Applied AI Summit schedule

I am a highly motivated and driven technologist and entrepreneur with a passion for Business Development, Digital Transformation, and Operational Management with advanced cross-functional tactical planning and problem solving skills. Throughout the years, I accumulated domain expertise in creating successful technology companies, negotiating and leading deals of mergers, acquisitions and IPOs with deep expertise in strategy, regulations, policies, controls and standards. With a background rooted in financial, medical and data sectors, I developed multiple AI and data systems by bridging between business, HR, financial and technology sectors that revolutionized operational performance; thereby adding value and advancements to my companies and leadership teams. I have a track record of leading teams towards success, partnering with internal and external stakeholders to optimize business and financial performance.

Digital Technology leadership program and MBA graduate with over 16 years of experience. Driving productivity benefits across Finance, Supply Chain, Commercial Operations, and Human Resource Operations by executing strategic programs, improving processes, and deploying artificial intelligence technologies. Leveraging analysis and collaborative leadership to drive organizational improvements and enhance user experience.

Expertise in robotic process automation, artificial intelligence, cybersecurity, technology risk and compliance. Leading globally distributed teams and have lived/worked in USA, India, Italy, China.

Driving AI Innovation with Machine Learning in the Enterprise

GM Financial, the wholly-owned captive finance subsidiary of General Motors, will discuss their AI journey with Machine Learning, Deep Learning, and Natural Language Processing. In this session, GM Financial will discuss their challenges, technology choices, and initial successes:

· Addressing a wide range of Machine Learning use cases, from credit risk analysis to improving customer experience

· Implementing multiple different tools (including TensorFlow™, Apache Spark™, Apache Kafka®, and Cloudera®) for different business needs

· Deploying a multi-tenant hybrid cloud environment with containers, automation, and GPU-enabled infrastructure

Gain insights from this enterprise case study, and get perspective on Kubernetes® and other game-changing technology developments.

Lynn Calvo is the AVP of Emerging Data Technology at GM Financial where he introduced big data analytics and machine learning capability into the lines of business and continues work with enabling its application for multiple AI use cases. Lynn is a recognized inventor on three US patents. He holds a master’s in Computer Science and is a former IT consultant servicing Fortune 50 clients with big data, data center, and security next-generation research. Lynn’s forward thinking and depth of knowledge allow him to skillfully leverage his 30-years of results-oriented IT experience into a winning model for GM Financial.

Interactive Improvement Between AI Algorithms and Business Insights

The adoption of Artificial Intelligence (AI) technologies among businesses is growing faster than ever before in recent years. With this remarkable growth, there are also emerging pain points with algorithms behaving as black-boxes and concerns that traditional human intelligence might become less relevant. This talk will touch on cases where human business insights help to improve AI models and where business insights are gained from AI models. Experience will be shared on developing AI solutions in an enterprise environment through this business-in-the-loop approach.

Biao "Bill" Chang is a Senior Data Scientist with eBay's Risk Management team. His work centers around developing scalable AI solutions for risk management and fraud detection on eBay's marketplace platform, spanning from problem definition, modeling development to model deployment and monitoring. Sample projects include building and productionizing real-time fraud detection model covering a portfolio of over 500 million listings monthly. Bill obtained his Ph.D. in engineering from the Georgia Institute of Technology and previously worked with Capital One on data analytics and data product.

Five years ago Adam talked here at ReWork on the future of AI in banking, he laid out a roadmap of things to come - previewing China’s Smile to Pay (which shortly after ballooned to 400 million users), smart contracts, personalised banking and other initiatives that have come to fruition. That said, Adam had also warned on the grave consequences of failure and significant challenges to arise both practical and regulatory which closely describes the state of quagmire we now find ourselves in.

From Alexa telling a 10-year old girl to touch a live plug with a penny, to regulatory breaches such as Uber test driving autonomous cars without state permission, running 6 red lights as a result.

Finance hasn’t been spared the controversy either, with multiple institutions losing significant wealth from poorly deployed AI, to colossal investment wastage by backing the wrong AI sectors.

In this talk Adam will share key insights & learnings on how organisations can better traverse these pitfalls by designing, building and deploying finance driven AI that is both sustainable and cost effective. He will also layout key milestones to enable future proofing imminent threats of supply chain failures and global talent shortages so that we can navigate the next five years of AI in finance with confidence.

