Speakers
Volker Tresp
Professor, Ludwig Maximilian University of Munich, Germany and Distinguished Research Scientist, Siemens
Volker Tresp is a Distinguished Research Scientist at Siemens and a Professor for Machine Learning at the Ludwig Maximilian University of Munich (LMU). He received a Diploma degree from the University of Goettingen, Germany, in 1984 and the M.Sc. and Ph.D. degrees from Yale University, New Haven, CT, in 1986 and 1989 respectively. Since 1989 he has been the head of various research teams in machine learning at Siemens, Research and Technology. He filed more than 70 patent applications and was inventor of the year of Siemens in 1996. He has published more than 150 scientific articles and administered over 25 Ph.D. theses. The company Panoratio is a spin-off out of his team. His research focus in recent years has been “Machine Learning in Information Networks” for modelling Knowledge Graphs, medical decision processes, perception, and cognitive memory functions. He has been the consortium lead of a number of publicly funded projects. Since 2011 he is also a Professor at the Ludwig Maximilian University of Munich where he teaches an annual course on Machine Learning.
Boris Kovalerchuk
Computer Science Department, Central Washington University, USA
Dr. Boris Kovalerchuk is a professor of Computer Science at Central Washington University, USA. He is author of three books on Data Mining (Kluwer, 2000), Visual and Spatial Analysis (Springer 2005), Visual Knowledge Discovery and Machine Learning (Springer, 2018), and over 170 other publications including a chapter in the Data Mining Handbook. His research interests are in machine learning, data mining and fusion, uncertainty modeling, visual analytics, image and signal processing. He has been a Principal Investigator of several research projects in these areas supported by the US Government agencies. Dr. Kovalerchuk served as a senior visiting scientist at the US Air Force Research Laboratory and as a member of several expert panels at the international conferences and panels organized by the US Government bodies.
Razvan Andonie
Computer Science Department, Central Washington University, USA
Dr. Razvan Andonie received the MS degree in mathematics and computer science from University of Cluj-Napoca, Romania, and the PhD degree from University of Bucharest, Romania. His PhD advisor was Solomon Marcus, fellow of the Romanian Academy. He is currently a professor of Computer Science at Central Washington University, USA. He was visiting professor at universities and research institutes from USA, Germany, France, Spain, Ireland, China, Switzerland, Luxemburg, U.K., etc. He is supervising PhD candidates in computational intelligence and machine learning. His actual research interests are computational intelligence techniques and applications, causality, hyperparameter optimization, deep learning, and transfer information.
Parisa Rastin
Computer Science Laboratory of Paris Nord (LIPN), Paris 13 University, France
Parisa Rastin is an academic researcher of the University of Paris 13, member of the Computer Science Laboratory of Paris Nord (LIPN) in the Artificial Learning and Applications (A3) team. She completed her PhD at the University of Paris 13 in collaboration with the data Scientist team of Mindlytix Co. She received a MSc degrees in Data Science and Machine Learning from the University of Paris 13 in 2015. Her PhD subject was: Automatic and Adaptive Learning for Relational Data Streams Clustering. Her primary research interests are in text mining, clustering and change detection in data streams, and unsupervised learning and complex data structures.
Jérémie Sublime
SEP - Ecole d'ingénieurs du numérique, Paris, France
Jérémie Sublime is currently an Associate Professor at the ISEP since September 2016, where he is a member of the DaSSIP (Data Systems Signal and Image Pricessing) team, and the head of the Business Intelligence Major. Furthermore, he is also a CNRS full researcher at the LIPN UMR 7030. He has a PhD in Applied Computer Science from AgroParisTech (University Paris-Saclay), an Engineer Degree in Software engineering & Computer Vision from the EISTI Cergy, as well as a Master’s Degree in Computer and Information Technologies from INHA University in South Korea. His research activities are focused on machine learning and data science problems and cover a wide spectrum of issues, ranging from unsupervised learning to image processing and change detection.
Nicoleta Rogovschi
Laboratory of Informatics Paris Descartes (LIPADE), Paris Descartes University, France
Nicoleta Rogovschi received her Master of Computer Science degree from Paris 13 University in 2006 in Machine Learning. She completed her Ph.D. in Computer Science (Probabilistic Unsupervised Learning) in 2009 in the Computer Science Laboratory of Paris 13 University. She is currently an Associate Professor in Computer Science at the Paris Descartes University. She’s research is with the Data Mining (GFD) Team. Her research interests include Probabilistic Learning, Unsupervised Learning, Clustering and Co-Clustering methods for different types of data. She is also a member of EGC, AFIA, IEEE, INNS, INNS AML group.
Nistor Grozavu
Computer Science Laboratory of Paris Nord (LIPN), Paris 13 University, France
Nistor Grozavu received his Master of Computer Science degree from Marseille II University in 2006 in Fundamental Informatics. He completed his Ph.D. in Computer Science (Unsupervised Learning) in 2009 in the Computer Science Laboratory of Paris 13 University. He is currently an Associate Professor in Computer Science at the Paris 13 University. His research is with the Machine Learning and Application Team from the LIPN Laboratory. His research interests include Unsupervised Learning, Transfer Learning, Dimensionality reduction, Collaborative Learning, Machine Learning by Matrix Factorization and content-based information retrieval. He is also a member of IEEE, INNS, INNS AML group.
Basarab Matei
Computer Science Laboratory of Paris Nord (LIPN), Paris 13 University, France
Basarab Matei is a member of the Computer Science Laboratory of Paris Nord (LIPN) in the LIPN UMR 7030 Artificial Learning and Applications (A3) team since September 2015 and Professor assistant (since 2004) at the Galilee Institute. He holds a Ph.D. in Applied Mathematics from Paris 6 University and a Msc degree in Applied Statistics and Optimization from the University of Bucharest. His research interests lie in the theoretical foundations as well as in the applications of artificial learning and data science, particularly for representation and optimization problems, as well as for unsupervised neural networks.
Guénaël Cabanes
Computer Science Laboratory of Paris Nord (LIPN), Paris 13 University, France
Guénaël Cabanes is an academic researcher of the University of Paris 13, member of the Computer Science Laboratory of Paris Nord (LIPN) in the LIPN UMR 7030 Artificial Learning and Applications (A3). He received a Ph.D. in Computer Science at the University of Paris 13, France, in 2010. He also received a Msc in Computer Model of Reasoning and Knowledges in 2007. His primary research interests are in data mining, unsupervised learning and complex structures.