology can transform businesses and our everyday life.
Silvia Chiappa
Silvia Chiappa
Researcher, Google DeepMind
Rebecca Fiebrink
Rebecca Fiebrink
Senior Lecturer, Goldsmiths
Dr. Rebecca Fiebrink is a Senior Lecturer at Goldsmiths, University of London. Her research focuses on designing new ways for humans to interact with computers in creative practice, including on the use of machine learning as a creative tool. Fiebrink is the developer of the Wekinator, open-source software for real-time interactive machine learning whose current version has been downloaded over 10,000 times. She is the creator of a MOOC titled “Machine Learning for Artists and Musicians,” which launched in 2016 on the Kadenze platform. She was previously an Assistant Professor at Princeton University, where she co-directed the Princeton Laptop Orchestra. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research, and Smule, where she helped to build the #1 iTunes app "I am T-Pain." She holds a PhD in Computer Science from Princeton University.
Joseph Barr
Joseph Barr
CAO, HomeUnion
Joe is a data scientist with 20-year track record of providing value to customers in government, healthcare, energy/utilities and finance.• Translating business issues into practical solutions • Applies decision sciences methodology and tools, including data, machine learning, statistics, operations research and econometrics Specialties: Expert in statistical modeling including predictive analytics, regression (OLS, logistic, Cox, generalized & mixed linear models), time series and forecasting, Bayesian methods, data mining, machine learning (boosting, neural nets, support vector machines, decision trees, neural networks and deep learning networks). Enthusiastic about heuristics: dimensionality-reduction, PCA, clustering. Strong business sense and experience. Holds doctorate degree in mathematics from the University of New Mexico. Sentry Endowed Chair of Data Science at the University of Wisconsin, Stevens Point and Chief Data Scientist at Auritas. Authors of several research articles. Journal editor & referee for World Scientific Semantic Computing and Robotics.
Konstantina Palla
Konstantina Palla
Postdoc Researcher, Oxford
I am a postdoc Researcher in the Oxford statistical machine learning group at the University of Oxford, working with prof. Yee Whye Teh. Before that, I completed my PhD in Machine Learning in the Computational and Biological Learning Lab at the University of Cambridge with Prof. Zoubin Ghahramani. I am interested in the development, theoretical analysis and application of Bayesian nonparametric models, a class of statistical methods able to automatically adapt their complexity to observed data. My recent work includes nonparametric models for relational data, clustering, reversible Markov chains, birth-death feature allocations and sparse graphs. Examples of applications are social networks for relational modelling, gene expression analysis for temporal and non-temporal clustering, ion channel patch clamp recordings for reversible Hidden Markov models, relational data for sparse dynamic graphs and evolving disease symptoms for latent dynamic feature allocations.
Igal Raichelgauz
Igal Raichelgauz
CEO & Founder, Cortica
Igal brings with him extensive experience in algorithms & architectures for high-dimensional and high-volume data processing flows. Prior to founding CORTICA, Igal was CTO of LCB Ltd. and managed Rimon project, developing high-end systems for real-time voice recognition. Prior to LCB, Igal was product engineer and technical leader at Intel Haifa product development division, leading characterization and analysis activities of 90nm Cache arrays. Igal has a B.Sc. in EE from Technion with several awards, and more than 3 years of research activities at the Technion, in the area of non-linear natural dynamical computational systems. Prior to Intel, Igal was a Co-Founder of a start-up company (Figment) providing advanced content services to cellular network providers in Israel, among them to Partner Communications. Igal served in elite intelligence unit in IDF, leading a team of data production and analysis. Igal is part of a forum dealing with Israeli strategic homeland security issues.
Andreas Mueller
Andreas Mueller
Lecturer Data Science, Columbia University
Andreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to Machine Learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. You can find his full cv here. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.
