Applied Data Science Invited Talks

In the past 20 years, the practical examples of KDD/data mining have become so ubiquitous that it’s almost impossible to imagine a new venture that isn’t based on data science. Uber, Facebook, 23andMe, Tesla – they aren’t just technology companies; they are data companies. And yet the reality is that these companies are still anomalies. Large, successful companies usually still treat KDD as either an afterthought or as an experiment. It’s not core to how they run the business.

As practitioners we compound this problem by concentrating our efforts on valuable business problems; but ones which are usually on the periphery of the business. We do this because changing the heart of how a company operates requires more than just process or technology changes. It requires cultural changes. And these cultural changes usually trigger corporate antibodies adverse to anything new.

This talk will review some practical realities of instituting data-driven decisions in a very large multi-national company.

Jonathan Becher serves as Chief Digital Officer and head of the SAP Digital business unit, which allows individuals to discover, buy, use, and renew SAP and third-party offerings with minimal human interaction. Jonathan reports to Bill McDermott, CEO of SAP.

Jonathan joined SAP from its acquisition of Pilot Software, where he was chief executive officer. Beforehand, he was CEO of Accrue Software, a publically-traded Web site analytics company, and CEO and co-founder of NeoVista Software, a pioneer in predictive analytics and data mining.

Jonathan authors the popular blog, Managing By Walking Around, is a contributor to LinkedIn Today, and a published author on multiple subjects. Jonathan is a board member for the Churchill Club, Silicon Valley’s premier business forum, and an advisor for numerous early-stage technology companies.

Jonathan holds a master’s degree in computer science from Duke University, and a bachelor’s degree in computer engineering from the University of Virginia.

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This talk will cover recent innovations in large-scale machine learning and their applications on massive, real-world data sets at Verizon. These applications power new revenue generating products and services for the company and are hosted on a massive computing and storage platform known as Orion. We will discuss the architecture of Orion and the underlying algorithmic framework. We will also cover some of the real-world aspects of building a new organization dedicated to creating new product lines based on data science.

Jeff Stribling is Associate Director of Product Management at Verizon. In his current role, he leads Product for the Big Data and Analytics group, where he and his team are developing big data products in the areas of advertising, IoT, cybersecurity and network health management, based on scalable machine learning algorithms. Prior to joining Verizon, he spent 13 years at Hitachi Communication Technologies America where he was Vice President of Technology Support and Product Management, developing last-mile fiber-optic networking equipment for distribution of triple-play services. Throughout his career, Dr. Stribling held various systems engineering and systems integration positions at several leading technology companies including Geyser Networks, Iospan Wireless and Lucent Technologies. Dr. Stribling holds a Ph.D. and M.S. in Electrical Engineering from Stanford University and a B.S. degree with honors in Electrical Engineering and Computer Science from the University of California, Berkeley.

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The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: “computational social science.” Against these exciting developments stands a stubborn fact: that in spite of many thousands of published papers, there has been surprisingly little progress on the “big” questions that motivated the field in the first place—questions concerning systemic risk in financial systems, problem solving in complex organizations, and the dynamics of epidemics or social movements, among others. In this talk I highlight some examples of research that would not have been possible just a handful of years ago and that illustrate the promise of CSS. At the same time, they illustrate its limitations. I then conclude with some thoughts on how CSS can bridge the gap between its current state and its potential.

Duncan Watts is a principal researcher at Microsoft Research and an AD White Professor at Large at Cornell University. Prior to joining MSR in 2012, he was from 2000-2007 a professor of Sociology at Columbia University, and then a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group.
Watts’ research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review, and has been recognized by the 2009 German Physical Society Young Scientist Award for Socio and Econophysics, the 2013 Lagrange-CRT Foundation Prize for Complexity Science, and the 2014 Everett Rogers Prize.
Watts is the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton, 2003); Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press, 1999); and most recently Everything is Obvious: Once You Know The Answer (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.

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An important property of embedded learning systems is the ever-changing environment they create for all algorithms operating in the system. Optimizing the performance of those algorithms becomes a perpetual on-line activity rather than a one-off task. I will review some of these challenges in autonomous vehicles. I will discuss Bayesian optimization methods and their application in robotics and scientific applications, focusing on scaling up the dimensionality and managing multi-fidelity evaluations. I will finish with lessons learned and thoughts on future directions as these methods move into embedded systems.

