Boris Lublinsky is a software architect at Lightbend, where he specializes in big data, stream processing, and services. Boris has over 30 years experience in enterprise architecture and has been accountable for setting architectural direction, conducting architecture assessments, and creating and executing architectural roadmaps in fields such as big data (Hadoop-based) solutions, service-oriented architecture (SOA), business process management (BPM), and enterprise application integration.
Dean Wampler leads the growing engineering team that's building Lightbend Fast Data Platform (FDP), the industry-leading platform for streaming data processing at scale, using Spark, Flink, Lightbend Reactive Platform, Kafka, HDFS, management and monitoring tools, all running on Mesosphere DC/OS. Dean conceived of FDP and also developed the original product plan and evangelism tools.
Dr. Erin LeDell is the Chief Machine Learning Scientist at H2O.ai, an artificial intelligence company in Mountain View, California where she works on developing open source software for scalable machine learning. Before joining H2O.ai, she received her PhD from UC Berkeley and was Principal Data Scientist at two San Francisco Bay Area startups.
Srini leads the Learning Platform team at Dataquest, which consists of product and engineering. We're a tightly integrated team focused on improving the learning experience and serving the rest of the organization.
Senior Offering Manager, Data Science Cross-Product Initiatives, IBM
Ted Fischer is a data science leader and senior offering manager responsible for cross-product initiatives in IBM's Data Science Portfolio. Ted was formerly the offering manager for IBM SPSS Modeler where he initiated a redesign of the product interface. Before moving to offering management, Ted Fischer had worked as a data scientist consultant to U.S. government agencies to find fraud and risk using IBM SPSS Modeler. At one government customer, Ted both built predictive models and provided technical leadership to a team of other data scientists. The resulting models deployed in IBM SPSS Modeler found billions of dollars of fraud -- and the work resulted in two awards from the Association of Management Consulting Firms. Ted has expertise as a people manager of data scientists and managing data science projects.
Ferenc Katai, Ph.D.
Offering Manager for CPLEX Optimization Studio (CPLEX/CPO/OPL)
Offering Manager for CPLEX Optimization Studio (CPLEX/CPO/OPL), IBM
Ferenc got his Ph.D. at Tokyo Institute of Technology on "Scheduling chemical systems with GA and stochastic heuristics". He became assistant professor for a few years at the same University, then joined ILOG as a consultant building/delivering optimization models for business problems in Japan. Then he led the technical team in the Japanese office as a director of consulting delivering many interesting client projects for top companies like Sony, Toyota, Mitsubishi Chemicals, etc. After returning to Europe he started managing OPL Studio as a product manager and then later on added CPLEX and CP OPtimizer to his portfolio, soon these three became CPLEX Optimization Studio. Ferenc has an over 25 years experience on the optimization market as a researcher, consultant and now as an Offering Manager setting the directions of mathematical SW in IBM.
Senior Software Developer, IBM Db2 Event Store, IBM
Daniel Zilio is a senior developer in the IBM Db2 Event Store team, and is working with Spark and client-based applications. This includes Spark SQL, Scala applications, Python, JDBC, and integration with IBM Streams.
Daniel was in the IBM Data Server Manager and DB2 Optimizer groups. His work included: DB design algorithms, query planning and tuning, column organization advising, and performance monitoring.
Before joining IBM, Daniel obtained his PhD from the University of Toronto in the area of physical DB design selection, which included creating automatic partition and index selection algorithms. During his PhD, he had a student fellowship internship at IBM Yorktown Heights Research Center in 1994 and was an IBM Center for Advanced Studies Student Fellow from 1994 to 1997.
Svetlana implemented (with colleagues) a large number of SPSS analytic algorithms from 2000 to 2018. She is representing IBM in the Data Mining Group working on PMML and PFA standards, managing recent PMML releases. She is now a Developer Advocate in Chicago.
Luciano Resende is a Data Science Platform Architect at IBM CODAIT (formerly Spark Technology Center). He has been contributing to open source at The ASF for over 10 years, he is a member of ASF and is currently contributing to various big data related Apache projects around the Apache Spark ecosystem. Currently, Luciano is contributing to Jupyter Ecosystem projects building scalable, secure and flexible Enterprise Data Science platform.
Raj specializes in data science and all things geospatial. Raj pioneered Web mapping-as-a-service in the late 1990s with Syncline, a startup he co-founded, and worked with many startups on data issues. Prior to IBM, he led geospatial data interoperability projects for the Open Geospatial Consortium. He has a PhD in Information Systems in Planning from MIT.
Senior Data Scientist, Data Science Elite Team, IBM
Victor Terpstra is a member of the IBM Data Science Elite team, specializing in decision optimization. He obtained a PhD in the area of systems and control, AI and scheduling. Victor has 20+ years of industry experience building operational decision support and optimization systems in areas like finance, oil&gas, transportation, energy, process industry, electronics, manufacturing and supply-chain.
Justin McCoy is an Austin area Software Developer sculpting a beautiful and simplified world through software and art. With 12 years of experience bringing the latest open source technologies to enterprise with IBM’s big iron, he knows what it takes to move from idea to production. With a passion for the client experience, he is currently focused on Cloud Computing, Watson, Serverless, and Data Science. You’ll usually find him playing ultimate frisbee, running, or talking about the future of technology.
Sidney is the Chief Data Scientist for IBM's North American data science technical sales team. She looks to bring her software engineering and data science expertise to drive and explore new opportunities for IBM's data science customers. This experience enables her to influence the direction of IBM’s data science offerings and she has gained the reputation as one of the team's most highly regarded experts. Sidney has more than 10 years experience as a software engineer and 8 years as a data scientist.
