CURRICULUM VITAE
`
`Dr. David W. Franke
`
`Chief Scientist, Vast.com
`
`Adjunct Associate Professor
`Department of Computer Science
`The University of Texas at Austin
`E-mail: dfranke@cs.utexas.edu
`
`Senior Member, AAAI
`
`EDUCATION
`
`1992 Ph.D. Computer Science
`The University of Texas at Austin
`Dissertation Advisor: Professor Benjamin J. Kuipers
`Dissertation: A Theory of Teleology
`1977 M.S. Computer Science
`The Pennsylvania State University
`Advisor: Professor Donald B. Johnson
`1976 B.S. Mathematics, Summa Cum Laude
`The University of Oklahoma
`
`WORK HISTORY
`
`Chief Scientist
`
` Vast.com, Austin TX
`
`(cid:120) Data mining and data analysis for market insights, descriptive models, and predictive models
`for consumer considered purchases (automotive, real estate, travel).
`(cid:120) Lead the data science team.
`
`Member Technical Staff
`
` Google, Mountain View CA
`
`(cid:120)
`
`Search Quality, Eval, Research Team
`o Managed engineering team that supported click-based evaluation of search
`experiments.
`o Research on user tasks and user intent in search queries.
`
`Associate Professor
`
`St. Edward's University, Austin TX
`
`(cid:120) Taught undergraduate Computer Science courses in New College.
`(cid:120) Taught graduate Master of Computer Information Systems (MCIS) courses in the School of
`Management and Business.
`
`Distinguished Technical Fellow
`
` Trilogy Software Inc., Austin TX
`
`(cid:120) Manage intellectual property: evaluate internally developed technology for patent potential,
`coordinate interaction with patent attorneys in developing patent disclosures.
`Initial investigation and implementation of text mining for Trilogy's automotive demand
`intelligence network.
`
`(cid:120)
`
`0001
`
`Versata 2003
`Ford Motor v. Versata
`CBM2016-00101
`
`

`
`(cid:120) Optimization modeling (linear programming) and prototype development for price
`optimization tool.
`Implementation of data mining (association rule mining) for Trilogy's Sales Optimizer tool.
`Product research and development, technical lead.
`Principle architect and implementer on Trilogy's flagship product SalesBUILDER, a product
`configuration tool.
`(cid:120) Technology evaluation of potential acquisitions.
`
`(cid:120)
`(cid:120)
`(cid:120)
`
`Senior Member, Technical Staff
`
`Microelectronics and Computer Technology
`Corporation (MCC), Austin TX
`
`(cid:120) Developed research proposal and "selling" materials for Hardware/Software CoDesign
`project. Pitched the project to potential funding companies, conducted initial project research.
`(cid:120) Managed team that developed a VHDL (VHSIC Hardware Description Language) compiler
`and simulation environment. The resulting product had greater language coverage and better
`performance than any commercial tool available at the time.
`Promoted use of GNU tools and supported same in transition from LISP to C/C++
`environments.
`(cid:120) Research on design knowledge representation for digital system designers.
`
`(cid:120)
`
`Senior Member, Technical Staff
`
`Texas Instruments, Inc., Austin TX
`
`(cid:120)
`
`(cid:120) Research and development of decision support tools (quantitative decision analysis, expert
`systems). Principle researcher, team lead, project manager for quantitative decision analysis
`tool.
`PC software product development - disk storage management, speech recognition products.
`Team lead, project manager.
`(cid:120) Computer architecture research and design (memory system architecture).
`(cid:120) Operating system design and implementation for a proprietary 16-bit minicomputer. Team
`lead.
`
`TEACHING
`
`The University of Texas at Austin
`
`David is an Adjunct Associate Professor in the Computer Sciences Department at UT. He also
`teaches in the Masters of Business Analytics program in the McCombs School of Business at UT.
`
`Courses
`
`(cid:120) Computer Science (Undergraduate Computer Science degree program)
`o CS 378 - Big Data Programming
`
`(cid:131) The map-reduce programming paradigm is a fundamental tool used in
`processing large data sets, and is supported in current tools such as Hadoop.
`Apache Spark offers another programming paradigm for processing large
`data sets. In this course the student will gain an understanding of the
`concepts embodied in map-reduce, and will investigate how map-reduce is
`used to address various problems in processing and analyzing large data sets.
`This course will explore map-reduce as implemented in Hadoop, as well as
`the associated distributed file system (HDFS). In this course you will gain an
`understanding of the concepts offered and supported in Spark, and will
`investigate how to apply these concepts to address various problems
`including those you addressed using map-reduce.
