24-31 August 2018
The Data Science Summer School is organized for the first time in Romania by the Bucharest University of Economic Studies, with the support of the International Relations Office, the Faculty of Cybernetics, Statistics and Informatics, and the Faculty of Finances and Banking, together with renowned specialists from the USA, Norway, Spain, Slovenia and Austria.
The goal of the summer school is to familiarize students with relevant state of the art topics in data science. The program will cover fundamentals of data science and focus on the following key data science topics:
- Machine learning
- Text mining
- Data management
- Data visualization
- Applications in finance and economics
The program will consist of a combination of lecture-style talks introducing various data science paradigms and methods, hands-on sessions, short high-level inspirational talks on data science related projects, and student projects.
The summer school aims to have a practical orientation, with Pyhton being used to exemplify many of the topics covered at the summer school.
At the end of the summer school, the students are expected to have an understanding of key paradigms used in data science and be able to practically apply them in data science projects.
- Familiarity with computer programming, interest in working with data, enthusiasm, and willingness to learn new things!
- Basic knowledge of linear algebra, probability theory, knowledge representation would be useful, though not strictly necessary.
- English, at least B2 (according to European language levels – Self Assessment Grid)
- In order to ensure a sufficient level of knowledge, the Summer School is aimed at prospective students with at least a Bachelor’s Degree.
|09:00-12:00||Fundamentals of Data Science||Data Science with Python||Intro to Machine Learning||Hands-on Machine Learning||Machine Learning in Finance||Text Mining Application||Data Visualization Principles and Frameworks||Student Projects|
|14:00-17:00||A Crash Course in Statistics||Hands-on: Statistics with Python||Deep Learning with Neural Networks||Intro to Text Mining||Financial Data Science Workshop||Data Management||Hands-on: Data Management and Visualization||Student Projects (incl. presentations of students projects)|
GATHERING AND DINNER
|Dumitru Roman (SINTEF / University of Oslo, Norway)||James Hodson (Cognism / AI for Good Foundation, USA)||Marko Grobelnik (Jozef Stefan Institute, Slovenia)||Razvan Bunescu (Ohio University, USA)|
camp fire/ barbecue
|Dan Nicolae (University of Chicago, USA)||Ioan Toma (University of Innsbruck, Austria)||Carlos A. Iglesias (Universidad Politécnica de Madrid, Spain)|
Dan Nicolae is Professor and Chair of Statistics at University of Chicago where he is also Professor in the Department of Medicine, Section of Genetic Medicine. Originally from Craiova, Dan Nicolae graduated from “Facultatea de Matematica” of University of Bucharest in 1995, and has obtained his PhD in Statistics from University of Chicago in 1999. He has held visiting positions at deCode Genetics in Iceland, University of Oxford and UCLA. His research focus is on developing statistical and computational methods for understanding the human genetic variation and its influence on the risk for complex traits, with an emphasis on asthma related phenotypes. The methodology developed in his group is based on foundations in high-dimensional inference, machine learning and data science. The current focus in his statistical genetics research is centered on data integration and system-level approaches using large datasets that include clinical and environmental data as well as various genetics/genomics data types: DNA variation, gene expression (RNA-seq), methylation and microbiome. Dan Nicolae has advised more than 50 researchers at all levels (Master, PhD and Post-Doctoral) and has published more than 130 articles in scientific journals. He has been member of numerous Advisory and Editorial Boards and has served on panels in United States, Canada, UK and European Union.
Razvan Bunescu received the PhD degree in computer science from the University of Texas at Austin, in 2007, with a thesis on machine learning methods for information extraction. He is currently an associate professor of electrical engineering and computer science at Ohio University. His research interests lie in the general area of machine learning, with a focus on applications in computational linguistics, biomedical informatics, and more recently computer architecture and music analysis. His research has been funded by grants from the National Science Foundation and the National Institutes of Health.
