Our speaker lineup includes leading data scientists, software engineers and machine learning researchers from international companies and both domestic and foreign universities who apply deep learning to real-world problems.
We are excited to announce that experienced professors and instructors from Virginia Commonwealth University (USA), University of Texas (USA), University of Portsmouth (UK), Ulsan University (South Korea), Gdansk University of Technology (Poland), and Intel (from San Diego, USA and from Gdansk, Poland) have already confirmed their attendance. They will give keynote presentations and lead mini-courses during the Deep Learning Summer School.
Paul Rad, The University of Texas at San Antonio, USA
Professor Paul Rad research area covers topics ranging from cloud computing, machine learning to data analytics. His work resulted in many research papers focused on the application of deep learning for robot navigation, detection of pedestrians, real-time face identification in crowds, voice-based authentication and many others. He is also the author of many patent applications, many of them in cloud computing. Recently, he has been the co-principal investigator for the creation of a cloud-computing testbed that will let researchers develop and experiment with new cloud architectures and applications ($10 million US NSF grant).
Milos Manic, Virginia Commonwealth University, USA
Dr. Milos Manic is a Professor with Computer Science Department and Director of Modern Heuristics Research Group at Virginia Commonwealth University. His work in Computational Intelligence and Deep Learning models with applications in Energy, Cyber Security, and Human-Machine Interfacing resulted in over 30 completed research projects and over 150 refereed articles and several U.S. patents.
Dr. Manic has been active IEEE volunteer as IEEE IES Officer, Associate Editor of TIE, TIE IEEE Transactions, founding chair of IEEE Resilience Technical Committee, and is serving as General Chair of IECON 2018 to be held in Washington D.C.
Hui Yu, University of Portsmouth, UK
Hui Yu, University of Portsmouth, UK Professor is a Reader in the School of Creative Technologies. He previously held an appointment with the University of Glasgow. He has won prizes for his study and research include Excellent Undergraduate Prize (provincial level), the Best PhD Thesis Prize, EPSRC DHPA Awards (PhD) and Vice Chancellor Travel Prize. Prof. Yu is an Associate Editor of IEEE Transactions on Human-Machine Systems and Neurocomputing journal. He is a member of the Peer Review College, the Engineering and Physical Sciences Research Council (EPSRC), UK. Prof. Yu has published many research papers focused on practical applications of machine learning. For example, he has analyzed the Long Short-Term Memory (LSTM) model to learn the gait patterns exhibited in neurodegenerative diseases, image saliency detection and 3D reconstruction.
Kang-Hyun Jo, University of Ulsan, Korea
Prof. Jo, Kang-Hyun, Ph.D., is with University of Ulsan as a professor in charge of Intelligent Systems Laboratory. He has served as the vice dean of e-Vehicle Graduate Institute and of College of Engineering. He has cooperated with many universities and has served as a director of societies like: ICROS, KMMS (both in Korea), and SICE (Japan). He is currently contributing himself as an (associate or guest) editor for a few international journals like IJCAS, TCCI, IEEE IES II, etc. Until now, he has published more than hundred technical papers with peer reviews. Professor published and demonstrated many practical applications of neural networks (including deep models) mainly in the area of video surveillance, traffic sign classification, vehicle detection, classification of human carrying baggage, etc.
Sebastian Raschka, Michigan State University, USA
Sebastian Raschka is developing novel computational methods in the field of computational biology (Ph.D. in December 2017). Among others, his research activities include the development of new deep learning architectures to solve problems in the field of biometrics. His book “Python Machine Learning”, awarded in ACM Computing Reviews’ Best of 2016, is a bestselling title at Packt and Amazon.com, and was translated into German, Korean, Chinese, Japanese, Russian, and Italian. Soon, his new book titled „Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python“ will be published. In his free time, Sebastian loves contributing to the open source projects. Methods implemented by him are now successfully used in machine learning competitions such as Kaggle.
Paweł Czarnul, Gdansk University of Technology, Poland
Paweł Czarnul obtained his D.Sc. degree in 2015 following his Ph.D. in 2003, both in computer science. He is the v-Dean for Cooperation & Promotion and the Head of Computer Architecture Department. His research interests include: high performance computing – parallel and distributed processing. He is the author of over 70 publications in the area of parallel and distributed processing, including HPC systems, in particular two recent books:„Integration of Services into Workflow Applications” and „Parallel Programming for Modern High Performance Computing Systems” (both by CRC). Laureate of national (6) and international (3) prizes. He was a PI or participated in 16 projects. Professor is a member of Smart Specialisations of Pomerania region council in the area of „Interactive technologies in an environment saturated with information”.
