
WELCOME


Christian Szegedy - Staff Research Scientist - Google
Binge-watching YouTube for Fun and Profit
Christian Szegedy - Google
Deep Learning for Formal Reasoning
Deep learning has transformed machine perception in the past five years. However, recognizing patterns is a crucial feature of intelligence in general. Here we give a short overview on how deep learning can be utilized for formal reasoning, especially for reasoning in large mathematical theories. The fact that pattern recognition capabilities are essential for these tasks has wider implications for other tasks like software synthesis and long term planning in complicated environments. Here is will give a short overview on some methods that leverage deep learning for such tasks.
Christian Szegedy is research scientist at Google, working on deep learning for computer vision, including image recognition, object detection and video analysis. He is the designer of the Inception architecture which set new state of the art on the latest ImageNet benchmark in the latest Large Scale Visual Recognition Competition. Before joining Google in 2010, he was scientist at Cadence Research Laboratories in Berkeley devising algorithms for chip design. His background is discrete mathematics and mathematical optimization. Christian got his PhD from the University of Bonn in applied mathematics in 2005.

Alejandro Jaimes - Acesio
Alejandro (Alex) Jaimes is CTO & Chief Scientist at Acesio. Acesio focuses on Big Data for predictive analytics in Healthcare to tackle disease at worldwide scale, impacting individuals and entire populations. We use Artificial Intelligence to collect and analyze vast quantities of data to track and predict disease in ways that have never been done before- leveraging environmental variables, population movements, sensor data, and the web. Prior to joining Acesio, Alex was CTO at AiCure and prior to that he was Director of Research/Video Product at Yahoo where he led research and contributions to Yahoo's video products, managing teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. His work focuses on Machine Learning, mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. He has published widely in the top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc), has been a visiting professor (KAIST), and is a frequent speaker at international academic and industry events. He is a scientist and innovator with 15+ years of international experience in research leading to product impact (Yahoo, KAIST, Telefonica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs). He has worked in the USA, Japan, Chile, Switzerland, Spain, and South Korea, and holds a Ph.D. from Columbia University.




Kevin Murphy - Research Scientist - Google
Semantic Image Segmentation Using Deep Learning & Graphical Models
Kevin Murphy - Google
Semantic Image Segmentation Using Deep Learning & Graphical Models
Kevin Murphy is a research scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision and NLP. Before joining Google in 2011, he was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a postdoc at MIT. Kevin got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals, as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research).

Andrew Ng - Baidu
Dr. Andrew Ng is Chief Scientist at Baidu. He leads Baidu Research, which comprises three interrelated labs: the Silicon Valley AI Lab, the Institute of Deep Learning and the Big Data Lab. The organization brings together global research talent to work on fundamental technologies in areas such as image recognition and image-based search, speech recognition, natural language processing and semantic intelligence. In addition to his role at Baidu, Dr. Ng a faculty member in Stanford University's Computer Science department, and Chairman of Coursera, an online education platform that he co-founded. Dr. Ng is the author or co-author of over 100 published papers in machine learning, robotics and related fields. He holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.



COFFEE


Richard Socher - Founder - you.com
Recursive Deep Learning for Modelling Compositional and Grounded Meaning
Richard Socher - you.com
Richard Socher is the founder of you.com and previous Chief Scientist at Salesforce. He was also previously the CEO and founder of MetaMind, a startup that seeked to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng and won the best Stanford CS PhD thesis award. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision.
He was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo! Key Scientific Challenges Award, a Microsoft Research PhD Fellowship in 2012 and a 2013 "Magic Grant" from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.




Hassan Sawaf - Senior Director, Human Language Technology - eBay
How AI & Deep Learning Transform 800 Million iIems in 190 Countries: eBay’s Approach
Hassan Sawaf - eBay
How AI and Deep Learning Transform 800 Million items in 190 Countries: eBay’s Approach
In today’s global ecommerce landscape, borders are artificial, and consumers expect to transact anytime, anywhere. The challenge for eBay – one of the world’s largest online marketplaces – is enabling the best-possible commerce experience across 800 million listings from 25 million sellers in 190 countries and multiple languages. Find out how eBay’s product experience team is leveraging AI and deep learning to improve the user experience for today’s global consumer – and how they’re building machine translation and information extraction engines specific to commerce at scale.
With nearly 20 years of expertise in language technologies, Hassan Sawaf is well-recognized in the field of machine translation. As senior director of machine translation at eBay, Hassan develops tools and services in language technology to open new markets and enable more opportunities for eBay customers around the world. In his role, Hassan focuses on eBay’s efforts in emerging markets and cross border trade, helping the company develop new ways to solve the language challenge inherent in global online commerce. For the past year, Hassan and his team have developed and implemented language solutions that enable eBay shoppers to more easily access the items they need and love in their native languages, and offer seamless translation options for businesses selling on eBay. Prior to joining eBay in April 2013, Hassan held management roles with several companies in the language solutions space, including AIXPLAIN AG, AppTek Inc., Net2Voice and SAIC. Hassan speaks fluent English, German and Arabic, holds two patents, is a member of the board of directors at idenTV LLC and BlueOSS, and continues to be active in the Angel investor community. He holds a Master of Science degree in computer science from Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, and attended the university for his Ph.D. studies.