Adam McMurchie is the Lead Cloud Data Engineer at NatWest. He was previously the leader in Devops and an A.I expert working in the banks SAO platform on the forefront of technology development in finance. With a broad exposure to a range of technologies, Adam drives an ethos of simplification, cloud agnosticism and specialises in spotting the next trends in fin tech. Additionally, Adam also has a background in science with a physics degree specialising in NeuroComputing and is a polyglot linguist & seasoned translator. Adam has pooled these skills to deliver full stack novel solutions from tensor flow driven mobile apps, to personalized banking chatbots. Adam also develops apps designed around the ethos of Social Utility, including Flood/Storm reporting, EV Vehicle bay monitoring and preservation of endangered languages.

Sunanda Parthasarathy, is a Senior Director of Enterprise Analytics at CVS Health where she leads teams of data scientists, analysts & solution strategists that aim to improve health outcomes for all using machine learning & causal inference techniques. Prior to this, she led data science teams at Wayfair and DataXu. As part of this journey she has hired and scaled multiple high performing ML teams and takes an active part in the career pathing discussions of this emerging field. She is an active member in the local start-up, tech and data science community and is the co-organizer of one of the oldest meetup groups in Boston- The Data Scientist and the co-lead of the Women in Big Data Boston Chapter. She enjoys bringing together leading minds in the field of data science and AI, to engage in thought leadership in this nascent field. Her contributions to the field have been recognized by multiple awards and mentions including The Emerging Exectuive Award from MassTLC 2018 and REWORK’s 30 influential women advancing AI in Boston. Before entering the data science world, she was a Princeton Postdoctoral fellow, working on the forefront of the next generation quantum materials that will replace silicon in a computer chip. Prior to that she received her PhD in physics from Purdue University working on solving open problems in the field of transport physics. Her research accomplishments have been recognized in the form of many awards including the H.Y.Fan Award for excellence in physics research.

Architecting a Real-Time Optimization Platform for Driver Positioning Products

At Lyft, we think a lot about trading off between the immediacy and quality of the response in automated decision-making. On one end, machine learning dominates products that require near-instantaneous feedback such as in fraud and customer support. On the other end, we have complicated workflows that crawl through user graphs to derive weekly macro-level business insights. This talk focuses on the “in-betweens” such as driver positioning and rider-driver matching that require time to aggregate market-level signals before any useful decision can be made. We explore the design principles that we have come to recognize in developing scalable infrastructure that enable fast, iterative, Science-heavy model and product development of real-time optimization workflows.

Key Takeaways:

• Science DevOps is every bit as important as building the Science models
• It's important for Research/Data Scientists to develop models with an understanding of the Eng infra and affect its development
• Similarly, it's important for Eng to work closely with Science to understand the infra needs and not over-index on a specific business application.

Hao Yi is a Research Scientist at Lyft. On the Driver Positioning team, Hao Yi leads the development of the optimization framework and models that power the Personal Power Zones and Hot Spots products that replaces the Driver Prime Time dynamic pricing experience. Previously, Hao Yi combated transaction and driver fraud on the Integrity team. There, he championed new approaches and led ML improvements that helped Lyft achieve best-in-class status in fraud rate within the ridesharing industry. On Support Experience, Hao Yi developed deep learning models for support ticket classification and routing that led to massive reductions in false positive tickets and manual ticket re-routing. Before Lyft, Hao Yi worked on drone traffic management with NASA Ames as a graduate at Stanford.

AI for Diverse Avenues/Applications: E-Commerce

I am a data science manager at Facebook. I work on Marketplace, where we enable people to connect via commerce on Facebook, with a focus on using applied data and analytics to build eCommerce behavior. Prior to coming to Facebook, I worked as a statistician for Capital One where I built risk and revenue models for the credit card business.

AI in Enterprises with Specific Relevance to Retail

Artificial intelligence is changing the business landscape across wide variety of industries, Retail being one of them heavily influenced and transformed by the recent advancement of AI. This talk with delve deep into • Opportunity in large-scale retail enterprise • Ai brings competitive advantage - Use of AI in retail from traditional ML (personalization, forecasting, optimization, etc) to Deep learning (behavioral analysis, chatbots, surveillance, knowledge graph, etc – dealing in voice, text and video). Talk of specific use case with relevant examples • Challenges in large scale AI implementation – data, computing, talent • AI in Target Corp – where, how • Where are we heading? Where does different technological evolutions converge – IoT, AI, blockchain, etc

Prakash Mall is a senior director of intelligent digital robotics and AI products including intelligent RPA, enterprise chatbots and video management and video analytics for surveillance. Prakash leads engineering for many product groups dealing with large scale distributed computing to AI with deep learning into text (NLP) and video. Prakash has over 20 years of experience building enterprise scale products in the Retail, Insurance and Healthcare sectors. His expertise is in building scalable open source systems and driving business value using emerging technologies. He has a proven track record of building and managing large global teams of 150+. Prior to joining Target, Prakash spent over 12 years at IBM including 8 years at the company’s US office.