Jacek Ruminski, Gdansk University of Technology, Poland
Prof. Jacek Ruminski (Ph.D. in Computer Science, habilitation in Biocybernetics and Biomedical Engineering) is a head of Biomedical Engineering Department at GUT. He has spent about 2 years working on projects at different European institutions. He was a coordinator or an investigator in about 20 projects receiving a number of awards, including for best papers, practical innovations (7 medals and awards) and also the Andronicos G. Kantsios Award. Prof. Ruminski is the author of about 210 papers, and several patent applications and patents. Recently he was a main coordinator of the European eGlasses project focused on HCI using smartglasses. His research is focused on application of machine learning in healthcare.
Kasun Amarasinghe, Virginia Commonwealth University, USA
Kasun is a PhD candidate at the Department of Computer Science, VCU, USA. He has gained experience on deep learning in both theoretical and application fronts and has multiple publications to his name. He is interested in applying Deep learning algorithms to real world research problems in a different areas such as energy systems, cyber-security and medicine.
Mrinmoy Maity, Indiana University Bloomington, USA
Mrinmoy is a PhD candidate in Computer Science at Indiana University Bloomington with a focus on Deep Learning. More specifically, he is interested in optimizing deep neural networks using less computations and limited storage spaces to enable on device operations on embedded devices in real-time. He mostly focuses on applications in audio domain although his research interests are generic enough to be applied to areas like automated driving and natural language processing. He also holds a broader interest in Artificial Intelligence in areas like Generative models and Reinforcement learning. During Summer of 2016, he worked as Data Science intern at Intel Nervana on Autoregressive Generative models.
Alicja Kwasniewska Intel, San Diego, USA and Gdansk Univ. of Technology, Poland
Graduated with distinction from Gdansk University of Technology, receiving the Bachelor (2014) and the Master (2015) Degree in Biomedical Engineering – Informatics in Medicine. As a PhD candidate at Gdansk University of Technology, she is conducting the research in the area of machine learning algorithms for computer vision in remote healthcare. She also has 3 years of professional experience in cloud computing and monitoring, that she gained during working in Intel on various open source projects: OpenStack, Swan, Snap. Recently, she has been conducting a joint research in the field of neural networks with University of Texas, San Antonio and has been working in Intel, San Diego on deep learning algorithms for autonomous cars, smart home and healthcare.
Maciej Szankin, Intel, San Diego, USA
Received M.Sc. in Computer Science in 2016 at the Department of Computer Architecture, Gdansk University of Technology. In his master thesis he proposed methods for running machine learning algorithms on text in the distributed environment. At Intel he has 5 years of professional experience in distributed computing and server architecture. For over 1 year he contributed to the open cloud project OpenStack, with the focus on scheduler and virtual machines management. Currently works for Intel in San Diego, CA on smart home and IoT devices and their use with Deep Learning methodologies.
Krzysztof Czuszyński, Gdansk University of Technology, Poland
Received M.Sc., Eng. in Electronics in 2012 at the Department of Biomedical Engineering and M.Sc. at the Department of Applied Informatics in Management in 2015, both at Gdansk University of Technology. Author or co-author of many scientific articles and conference papers, four of which were granted with best paper awards. His current research activities and Ph.D. thesis relate to application of Machine Learning in gesture recognition for human system interactions. In particular, he is working with TensorFlow using RNNs on multiple GPU systems.
Paweł Rościszewski, Gdansk University of Technology, Poland
Paweł is a PhD candidate in Computer Science at GUT. His master thesis (2012) introduced KernelHive, a framework for automatic parallelization of computations across multi-level heterogeneous HPC systems with CPUs and GPUs. He co-developed MERPSYS, an environment for modeling and simulation of large-scale parallel application execution. His main research area is optimization of parallel applications, recently focusing on deep neural network training. During a PhD student exchange at the University of Milan he gained experience in machine learning and computer vision for industrial applications. He proposed and implemented significant optimizations in the process of parallel recurrent neural network training for acoustic modeling at VoiceLab.ai and established a new course called High Performance Machine Learning at GUT. Currently he is leading the TensorHive project, which aims for developing a lightweight computing resource management tool for distributed DNN training in TensorFlow.
Tomasz Stachlewski, Senior Solutions Architect, Amazon Web Services
Tomasz is a Solutions Architect at Amazon Web Services, where he helps companies of all sizes (from startups to enterprises) in their Cloud journey. He provides guidelines for creating cloud solutions that deliver the most value to his customers, and help take their IT to the next level. He is a big believer
in innovative technology such as serverless architecture, which allows organizations to accelerate their digital transformation. Before joining Amazon, he worked at LOT Polish Airlines, where he architected their first cloud projects, and at Accenture.
Viacheslav Klimkov, Applied Scientist, Alexa Language Technologies
Viacheslav Klimkov is an applied scientist who have been working in Text-to- Speech group at Amazon for the last 2.5 years. He has a master degree from National technical University of Ukraine „Kyiv Polytechnic Institute”and has vast experience in modeling speech for both speech synthesis and speech recognition systems.
The names of the other speakers from NVIDIA, Intel and other companies and universities will be published after we receive the proper confirmation.
The list of speakers may change for reasons beyond the control of the organizers.