Dr. Jeff Schneider is the engineering lead for machine learning at Uber’s Advanced Technologies Center. He is currently on leave from Carnegie Mellon University where he is a research professor in the school of computer science. He has 20 years experience developing, publishing, and applying machine learning algorithms in government, science, and industry. He has over 100 publications and regularly gives talks and tutorials on the subject.

Previously, Jeff was the co-founder and CEO of Schenley Park Research, a company dedicated to bringing machine learning to industry. Later, he developed a machine learning based CNS drug discovery system and commercialized it during two years as Psychogenics’ Chief Informatics Officer. Through his research, commercial, and consulting efforts, he has worked with dozens of companies and government agencies around the world.

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With hundreds of thousands of users, RapidMiner is the most frequently used visual workflow platform for machine learning. It covers the full spectrum of analytics from data preparation to machine learning and model validation. In this presentation, I will take you on a tour of machine learning which spans the last 15 years of research and industry applications and share key insights with you about how data scientists perform their daily analysis tasks. These patterns are extracted from mining millions of analytical workflows that have been created with RapidMiner over the past years. This talk will address important questions around the data mining process such as: What are the most frequently used solutions for typical data quality problems? How often are analysts using decision trees or neural networks? And does this behavior change over time or depend on the users experience level?

Dr. Ingo Mierswa is the founder and President of RapidMiner and as such responsible for strategic innovation and execution around the open source machine learning platform. Ingo is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Ingo, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Ingo, the entrepreneur, has been leading the company with growth rates of up to 300 percent per year over the first seven years. In 2012, he spearheaded the go-international strategy with the opening of offices in the U.S. as well as the UK and Hungary. After two rounds of fundraising, the acquisition of Radoop, and supporting the positioning of RapidMiner with leading analyst firms like Gartner and Forrester, Ingo takes a lot of pride in bringing the world’s best team to RapidMiner.

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We are all familiar with John Wanamaker’s famous quote: “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” What’s ironic is that Wanamaker’s dilemma is as much the marketer’s dilemma today as it was in the 1900’s. In an increasingly sophisticated marketing environment, where new marketing tools have enabled more precise targeting and marketing measurement than ever before, the challenge of solving Wanamaker’s dilemma has still loomed – that is, until now. In this talk, I will show you how at Amplero we have solved what’s not humanly possible to solve, making available to marketers an AI-powered marketing technology that makes use of dynamic machine learning at massive scale to enable smarter, more effective marketing. In particular, I will focus on the revolution of multi-armed bandit experimentation coupled with machine learning, highlighting what it means for today’s marketer, and specifically the marketer’s ability to directly impact business KPIs and customer lifetime value. As part of my presentation I will share experiences and examples from our work with BtoC enterprises with 10M’s of customers.

As Amplero’s Chief Scientist and CTO, Dr. Olly Downs leads the data science, technology and product teams responsible for building the company’s AI-based marketing technology platform. Olly is a machine learning scientist and serial technology entrepreneur, credited with bringing advanced analytics and machine learning methods to bear as the creative spark behind numerous early-stage technology companies. Olly specializes in applying abstract analytical ideas from mathematical, physical and statistical science to problems in the real world and commercializing them into significant businesses. He has recently served as Chief Scientist at Atigeo, Chief Scientist at Mindset Media (sold to Meebo, February 2011) and Director of Research at Pelago (sold to GroupOn, April 2011). As Principal Scientist at INRIX, the first technology spin-out from Microsoft Research, Olly delivered a world-first in the provision of real-time traffic information using a nationwide network of GPS-enabled probe vehicles. To date Olly’s work has resulted in more than 25 granted patents. Based in Seattle, Olly holds Ph.D. and MA degrees in Applied & Computational Mathematics from Princeton University, and BA, MA and MSci degrees in Experimental & Theoretical Physics from the University of Cambridge, UK.

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Every automaker is working on driver assistance systems and self-driving cars. Conventional computer vision used for ADAS is reaching its threshold because it is impossible to write code for every possible scenario as a vehicle navigates. In order to develop a truly autonomous car, deep learning and artificial intelligence are required. With deep learning, the vehicle can be trained to have super human levels of perception, driving safer than anyone on the road. An end-to-end artificial intelligence platform based on supercomputers in the cloud and in the vehicle enables cars to get smarter and smarter. Coupled with an extensive software development kit with vision and AI libraries and software modules, automakers, tier 1s, and startups can build scalable systems from ADAS to full autonomy.