Yao Zhang
Yao Zhang
Founder & CEO, Roboterra
Roman Yampolskiy
Roman Yampolskiy
Associate Professor, University of Louisville
Dr. Roman V. Yampolskiy is a Tenured Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. He is the founding and current director of the Cyber Security Lab and an author of many books including Artificial Superintelligence: a Futuristic Approach. During his tenure at UofL, Dr. Yampolskiy has been recognized as: Distinguished Teaching Professor, Professor of the Year, Faculty Favorite, Top 4 Faculty, Leader in Engineering Education, Top 10 of Online College Professor of the Year, and Outstanding Early Career in Education award winner among many other honors and distinctions. Yampolskiy is a Senior member of IEEE and AGI; Member of Kentucky Academy of Science, and Research Advisor for MIRI and Associate of GCRI. Roman Yampolskiy holds a PhD degree from the Department of Computer Science and Engineering at the University at Buffalo. He was a recipient of a four year NSF (National Science Foundation) IGERT (Integrative Graduate Education and Research Traineeship) fellowship. Before beginning his doctoral studies Dr. Yampolskiy received a BS/MS (High Honors) combined degree in Computer Science from Rochester Institute of Technology, NY, USA. After completing his PhD dissertation Dr. Yampolskiy held a position of an Affiliate Academic at the Center for Advanced Spatial Analysis, University of London, College of London. He had previously conducted research at the Laboratory for Applied Computing (currently known as Center for Advancing the Study of Infrastructure) at the Rochester Institute of Technology and at the Center for Unified Biometrics and Sensors at the University at Buffalo. Dr. Yampolskiy is an alumnus of Singularity University (GSP2012) and a Visiting Fellow of the Singularity Institute (Machine Intelligence Research Institute). Dr. Yampolskiy’s main areas of interest are AI Safety, Artificial Intelligence, Behavioral Biometrics, Cybersecurity, Digital Forensics, Games, Genetic Algorithms, and Pattern Recognition. Dr. Yampolskiy is an author of over 100 publications including multiple journal articles and books. His research has been cited by 1000+ scientists and profiled in popular magazines both American and foreign (New Scientist, Poker Magazine, Science World Magazine), dozens of websites (BBC, MSNBC, Yahoo! News), on radio (German National Radio, Swedish National Radio, Alex Jones Show) and TV. Dr. Yampolskiy’s research has been featured 250+ times in numerous media reports in 22 languages.
Louis Monier
Louis Monier
Head of AI, AirBnB
Ryan Keisler
Ryan Keisler
Head of ML, Descartes Labs
Ryan Keisler is the head of machine learning at Descartes Labs, a startup at the intersection of geospatial data and machine learning. His work draws on his background in physics, data analysis, and machine learning. Prior to joining Descartes, Ryan received his PhD in physics from the University of Chicago and worked as a cosmologist at Stanford University.
Timnit Gebru
Timnit Gebru
Ph.D Student, Stanford
PhD student in the Stanford AI Laboratory, studying computer vision under Fei-Fei Li. My main research interest lies in data mining large scale publicly available images to gain sociological insight. Prior to joining the lab I worked at Apple designing circuits and signal processing algorithms for various Apple products including the first ipad.
Davide Feltoni Gurini
Davide Feltoni Gurini
CEO & Founder, Datalytics Srl
CEO & Founder of Datalytics S.r.l also hold a Ph.D. in Artificial Intelligence and Computer Engineering. I have a deep passion in new Technologies, Big Data Applications and Databases, and I also love teaching people to share my knowledge. In 2013 I've founded my company, Datalytics, that keeps me focused on emerging Technologies, Business Development techniques and Team Leading. I hold a Ph.D. at Roma Tre University regarding Artificial Intelligence applyed to Recommender Systems in Social Media. I've published and presented papers to the best conferences in Italy, USA, Brazil, Denmark, Chile and China. You can also refer to my linkedin profile for any other info -> https://www.linkedin.com/in/davidefeltoni/
Gunnar Carlsson
Gunnar Carlsson
Co-founder, Ayasdi Inc.
