
REGISTRATION & LIGHT BREAKFAST
Luis Oros - Pearson
Luis Helps Pearson tap into the potential of advanced computing and data science by finding product-market fit of lab R&D. Paths to market are explored through new prototypes and business model innovation.
Prior to joining the Lab, Luis was a product owner leading innovation efforts in the Future Technologies Lab and Higher Education businesses. Before joining Pearson he created a novel student-centered curriculum while teaching in Hawaii as part of Teach For America. He has a BA in neuroscience from Johns Hopkins University and an MA in education from the university of Hawaii.


THEORY & APPLICATIONS OF ARTIFICIAL INTELLIGENCE


Joanna Bryson - Professor - University of Bath
Why AI Must Be Biased, and How We Can Respond
Joanna Bryson - University of Bath
Why AI Must Be Biased, and How We Can Respond
Like physics and biology, computation is a natural process with natural laws. We are making radical progress in artificial intelligence because we have learnt to exploit machine learning to capture existing computational outputs developed and transmitted by humans with human culture. This powerful strategy unfortunately undermines the assumption that machined intelligence, deriving from mathematics, would be pure and neutral, providing a fairness beyond what is present in human society. In learning the set of biases that constitute a word's meaning, AI also learns patterns some of which are based on our unfair history. Addressing such prejudice requires domain-specific interventions.
Joanna J. Bryson is a transdisciplinary researcher on the structure and dynamics of human- and animal-like intelligence. Her research covers topics ranging from artificial intelligence, through autonomy and robot ethics, and on to human cooperation. She holds degrees in Psychology from Chicago (AB) and Edinburgh (MPhil), and Artificial Intelligence from Edinburgh (MSc) and MIT (ScD). She has additional professional research experience from Oxford, Harvard, and LEGO, and technical experience in Chicago's financial industry, and international organization management consultancy. Bryson is presently a Reader (associate professor) at the University of Bath, and an affiliate of Princeton's Center for Information Technology Policy.




Rohan Agrawal - Machine Learning Engineer - Spotify
Personalized Playlists at Spotify
Rohan Agrawal - Spotify
Personalized Playlists at Spotify
Spotify has released a wide range of personalized features, out of which we will cover two personalized playlist products: Discover Weekly and Release Radar. We will go through some of the Machine Learning models that go into making these playlists. We will talk about some of the challenges faced while building such products, lessons learnt and open areas for improvement.
Rohan is a Machine Learning Engineer at Spotify where he works on Collaborative Filtering and NLP models which power the Discover Weekly and Release Radar features. During his time at Spotify, he has also worked on real time models for ad targeting, playlist recommendations as well as providing a Personalization Service as a piece of infrastructure to other teams within Spotify. Prior to joining Spotify, Rohan was at Columbia University from where he obtained a Masters Degree in CS.




Ed Chow - Programme Manager, Jet Propulsion Lab - NASA
AUDREY for Next Generation First Responders
Ed Chow - NASA
AUDREY for Next Generation First Responders
NASA Jet Propulsion Laboratory is developing the AUDREY (Assistant for Understanding Data through Reasoning, Extraction, and sYnthesis) technology to support NASA and Department of Defense needs for data analytics, autonomy, test & evaluation, and cyber security applications. This presentation will focus on the application of the AUDREY technology to support the Department of Homeland Security Next Generation First Responder Program. We will discuss the architecture and application of the collaborative AUDREY artificial intelligence personal assistant with simultaneous reasoning and learning capabilities to address the dynamic environment, insufficient training set, unreliable communications, and incomplete sensor data challenges.
Dr. Edward Chow is the Manager of the Civil Program Office at the NASA Jet Propulsion Laboratory (JPL) and the JPL Principal Investigator for the Real-time Automated Insight Engine for Data to Decision (RAID) Project funded by OSD T&E S&T C4T to develop the next generation artificial intelligence technologies to enable system of system test automation in distributed T&E environment. Dr. Chow received his Ph.D. in Electrical Engineering from University of Southern California in 1988. Dr. Chow is the recipient of the NASA Exceptional Engineering Achievement Medal and the JPL Lew Allen Award.