Danny Shapiro is Senior Director of NVIDIA’s Automotive Business Unit, focusing on solutions that enable faster and better design of automobiles, as well as in-vehicle solutions for self-driving cars, infotainment systems, and digital instrument clusters. Danny holds a BSE in Electrical Engineering and Computer Science from Princeton University and an MBA from the Hass School of Business at UC Berkeley. Danny serves on the Advisory Boards for the LA Auto Show, the Connected Car Council and the NVIDIA foundation that focuses on computational solutions for cancer research.

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The QQ (800M monthly users) and Wechat (700M monthly users) are the two largest instant messaging / social networks in China. Tencent Social Ads is the advertising system for both Wechat and QQ, serving well over 10B page views per day, for hundred million daily users.

We strive to understand as much as possible on our users’ multiple aspects, so as to serve the best personalized ads for them. The rich user behaviors on Tencent’s many products lay a solid foundation in user profiling. We develop audience targeting on many dimensions, including demographics, interests, intents, transactions, physical locations, and access environment, etc.

In this presentation, we will share our experience in large-scale user data mining for audience targeting, and discuss the challenges we face and the solutions we have employed:

• Some demographics data are obtained from user input, and thus would have gaps in both accuracy and coverage. We will discuss the techniques in calibrating and verifying these data.
• We infer user interests from their social behaviours. For example, most QQ groups are not labelled properly, but by applying a large-scale topic model on the QQ memberships, we can effectively classify most QQ groups into an interests taxonomy. We also infer user interests from user’s physical location check-ins and uploaded photos.
• User data can be collected from many diverse sources, including behaviors in various Tencent products, click and conversion in ad platform, and even seed customers collected by advertisers. We’ll discuss the systems to merge these diverse data to provide a coherent view for our advertisers.
• High quality user labels are usually sparse. We implemented an algorithm for advertisers to reach more potential customers through user similarity computation based on user features as well as social graph explorations. We’ll describe the system’s impact on actual advertising campaigns.
• Top advertisers demand rich audience targeting solutions in combination of their own customer data, Tencent’s platform users data, and possibly other 3rd-party data. We’ll discuss the data exchange platform that can facilitate various data applications together with 3rd-party DSPs and DMPs.

Ching Law is General Manager of Social & Performance Ads Department at Tencent, overseeing engineering, marketing, operations, and products. Ching is instrumental in the development of GDT, the data and technology platform for Tencent Social Ads.

Ching has over ten years of experience in digital marketing and online advertising systems. Before joining Tencent in 2012, Ching worked at Google for eight years, where he led multiple audience & contextual targeting projects in AdSense, Google dictionary and Cantonese input system.

Ching received his Ph.D. in Computer Science from Massachusetts Institute of Technology.

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Recently, learning deep models from dense data has received a lot of attention in tasks such as object recognition and signal processing. However, when dealing with non-sensory data about real-world entities, data is often sparse; for example people interaction with products in e-Commerce, people interacting with each other in social networks or word sequences in natural language. In this talk, I will share lessons learned over the past 10 years when learning predictive models based on sparse data: 1) how to scale the inference algorithms to distributed data setting, 2) how to automate the learning process by reducing the amount of hyper-parameters to zero, 3) how to deal with Zipf distributions when learning resource-constrained models, and 4) how to combine dense and sparse-learning algorithms. The talk will be drawing from many real-world experiences I gathered over the past decade in applications of the techniques in gaming, search, advertising and recommendations of systems developed at Microsoft, Facebook and Amazon.

Ralf is Director of Machine Learning at Amazon and Managing Director of the Amazon Development Center Germany. He works on problems of demand forecasting, scalable machine learning, computer vision and linking structured content. In 2011, he worked at Facebook leading the Unified Ranking and Allocation team. From 2000 - 2011, he worked at Microsoft Research and was co-leading the Applied Games and Online Services and Advertising group which engaged in research at the intersection of machine learning and computer games and in the areas of online services, search and online advertising combining insights from machine learning, information retrieval, game theory, artificial intelligence and social network analysis. Ralf was Research Fellow of the Darwin College Cambridge from 2000 - 2003. He has a diploma degree in Computer Science (1997) and a Ph.D. in Statistics (2000). Ralf¹s research interests include Bayesian inference and decision making, computer games, kernel methods and statistical learning theory. He is one of the inventors of the Drivatars system in the Forza Motorsport series as well as the TrueSkill ranking and matchmaking system in Xbox 360 Live. He also co-invented the adPredictor click-prediction technology launched in 2009 in Bing¹s online advertising system.