LUNCH


Charlie Tang - Research Scientist - Apple
Deep Learning with Structure: How Neural Nets can Leverage Domain-Specific Knowledge in Computer Vision
Charlie Tang - Apple
Deep Reinforcement Learning Advancements and Applications
Recent advances in Deep Reinforcement Learning have captured the imagination of both the AI researchers and the general public. Combining the latest Deep Learning technology with Reinforcement Learning techniques has led to stunning breakthroughs, surpassing human level performances at Atari games and the game of Go. Furthermore, Deep RL is being successfully adopted in a variety of fields such as robotics, control systems, translation, dialogue systems, and others. This talk will explore the intuitions, algorithms, and theories that have led to the recent success of Deep RL. A survey of exciting Deep RL applications and tough challenges ahead will also be discussed.
Charlie obtained his PhD in 2015 in Machine Learning from the University of Toronto, advised by Geoffrey Hinton and Ruslan Salakhutdinov. His thesis focused on various aspects of Deep Learning technology. Charlie also holds a Bachelors in Mechatronics Engineering and Masters in Computer Science from the University of Waterloo. After his PhD, along with Ruslan Salakhutdinov and Nitish Srivastava, Charlie co-founded a startup focused on the application of Deep Learning based vision algorithms. Currently, Charlie is a research scientist at Apple Inc. Charlie's research interests include Deep Learning, Vision, Neuroscience and Robotics. He is one of the few competitors to have reached the #1 ranking on Kaggle.com, a widely popular machine learning competition platform. Charlie is also a Canadian national chess master.




Modar Alaoui - CEO & Founder - Eyeris
Emotion Recognition through Deep Learning for a better Ambient Intelligence
Modar Alaoui - Eyeris
Vision AI for Augmented Human Machine Interaction
This session will unveil the latest vision AI technologies that ensure safe and efficient human machine interactions in the industrial automation context. Today’s human-facing industrial AI applications lack a key element for Human Behavior Understanding (HBU) that is critical for augmented safety and enhancing productivity. The second part of this session will detail how real-world applications can benefit from a comprehensive suite of visual behavior analytics that are readily available today.
Modar is a serial entrepreneur and expert in AI-based vision software development. He is currently founder and CEO at Eyeris, developer of a Deep Learning-based emotion recognition software, EmoVu, that reads facial micro-expressions. Eyeris uses Convolutional Neural Networks (CNN's) as a Deep Learning architecture to train and deploy its algorithm in to a number of today’s commercial applications. Modar combines a decade of experience between Human Machine Interaction (HMI) and Audience Behavioral Measurement. He is a frequent keynoter on “Ambient Intelligence”, a winner of several technology and innovation awards and has been featured in many major publications for his work.




Adam Hanina - CEO - AiCure
Using Artificial Intelligence to Improve the Quality of Care
Adam Hanina - AiCure
Using Artificial Intelligence to Improve the Quality of Care
Every year 125,000 people die in the US because medications are not taken properly. Globally, this contributes to tens of millions of preventable deaths. In clinical research, pharmaceutical companies have no idea if subjects are actually taking the experimental drugs that are being tested.
Adam Hanina, CEO of AiCure will be presenting the first artificial intelligence platform focused towards accurately monitoring, interacting, predicting and intervening with patients in real-time. The National Institutes of Health stated that AiCure “has the ability to significantly impact drug research and therapy” through the ubiquitous mobile phone platform that exists today. In this session, Adam will overview the technology, its impact, and the importance of understanding and designing adaptive human machine interfaces.
Adam Hanina is co-founder and CEO of AiCure, an artificial intelligence company that has developed facial recognition and motion-sensing technology to confirm medication ingestion by clinical trial participants and high-risk patients in real time. He is a passionate advocate for the use of healthcare software as a population health tool and has directed much of his previous work to this end. He has acted as a subject-matter expert on medication adherence technologies for the National Institutes of Health (NIH) and is currently a principal investigator for multiple NIH innovation grants.