COFFEE
VIDEO ANALYSIS & IMAGE RECOGNITION
Carl Vondrick - Google
Predictive Vision
Our research studies Predictive Vision with the goal of anticipating the events that may happen in the immediate future. To tackle this challenge, we present predictive vision algorithms that learn directly from large amounts of raw, unlabeled data. Capitalizing on millions of natural videos, our work develops methods for machines to learn to anticipate the visual future, forecast human actions, and recognize ambient sounds.
Carl Vondrick is a research scientist at Google and he will be an assistant professor at Columbia University in fall 2018. He received his PhD from the Massachusetts Institute of Technology in 2017. His research was awarded the Google PhD Fellowship, the NSF Graduate Fellowship, and is featured in popular press, such as NPR, CNN, the Associated Press, and the Late Show with Stephen Colbert.




Kamelia Aryafar - Senior Data Scientist - Etsy
Learning to Rank with Deep Visual Semantic Features
Kamelia Aryafar - Etsy
Learning to Rank with Deep Visual Semantic Features
Search is an important problem for modern e-commerce platforms such as Etsy. As a result, the task of ranking search results automatically or the so-called learning to rank is a multibillion dollar machine learning problem.In this talk, we first review Etsy's approach to learning to rank using a few hand-constructed features based on the Etsy listing's text-based representation.
We then discuss a multimodal learning to rank model that combines these traditional text-based features with visual semantic features transferred from a deep convolutional neural network. We show that a multimodal approach to learning to rank can improve the quality of ranking in an experimental setting.
Kamelia Aryafar, Ph.D. is a senior data scientist with Etsy's Data Science team since 2013. She works on building scalable machine learning and computer vision tools to curate a personalized experience for Etsy users. Prior to Etsy she was doing a Ph.D. in computer science and machine learning in Drexel University, building large-scale music classification models.




Soumith Chintala - Research Engineer - Facebook
Unsupervised Learning Using Adversarial Networks
Soumith Chintala - Facebook
Unsupervised Learning Using Adversarial Networks
Unsupervised learning has become one of the most important problems in AI. In this talk I will give an overview of unsupervised learning with a powerful learning method called Generative Adversarial Networks, and talk about it’s applications.
Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. Prior to joining Facebook in August 2014, he worked at MuseAmi, where he built deep learning models for music and vision targeted at mobile devices. He holds a Masters in CS from NYU, and spent time in Yann LeCun's NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.




Rahel Jhirad - Director, Data Science - Hearst
Multi-Modal Deep Learning: Connecting Text to Images
Rahel Jhirad - Hearst
Rahel Jhirad is the Director of Data Science at Hearst. She holds a MS in Mathematics from NYU and a PhD in Economics from Princeton. She has worked in finance for 20 some years as a quant, risk manager and trader. She has been working with machine learning algorithms at scale since 2010, building models and advising Fortune 100 companies.
She joined Hearst in 2015 and leads their data science team. She works with content, clickstream, ad, audience, off-platform data using numerous machine learning algorithms including Natural Language Processing, Topic Modelling, Recommender Systems, A/B Testing, Image and Object Recognition, and other Machine Learning and Deep Learning tools. She runs a popular meetup called Economics and Big Data whose goal it is to bring the researchers in computer science and machine learning and researchers social science closer together. She is a strong advocate of cross-disciplinary literacy.



LUNCH
SPEECH RECOGNITION & NLP


Antoine Bordes - Research Scientist - Facebook AI Research
Language Understanding with Memory Networks
Antoine Bordes - Facebook AI Research
Language Understanding with Memory Networks
Despite recent advances in AI, deep understanding of natural language by machines still remains highly challenging. In this talk, we will present Memory Networks, an attention-based neural network architecture that operates an external symbolic memory component to perform reasoning. Memory Networks can achieve interesting performance on various tasks of question answering and dialogue management, and appear to be a promising avenue towards a better machine comprehension of language.
Antoine Bordes is a research scientist at Facebook Artificial Intelligence Research. Prior to joining Facebook in 2014, he was a CNRS researcher in Compiegne in France and a postdoctoral fellow in Yoshua Bengio's lab of University of Montreal. He received his PhD in machine learning from Pierre & Marie Curie University in Paris in 2010 with two awards for best PhD from the French Association for Artificial Intelligence and from the French Armament Agency. Antoine’s current interests are centered around natural language understanding using neural networks, with a focus on question answering and dialogue. He published more than 40 papers cited more than 3,000 times.