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Panelist: Mark Gorenberg, Managing Director, Zetta

Mark has an enviable track record when it comes to identifying and nurturing enterprise software that delivers exceptional returns. During his 20 years in venture capital, he’s generated extraordinary results, particularly in analytics where his portfolio has realized a 149 percent annual rate of return. Prior to Zetta Venture Partners, Mark served as managing director at Hummer Winblad, the first fund focused on enterprise software. There, his most publicized successes in analytics included Omniture (IPO and acquired by Adobe); AdForce (IPO and acquired by CMGI) and Scopus Technologies (IPO and acquired by Seibel Systems). Today, he sits on the boards of the next generation of leading companies like Domo, Inside Sales, EventBoard and FollowAnalytics.

Mark started his technology career at Sun Microsystems working on its SPARCstation workstations.

Beyond his role as venture capitalist and entrepreneur, Mark is a trusted advisor to some of the country’s most influential technology institutions. He serves on the President’s Council of Advisors on Science and Technology (PCAST), the Board of Trustees for Massachusetts Institute of Technology and the National Venture Capital Association.

He graduated from MIT and received Masters degrees from the University of Minnesota and Stanford University in Electrical Engineering.

Panelist: Tim Guleri, Managing Director, Sierra Ventures

Tim Guleri is a Managing Director of Sierra Ventures, a California based early stage venture capital fund. Sierra’s ten venture funds have committed capital exceeding $1.6 Billion.

Tim’s investment focus is Information Technology Software companies. Additionally, Tim has helped execute on Sierra’s India strategy and investments. As a venture capitalist, Tim’s taken two companies public; Sourcefire in 2007 (Nasdaq: FIRE) which was acquired by Cisco for $2.7B and MakeMyTrip.com (Nasdaq: MMYT). Additional exits have been Greenplum (EMC Corporation), Net6 (Citrix Systems) AirMedia (AOL), Makara (RedHat), Red Aril (Hearst), Runa (Staples), Bina (Roche) and Appcelerator (Axway). Tim currently serves on the BOD of Treasure Data, Alpine Data Labs, Nexenta, Hired, LeadGenius, Phenom People and Shape Security.

Prior to Sierra, Tim learnt the ropes as an entrepreneur. He was founder and CEO of Octane Software from 1996 to 2000. He successfully led Octane’s merger with Epiphany (Nasdaq: EPNY), a transaction valued at $3.2 billion in 2000. Before Octane, Tim was Vice President of field operations at Scopus Technology. Scopus went public in 1995 and was acquired by Siebel Systems (Nasdaq: SEBL) in 1997 for $800M. Tim was instrumental in growing the company to $100 million in revenue and to 600 employees from 1992 to 1996.

Tim holds a MS from Virginia Tech, and a BSEE from India.

Panelist: Matt Ocko, Co-founder and Co-Managing Partner, Data Collective

Matt Ocko, co-founder and co-Managing Partner of Data Collective (DCVC), has three decades of experience as a technology entrepreneur and VC, in the US and globally. Matt’s prior investments include Cotendo (AKAM), Zynga (ZNGA), Facebook (FB), XenSource (CTRX), UltraDNS (NSR), FlashSoft (SDNK), Fortinet (FTNT), Aggregate Knowledge (NSR), Virtuata (CSCO), DataMirror (IBM), Couchbase, Ayasdi, Kenshoo, D-Wave Systems, MetaMarkets, Uber, AngelList, and many others, including multiple additional acquisitions by Google, Facebook, Netapp, and other Fortune 1000 tech companies. Matt founded Da Vinci Systems, a pioneering e-mail software vendor with over 1 million users world-wide prior to its acquisition. He is an inventor on over 40 granted or in-process patents in areas as diverse as enterprise hardware and fraud detection. Matt holds a degree in Physics from Yale University and has been active in helping develop China’s venture capital and technology regulatory framework for two decades.