PANEL SESSION: Deep Learning in the Real-World

Conversation & Drinks on the Terrace

COFFEE

REGISTRATION & LIGHT BREAKFAST
Patrick Ehlen - Loop AI Labs
Patrick Ehlen, PhD, is a cognitive scientist and Head of Deep Learning at Loop AI Labs. He specializes in representation learning for semantics, pragmatics, and concept acquisition. He developed natural language and context resolution technologies at AT&T Labs, and worked on methods to extract concepts and topics from ordinary spontaneous conversations among people as part of the DARPA CALO project at CSLI/Stanford. He has produced 45 research publications in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, and human concept learning. He joined Loop AI Labs to help usher in a new era of cognitive computing services.




Tara Sainath - Senior Research Scientist - Google
Deep Learning Speech Recognition Advancements at Google
Tara Sainath - Google
Multichannel Signal Processing with Deep Neural Networks for Automatic Speech Recognition
Automatic Speech Recognition systems commonly separate speech enhancement, including localization, beamforming and postfiltering, from acoustic modeling. In this talk, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel model.
I received my PhD in Electrical Engineering and Computer Science from MIT in 2009. The main focus of my PhD work was in acoustic modeling for noise robust speech recognition. After my PhD, I spent 5 years at the Speech and Language Algorithms group at IBM T.J. Watson Research Center, before joining Google Research. I have co-organized a special session on Sparse Representations at Interspeech 2010 in Japan. I have also organized a special session on Deep Learning at ICML 2013 in Atlanta. In addition, I am a staff reporter for the IEEE Speech and Language Processing Technical Committee (SLTC) Newsletter. My research interests are mainly in acoustic modeling, including deep neural networks, sparse representations and adaptation methods.



Marc Delingat - Senior Director, Global Product Experience - eBay
How AI & Deep Learning Transform 800 Million iIems in 190 Countries: eBay’s Approach
Marc Delingat - eBay
How AI and Deep Learning Transform 800 Million iIems in 190 Countries: eBay’s Approach
In today’s global ecommerce landscape, borders are artificial, and consumers expect to transact anytime, anywhere. The challenge for eBay – one of the world’s largest online marketplaces – is enabling the best-possible commerce experience across 800 million listings from 25 million sellers in 190 countries and multiple languages. Find out how eBay’s product experience team is leveraging AI and deep learning to improve the user experience for today’s global consumer – and how they’re building machine translation and information extraction engines specific to commerce at scale.
As Senior Director Global Product Experience, Marc Delingat is responsible for the global customer experience outside of the United States. Marc is focused on building innovative products for eBay’s buyers and sellers, to break down language and market barriers in global commerce, and expand eBay’s geographic footprint.
In this role, Marc looks for new opportunities to research, develop, and bring to market new product solutions for emerging markets and improve cross border trade to open up the world’s selection of merchandise. His organization includes product management, applied research, and engineering in human language technology, localization, global product experience, global architecture, and internationalization for all of eBay’s web and mobile products. A proficient English and German speaker, Marc has a keen interest in combining design, technology, science, and language, to build products customers across the world love. Beginning in 2013, he formed the eBay Human Language Technology Research team with the charter to bridge the language gap in global commerce. In 2014 he led the acquisition of parts of AppTek to build out the team and acquire key technologies and intellectual property to further eBay’s leadership position in this area. As a result, eBay is a oft-cited leader in machine translation innovations specific to the field of commerce.


Claudia Perlich - Dstillery
Claudia Perlich currently acts as Chief Scientist at Dstillery and designs, develops, analyzes and optimizes the machine learning that drives digital advertising. An active industry speaker and frequent contributor to academic and industry publications, Claudia was recently named winner of the Advertising Research Foundation’s (ARF) Grand Innovation Award, was selected as member of the Crain’s NY annual 40 Under 40 list, WIRED’s Smart List, and FastCompany’s 100 Most Creative People. She has published over 50 scientific articles, and holds multiple patents in machine learning. Claudia has a PhD in Information Systems from NYU and worked in the Predictive Modeling Group at IBM’s Watson Research Center, concentrating on data analytics and machine learning for real-world applications. She also teaches in the NYU Stern MBA program.


Lauren Berkowitz - One Llama Labs
Lauren Berkowitz is a leader in launching and commercializing emerging technologies. Prior to joining audio artificial intelligence company, One Llama Labs, where she was SVP of Product and Business Development, Lauren led major technology divisions for companies including Bertelsmann, Sony and EMI and consulted with numerous clients including Comcast and the American Red Cross on the optimum use and timing of integrating emerging technologies into core business. Having recently left One Llama Labs, Lauren is currently working on a stealth bio-inspired multi-modal AI project. Lauren holds JD and MBA degrees from Suffolk University and a BS from Boston University.