`
`0002
`
`

`
`o CS 363D - Introduction to Data Mining
`
`(cid:120) McCombs School of Business (Master of Business Analytic Program)
`o MIS 381N.1 - Intro to Data Management
`
`(cid:131)
`
`Fundamental to any analytics initiative is the data stored in database
`management and other data storage systems. It is often said 70-80% of the
`time in doing analytics goes towards extracting, cleaning, and transforming
`data. This class is designed for business analytics students to explore various
`concepts of data management and develop expertise in data querying and
`processing. The following are some learning outcomes of this course:
`
`(cid:131) Understand different types of data models
`
`(cid:131) Develop skills to model organizational data using Entity-
`Relationship models using modeling tools
`
`(cid:131) Analyze functional dependencies and normalization, and design
`relational databases
`
`(cid:131) Gain expertise in data definition and data manipulation using
`Structured Query Language (SQL)
`
`(cid:131) Understand concepts behind building data warehouse and big data
`systems
`
`(cid:131) Gain working knowledge in Big Data storage processing using
`Hadoop map-reduce and Spark
`
`(cid:131) Understand Big Data ecosystems and analytics
`
`St. Edward's University
`
`From June 2005 through August 2007, David was an Assistant Professor at St. Edward's University,
`with a joint appointment to New College and the School of Management and Business. In New
`College, he taught courses for the P.A.C.E. Computer Systems Management degree program. In the
`School of Management and Business, he taught courses for the MS in Computer Information
`Systems (MCIS) program.
`
`Courses
`
`(cid:120) New College (Computer Systems Management degree program)
`o COSC 1323 - Computer Science Concepts I: Introduction to Programming
`
`(cid:131) This course introduces students to fundamental aspects of the field of
`computing, focusing on problem-solving and software design concepts and
`their realizations as computer programs using JAVA. Topics include
`procedural abstraction, control structures, iteration, recursion, data types and
`representation, arrays, records, and user-defined types. Introduction to a
`high-level language, for the purpose of gaining mastery of these principles,
`will be done in a closely coordinated laboratory experience.
`o COSC 2325 - Computer Science Concepts II: Data Structures
`
`0003
`
`

`
`(cid:131) This course moves students into the domain of software design, introducing
`principles that are necessary for solving large problems. With an emphasis
`on the software design process, topics include abstract data types,
`specifications, complexity analysis and file organization, basic data
`structures (queues, stacks, trees, linked lists) and transformations (sorting
`and searching) are introduced as fundamental tools that are used to aid this
`process. Time and space analysis and verification are also included.
`Applications of these topics emphasizing software design will be developed
`in JAVA. Prerequisite: COSC 1323 and COSC 1123
`o MATH 2310 - Mathematics of Business
`
`(cid:131) A non-calculus based business mathematics course designed to meet the
`mathematics requirement for New College business majors. Topics include a
`review of algebra, the mathematics of finance, application of the exponential
`function, linear equations and matrices, Leontief input-output models,
`methods of maximization and minimization with constraints and the simplex
`method, graph theory, analysis of charts and graphs, and the basic principles
`or probability. Basic game theory and Markov chains may also be included.
`Prerequisite: MATH 1314.
`o MATH 2315 - Discrete Mathematics
`
`(cid:131) An introduction to topics and problems in mathematics that are commonly
`used in computer science and information systems analysis, design and
`operations. These topics include principles of counting, logic, set theory,
`mathematical induction, relations and functions, and an introduction to
`computational complexity and to graph theory. Prerequisite: MATH 2312.
`
`(cid:120)
`
`School of Management and Business (Masters of Computer Information Systems degree
`program)
`o MCIS 5301, ISMG 5301 - Introduction to Programming
`
`(cid:131) This course teaches the basic skills of programming a computer using a
`high-level language and a visual development environment. It introduces
`data types, arrays, structures, algorithm design, control structures, loops,
`procedures, data abstraction, and object-oriented programming. Students are
`required to develop algorithms and write computer programs.
`o MCIS 6306, ISMG 6306 - Database Systems
`
`(cid:131) This course provides an overview of modern database systems, including the
`critical issues for success in management of databases, such as designing,
`modeling, creating, querying, programming and administering a database.
`Different systems and system architectures also are examined. Prerequisite:
`at least concurrent with MCIS 5301 and MCIS 5100.