CourseMatch: Using Word Embeddings for Scaling Access to Online Education during COVID

At Coursera our mission is to provide everyone, anywhere access to high quality education to transform their lives. As the COVID crisis forces teaching and learning to shift online, there is an acute need to support both universities and students at this time. To enable universities and students to take advantage of the large number of high quality, online courses on Coursera, we have utilized word embeddings to map on-campus university catalogs to their online Coursera equivalents, ensuring students can continue their studies and universities can support their programs. During this presentation we will review the theory behind world embeddings and discuss this novel application in greater detail.

Vinod Bakthavachalam is a Data Scientist working with the Content Strategy and Enterprise teams, focusing on using Coursera's data to understand what are the most valuable skills across roles, industries, and geographies. Prior to Coursera, he worked in quantitative finance and studied Economics, Statistics, and Molecular & Cellular Biology at UC Berkeley.

Enriching Creativity in Applied AI with Interdisciplinary Backgrounds and Diverse Perspectives

Today AI is being created by a very homogeneous set of people. Not only it is homogeneous in terms of race and color but also in terms of academic backgrounds. I am a very unlikely researcher to become an AI engineer but I believe it is crucial so that AI created by heterogenous set of people is more fair and inclusive. I will be sharing my personal journey into AI coming from engineering, applied mathematics, biophysics and music background. I will also be sharing my machine learning project at AT&T: how I am utilizing machine learning to detect network outage and prevent unnecessary dispatch in an event of network outage.

Himani Agrawal, PhD is a Machine Learning Engineer at AT&T Chief Data Office where she is applying machine learning in the telecommunications industry to proactively predict network outage and avoid customer dispatch. She has a very interdisciplinary research background encompassing fields such as applied mathematics, biophysics and data science. Apart from work, she is passionate about promoting women in technology and actively participates with the Anita Borg Institute, Women in Machine Learning and Data Science, Women in Machine Learning and Society of Women Engineers. She is actively involved in the machine learning and data science communities at AT&T: Data Night Live and Data Powered Insights. She has had speaking engagements at international conferences like Grace Hopper Celebration of Women in Computing, Society of Women Engineers Annual Conference, American Society of Mechanical Engineers and Society of Engineering Science Annual Technical Meeting. She is passionate about Italian Classical singing and currently trains with University of North Texas Voice Professor Dr. Lauren McNeese.

Data Scientists Should Build Dashboards (and write ETL too)

Bringing machine learning (ML) powered products to a large production environment requires a diverse skillset: ML experts who can identify which approach/algorithm will best fit the problem at hand, engineers who can take the prototypes from the ML experts and productionize them, and analysts who can measure how successful the solution has been. It is common to see companies organize themselves into teams that are responsible for each of these tasks: data scientists, data engineers and data analysts respectively. At Shopify we believe this is sub-optimal, as understanding of the actual problem being solved leaks at each interface. Instead we build data science teams that are responsible for all three of these tasks. This talk will outline how we organize our data science teams to meet these challenges and bring ML powered products to production.

Kyle Tate is a senior data science lead at Shopify. He leads the teams responsible for building the data products that power Shopify Capital, Fraud Protect for Shopify Payments and Shopify's internal risk management. Kyle is interested in taking the ideas and techniques from the pages of machine learning textbooks and papers and bringing them to life in the real world.

Creating Applications That Converse, Understand and Become More Intelligent

Advances in natural language processing (NLP) are increasingly eroding the barriers between frictionless human-machine interaction, creating opportunities to develop nuanced and natural communication techniques that more accurately convey person-to-person dialogue. In this talk, I'll discuss how convolutional neural networks and long short-term memory networks are enabling more sophisticated NLP systems and building the necessary components to lay the foundation for advanced dialogue.

Margaret is a Vice President of Software Engineering for Conversational AI Platforms at Capital One. She sets the technology strategy for Eno, Capital One’s banking chatbot, as well as enterprise messaging capabilities for email, push and SMS. Margaret is also an advocate for closing the gap in women in technology as a member of Capital One’s Women in Technology working group. Margaret has been with Capital One for 20 years, in roles of increasing responsibility within Technology and Operations. She holds a BS in Operations Research & Industrial Engineering from Cornell University and her MS and PhD from Lehigh University in the same field. Prior to joining Capital One, she spent three years as an Assistant Professor at the University of Virginia, in Systems Engineering. Her research interests have been varied, but always along the common theme of using technology to solve complex business problems. Margaret is a board member of CodeVA.org and the Computer Science Industrial Advisory Board at Virginia Commonwealth University. Margaret lives in Richmond, VA with her husband and two teenage daughters.