Patrick Ehlen - Loop AI Labs
Patrick Ehlen, PhD, is a cognitive scientist and Head of Deep Learning at Loop AI Labs. He specializes in representation learning for semantics, pragmatics, and concept acquisition. He developed natural language and context resolution technologies at AT&T Labs, and worked on methods to extract concepts and topics from ordinary spontaneous conversations among people as part of the DARPA CALO project at CSLI/Stanford. He has produced 45 research publications in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, and human concept learning. He joined Loop AI Labs to help usher in a new era of cognitive computing services.




Clement Farabet - VP of AI Infrastructure - NVIDIA
Real-Time, Content-Driven Representations at Twitter
Clement Farabet - NVIDIA
Industry-Grade Deep Learning
Today’s AI is arming humans with superpowers — from aiding doctors to make better diagnoses to helping the public move around safely. Entire industries are being redefined, and new ones are emerging as well. AI, today mostly powered by Deep Learning, is a powerful tool, but one that is not trivial to master and integrate into existing industry workflows. NVIDIA has enabled the current AI boom by providing critical compute power necessary for scientists to solve a wide range of AI problems, specifically challenging perception problems. Today NVIDIA is investing in higher-level abstractions to solve even more complex innovations, like self-driving cars, and help other industries leverage Deep Learning. In this talk, you’ll learn how Deep Learning has evolved over the past 10 years, how we enabled this field, and continue to do so; what we are doing to get to fully autonomous cars; and how we are building platforms to enable anyone to create value with Deep Learning. I will also talk about research at NVIDIA and how we operate a fast-moving R&D team to rapidly transfer research into products.
Clement Farabet is VP of AI Infrastructure at NVIDIA. His team is responsible for building NVIDIA’s next-generation AI platform, to enable a broad range of applications, from autonomous cars to healthcare. Clement received a PhD from Université Paris-Est in 2013, while at NYU, co-advised by Laurent Najman and Yann LeCun. His thesis focused on real-time image understanding, introducing multi-scale convolutional neural networks and a custom hardware arch for deep learning. He cofounded Madbits, a startup focused on web-scale image understanding, sold to Twitter in 2014. He cofounded Twitter Cortex, a team focused on building Twitter’s deep learning platform for recommendations/search/spam/nsfw/ads.


Aaron Steele - CartoDB
Beyond the Headless Map
Many of today’s maps are traditional, giving you a full picture of a particular space, like your current location on a Google Map. But there’s a growing trend of headless maps. Consider Tinder which is a giant map of potential partners where you never see a map. With IoT we can imagine going beyond the headless map into a world of no maps at all. For example, you say “ok car, let’s go to LA” and it maps out the best way to get there from streams of real-time sensor data from roads, satellites, traffic, weather, etc. In this talk we explore a future where we might stop looking at our maps altogether, because IoT is the map.
Aaron Steele is Sr. Vice President of Technical Business Development at CartoDB where he thinks creatively about the future and is responsible for company growth. Aaron holds a degree in Computer Science from the University of California, Berkeley. Before CartoDB, Aaron was CTO and Principal Software Engineer at World Resources Institute. His career started in academia, bridging gaps between biodiversity science, big data, and the web.




Bart Peintner - CTO, Chief Scientist, Founder - Loop AI Labs
Understanding is Power: Deep Learning for Text Understanding
Bart Peintner - Loop AI Labs
Bart is the Co-Founder, CTO and Chief Scientist of Loop AI Labs, which develops technology that helps machines understand the human world. His current focus is on extracting markers of a person’s interests, personas, and life context from social data and other unstructured data. For the past 12 years Bart has led and contributed to several projects related to personalization and learning, including Ph.D. research on personalized robotics for older adults, a calendaring system that learns its user's preferences, and a collaborative task management tool that learns how individuals interact with people and web services.


Katherine Gorman - Talking Machines
Katherine Gorman is the Executive Producer of the Talking Machines podcast and the Creative Director of Tote Bag Productions. After a decade as a daily news producer for public radio, she pursued her passion for science reporting and launched the podcast with host Harvard Professor Ryan Adams. Through clear explanations of fundamental concepts and in depth interviews with those at the forefront of research, Talking Machines introduces the reality of machine learning to a wide audience. Early in its first season, the show has already become one of the most popular tech news podcasts on the global iTunes charts. http://www.thetalkingmachines.com/



Arno Candel - Chief Architect - H2O.ai
WORKSHOP: Scalable Data Science and Deep Learning with H2O
Arno Candel - H2O.ai
Scalable Data Science and Deep Learning with H2O
In a world where Data Science has become a driving force for innovation and profitability, organizations are in an arms race to leverage Machine Learning as a competitive differentiator. H2O is a leading open-source in-memory Machine Learning platform designed for distributed compute clusters. H2O’s vision is to democratize scalable Machine Learning with its open-source Java code base that integrates with everyday tools such as R, Python, Hadoop and Spark. H2O’s friendly User Interface makes it easy for data scientists to train models and share results with business stakeholders. Models can easily be put into production with auto-generated Java scoring code. This talk will spotlight H2O Deep Learning and its ease of use and scalability on large real-world datasets and showcase its versatility across multiple applications.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.
Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine. Follow him on Twitter: @ArnoCandel.