Stephen Scarr - CEO - Info.com & eContext
Importance of Accurately Labeled Data for Topical Machine Learning
Stephen Scarr - Info.com & eContext
Importance of Accurately Labeled Data for Topical Machine Learning
Accurate data plays a critical role producing useful machine learning models. Most available training sets create models outputting either generic topic classifications or unstructured flat entities. Attempts at granular, hierarchical outputs are sub-optimal, even when trained on corpuses in a single vertical, because of the significant ambiguity of natural language. Automatically labeling training data with hundreds of thousands of hierarchical topics produces flexible, structured classifiers in any vertical. In this session, we’ll address: • The role taxonomy plays in machine learning • Benchmarking opportunities for better machine learning results • Improving the machine learning model with labeled topic data
As CEO of Info.com and eContext, Stephen is responsible for all aspects of development at both companies and has more than 20 years of experience in managing businesses. Stephen has a strong marketing background and a passion for big data and analytics.
Info.com is an independent search platform with 8 million unique users. From a single search query, Info.com provides results from the leading search engines. Info.com is also partnered with eight vertical search providers. Info.com owns Info.co.uk and Info.com.au and has Chicago and London offices.




Devin Gaffney - PhD Student - Northeastern Universtiy
Bot Analytics: Simulating Random Walks to Predict Network Interventions
Devin Gaffney - Northeastern Universtiy
Bot Analytics: Simulating Random Walks to Predict Network Interventions
Chat Bots have been described as a potentially large growth area over the next few years - what sorts of new strategies can we use to predict the behaviors of users interacting with chat bots, and how can we detect and predict interventions designed to maximize desired user behaviors?
Devin Gaffney is a PhD Student at Northeastern University’s Network Science Institute. His current research interests are focused on online human interaction, and to what extent general patterns of interaction can inform us of how people socialize and organize. His dissertation is specifically looking at how individuals migrate across platforms and across topics, and how that dynamic process can be described in terms of social interaction.



Tara Sainath - Senior Research Scientist - Google
Multichannel Signal Processing with Deep Neural Networks for Automatic Speech Recognition
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.



COFFEE
ECONOMIC IMPACT OF ARTIFICIAL INTELLIGENCE

PANEL: How Can Machine Intelligence Provide a Competitive Edge in Business?
Ethan Rosenthal - Dia&Co
Ethan Rosenthal is the first Data Scientist hire at Dia&Co, a personal styling service for plus-size women. He builds data science products for enhancing the customer experience through increased personalization while optimizing over constraints inherent to a rapidly scaling startup. This work ranges from recommender systems to integer programming to natural language processing. Before Dia&Co, Ethan was a Data Scientist at Birchbox and an academic physicist. He received his PhD in Physics from Columbia University where he studied unconventional superconductors.


Alexander Statnikov - American Express
Alexander Statnikov is a Vice President of Digital Modeling and Machine Learning at American Express. He plays an essential role in leading Machine Learning and Big Data Analytics activities for the company. He is currently managing teams of data scientists responsible for developing a variety of machine learning models and data products for customer acquisition, underwriting, credit and fraud management. Prior to joining American Express, Alexander was an Associate Professor at New York University specializing in causal discovery and various areas of data science and machine learning. Alexander is an author of more than 50 journal articles, 5 books and monographs, and inventor of 13 U.S. patents (issued or pending).