Gunnar Carlsson is a mathematician who has worked in the area of topology, the study of shape, since the 1970's. He has taught at University of Chicago, University of California (San Diego), Princeton University, and since 1991 at Stanford University. He has served has the chair of the Mathematics Department at Stanford, and is one of the leading topologists in the world. In 2000, he initiated a project to apply topological methods to the analysis of large and complex data sets, and from 2005-2010 led a large multi-university initiative around the developing area of topological data analysis (TDA). He is a cofounder of Ayasdi Inc., which is a company which develops artificial intelligence applications based on TDA, and has retired from Stanford to work with Ayasdi.
Lukas Biewald
Lukas Biewald
Founder, Crowdflower
Lukas Biewald is the founder and Chief Data Scientist of CrowdFlower. Founded in 2009, CrowdFlower is a data enrichment platform that taps into an on-demand to workforce to help companies collect training data and do human-in-the-loop machine learning. Following his graduation from Stanford University with a B.S. in Mathematics and an M.S. in Computer Science, Lukas led the Search Relevance Team for Yahoo! Japan. He then worked as a senior data scientist at Powerset, acquired by Microsoft in 2008. Lukas was featured in Inc Magazine’s 30 Under 30 list. Lukas is also an expert level Go player.
Reza Zadeh
Reza Zadeh
Founder & CEO, Matroid
Reza Bosagh Zadeh is Founder CEO at Matroid and an Adjunct Professor at Stanford University. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. Reza received his PhD in Computational Mathematics from Stanford under the supervision of Gunnar Carlsson. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Boards of Microsoft and Databricks. As part of his research, Reza built the Machine Learning Algorithms behind Twitter's who-to-follow system, the first product to use Machine Learning at Twitter. Reza is the initial creator of the Linear Algebra Package in Apache Spark. Through Apache Spark, Reza's work has been incorporated into industrial and academic cluster computing environments. In addition to research, Reza designed and teaches two PhD-level classes at Stanford: Distributed Algorithms and Optimization (CME 323), and Discrete Mathematics and Algorithms (CME 305).
Harm van Seijen
Harm van Seijen
Research Manager, Microsoft
Harm van Seijen is a Research Manager at Microsoft. His research is centered on scalability of reinforcement learning, with the goal of addressing the fundamental challenges related to natural conversations between humans and artificial agents in real-world setting. Prior to joining Maluuba, Harm was a postdoc at the University of Alberta, working alongside professor Richard Sutton on novel reinforcement learning techniques. He completed his PhD at the University of Amsterdam.
Katherine Heller
Katherine Heller
Assistant Professor, Duke University
Katherine is an Assistant Professor at Duke University, in the Department of Statistical Science and at the Center for Cognitive Neuroscience. Prior to joining Duke she was an NSF Postdoctoral Fellow, in the Computational Cognitive Science group at MIT, and an EPSRC Postdoctoral Fellow at the University of Cambridge. Her Ph.D. is from the Gatsby Unit, where her advisor was Zoubin Ghahramani. Katherine's research interests lie in the fields of machine learning and Bayesian statistics. Specifically, she develops new methods and models to discover latent structure in data, including cluster structure, using Bayesian nonparametrics, hierarchical Bayes, techniques for Bayesian model comparison, and other Bayesian statistical methods. She applies these methods to problems in the brain and cognitive sciences, where she strives to model human behavior, including human categorization and human social interactions.
Get to know the speakers
Presenting companies include
Duke University
Matroid
Microsoft
Datalytics
Stanford
Descartes Labs
Roboterra
Louisville
AirBNB
Columbia University
Cortica
Oxford
HomeUnion
Goldsmiths
Google DeepMind
First Utility
Apple
Craft ai
Google
Kindred.ai
Microsoft
Virginia Tech
Google

Agenda

9am-6pm EST // 6am-3pm PST // 3pm-12am CEST // 9pm-6am GMT+8
MACHINE LEARNING RESEARCH
DEEP LEARNING, NLP & CHATBOTS
STARTUPS, INDUSTRY & SOCIETY
DEMOS, TUTORIALS & applied AI
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MACHINE LEARNING RESEARCH
COMPUTER VISION NLP / CHATBOTS
APPLIED AI: STARTUPS, INDUSTRY & SOCIETY
DEMOS & TUTORIALS
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