WORKSHOP with H2O - in the Duxbury Room
Main Stage Continued - Ballroom


Daniel McDuff - Director of Research - Affectiva
Emotion Intelligence to our Digital Experiences
Daniel McDuff - Affectiva
Turning Everyday Devices into Health Sensors
Today's electronics have very sensitive optical and motion sensors. These can captures subtle signals resulting from cardiorespiratory activity. I will present how webcam(s) can be used to measure important physiological parameters without contact with the body. In addition, I will show how an ordinary smartphones can be turned into a continuous physiological monitors. Both of these techniques reveal the surprising power of devices with around us all the time. I will show how deep learning are helping us create highly scalable and low-cost applications based on these sensor measurements.
Daniel McDuff is Principal Research Scientist at Affectiva. He is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotions (currently with 8B+ data points). Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR and the Center for Integrated Medicine and Innovative Technology (CIMIT). His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel is also a Research Affiliate at the MIT Media Lab.



WELCOME
Meng Wang - Orbeus
Meng co-founded Orbeus with the vision to use face and image recognition technology to quickly find photos and key sections of videos. Meng has 8+ years of experience in computer vision and image processing, including roles at Google Research, Google Search and YouTube. He lead the development of large scale image indexing & visualization infrastructure, which was later launched in Google Labs. He also co-developed YouTube 2K and 4K and 3D formats. Meng was enrolled in Boston University’s image processing PhD program and conducted research at MIT and MERL prior to Orbeus. Meng is credited with 15 patents.



COFFEE
Amy Robinson - EyeWire
EyeWire Presents: Pipeline to Neuropia
Amy Robinson is an expert in crowd sourcing. She is the Executive Director of EyeWire, a game to map the brain played by nearly 200,000 people worldwide. In EyeWire, gamers solve 3D puzzles that map out neurons, allowing neuroscientists to chart synaptic connections among neurons and thus begin to decipher the mysteries of how we see. EyeWire is the first of many games that will invite the world to make discoveries about how the brain works. Amy is a long time TEDster and founded the TEDx Music Project, a collection of the best live music from TEDx events around the world. Amy writes the Neurotech series for Scientific American. She was named to the Forbes 30 Under 30 in 2015.




Ryan Adams - Assistant Professor of Computer Science - Harvard
Taking Humans out of the Deep Learning Loop
Ryan Adams - Harvard
Taking Humans out of the Deep Learning Loop
Machine learning frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience and heuristics. This pain is particularly acute in deep learning and there is great appeal in automatic approaches that optimize learning algorithms for the problem at hand. I will discuss how this can be tackled with Bayesian optimization, surpassing human experts on many deep learning problems. I'll also talk about applying these ideas across the sciences, and discuss how our new startup, Whetlab, is making it easy to use Bayesian optimization over the wire.
Ryan Adams is an Assistant Professor of Computer Science at Harvard. He received his Ph.D. in Physics at Cambridge as a Gates Scholar. He was a CIFAR Junior Research Fellow at the University of Toronto before joining the faculty at Harvard. His Ph.D. thesis received Honorable Mention for the Savage Award for Theory and Methods from the International Society for Bayesian Analysis. Dr. Adams has won paper awards at the International Conference on Machine Learning, the International Conference on Artificial Intelligence and Statistics, and the Conference on Uncertainty in Artificial Intelligence. He has also received the DARPA Young Faculty Award and the Sloan Fellowship. Dr. Adams is the CEO of Whetlab, a machine learning startup, and co-hosts the popular Talking Machines podcast.



Aaron Steele - Sr. VP of Technical Business Development - CartoDB
The Deep Blue Dot
Aaron Steele - CartoDB
Beyond the Headless Map
Many of today’s maps are traditional, giving you a full picture of a particular space, like your current location on a Google Map. But there’s a growing trend of headless maps. Consider Tinder which is a giant map of potential partners where you never see a map. With IoT we can imagine going beyond the headless map into a world of no maps at all. For example, you say “ok car, let’s go to LA” and it maps out the best way to get there from streams of real-time sensor data from roads, satellites, traffic, weather, etc. In this talk we explore a future where we might stop looking at our maps altogether, because IoT is the map.
Aaron Steele is Sr. Vice President of Technical Business Development at CartoDB where he thinks creatively about the future and is responsible for company growth. Aaron holds a degree in Computer Science from the University of California, Berkeley. Before CartoDB, Aaron was CTO and Principal Software Engineer at World Resources Institute. His career started in academia, bridging gaps between biodiversity science, big data, and the web.