Steve Ardire - SignalAction.AI
Contextual AI for Digital Behavioral Health from SignalAction.AI
COVID-19 has filed the Mental Health Crisis especially for the world’s youth where depression rates tripled during the pandemic and where 1 in 4 people in the 18-24 age bracket have seriously considered committing suicide. Human behavior is messy, and most brain activity is nonconscious, so the best way to address is using multimodal analysis i.e., spoken language, emotional, facial, behavioral inputs with human-like understanding to reveal intent, nuanced perceptions, anxieties for more meaningful insights. If you detect fear and anxiety perhaps depression starts becoming manifest so would be incredibly helpful for therapists to have real-time access to session data that shows behavior and emotional states in granular detail to analyze the situation better.
Steve is the Co-Founder of SignalAction.AI Contextual AI for Digital Behavioral Health, an AI startup ‘force multiplier' and Quintessential 'Merchant of Light'. He built his personal brand as AI startup ‘force multiplier' ( in which he advised 25 AI startups over the past 7 years ) shaping serendipity to connect and illuminate the dots that matter leveraging deep relationship capital with incisive business strategy to deliver the best results.


Kathryn Hume - Fast Forward Labs
Kathryn Hume leads marketing for Fast Forward Labs, a machine intelligence research company, and teaches courses on law and technology at the University of Calgary. Prior to joining Fast Forward Labs, she advised global law firms on data privacy and security, and managed Intapp's Risk Roundtable, a seminar series about legal risk management. Holding a doctorate in comparative literature from Stanford, speaks multiple languages and excels at helping organizations innovate by mapping new technologies to vertical business problems.


Hanlin Tang - Intel
Deep Learning in Production
Deep learning algorithms are now transiting from proof-of-concepts or academic research to production deployments in industry. In this talk, we discuss lessons learned in engineering algorithms at scale, from model development to inference optimizations to performance monitoring in the field. We also present the Intel Nervana portfolio of software and hardware to enable faster deployments.
Hanlin is an engineering lead at Intel’s AI Products Group. He leads AI projects with federal agencies as well as research initiatives in computer vision. Hanlin joined Intel from its acquisition of the deep learning startup Nervana Systems. At Nervana and now Intel, Hanlin co-developed the open-source deep learning framework neon, and built algorithms used in applications ranging from satellite imagery analysis to neural spiking predictions. Hanlin obtained his Ph.D. from Harvard University, where he investigated the role of recurrent neural networks in human cortex.




Anasse Bari - Clinical Assistant Professor of Computer Science - NYU
Swarm Intelligence Algorithms
Anasse Bari - NYU
Anasse Bari (Ph.D.) is Fulbright scholar and a clinical assistant professor of computer science at New York University who has many years of predictive modeling and data mining experience. Dr. Bari has recently worked closely with leadership of the World Bank Group as a data scientist where he was leading the design of enterprise data analytics projects. Bari is the co-author of the book "Predictive Analytics for Dummies", by Wiley. Anasse is a renowned speaker and his research has been focused on predictive analytics, data mining, and information retrieval.




Max Sklar - Machine Laarning Engineer - Foursquare
Marsbot: A Personal Assistant From Foursquare
Max Sklar - Foursquare
Marsbot: A Personal Assistant From Foursquare
Foursquare’s local recommendation engine uses a variety of machine learning and NLP techniques to select places for users in a city. This year we launched Marsbot, a new concept for delivering these recommendations in the form of text messages. With the recent focus in the industry on chatbots and text applications, this talk will cover how we are thinking about this change from a product perspective, and some of the lessons learned along the way.
Max Sklar is an engineer and data scientist at Foursquare. As part of the engineering team, Max focuses on using both machine learning and heuristics to develop new features and products. Over the last year, he has led the development effort of Marsbot, a bot that texts local recommendations to users based off of where they go in the real world. Max has spoken at a variety of conferences, universities, and meetups. He holds an M.S. in Information Systems from NYU, and a B.S. in Computer Science from Yale.