`o MCIS 6310 - Systems Analysis and Design
`
`(cid:131) This course introduces students to a software development process, system
`analysis, system design, requirements identification and collection, data
`modeling, design of an interface and data management. Students will
`develop an understanding of the iterative software development process and
`develop system requirements and a system design through use of the Unified
`
`0004
`
`

`
`Modeling Language (UML) and a visual modeling tool. In addition to
`object-oriented and iterative methods, structured analysis and design
`techniques will also be discussed as an alternative. Prerequisites: MCIS
`5100, MCIS 5301, and MCIS 6306.
`o MCIS 6313 - Data Warehouse and Data Mining
`
`(cid:131) This course covers the fundamentals of data warehousing architecture and
`the issues involved in planning, designing, building, populating and
`maintaining a successful data warehouse. The course introduces students to
`data mining, and how it relates to data warehousing. Specific topics covered
`include the logical design of a data warehouse, the data staging area and
`extract-transform-load processing, the use of multi-dimensional analysis
`using OLAP techniques, and coverage of the knowledge discovery process
`using data mining techniques for associations, classification, clustering, and
`regression. Prerequisites: MCIS 6306.
`o MCIS 6314 - Web Programming
`
`(cid:131) This course introduces students to concepts, architectures, and programming
`and scripting languages used to construct Web sites and Web applications.
`The course explores client-side and server-side scripting and programming
`techniques and languages used to implement today’s Web applications
`including database-based applications. Prerequisites: MCIS 5301, MCIS
`6306, MCIS 6308.
`o MCIS 6315 - Information Systems Capstone
`
`(cid:131) This course provides a system-oriented view of the organization and its
`relation with information technology. It also addresses the information
`system function within the organization and how disparate technologies and
`computer platforms and networks can be integrated to provide a flexible and
`efficient infrastructure for the organization. Students work on a planning,
`design, implementation or re-engineering project that includes a thorough
`investigation of an information system and the formulation and evaluation of
`strategies that determine the character, direction and success of an
`organization. Ethical issues also are identified and analyzed. This project
`may be undertaken in collaboration with capstone students from the MBA
`Program. Prerequisites: Final trimester.
`
`Mu Sool Won
`
`David is 5th Don in Mu Sool Won, and teaches the traditional Korean martial art at the headquarters
`school in north Austin under the leadership of Grandmaster Byung In Lee.
`
`PAPERS
`
`(cid:120)
`
`Journals
`o
`o
`o
`
`"Configuration Research and Commercial Solutions", in AI EDAM (special issue on
`Configuration Design), Vol. 12, No. 4 (Sept. 1998), pp. 295-300.
`"Deriving and Using Descriptions of Purpose", in IEEE Expert (special track on
`Functional Reasoning), Vol. 6, No. 2 (April 1991), pp. 41-47.
`"Embedding Rule Inferencing in Applications", in IEEE Expert (special track on
`Object-Oriented Programming in AI), Vol. 5, No. 6 (December 1990), pp. 8-14.
`
`0005
`
`

`
`(cid:120) Conferences, Workshops
`o
`o
`o
`
`"A Theory of Teleology", Ph.D. Dissertation, TR AI93-201, Artificial Intelligence
`Laboratory, The University of Texas at Austin, May 1993.
`"Design Automation Technology for CoDesign: Status and Directions", IEEE
`International Symposium on Circuits and Systems, May 1992.
`"Acquisition of Teleological Descriptions", SPIE Intelligent Information Systems
`Conference on Applications of Artificial Intelligence X: Knowledge Based Systems,
`April 1992.
`"Classifying and Indexing Design Modifications via Descriptions of Purpose", AAAI
`Spring Symposium Series, Symposium on Computational Considerations in
`Supporting Incremental Modification, March 1992.
`"Rule-Based Problem Solving in Applications", Third Workshop on Object-Oriented
`Programming in AI, AAAI-91.
`"Hardware/Software CoDesign: A Perspective", in Proceedings of the 13th
`International Conference on Software Engineering, Austin TX, May 13-16, 1991,
`pp. 344-352 (coauthor: Martin K. Purvis).
`"What Simulationists Need to Know About Their Problems", in Proceedings of the
`1990 Winter Simulation Conference, panel on "AI: What Simulationists Really Need
`to Know", Dec. 10, 1990.
`"Representing and Acquiring Teleological Descriptions", 1989 Workshop on Model-
`Based Reasoning.
`"Component-Connection Models", 1989 Workshop on Model-Based Reasoning
`(coauthor: Daniel Dvorak).