As Program Director for the IBM Data Science Elite team (goo.gl/8ZrfVD), I love working in this dynamic and multi-cultural environment, sharing knowledge, continuously learning and innovating, and most importantly: focusing on improving the world around us by leveraging expertise in mathematics, combined with the latest technology.
Hemen is a senior solution architect in the customer success team. He focuses on providing technical solutions for customer’s problems using H2O.ai’s products. Additionally, he also works on adding customer requested features to the Driverless AI product. Before joining H2O, Hemen was working for customers like Adobe and HP in various software development and solution/project delivery positions. Hemen completed his Bachelors in Computer Engineering from University of Mumbai. Hemen has a passion for creating tools and believes in sharing them with larger community, Getbhavcopy (www.getbhavcopy.com) is one of the tools widely adopted in India for downloading daily data from Indian stock exchanges for technical analysis. During his free time Hemen enjoys spending time with his family, mostly cooking something special on weekends and spending time reviewing his daughter’s school homework while keeping himself informed of the latest in the field of technology.
Right-brained and hypothesis driven, I prefer to work backwards, framing the big picture before diving head first into the guts. I tend to fixate on the important yet often uncomfortable questions. I don't really bring an encyclopedia of technical whizbang to a team, just a lot of hard-earned common sense and bad jokes.
Steve Barbee
Offering Manager of SPSS Predictive Analytics Algorithms; Data Scientist
Offering Manager of SPSS Predictive Analytics Algorithms; Data Scientist, IBM
I'm Steve Barbee, an IBM Data Scientist and Offering Manager focusing primarily on algorithms for SPSS Predictive products. My educational background is in physics & math as well as plasma physics (MSE, Columbia U.) a long time ago. Soon after my MSE, I was a pilot line semiconductor process engineer in IBM's Microelectronics Division and rose up the ranks to Sr. Engineering Manager in the Advanced Technology Department. My first introduction to machine learning was around 1997 when I assigned one of my staff in the IBM East Fishkill Semiconductor Lab to investigate a group investigating neural networks at the SEMATECH consortium. That was when I first learned of the concept of "overfitting." I proposed to my IBM Research Division managers in 2003 that we start analyzing some of the data generated from an automated fab where I was located. With a green light, I jumped with both feet into data mining (the common term at that time) leading a small team to see if we could predict chip yield in the 300mm wafer line in East Fishkill. That work led to a thesis topic and by 2007, I added a MS in Data Mining to my bio and soon retired from IBM to land a job at SPSS since I immediately fell in love with Clementine (now IBM SPSS Modeler) in grad school. During my 6 years in technical pre-sales in SPSS and IBM, I received a Sales Eminence Award and membership in the 100 Percent Club. I then went from performing proofs of concept to the new discipline of Offering Management where I could influence the direction of algorithm development in SPSS products. Over my 40-year, SPSS-interrupted, career at IBM I've received an Outstanding Innovation Award and was an IBM Leading Inventor twice while accumulating 88 patents of which the most recent 4 are in the field of data science.
Gabriela de Queiroz is a Data Scientist and the founder of R-Ladies, a world-wide organization for promoting diversity in the R community with over 90 chapters all around the world. She likes to mentor and shares her knowledge through mentorship programs, tutorials and talks. She has worked in several startups and where she built teams, developed statistical models and employed a variety of techniques to derive insights and drive data-centric decisions. She holds 2 Master’s: one in Epidemiology and one in Statistics.
SPEAKERS
Shadi Copty
Director, Data Science & Machine Learning Offerings
Director, Data Science & Machine Learning Offerings, IBM
Shadi Copty is responsible for the business and strategy for IBM Analytics' Data Science and Machine Learning offerings. Prior to this role, he led the Data Science practice in IBM Watson Customer Engagement, which included top Machine Learning, Optimization and Statistical learning experts that invented, built, and supported the models that became the heart of the business's cognitive engine. Prior to that Mr Copty held various technical and business leadership roles in IBM Research, where he started his career 17 years ago. He holds a BSC in Computer Science and an MBA from the Technion, Israel.
Hemant Suri
Sr. Manager, Hybrid Data Warehouse Offering Management
Kirk is a Principal Data Scientist with a decade of experience writing predictive models on Wall Street. Noteworthy experience in the areas of Trade Surveillance and Financial Crimes. Held roles as a Software Architect specializing in big data and distributed systems. Routinely provides solutions to large enterprises migrating from a traditional business-intelligence practice to a modern data science at scale operation. Passions include distributed computing, outlier analysis, Scala and Spark. Proctors data science workshops for fortune 100s.
Robert has over 12 years of experience working on various projects related to Artificial Intelligence, Robotics, IoT, Enterprise & Embedded Software. His primary focus at Hortonworks is building communities around Big Data and Data Science, and enabling Enterprises to accelerate adoption of cutting edge open-source technologies.
Harsh Shah is Partner Solutions Engineer at Hortonworks. Works on the Partner Engineering team at Hortonworks with a broad ecosystem of partners. In his current role he leads the certification process for partner solutions with Hortonworks Data Platform (HDP) and Hortonworks Data Flow (HDF).
Chief Data Scientist, Client Insights, IBM Watson FSS
Enthusiastic data scientist and team leader with 20+ years in analytical software for the financial services sector with focus on risk management, compliance and customer analytics. My career spans all risk types - market risk, credit risk, operational risk, liquidity risk, portfolio management. I've worked in many areas from offering management to development, documentation to support, relationship management to sales/pre-sales/demonstrations.