CONVERSATION & DRINKS

REGISTRATION & LIGHT BREAKFAST
Luis Oros - Pearson
Luis Helps Pearson tap into the potential of advanced computing and data science by finding product-market fit of lab R&D. Paths to market are explored through new prototypes and business model innovation.
Prior to joining the Lab, Luis was a product owner leading innovation efforts in the Future Technologies Lab and Higher Education businesses. Before joining Pearson he created a novel student-centered curriculum while teaching in Hawaii as part of Teach For America. He has a BA in neuroscience from Johns Hopkins University and an MA in education from the university of Hawaii.


STARTUP SESSION


Lou Auguste - Founder & CEO - AlexaPath
MLCV for Automated Cervical Cancer Diagnosis
Lou Auguste - AlexaPath
A Yale-educated humanitarian, Lou was inspired to develop a rapid, cost-efficient solution for the global pathology shortage following a year he spent in Haiti as a member of USAID's Earthquake Recovery Team. Consultations with University College London, and collaboration with NYU Polytechnic, enabled Lou to begin development of the mWSI technique in 2013. In addition to founding Alexapath, Lou formed Team Live Longer, a joint effort between Haitian Doctors Abroad (AMHE), and Alexapath. Lou was recognized for his contributions to the field of innovation by Virgin Media in 2013, was honored at the AMHE's 2015 Spring Gala for his continued efforts to develop mobile health care solutions, and most recently was awarded Impact Fellowship from Singularity University.




Sravan Puttagunta - CEO - Civil Maps
Mixed Reality Maps for Autonomous Cars and Future Value Added Services for Passengers
Sravan Puttagunta - Civil Maps
Mixed Reality Maps for Autonomous Cars and Future Value Added Services for Passengers
Self Driving Cars need to groom passengers to feel safe and comfortable during a ride. Therefore it is important for the car to communicate it's capabilities and intentions in a way that the user has complete confidence in the decision making capabilities of the car. With the advancements in 3D mapping, ADAS safety systems, and complex decision engines; a passenger of the future will be informed of all the relevant information at the right time with the appropriate context for them to feel connected with the vehicle. This feedback loop is the pre-requisite step for passengers to feel in control of a car that is operating autonomously. In this presentation we will go over some of the key technologies such as Distributed AI, Cloud Robotics, Augmented Reality Human Machine Interfaces, Discovery services and other Value Added Services that will become important for passengers as cars become more autonomous.
Sravan founded Civil Maps, a computer vision robotics company that crowdsources 3D Semantic Maps and allows mobile robotics platforms to make decisions. He invented Video Fingerprinting for Linear Broadcast TV to compete with Neilson at his previous company. He wrote fairly substantive chunks of AI algorithms on cars which map the world in 3D--the most popular vehicular platform.
Sravan is the co-founder and CEO of Civil Maps, a computer vision robotics company that crowdsources 3D semantic maps and allows mobile robotics platforms to make decisions. In his previous work, he invented video fingerprinting for linear broadcast TV to track viewing habits and contributed to software that runs in more than 30 million TVs. He has written substantial portions of artificial intelligence (AI) algorithms on cars which map the world in 3D.


Shaunak Khire - EmmaAI
Applied AI in Finance
I will mostly be focusing on using bayesian methods together with vectors for word representation algorithms (eg: glove, word2vec like algos) to analyze data feeds for better decision making in an applied real world setting. This will be showcased by our ANN’s capability to disseminate unstructured topical information to output an analyst report and a near real time process by which the AI takes a decision based on the input to autonomously invest/trade in a particular financial security. Towards the end, I will also be speaking a bit more about the need to create and support open data protocols for personalization by parsing existing user datasets and allowing them to be freely imported across different types of AI agents.
Shaunak Khire, is the cofounder & CEO of Emma/MANSI AI, a company focused on building autonomous vertically integrated machine intelligence platform using bayesian methods & vectors for representation of words. Emma, the AI’s moniker, called the short term bottom on crude oil and the broader commodities complex in January. It began analyzing and writing analyst reports on US equities and bonds in March. In June 2016, Emma/MANSI began investing autonomously in in three financial securities GSK, Tesla and the 10Yr Bond.
Shaunak started out as a mobile security hacker at BT focusing on vulnerabilities in the Bluetooth standards, specifically the A2DP profiles on the Symbian and Java OS platforms. Previously Shaunak was the interim CTO & Board Member of a private equity backed DMP (data management platform), he has also served on the global board of the Mobile Marketing Association. Prior to that he was a fund manager at a small family office managing a diversified energy portfolio. Shaunak was a part of the Clinton Global Initiative tech & poverty alleviation groups and he coconstructed the MaghaCGI30 Index a social impact index that weighs constituents based on qualitative characteristics found typically in unstructured data such as corporate CSR reports. He is a contributor for TechCrunch & Recode and has been on MIT Technology Review’s 35 under 35 list in 2012.




