Colleen Farelly
Workshop Title (1): Geometry, Data and One Path Into Data Science
Abstract: This presentation will provide an overview of data science, detail my own journey into data science, and dive into some applications related to machine learning methods rooted in geometry and topology.
Workshop Title (2): Applying Topological Data Analysis (Part 1)
Abstract: This workshop will overview persistent homology and the Mapper algorithm from a theoretical perspective and through exploring a dataset in Python with these methods.
Workshop Title (3): An Introduction to Machine Learning
Abstract: This talk will overview some common tasks in machine learning (supervised learning, dimensionality reduction...), as well as some more advanced types of machine learning (like spatial, computer vision, and text algorithms).
Biography:
Colleen M. Farrelly is a senior data scientist at Datasembly whose work has focused on natural language processing, topological data analysis, longitudinal data modeling/time series applications, and measurement models across many different industries. Outside of machine learning and mathematics, Colleen enjoys water sports and creative writing.
Aurelie Jodelle Kemme
Workshop Title: An Introduction to Machine Learning (Part 2)
Abstract: This talk will overview some common tasks in machine learning (supervised learning, dimensionality reduction...), as well as some more advanced types of machine learning (like spatial, computer vision, and text algorithms).
Biography:
Aurelie Jodelle Kemme is a Cameroonian data scientist who holds an MS in Industrial Mathematics from AIMS Cameroon. She is currently an MS researcher at Quantum Leap Africa in Rawanda.
Kaisa Miettinen
Talk Title: Data-Driven Multiobjective Optimization with Interactive Methods
Abstract:
In data analytics, we can use descriptive analytics to understand the data or predictive analytics to make predictions, but this is not always sufficient to know what actions to take to reach desired outcomes. To make recommendations or decisions based on the data, we need prescriptive or decision analytics. We can fit models in the data and derive decision problems. If the decision to be made is characterized by multiple conflicting objectives to be optimized, we can support decision-making by applying appropriate multiobjective optimization methods.
In this talk, I discuss different elements of a seamless chain from data to data-driven decision support involving multiobjective optimization and give examples of different elements. Eventually, the derived multiobjective optimization problem is solved with an appropriate interactive method. In that way, the decision-maker with domain expertise can augment information contained in the data and direct the solution process with one’s preferences. At the same time, the decision-maker gains insight into the interdependencies and trade-offs among the conflicting objectives and can get convinced of the quality of the most preferred solution. In addition, I give some examples of data-driven decision-making problems. In addition, I give an overview of the modular, open-source software framework DESDEO containing different interactive methods.
Biography:
Kaisa Miettinen is Professor of Industrial Optimization at the University of Jyvaskyla. Her research interests include theory, methods, applications and software of nonlinear multiobjective optimization including interactive and evolutionary approaches. She heads the Research Group on Multiobjective Optimization and is the director of the thematic research area called Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, www.jyu.fi/demo). She has authored over 200 refereed journal, proceedings, and collection papers, edited 17 proceedings, collections and special issues and written a monograph Nonlinear Multiobjective Optimization. She is a member of the Finnish Academy of Science and Letters, Section of Science and has served as the President of the International Society on Multiple Criteria Decision Making (MCDM). She belongs to the editorial boards of seven international journals. She has previously worked at IIASA, International Institute for Applied Systems Analysis in Austria, KTH Royal Institute of Technology in Stockholm, Sweden and Helsinki School of Economics, Finland. She has received the Georg Cantor Award of the International Society on MCDM for independent inquiry in developing innovative ideas in the theory and methodology.
Diletta Martinelli
Workshop Title: An Introduction to Linear Algebra and Matrix Computations
Abstract:
Linear algebra and, in particular, matrix computations are at the core of any scientific endeavor! From pure mathematics subjects studied for centuries, such as projective geometry and differential equations, to the more recent applications to data science and machine learning.
“The Matrix is everywhere!”
In this workshop, we will give an introduction to matrix computations in connection to solutions of systems of linear equations. In particular, we will describe the Gaussian Elimination algorithm and discuss the geometry of the space of solutions of linear systems.
No prior background will be assumed for the workshop, more than on the general theory we will adopt on “hands-on” approach focusing more on examples and exercises.
Biography:
Diletta Martinelli is an assistant professor working at the University of Amsterdam, her field of research is algebraic geometry with a special focus on higher dimensional birational geometry and moduli theory. She graduated from Imperial College London in November 2016 and she held postdoctoral positions at the University of Edinburgh, MSRI Berkeley and the University of Glasgow, before arriving in Amsterdam in October 2019. In the past years she has been involved in a variety of projects in developing countries. She taught masterclasses in several African countries and in Pakistan and has supervised several international students. She is also involved in several projects in outreach and in improving gender-balance in the academic setting, and more generally aimed toward the creation of a more diverse and inclusive environment.
Qin Li
Workshop Title: Kinetic Theory and Data Science: who feeds whom what?
Abstract: TBA
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