10:00 - 13:00 (Aula 01-09)

Natalia da Silva (PhD, Iowa State University): Es profesora adjunta en el Instituto de Estadística de la Universidad de la República en Montevideo (UDELAR-IESTA). Trabaja en investigación en métodos de aprendizaje supervisado, predicción, análisis de datos exploratorios y gráficos estadísticos en colaboración con Di Cook y Heike Hofmann. Es cofundadora de R-Ladies Ames y R-Ladies Montevideo y Chair de LatinR2018.

Abstract: Artificial intelligence, is playing a significant role in creative activities such as music, architecture, fine arts, and science. Indeed, the computer is already a canvas, a brush, a musical instrument,... However, we should aim at more ambitious relations between computers and creativity. Rather than just seeing the computer as a tool to help human creators, we could see it as a creative entity at least to some degree. With this understanding in mind, an operational, and widely accepted, definition of creativity is: "a creative idea is a novel and valuable combination of known ideas". In other words, physical laws, theorems, musical pieces can be generated from a finite set of existing elements and, therefore, creativity is an advanced form of problem solving that involves memory, analogy, learning, and reasoning under constraints, among others, and is therefore possible to replicate by means of computers.

This view has triggered a new subfield of Artificial Intelligence called Computational Creativity. This talk addresses the question of the possibility of achieving computational creativity through some examples of computer programs capable of replicating aspects of creative behavior. We end with some reflections on the recent trend of “democratisation" of creativity by means of assisting and augmenting human creativity.

¿QUE SABEN DE NOSOTROS LAS REDES SOCIALES?

Subir información a las redes sociales puede representar un verdadero peligro si no se maneja con cuidado. Datos sensibles, fotografías o actividades comerciales pueden representar un verdadero interés para una población delictiva poniendo en situación crítica a sus propietarios.

Gastón Miguel Semprini: Licenciado en Sistema de Información, Universidad de Belgrano, Jefe del Departamento de Informática Forense del Poder Judicial de Rio Negro. “Master en Informática Forense y Pericial” proporcionado por el Instituto Europeo de Estudios Empresariales de España, “Experto en Informática Forense”, Universidad Tecnológica Nacional de Avellaneda.

Aplicaciones de grafos y programación entera para la personalización de ofertas en la industria retail.

Para un retailer, la implementación de promociones personalizadas es un medio para evitar el efecto negativo de las promociones masivas, como por ejemplo efectos de acumulación de stock por parte de los clientes aprovechando descuentos en productos que de todos modos comprarían. Las promociones personalizadas se apoyan en las preferencias heterogéneas de individuos para productos dentro de una categoría, y en sus diferentes reacciones a los precios descontados. En este trabajo consideramos el diseño de promociones personalizadas, y la predicción de las respuestas de los clientes a tales promociones sobre una categoría de productos. Finalmente, en base a esas predicciones, estudiamos el problema de optimizar el subconjunto de productos a promocionar durante la visita de un cliente a la tienda.

Los inputs requeridos por nuestra propuesta incluyen datos de panel con la historia de transacciones etiquetadas con el ID de cada cliente, información sobre el mix de productos disponible para una categoría de productos, y la identificación de productos que estaban en promoción en el momento de cada visita por parte de cada cliente.

Las preferencias de cada cliente se representan con un grafo acíclico dirigido (DAG). El DAG se construye en base a los datos históricos que revelan las preferencias de cada cliente. En el DAG, cada nodo i representa un producto, y el arco dirigido (i,j) representa la relación “el producto i es preferible sobre el producto j”.

Desde el punto de vista teórico, proveemos cotas computacionalmente tratables para calcular tanto la probabilidad de observar un DAG como las probabilidades de compra (que en general son problemas #P-hard). Luego, tomando la colección de DAGs representando la base de clientes, calibramos sobre sí variantes del modelo multinomial logit (MNL), junto a dos benchmarks que constituyen estados del arte en la materia (pero que no usan la estructura de DAGs). Finalmente, testeamos tanto nuestro modelo basado en DAGs como los dos benchmarks para el diseño de promociones personalizadas. Vía una formulación MIP, consideramos el problema de optimizar el subconjunto de productos a ofrecer en promoción para un cliente particular, dado un surtido de productos prefijado, con el objetivo de maximizar ingresos.