Johann Hauswald - Co-Founder & VP, Engineering - Clinc
Lucida: Infrastructure for Emerging Intelligent Web Services
Johann Hauswald - Clinc
Lucida: Infrastructure for Emerging Intelligent Web Services
As Intelligent Personal Assistants (IPA) such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, webservice companies are now providing image, speech, and natural language processing web services as the core applications in their datacenters. These emerging applications require machine learning and are known to be significantly more compute intensive than traditional cloud based web services, giving rise to a number of questions surrounding the designs of server and datacenter architectures for handling this volume of computation.
I am a Ph.D. student in the Department of Computer Science and Engineering at the University of Michigan advised by Jason Mars, Lingjia Tang, and Trevor Mudge. I am part of Clarity Lab and affiliated with the Computer Engineering Lab. My research focuses on system design for emerging cloud-based applications. I am co-founder and VP of Engineering at clinc, an Ann Arbor, MI based startup designing personal assistant technologies.


COFFEE
REAL WORLD APPLICATIONS OF MACHINE INTELLIGENCE


Avneesh Saluja - Machine Learning Scientist - Airbnb
Extracting Customer Insights at Airbnb
Avneesh Saluja - Airbnb
Extracting Customer Insights at Airbnb
Abstract: Airbnb has had over 100 Million guest arrivals over all time, and over 40 Million in 2015 alone. Naturally this exponential growth is a challenge to deal with from a customer service perspective. The first part of this talk will thus concentrate on how we can respond to and address customer issues in a shorter amount of time, while maintaining a high level of customer satisfaction. In addition, customer issues in aggregate can tell us a lot about potential product improvements and changes, and the second part of this talk will discuss how we extract and categorize these potential improvements from vast amounts of customer service tickets.
Avneesh is currently a Machine Learning Scientist at Airbnb, where he leads efforts on building a common, scalable machine learning infrastructure that enables data scientists and engineers to explore, train, and deploy models with minimal effort. He has concentrated on leveraging the vast amounts of text data on the site to enable the next generation of data products within the company. Avneesh completed his PhD in natural language processing from Carnegie Mellon University in 2015 (where his thesis focused on building contextually richer models for translating human languages), and his undergraduate degree in electrical engineering from Stanford University in 2007. In a prior life (before grad school), he was a structured equity products trader at Goldman Sachs.




Yan Karklin - Principal Data Scientist - Knewton
Machine Learning for Adaptive Learning
Yan Karklin - Knewton
Machine Learning for Adaptive Learning
An effective adaptive learning system must identify deficiencies, provide appropriate remediation, and report actionable insights to students, teachers, and administrators. Machine learning aids in this mission by allowing us to infer aspects of students and educational content based on data, enabling any individual student on the system to benefit from the collective experience of many students. A critical task benefitting from this approach is estimating a student’s proficiency in one or more conceptual areas based on their and other students’ previous activity on similar content. In this talk, I’ll review this problem, its associated challenges, and some of the solutions that have been proposed. I’ll then present some recently published results comparing a class of Bayesian models based on item response theory with a deep learning approach employing recurrent neural networks.
Yan comes to Knewton with a strong background in computation, machine learning, and statistical data analysis. He got his Ph.D. in Computer Science at Carnegie Mellon University, where he was also involved in the Center for the Neural Basis of Cognition, an interdisciplinary program covering computational neuroscience and related fields. He received the prestigious Department of Energy (DOE) Computational Science Graduate Fellowship, and spent time at the DOE Lawrence Livermore National Lab developing sophisticated machine learning methods for automatically predicting RNA function. Prior to joining Knewton, he was a Howard Hughes Medical Institute fellow at New York University, where his research focused on understanding statistical patterns in images and sounds, and on formulating theories of information processing in the brain. In 2009, Yan published research in Nature and has taught courses in both computer science and neuroscience.