LUNCH


Dhruv Batra - Assistant Professor/Research Scientist - Georgia Institute of Technology/Facebook AI Research (FAIR)
Visual Question Answering and CloudCV
Dhruv Batra - Georgia Institute of Technology/Facebook AI Research (FAIR)
Habitat: A Platform for Embodied AI Research
We present Habitat, a new platform for the development of embodied artificial intelligence (AI). Training robots in the real world is slow, dangerous, expensive, and not easily reproducible. We aim to support a complementary paradigm – training embodied AI agents (virtual robots) in a highly photorealistic 3D simulator before transferring the learned skills to reality.
The ‘software stack’ for training embodied agents involves datasets providing 3D assets, simulators that render these assets and simulate agents, and tasks that define goals and evaluation metrics, enabling us to benchmark scientific progress. We aim to standardize this entire stack by contributing specific instantiations at each level: unified support for scanned and designed 3D scene datasets, a new simulation engine (Habitat-Sim), and a modular API (Habitat-API).
The Habitat architecture and implementation combine modularity and high performance. For example, when rendering a realistic scanned scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (FPS) running single-threaded and can reach over 10,000 FPS multi-process on a single GPU! Finally, we describe the Habitat Challenge, an autonomous navigation challenge that aims to benchmark and advance efforts in embodied AI.
Key Takeaways:
- Photo-realistic simulators are the future
Dhruv Batra is an Assistant Professor in the School of Interactive Computing at Georgia Tech and a Research Scientist at Facebook AI Research (FAIR).
His research interests lie at the intersection of machine learning, computer vision, natural language processing, and AI, with a focus on developing intelligent systems that are able to concisely summarize their beliefs about the world with diverse predictions, integrate information and beliefs across different sub-components or `modules' of AI (vision, language, reasoning, dialog), and interpretable AI systems that provide explanations and justifications for why they believe what they believe.
In past, he has also worked on topics such as interactive co-segmentation of large image collections, human body pose estimation, action recognition, depth estimation, and distributed optimization for inference and learning in probabilistic graphical models.
He is a recipient of the Office of Naval Research (ONR) Young Investigator Program (YIP) award (2017), the Early Career Award for Scientists and Engineers (ECASE-Army) (2015), the National Science Foundation (NSF) CAREER award (2014), Army Research Office (ARO) Young Investigator Program (YIP) award (2014), Outstanding Junior Faculty awards from Virginia Tech College of Engineering (2015) and Georgia Tech College of Computing (2018), two Google Faculty Research Awards (2013, 2015), Amazon Academic Research award (2016), Carnegie Mellon Dean's Fellowship (2007), and several best paper awards (EMNLP 2017, ICML workshop on Visualization for Deep Learning 2016, ICCV workshop Object Understanding for Interaction 2016) and teaching commendations at Virginia Tech. His research is supported by NSF, ARO, ARL, ONR, DARPA, Amazon, Google, Microsoft, and NVIDIA. Research from his lab has been extensively covered in the media (with varying levels of accuracy) at CNN, BBC, CNBC, Bloomberg Business, The Boston Globe, MIT Technology Review, Newsweek, The Verge, New Scientist, and NPR.
From 2013-2016, he was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, where he led the VT Machine Learning & Perception group and was a member of the Virginia Center for Autonomous Systems (VaCAS) and the VT Discovery Analytics Center (DAC). From 2010-2012, he was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed academic computer science institute located on the University of Chicago campus. He received his M.S. and Ph.D. degrees from Carnegie Mellon University in 2007 and 2010 respectively, advised by Tsuhan Chen. In past, he has held visiting positions at the Machine Learning Department at CMU, CSAIL MIT, Microsoft Research, and Facebook AI Research.
Webpage: http://cc.gatech.edu/~dbatra



END OF SUMMIT

LIGHT BREAKFAST
Patrick Ehlen - Loop AI Labs
Patrick Ehlen, PhD, is a cognitive scientist and Head of Deep Learning at Loop AI Labs. He specializes in representation learning for semantics, pragmatics, and concept acquisition. He developed natural language and context resolution technologies at AT&T Labs, and worked on methods to extract concepts and topics from ordinary spontaneous conversations among people as part of the DARPA CALO project at CSLI/Stanford. He has produced 45 research publications in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, and human concept learning. He joined Loop AI Labs to help usher in a new era of cognitive computing services.