Marko Grobelnik is an expert in the areas of analysis and knowledge discovery in large complex databases. Marko collaborates with major European and US academic institutions and consults for industries such as British Telecom, Microsoft Research, Nature, New York Times, Bloomberg and Accenture. Marko is the author of several books in the area of machine learning, data mining, text mining and semantic technologies and author of many scientific papers. He is also W3C AC representative for JSI, CEO of the company Quintelligence and co-founder of the company Cycorp Europe. Marko also served as a program chair for the European Machine Learning conference (ECMLPKDD 2009) and for the European Semantic Web Conference (ESWC 2011). In terms of project experience, Marko served as the technical coordinator for the projects FP6 IST-World and FP7 VIDI and as scientific coordinator for the FP7 project X-LIKE; he was a member of the project management board in several FP6 and FP7 Projects (SEKT, NEON, ACTIVE and COIN). In 2016, Marko was appointed as the Digital Champion of Slovenia.
Carlos A. Iglesias received the telecommunications engineering degree and the PhD degree in telecommunications, both from the Universidad Politécnica de Madrid (UPM), in 1993 and 1998, respectively. He is an associate professor in the Telecommunications Engineering School, UPM, Spain and Head of the Intelligent Systems Research Group, since 2014. He has been a principal investigator on numerous research grants and contracts in the field of advanced social and IoT systems, funded by the regional, national and European bodies. His main research interests include social computing, multiagent systems, information retrieval, sentiment and emotion analysis, linked data, and web engineering. He participates in the Big Data Value Association in the groups of Data Visualization and Finance.
James Hodson is a researcher, entrepreneur, and social activist. His work spans Machine Learning, Economics, Sustainable Global Development, Sociology, and Philosophy. James currently serves as Chief Science Officer of Cognism, an AI-powered Financial, Human Resources, Sales, and Marketing intelligence platform; as CEO of the AI for Good Foundation; and as Senior Researcher at the Artificial Intelligence Department of the Jozef Stefan Institute, Slovenia. Through these roles, James seeks to effect lasting change in our economies, societies, and scientific understanding. He is an early stage investor in technology-focussed start-ups, nurturing leadership, teams, energy, and innovation. Previously, James built and directed the AI Research group at Bloomberg in New York, a 20+ research focussed group across ML, NLP, Knowledge Engineering, Reasoning, and Inference. He was a researcher at the German National Research Laboratory for Artificial Intelligence (DFKI), and holds patents across Machine Translation and Network Inference.
Dr. Ioan Toma is the COO and co-founder of ONLIM GmbH, an Austrian startup focusing on Chatbots and Intelligent Assistants, being responsible for the research activities of the company. In the past 8 years, he was a senior researcher at Semantic Technology Institute (STI) Innsbruck, University of Innsbruck, Austria. Ioan’s current research areas include Semantics, Knowledge Graphs, Chatbots and Intelligent Assistants. Ioan received a Ph.D. in Computer Science from the University of Innsbruck, Austria, with a thesis on Modelling and Ranking Semantic Web services based on Non-functional properties and a Diploma of Engineering and a Master’s degree in Computer Science from the Technical University of Cluj-Napoca, Romania. Ioan has been involved in a number of research projects at national and European levels (e.g. ASG, DIP, eFreight, ENVISION, EuTravel, FITMAN, Grisino, LarKC, LDBC, LDCT, MSEE, OntoHealth, RENDER, ServiceWeb3.0, SEALS, SESA, and SOA4All). He has published around 85 articles as book chapters, conference papers, workshops papers and journal articles. Ioan has also co-organised multiple workshops and has been a member of many conference and workshop program committees.
Dumitru Roman works as a Senior Research Scientist at SINTEF (Norway) – the largest independent research organization in Scandinavia. He has wide experience with initiating, leading, and carrying out data-driven and research-intensive projects, participating in dozens of large international projects during the past 14 years in which he has collaborated with large numbers of private companies, public sector organizations, universities, and research institutes. He is currently active in the data management field, focusing on innovation projects enabling data-driven business products and services. He holds an adjunct associate professorship at the University of Oslo, Norway.