Erik Andrejko - Head of Data Science - Climate Corporation
Moore Meets Malthus - Machine Intelligence in Food Production
Erik Andrejko - Climate Corporation
Moore Meets Malthus - Machine Intelligence in Food Production
A recent confluence of factors has created unique opportunities to apply machine intelligence to agricultural production to increase farming efficiency and profitability. Technology is key to meeting the growing production demands caused by increasing global population and changing consumption patterns. To effectively address the challenge, we must utilize increasingly localized precision agriculture, analyze enormous data volumes, and bring machine learning powered solutions to the market. This talk considers the opportunities and challenges in building and delivering analytics-driven tools to augment human decision making as a means to address the world’s largest optimization problem: optimizing global food production.
Erik leads the data science and research organization, which applies large-scale statistical machine learning and data science to solve challenging problems in numerous domains including climatology, agronomic modeling and geospatial applications. Erik's contributions to The Climate Corporation include defining the data science vision and leading the research underpinning pioneering products including Climate Basic and Climate Pro. Previously, Erik worked at several Bay-area start ups. He has a B.S. in Computer Science from Arizona State University and a PhD in Mathematics from University of Wisconsin-Madison.




Ben Klein - Applied Researcher - eBay
Finding Similar Listings at eBay Using Visual Similarity
Ben Klein - eBay
Finding Similar Listings at eBay Using Visual Similarity
Ben Klein is an Applied Researcher at eBay NYC, where he works on computer vision, machine learning, and deep learning. Ben leads eBay’s efforts on applying computer vision for recommendation systems applications. Prior to joining eBay, Ben worked at Microsoft Research, as a machine learning researcher and developed algorithms that are used by Microsoft Xbox. Ben holds a Master’s degree in Computer Science from Tel-Aviv University and his work has been published in CVPR and ECCV.



LUNCH

APPLICATIONS OF MACHINE INTELLIGENCE IN HEALTHCARE

PANEL: Machine Intelligence in Healthcare
Kadija Ferryman - Data & Society
Kadija Ferryman is a cultural anthropologist whose research centers on the ethics of emerging technologies in biomedical research and health care. Specifically, her work examines the ethical dimensions of biomedical research that uses genomics to address racial disparities in health. Using ethnographic methods, she examines how values shape efforts to use genomic data to improve health outcomes and reduce chronic disease risk.
Dr. Ferryman holds degrees in anthropology from Yale (BA) and the New School for Social Research (PhD).
Currently, Dr. Ferryman is a Postdoctoral Scholar at the Data & Society Research Institute. She will be researching the promise and potential pitfalls of data collection and analysis in precision medicine.
Before completing her PhD, she was a public policy researcher at the Urban Institute in Washington, DC, where she studied housing discrimination and public housing redevelopment.


Arjun Krishnan - Princeton Uni
Arjun Krishnan is a computational biologist, currently working as a postdoctoral researcher at Princeton University. He'll soon be an Asst. Professor at Michigan State University. Arjun's research involves using statistics and machine learning to build data-driven models of how genes function differently in different tissues in the human body and how these tissue-specific functions relate to complex diseases.


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.