Los experimentos numéricos en datos de panel reales sobre 27 categorías de productos muestran que nuestra propuesta permite predicciones más certeras respecto a las compras de los clientes que los benchmarks testeados, con mejoras del orden de 10%. Finalmente, nuestro MIP para ofrecer promociones personalizadas permiten mejoras del 26% (en promedio) sobre los resultados que exhibe el dataset.

Trabajo conjunto con Srikanth Jagabathula y Dmitry Mitrofanov (New York University).

Prof. Ignacio E. Grossmann: is the R. R. Dean University Professor of Chemical Engineering, and former Department Head at Carnegie Mellon University. He obtained his B.S. degree at the Universidad Iberoamericana, Mexico City, in 1974, and his M.S. and Ph.D. at Imperial College in 1975 and 1977, respectively.  He is a member and former director (2005-2015) of the “Center for Advanced Process Decision-making,” an industrial consortium that involves about 20 petroleum, chemical, engineering and software companies. He is a member of the National Academy of Engineering. He has received the following AIChE awards, Computing in Chemical Engineering, William H. Walker for Excellence in Publications, Warren Lewis for Excellence in Education, and Research Excellence in Sustainable Engineering. In 2003 he received the INFORMS Computing Society Prize, and is currently a Fellow of INFORMS. In 2015 he was the first recipient of the Sargent Medal by the IChemE. He has honorary doctorates from Abo Akademi in Finland, University of Maribor in Slovenia, Technical University of Dortmund in Germany, University of Cantabria in Spain, and from the Russian Kazan National Research Technological University. He has been named Thomson Reuters Highly Cited Researcher in 2014-2016. His research interests are in the areas of mixed-integer, disjunctive and stochastic programming, energy systems including petroleum, shale gas and biofuels, water networks, and planning and scheduling for enterprise-wide optimization, and reliability optimization. He has authored more than 500 papers, several monographs on design cases studies, and the textbook “Systematic Methods of Chemical Process Design,” which he co-authored with Larry Biegler and Art Westerberg.  He has graduated 58 Ph.D. and 16 M.S. students.

Recent Theoretical and Computational Advances in the Optimization of Process Systems under Uncertainty

Optimization under uncertainty has been an active and challenging area of research for many years. However, its application in Process Systems has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust optimization vs. stochastic programming), handling of nonlinearities (most work addresses linear problems), large computational expense (orders of magnitude larger than deterministic models), and difficulty in the interpretation of the results by non-expert users.

In this lecture, we describe recent advances that address some of these barriers. We first describe the basic concepts of robust optimization, including the robust counterpart, showing its connections with semi-infinite programming. We also we explore the relationship between flexibility analysis and robust optimization for linear systems. A historical perspective is given, which shows that some of the fundamental concepts in robust optimization have already been developed in the area of flexibility analysis in the 1980s. We next consider two-stage and multi-stage stochastic programming in the case of exogenous parameter, for which we describe acceleration techniques for Benders decomposition, hybrid sub-gradient/cutting plane methods for Lagrangean decomposition, and sampling techniques. We address both mixed-integer linear and nonlinear stochastic programs, including integer recourse. We then address the generalization to the case of both exogenous and endogenous parameters, which gives rise to conditional scenario trees for which theoretical properties are described to reduce the problem size. To avoid ad-hoc approaches for setting up the data for these problems, we describe approaches for handling of historical data for generating scenario trees. Finally, we illustrate the application of each of these formulations in demand-side management optimization, planning of process networks, chemical supply chains under disruptions, planning of oil and gas fields, and optimization of process networks, all of them under some type of uncertainty.

Octavio Lange Ingeniero Agrónomo (UBA) y Magíster en Administración de Negocios (UNICEN): Nacido en Tandil, con más de 6 años especializándose en tecnologías digitales en el sector agropecuario. Asesor CREA de la región Mar y Sierras y responsable del proyecto Palenque de la Fundación Sadosky.

El proyecto Palenque como facilitador del ecosistema AgTech.

 Día tras día surgen nuevas y mejores herramientas para agregar valor o resolver problemáticas del. sector agropecuario, tanto públicas como privadas. El. inconveniente es que cada actor brinda soluciones estancas con bajo grado de colaboración entre ellas. El. desafío es acordar un lenguaje común y lograr que estas herramientas puedan interoperar entre sí para alcanzar mejores soluciones y generar mayor conocimiento colectivo y conectivo

ANDES es la plataforma digital que implementa la Historia Única Digital de Salud (HUDS) en la provincia de Neuquén y tiene próximas implementaciones en otras provincia. Es un proyecto gratuito, de código abierto, basado íntegramente en HL7, FHIR y SNOMED. Compartiremos experiencias, aciertos y errores en la creación de este ecosistema de aplicaciones de salud.