Jianxiong Xiao - Assistant Professor - Princeton University
Deep Visual Learning Beyond 2D Object Recognition
Jianxiong Xiao - Princeton University
3D Deep Learning for Robot Perception
Deep learning has made unprecedented progress in artificial intelligence tasks from speech recognition to image recognition. In both, we ask our algorithms to reason about features in the most appropriate dimension: for natural language, we feed one-dimensional one-hot vectors of words as input to a recurrent neural network, whereas in image processing, we use two-dimensional filters over pixels in a convolutional network. However, as we are physically living in a three-dimensional world, for robot perception, it is more natural and often more useful to use three-dimensional representations and algorithms to reason about the 3D scene around us.
In this talk, I will share our recent experiences on 3D deep learning at three different levels for robot perception: local part, whole object, and global scene. At the local part level, we have developed an algorithm to learn 3D geometric descriptors to match local 3D keypoints, which is a critical step in robot mapping. At the object level, we have developed an object detector to slide a window in 3D using 3D convolutional neural networks. At the global scene level, we propose a novel approach to feed the whole 3D scene into a deep learning network, and let the network automatically learn the 3D object-to-object context relationship for joint inference with all the objects in a scene. To support 3D deep learning research, I will introduce "Marvin", a deep learning software framework to work with three-dimensional deep neural networks.
Jianxiong Xiao is an Assistant Professor in the Department of Computer Science at Princeton University and the director of the Princeton Vision Group. He received his Ph.D. from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT). His research focuses on bridging the gap between computer vision and robotics by building extremely robust and dependable computer vision systems for robot perception. Especially, he is interested in 3D Deep Learning, RGB-D Recognition and Reconstruction, Place-centric 3D Context Modeling, Synthesis for Analysis, Deep Learning for Autonomous Driving, Large-scale Crowd-sourcing, and Petascale Big Data. His work has received the Best Student Paper Award at the European Conference on Computer Vision (ECCV) in 2012 and Google Research Best Papers Award for 2012, and has appeared in popular press in the United States. Jianxiong was awarded the Google U.S./Canada Fellowship in Computer Vision in 2012, MIT CSW Best Research Award in 2011, and two Google Research Awards in 2014 and in 2015. More information can be found at: http://vision.princeton.edu.


Aditya Khosla - Research Assistant - MIT
Predicting Human Visual Memory using Deep Learning
Aditya Khosla - MIT
Aditya Khosla is a Research Assistant at MIT working on deep learning for computer vision and human cognition. He is interested in developing machine learning techniques that go beyond simply identifying what an image or video contains, but instead predict the impact visual media has on people e.g., predicting whether someone would like an image or not, and whether they would remember it. He is also interested in applying computational techiques to predictably modify these properties of visual media automatically. He is a recipient of the Facebook Fellowship, and his work on predicting image popularity and modifying face memorability has been widely featured in popular media like The New York Times, BBC, and TechCrunch. For more information, visit my website: http://mit.edu/khosla



Alexander Schwing - Fields Postdoctoral Fellow - University of Toronto
Deep Learning meets Structured Prediction
Alexander Schwing - University of Toronto
Deep Learning meets Structured Prediction
Many holistic prediction challenges for real-world applications involve reasoning about several random variables which are statistically related. Markov random fields (MRFs), and energy minimization methods in general, are a great mathematical tool to encode such dependencies. Within this talk we'll show how to combine MRFs with deep learning algorithms to estimate more complex non-linear representations, while taking into account the dependencies between the output variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form MRF potentials. We then demonstrate its applicability and generality using a variety of 3D scene understanding tasks such as semantic image segmentation, 3D layout prediction and image tagging.
Alexander Schwing is currently a Fields Postdoctoral Fellow at University of Toronto working with Raquel Urtasun. He graduated with a diploma degree in electrical engineering and information technology from Technical University Munich (TUM) and completed his PhD in computer science at ETH Zurich, collaborating mainly with Raquel Urtasun (University of Toronto), Tamir Hazan (Haifa University) and Marc Pollefeys (ETH Zurich). His research focuses on optimization algorithms for inference and learning tasks and his work is motivated among others by applications arising from monocular 3D scene understanding topics. For more information, please browse to http://alexander-schwing.de.


Aude Oliva - Principal Research Scientist - MIT
Learning Deep Features for Scene and Place Recognition
Aude Oliva - MIT
Learning Deep Features for Scene and Place Recognition
With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these deep architectures. Using a combination of neuro-imaging techniques of the human brain, and computer science methods, the talk will show how meaningful information emerge in the human brain, and within deep learning architectures, and how CNN architectures can benefit from neuroscience.