Jalak Jobanputra - Future\Perfect Ventures
Jalak Jobanputra is Founding Partner of Future\Perfect Ventures, a venture capital fund in NYC focused on early stage investments in next generation technology including blockchain and machine learning. Before founding Future\Perfect Ventures in 2013, Jobanputra was the Director of Mobile Investments in Emerging Markets at Omidyar Network. She has 20 years experience in venture capital, impact investing, media and technology. She was previously Senior Vice President at the New York City Investment Fund (NYCIF), a private economic development fund, where she managed the fund’s technology and digital media venture investments. While there, Jobanputra spearheaded the formation NYCSeed in 2008, and helped launch the FinTech Innovation Lab, which has since been replicated in London and Hong Kong. Jobanputra worked closely with the Bloomberg administration and NYCEDC to implement initiatives to help diversify the NYC economy through NYC’s growing tech/digital sectors. Prior to NYCIF, she was a Principal at New Venture Partners, a $300 million early stage venture fund that commercialized technology out of corporate labs. At NVP, Jobanputra incubated a range of technologies, including speech recognition/NLP, 3D displays, video surveillance, 4G wireless broadband, and music recommendation software. From 1999- 2003, Jobanputra was at Intel Capital in Silicon Valley, where she invested in enterprise software, internet and digital media startups. In 1997 in NYC, in the early days of Silicon Alley, she launched and managed product development for online financial information startup Horsesmouth, where she also learned to code and help build a content recommendation engine. She began her career in media, telecom and tech investment banking at Lehman Brothers and Broadview in NYC and London. Jobanputra currently sits on the Board of Directors for the Center for an Urban Future, Advisory Board of Loreal’s Women in Digital Initiative, is a member of Mayor DeBlasio’s Broadband Taskforce, and served on Secretary Clinton’s Women’s Leadership Council. Jobanputra spent four months setting up microfinance programs and training women entrepreneurs in Dar es Salaam, Tanzania after receiving her MBA from the Kellogg School of Management in 1999. She graduated magna cum laude from the University of Pennsylvania with a BA in Communications from the Annenberg School and a BSE in Finance from the Wharton School. Her blog The Barefoot VC has repeatedly been cited as a Top 10 investor blog in several publications and she is a frequent guest on Bloomberg TV, CBNC and Fox Business news.




Olexandr Isayev - Research Scientist - University of North Carolina
How Can AI Help in Designing Better and Safer Medicine
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.


APPLICATIONS OF MACHINE INTELLIGENCE IN & VEHICLES & ROBOTICS
Dan Shiebler - TrueMotion
Making Deep Learning Work on Messy Sensor Data
The smartphone is perhaps the most powerful machine in human history. People take it with them everywhere they go, and it provides essentially limitless entertainment, knowledge, and utility. Almost every phone comes equipped with sensors that passively generate enormous amounts of data, such as the GPS, IMU, magnetometer and barometer. In order to take advantage of the complex higher order relationships in this sensor data, we turn to deep learning. But making deep learning algorithms work on noisy, unreliable and poorly labeled smartphone sensor data can be tricky.
Dan is a Data Scientist at TrueMotion, where he builds machine learning algorithms that use smartphone sensors to understand and score driving behaviors. Dan leads TrueMotion's efforts on developing smartphone IMU algorithms to detect hard brakes and distracted driving. Dan is also a guest speaker at the NYC Data Science Academy. In the past, he has worked as a neurosurgery researcher at Rhode Island Hospital, as a Digital Humanities Programmer at the Brown University Library, and as a Computational Biology Software Consultant for the Weinreich Lab at Brown University. Dan graduated from Brown University in 2015.




Marc Gyongyosi - CEO & Founder - IFM Technologies
Intelligent Flying Machines That Count Inventory
Marc Gyongyosi - IFM Technologies
Marc Gyongyosi is the Founder and CEO of IFM Technologies, a Chicago based Computer Vision and Robotics Startup. At IFM he is creating autonomous flying robots for automated indoor data collection. His expertise is in visual-inertial navigation and GPU programming. Previously, Marc worked at a startup developing a novel 3D vision system for self-driving cars with funding from the NSF and Google. Before that, Marc was a part of BMW's Advanced Robotics R&D group in Munich where he worked on lightweight collaborative robots.



END OF SUMMIT
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MI TALENT EXPO - Seaport Room - Networking break to meet potential new employees
Coffee Break & Recruitment - 14:50 - 15:25