`
`o
`
`o
`o
`
`o
`
`o
`o
`
`(cid:120) Technical Reports
`o
`o
`o
`o
`
`"MCC CoDesign Exploratory Initiative: Hardware/Software CoDesign Workshop
`Report", MCC Technical Report EI-264-90 (coauthor: Martin K. Purvis).
`"MCC CoDesign Exploratory Initiative: Preliminary Report", MCC Technical
`Report EI-222-90 (coauthor: Martin K. Purvis).
`"CAD Inference Engine (CADIE) Research Prototype User's Guide", MCC
`Technical Report CAD-047-90.
`"CC: Component-Connection Models for Qualitative Simulation, A User's Guide",
`TR AI90-126, Artificial Intelligence Laboratory, The University of Texas at Austin.
`(coauthor: Daniel L. Dvorak).
`"CADRES: CAD Design Knowledge Representation System", MCC Technical
`Report CAD-198-89 (coauthors: David E. Newton, Richard P. Johns).
`
`o
`
`Patents
`
`(cid:120) Method for generating and displaying tree structures in a limited display area, (with Carroll
`R. Hall) Patent Number: 4,710,763. Dec. 1, 1987.
`(cid:120) Method and apparatus for goal processing memory management, (with John Lynch) Patent
`Number 5,369,732, Nov. 29, 1994.
`(cid:120) Method and apparatus for configuring systems, (with John Lynch) Patent Number 5,515,524,
`May 7, 1996.
`(cid:120) Method and apparatus for configuring systems, (with John Lynch) Patent Number 5,708,798,
`Jan. 13, 1998.
`(cid:120) Method and apparatus for configuring systems, (with John Lynch) Patent Number 6,002,854,
`Dec. 14, 1999.
`(cid:120) Method and apparatus for configuring systems, (with John Lynch) Patent Number 7,043,407,
`May 9, 2006.
`(cid:120) Attribute based association rule mining, (with Nirad Sharma, Rohit Namjoshi) Patent
`Number 7,433,879, Oct. 7, 2008.
`
`0006
`
`

`
`(cid:120)
`
`(cid:120)
`
`(cid:120)
`
`(cid:120) Application state server-side cache for a state-based client-server application, (with Jude
`Britto, Rajasekhar Vinnakota, Douglas Gray, Deepti Gupta) Patent Number 8,019,811, Sep.
`13, 2011.
`System and method for efficiently generating association rules Patent Number 8,401,986,
`March 19, 2013.
`(cid:120) Predictive prefetching of data from remote client-state cache from server to update local
`client-state cache, (with Jude Britto, Rajasekhar Vinnakota, Douglas Gray, Deepti Gupta)
`Patent Number 8,832,184, Sep. 9, 2014.
`Systems, methods, and devices for measuring similarity of, and generating recommendations
`for, unique items, (with Joshua Levy) Patent Number 9,104,718, August 11, 2015.
`Systems, methods, and devices for measuring similarity of, and generating recommendations
`for, unique items, (with Joshua Levy) Patent Number 9,324,104, April 26, 2016.
`(cid:120) Application state server-side cache for a state-based client-server application, (with Jude
`Britto, Rajasekhar Vinnakota, Douglas Gray, Deepti Gupta) Patent Number 9,385,914, July
`5, 2016.
`Systems, methods, and devices for identifying and presenting identifications of significant
`attributes of unique items, (with Thomas Wilbur) Patent Number 9,465,873, October 10,
`2016.
`
`(cid:120)
`
`PROFESSIONAL ORGANIZATIONS AND ACTIVITIES
`
`(cid:120)
`
`Professional Society Memberships
`o Association for Computing Machinery
`o American Association for Artificial Intelligence
`o
`IEEE Computer Society
`(cid:120) Advisory Committee, Department of Computer Science, University of Texas at Austin
`(cid:120) Chair, Board of Advisors, School of Computer Science, University of Oklahoma
`(cid:120) Conference and Workshop Program Committees
`o Configuration Workshops for AAAI, IJCAI, and ECAI conferences.
`o Model Based Reasoning Workshop, AAAI-90, AAAI-91.
`o Qualitative Reasoning Workshop, QR-91.
`o Applications of AI X: Knowledge-Based Systems Conference.
`o First International Workshop on Hardware/Software CoDesign.
`o Computer Based System Design, ICCD-92.
`Paper Reviewer
`o CCSC-SC 2006
`(cid:120) CoChair, ICSE Workshop on Hardware/Software CoDesign
`(cid:120) Vast representative on the Industry Council for the MSIROM: Business Analytics program at
`the McCombs School of Business at the University of Texas.
`
`(cid:120)
`
`0007