Let the Models Speak: Model Interpreters

Curiosity kills. We have advanced to building machine learning algorithms for real-time predictions that can help us predict almost accurately every time. It’s like we have built a magic box that will answer all our questions. The more curios question is why did the magic box predict what it did. During the session I would like to share why you should/shouldn’t trust your model but you should explain the reasoning of your predictions that help Business users to understand your Magic box. I would like to share about how at AT&T we are using Model Interpreters to help business users identify the elements of the predictions that can give intuitive reasoning behind each prediction.

Sandeep David Golkonda is a Data Scientist at AT&T Chief Data Office he actively researches and applies Machine Learning and AI solutions for real-time analytics problems. His, current work is focused on building AI framework to optimize network events. He is an active member of School of AI and Teaching Assistant for machine learning and deep learning. He holds M.S in computer science and M.S in business analytics.

Growing up during some of the fastest economic growth times in India, Priyanka has always been as much curious in understanding how modern inventions advancing humankind by leaps and bounds affected the societal sanctities as much as she has been in figuring out the inner workings of sophisticated systems built around telecommunication, software, databases and now machine learning and AI analytics. After finishing her bachelors from College of Engineering in Pune(India), Priyanka came to the US to pursue her masters from USC and spent almost a decade working for companies like Qualcomm and Intel to define some of the then new experiences on smartphones and internet-of-things using audio/video and camera integration through bare-bone software to begin with, followed by computer vision analytics to bring connectivity and context to the solutions. After a long sprint in the Silicon Valley, Priyanka decided to explore the other side of her passion through technology and machine learning by moving to Dallas and working for Parkland Center of Clinical Innovation (PCCI) an advanced not-for-profit clinical research organization which aims at pioneering ways to create healthier communities. As a Director of ML/AI at PCCI, Priyanka and her team are now streamlining clinical/healthcare data model development workflows from data engineering, training/re-training, deployment to statistical model evaluation fielding unique challenges like high dimensionality nature of healthcare data, flexibility of tuning parameters during prospective testing under clinician supervision and upholding compliance to HIPAA standards at all times to name a few. Trained under some of most prudent software and system architects of the world, Priyanka wows for nothing less than superior software engineering practices including test-driven development, agile methodologies, DevOps and loves to be plugged into discussions of revolving around bringing these time-tested practices into mainstream ML/AI development cycles while keeping enough room to usher in a new phase of "Responsible AI"

Babar Bhatti is a technology entrepreneur, digital executive and expert in the areas of analytics and AI. He is co-founder of Dallas AI, a meetup of about 1600 members focused on AI and Deep Learning. He is a frequent speaker and presenter on the topic of AI, Machine Learning and Digital Technologies. He has recently spoken at Harvard Business School Club of DFW, Financial Executives International, UTD School of Business and many other conferences. Babar is currently a Principal at CoreLogic. Formerly, Babar was the CEO and Co-founder of MutualMind, a social media analytics company.

He holds dual MS from MIT in Engineering and Technology and Policy.

Dean Teffer is Principal Scientist for Machine Learning at JASK, which delivers the first AI-powered SOC platform that enables autonomous security operations. At JASK he is working with a highly talented team of developers, analysts and scientists to create a new type of network security product. Dean designs and develops model-driven machine learning systems for streaming and distributed production environments, with his specialties including anomaly detection, probabilistic modeling and clustering. Dean has worked for the past 9 years in network security, and his teams have made significant contributions in early detection for counter intelligence applications, among other achievements.

Dean previously served in program management roles at the UT Austin Applied Research Labs, Siemens, Precision Traffic Systems and Compuware. He has a PhD in Computer Engineering and a Masters in Physics from the University of Texas at Austin.

Clint Wheelock is the founder and managing director of Tractica. He leads all research operations at the firm, including management of its analyst team as well as client interactions and consulting engagements. His personal research focuses on artificial intelligence and user interface technologies.

Wheelock has an extensive background in market intelligence focused on emerging technologies. Most recently, he was founder and president of Pike Research, a leading market intelligence firm focused on the global clean technology industry, which was acquired by Navigant Consulting, after which Wheelock led the rebranded Navigant Research business as its managing director. In this role, he managed all aspects of company operations, including research, sales, marketing, finance, and operations. Prior to forming Pike Research, Wheelock was chief research officer at ABI Research, vice president at the NPD Group, and research director at In-Stat. Previous positions also include senior product management and strategic marketing roles at Qwest Communications and Verizon Communications, as well as prior experience in management consulting and private investment banking. Wheelock holds an MBA from the University of Dallas and a BA from Washington & Lee University.