Machine Intelligence Summit schedule

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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 (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 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 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.

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 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.

This session will be taking place 2.50pm - 3.30pm in the Seaport Room adjacent to the exhibition area.

The breakout session is an opportunity to advertise job openings in your company to 200+ attendees at the event; or to raise your profile if you are looking for a new role.

Participating companies include:

Capital One

At Capital One, we’re building a leading information-based technology company. Still founder-led by Chairman and Chief Executive Officer Richard Fairbank, Capital One is on a mission to help our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We measure our efforts by the success our customers enjoy and the advocacy they exhibit. We are succeeding because they are succeeding. Do you want to work for a tech company that writes its own code, develops its own software, and builds its own products? We experiment and innovate leveraging the latest technologies, engineer breakthrough customer experiences, and bring simplicity and humanity to banking. We make a difference for 65 million customers. We're changing banking for good. At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors, who love to solve real problems and meet real customer needs. We want you to be curious and ask “what if?”

ROSS Intelligence

ROSS Intelligence uses artificial intelligence to enable lawyers to do more than humanly possible. Our first product is ROSS, an artificially intelligent lawyer that can answer complex legal research questions. We are backed by Y-Combinator and count some of the best law firms in the industry as our customers. We are looking for engineers who are passionate about applying natural language understanding and machine learning to help humans find answers from data.

Locus Analytics

Locus Analytics, a research firm based in New York City, studies networks of relationships between businesses, jobs, products, and other economic entities. Drawing from linguistics, systems sciences, and graph theory, Locus has developed a universal framework for systematically describing economic entities across industry, scale, and geography. Existing economic datasets are highly fragmented, inconsistently structured, and opaque. Our mission is to use our technology to make the world’s economic data broadly accessible and readily navigable.

Metabolon

Metabolon is the world leader in big data biochemistry. Our metabolomics platform is driving advances in precision medicine, biomarker discovery, diagnostic testing, and genomic analysis. We are looking for machine learning experts to help us leverage this data stream to decode how metabolism and life work. Projects include: integrating knowledge about the network of metabolic interactions into our analysis, mining biological literature (and other unstructured data streams), and automating extraction and curation of metabolomics data from raw mass spectrometry signal.