Devavrat Shah - MIT
Tales of Bayesian Regression
In this talk, I will discuss few case studies for unusually accurate predictions with unstructured data. We develop and utilize the method of Bayesian Regression for what we call "Latent Source Model". The basic insight is quite simple. There are only few distinct ways in which phenomenon of interest occurs. Be it topics trending on Twitter, price variations in Bitcoin or choices consumers make in going to restaurants, shopping clothes or watching movies. The method is embarrassingly parallel and scalable while being highly accurate.
Devavrat Shah is an Associate Professor with the department of Electrical Engineering and Computer Science at MIT. He is a Co-Founder and Chief Scientist of Celect, which helps retailers decide what to put where by accurately predicting customer choice using omni-channel data. His primary research interest is in developing large-scale machine learning algorithms for massive unstructured data. He has made contributions to development of “gossip” protocols and “message-passing” algorithms which have been pillar of modern distributed data processing systems.
He has received 2010 Erlang Prize from INFORMS. He is a distinguished young alumni of his alma mater IIT Bombay.

Paul Murphy - Clarify
Deep Learning & Speech: Adaptation, the Next Frontier
The speech community is finally excited about deep learning, but we’re proceeding with caution. Adaptation is critical to understanding real-world speech data. We need to adapt to acoustics and language of course, but also to context. To date, DNNs have shown great promise, but their ability to adapt to the unexpected is still in question. This talk will look at where we are today, as well as the challenges still in front of us.
Paul Murphy is one of Clarify's founders and its CEO. Paul's career in software operations industry has spanned twenty years and three continents. Ten years were dedicated to understanding and building large systems on Wall Street for clients like J.P. Morgan and Salomon Brothers. Paul's work in this area allowed him to explore a broad range of computing solutions, from mainframes to web services, and the gamut of space-time tradeoffs required by dissimilar front and back office systems. Thirteen years ago, Paul moved to London to work at Adeptra, a pioneer in the use of automated outbound calling in the area of credit card fraud detection and prevention. As Adeptra's CTO, he developed all of the software which enabled Adeptra to place intelligent interactive outbound calls on behalf of clients. These systems made extensive use of text-to-speech and voice recognition technology. Since then Paul has dedicated his time to developing technologies that leverage emerging voice processing techniques.




Kevin O'Brien - CEO & Founder - GreatHorn
Of Robots and Response Times: Automating Cybersecurity Analysis
Kevin O'Brien - GreatHorn
Of Robots and Response Times: Automating Cybersecurity Analysis
Over the past two and a half years, both the sophistication and frequency of cybersecurity attacks assaulting organizations has risen dramatically. Operating at sub-detectable levels, today's hackers launch long-term assaults on entire industries, sharing data and exploiting weaknesses to penetrate network defenses. Establishing a more robust means of responding to these advanced persistent threats (or APT) requires a new model for looking at cybersecurity data. In this talk, we will explore how deep learning can be used to predict not only when these attacks are underway, but also how they propagate through markets and industries, and how cybersecurity professionals can reduce the time to both detection and response through the use of an automated prediction approach to breach mitigation.
Kevin is the CEO and founder of GreatHorn, his 6th startup. He has been working within the cybersecurity industry since the late 1990s, with a focus on penetration analysis and security within automated and complex infrastructure. He speaks and writes frequently on how security is evolving in response to today's complex business and technology ecosystems, especially within highly regulated industries such as healthcare, retail, and financial services, and has published multiple papers on threat classification automation, cloud security, and the establishment of anti-social-engineering firewalls.




Olexandr Isayev - Research Scientist - University of North Carolina
Computational Drug Discovery with Deep Learning
Olexandr Isayev - University of North Carolina
Olexandr Isayev is a Research Scientist at UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill. In 2008, Olexandr received his Ph.D. in computational chemistry. He was Postdoctoral Research Fellow at the Case Western Reserve University and scientist at the government research lab before joining UNC in 2013. Olexandr received “Emerging Technology Award” from the American Chemical Society (ACS) and the GPU computing award from NVIDIA in 2014. His research interests focus on making sense of chemical data with molecular modeling and machine learning.


Will Silversmith - EyeWire
EyeWire Presents: Pipeline to Neuropia
William Silversmith has been the technical lead of EyeWire since taking over from Mark Richardson in June 2013. With a Bachelors of Science from Rensselaer Polytechnic Institute, a passion for probing into deep questions about ourselves, and a drive to learn every aspect of his work, he has pushed the technology and design of EyeWire to where it is today. His role currently includes enhancing system scalability, designing and producing key game features, coordinating technical decisions from other contributors, and providing technical support and guidance to developing partnerships. In his spare time, he likes to read neuroscience papers, learn new languages, and is developing a nascent love of photography and design.