Panelist: Greg Sands, Founder and Managing Director at Costanoa Venture Capital

Greg is the Founder and Managing Director at Costanoa Venture Capital, an early stage venture capital firm focused on finding great entrepreneurs using technology to solve real problems and build lasting companies. Prior to founding Costanoa, Greg was a Managing Director at Sutter Hill, where he led early stage investments in Feedburner, QuinStreet, Yokou and Merced Systems. Before Sutter Hill, Greg was the first hire at Netscape Communications after its founding engineering team. Starting as a Product Manager, he wrote the initial business plan, coined the name Netscape, and created the SuiteSpot Business Unit, which he grew from zero to $150m in revenue.

Greg has an MBA from Stanford Graduate School of Business and graduated from Harvard College Magna Cum Laude. He currently serves as President of the Stanford DAPER (Athletics Department) Investment Fund and is a Board Member and Former President of the Western Association of Venture Capitalists.

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Panelist: Ingo Mierswa, Founder and President, RapidMiner

Dr. Ingo Mierswa is the founder and CTO of RapidMiner and as such responsible for the strategic innovation of the open source machine learning platform. Ingo is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Ingo, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Ingo, the entrepreneur, has been leading the company with growth rates of up to 300 percent per year over the first seven years. In 2012, he spearheaded the go-international strategy with the opening of offices in the U.S. as well as the UK and Hungary. After two rounds of fundraising, the acquisition of Radoop, and supporting the positioning of RapidMiner with leading analyst firms like Gartner and Forrester, Ingo takes a lot of pride in bringing the world’s best team to RapidMiner.

Panelist: Richard Rovner, Vice President of Marketing, MathWorks

Richard Rovner is Vice President of Marketing for MathWorks, the makers of MATLAB & Simulink. He leads the company’s 300-person marketing department and is responsible for strategic planning, product and technology strategy, partner management, web and programs marketing, and corporate communications. In this role, Richard has the opportunity to see how MATLAB and Simulink users advance the state-of-the-art in numerous applications and industries. Before joining MathWorks in 2001, Richard held senior marketing and sales positions at SAS. Rich spent the first part of his career as what is now called a data scientist, working for ten years developing applications in computer vision and image processing, machine learning, simulation, and statistical analysis. He has a B.S. in Applied Mathematics from Carnegie Mellon University and an M.S. in Computer Science from George Washington University.

Panelist: Udo Sglavo, Senior Director R & D, SAS

Udo Sglavo is a senior director of the data mining and forecasting R&D teams at SAS. He has the privilege to head up a team of highly skilled statisticians, data scientists, and mid-tier developers. Jointly they are in charge of creating industry-leading and award-winning software for data mining, machine learning, and large scale automatic forecasting. In addition he helps to define the specifics of the advanced analytics product portfolio at SAS, as well as the integration of analytics into solutions and visualization products.

Udo enjoys public speaking and talking to customers about how to apply analytics to solve business problems. He is comfortable working in an international environment, having established and run a virtual, global team of analytics domain experts.

Panelist: Dan Steinberg, President and Founder, Salford Systems

Dan Steinberg, President and CEO of Salford Systems, founded the company in 1983 just after receiving his Ph.D. in Economics at Harvard. He also served as a Member of Technical Staff at AT&T Bell Laboratories and Assistant Professor of Economics at the University of California, San Diego, and has participated in dozens of consulting projects for Fortune 100 clients. He has been honored by the SAS User’s Group International (SUGI) and led the modeling teams that won the KDDCup 2000 and the 2002 Duke/Teradata Churn modeling competition. Dr. Steinberg has published articles in statistics, econometrics, computer science, and marketing journals, and has been a featured data mining issues speaker for the American Marketing Association, American Statistical Association, the Direct Marketing Association and the Casualty Actuarial Society.

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This talk will explore how time - and timing - impact Netflix’s data science applications. Netflix enables stories from around the world to be discovered, enjoyed, and shared globally. Success in this space means finding and creating the right movies and TV shows, connecting them with the cultures and people who will enjoy them, and delivering a high-quality video streaming experience. As we lean on data science to help in these areas, the specific manner in which time is handled can impact results dramatically. I will share examples of how Netflix incorporates time in algorithm training, feature engineering, A/B test execution, and measurement of member behaviors.