Estonia es considerado el 1er país digital del mundo, en donde toda la información de salud es asegurada con tecnología Blockchain. En 2008 el Gobierno de la provincia del Neuquén tomó como referencia el modelo digital de Estonia para desarrollar su modelo de Integrabilidad. Presentaremos el resultado de esta experiencia, resumiendo las claves que permitieron su adopción y adaptación.

Working with Industrial Cyber-Physical System and Industry4.0-compliant Solutions (Education and Training Requirements)

We are witnessing rapid changes in the industrial environment, mainly driven by business and societal needs towards production customization and the digitalization of the economy, i.e., digitalization and interconnection of products, services, enterprises, and people. This trend is supported by new disruptive advances in the cross-fertilization of concepts and the amalgamation of information-, communication-, control- and mechatronics technology- driven approaches in traditional industrial systems. In this context, industrial informatics combine the progress achieved by the application of large distributed and networked computing systems on product and production system design, planning, engineering, and operation with the power of digital data that are produced by industrial processes and also collected by the Internet of Things. The technological, economic, and social impacts of these developments are so enormous that the whole process is labeled as the 4th Industrial Revolution.

In 2006, the term “Cyber-Physical Systems” (CPS) was coined to “refer to the integration of computation with physical processes”. CPS can be described as smart systems that encompass hardware and software, computational and physical components, seamlessly integrated and closely interacting to sense and to control in real-time the changing state of the real world. These systems involve a high degree of complexity at numerous spatial and temporal scales, and highly networked communications integrating their computational and physical components. As such CPS refer to Information-Communication-Control- Mechatronics-Systems (sensing, actuating, computing, communicating, etc.) embedded in physical objects, interconnected through several networks including the Internet, and providing citizens and businesses with a wide range of innovative applications based on digitalized data, information and services.

Ontologically the term Cyber-Physical Systems means hardware-software systems which tightly couple the physical world and the digitalized (virtual) world. In a CPS ecosystem, on the one hand every real physical object has one or more cyber representations, and on the other hand a cyber component or system can be linked to a physical representation i.e., an object in the 3-dimensional human-tangible world. Moreover, these objects are increasingly interconnected in real-operational-time, networked either permanently or communicate in an asynchronous manner from time to time, possessing all essential characteristics of real-time-critical systems.

Industrial Cyber-Physical Systems (ICPS) and Industry4.0-compliant solutions forge the core of real-world networked industrial infrastructures having a cyber-representation through digitalization of data and information across the enterprise, along the product and process engineering life-cycle and from suppliers to customers along the supply chain. As such the competitive performance of an ICPS mainly depends on the ability to effectively collect, analyze and use large-scale digitalized data and information from many different and often heterogeneous sources, to sustainably and efficiently manage, supervise and operate in the industrial environments. This effective information-driven interaction of ICPS with other CPS and enterprise systems, extending to all business processes, is viewed as vital to modern Industry4.0-compliant infrastructures.

There are many challenges ahead in the convergence of computing, control, mechatronics and communications for CPS and Industry4.0-compliant ecosystems. There is a need for investigating and learning a wide spectrum of foundations, research and technological fields. In this context, the plenary talk (i) addresses the penetration and proliferation of such ecosystems into the industrial environments, taking into account that the same trend is also evident in other domains such as energy, healthcare, manufacturing, military, transportation, consumer, enterprise, robotics, and smart cities, among others; and (ii) offers an overview of major requirements to learning, teaching, training students, technicians and engineers for working with ICPS and Industry4.0-compliant solutions.

After presenting the scientific and technical background behind Industrial Cyber-Physical Systems (ICPS), their relationship to other frameworks like Systems-of-Cyber-Physical Systems, Industrial Internet-of-Things (IIoT) and Industry4.0, and associated technology standardization/normalization initiatives, the audience/participants of the plenary talk will get a deep view about:

● Which is the minimal necessary pre-existing Know-How for understanding and working with ICPS and Industry4.0-compliant solutions?

● What and How to learn ICPS and Industry4.0-compliant solutions? (Recommendations for graduated and post-graduate students, and industrialists)

● What and How to teach ICPS and Industry4.0-compliant solutions? (Recommendations for trainees, professors, etc.)

● How to engineer and operate ICPS and Industry4.0-compliant solutions?