Caitlin Smallwood is the Vice President of Science and Algorithms at Netflix, where she and her team drive predictive decision models, algorithm / machine learning research, and experimentation science for all parts of the Netflix business. Caitlin is particularly passionate about personalization and other mechanisms of providing value to people through data.

Prior to joining Netflix in 2010, Caitlin worked at Intuit, Yahoo!, and several consulting firms (PwC, SRA), focusing on an array of analytic disciplines and products. She is experienced at leading strong teams and has built several data science groups from scratch. Caitlin holds an M.S. in Operations Research from Stanford University and a B.S. in Mathematics from The College of William and Mary.

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The time has come to manage information across the enterprise as a strategic asset and deliver to enterprise data consumers, experiences that rival their experiences managing information on the public web. People who use information technology at work are the same people that go home and use the Internet. Why should their information systems at work be harder to use—or less satisfying—than the systems they use at home?

In this presentation, serial entrepreneur and Tamr Co-Founder Andy Palmer exposes the dirty secret of messy, siloed, enterprise data. Pulling from first hand experience, Palmer shares how Fortune 500 companies are saving $100s of millions by automatically unifying and preparing enterprise data at scale—and empowering enterprise data and IT professionals to become the “Googlers” of their enterprise.

Andy Palmer is a serial entrepreneur and angel investor who has helped start, fund or found 50+ innovative companies in technology, health care and the life sciences, including pioneering big data analytics company Vertica Systems (acquired by HP). He also founded Koa Labs, a shared start-up space for entrepreneurs in Cambridge’s Harvard Square; and is a Research Affiliate at MIT CSAIL and co-presenter of the MIT edX course “Startup Success: How to Launch a Technology Company in 6 Steps.”

Previously, Palmer was a member of the core startup team and the SVP and CIO at Infinity Pharmaceuticals (NASDAQ: INFI); and held positions at innovative companies including Novartis, Bowstreet, pcOrder.com and Trilogy.

(BA, Bowdoin 1988; MBA, Tuck School of Business at Dartmouth, 1994)

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With today’s massive global adoption of personal computers, smartphones, tablets, and the emerging class of devices, we live in a time of unprecedented device proliferation. This gives rise to very fundamental challenges around the fragmentation of consumer identity. The big Internet giants of Google, Facebook, etc. have an inherent advantage in solving for this through their first-party data of made up of a stateful, logged-in base of user information. This creates an inequity across the digital and Internet economy in the ability to provide seamless experiences and solutions from understanding consumer identity. This talk will address how machine learning and data science best-practices can solve for this identity capability, and how it can be done in a privacy-safe and data-safe manner, with high precision and at massive scale.

Kamakshi will focus on the algorithms developed at Drawbridge that process a massive scales of data in near-real-time spans to solve for a single user’s identity across different domains, while still protecting consumer anonymity.

Kamakshi Sivaramakrishnan is the founder and CEO of Drawbridge, the leading technology company that is fundamentally changing the way in which brands and enterprises connect with people. Under Kamakshi’s leadership, Drawbridge has built the first technology-aided solution that addresses a problem central to all digital media: consumer-trusted cross-device identity.

Kamakshi received her PhD in Information Theory and Algorithms from Stanford University, and has been named one of Business Insider’s “Most Powerful Women in Mobile Advertising” for four consecutive years. She received the 2015 Women of Vision ABIE Award for Technology Entrepreneurship from the Anita Borg Institute. Kamakshi also has the unique distinction of her work being onboard NASA’s New Horizons mission to Pluto and the outer planetary system.

Randell Cotta is a Senior Data Scientist at Drawbridge, where he has spent the last three years focused on designing the intelligence behind their digital identity solution as well as the learning systems that leverage digital identity for their partners and underlying consumers. Randell received his PhD in theoretical physics from the SLAC Theory Group at Stanford, where he helped to define the interface between theories explaining dark matter and related experimental data. He was later a postdoc in the Particle Theory Group at UC Irvine before joining Drawbridge.

Among other honors, Drawbridge was recognized as the fastest-growing private company in the marketing and advertising space on the 2015 Inc. 5000, as well as the fastest-growing woman-led company, and sixth-fastest-growing overall.

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