Speakers

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.

Ralph Hinsche, NVIDIA, Germany

With more than 30 years of HPC experience Ralph started as a student in 1987 at Parsytec (Transputer, OCCAM) in Aachen/Germany. This was followed by head of department activities at several SUN Microsystems partners with the focus on HPC and he contributed to a national development project (parallel computer GIGAmachine). In 1996 he became a sales engineer at EUREM with a focus on “Wide Area Automation” (distributed intelligence). In his last position, he was Key Account Manager at circular for nearly 10 years mainly in the field of HPC. Again, there were close co-operations with SUN Microsystems and DELL. Ralph is now responsible within the DACH region as a Business Development Manager for GPU-Computing (Tesla) and Deep Learning at NVIDIA since 2014.

Alfredo Canziani, NYU Courant Institute of Mathematical Sciences, USA

Alfredo Canziani is a Post-Doctoral Deep Learning Research Scientist and Lecturer at NYU Courant Institute of Mathematical Sciences, under the supervision of professors KyungHyun Cho and Yann LeCun. His research mainly focusses on Machine Learning for Autonomous Driving. He has been exploring deep policy networks actions uncertainty estimation and failure detection, and long term planning based on latent forward models, which nicely deal with the stochasticity and multimodality of the surrounding environment. Alfredo obtained both his Bachelor (2009) and Master (2011) degrees in EEng cum laude at Trieste University, his MSc (2012) at Cranfield University, and his PhD (2017) at Purdue University. In his spare time, Alfredo is a professional musician, dancer, and cook, and keeps expanding his free online video course on Deep Learning and Torch.

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.

Piotr Januszewski

In January graduated with distinction from Gdansk University of Technology and received his Bachelor of Science in Computer Science. For engineer’s thesis he and his colleagues worked on “Lung cancer detection from computer tomography images using convolutional neural networks”. He continues his studies in Parallel Computing as specialisation and Data Analysis as supplementary specialisation at GUT, currently working on Transfer Learning in Deep Reinforcement Learning. He is president of GUT’s student research circle “Gradient”, where many students and graduates learn and practice their skills in Deep Learning domain. He also worked at Intel for almost 2 years as Undergraduate Software Engineer in two teams, one developing OpenCL user mode driver and the other one developing low-level framework for custom Intel hardware dedicated for learning artificial neural networks. He is currently working at Quantum Lab on Junior AI Specialist position, developing artificial intelligence for emotion understanding and analysis.

Karol Draszawka

Karol works as an assistant at the Department of Computer Architecture at GUT. Among courses which he teaches there is Artificial Intelligence as well as Analysis Methods for Big Data, the latter being the de facto introduction to deep learning techniques course at GUT. He is also a co-founder and co-supervisor of the student research circle ‘Gradient’ at GUT – a group gathering students interested in machine learning. His scientific interest spreads widely over artificial intelligence research field, with the main focus on supervised and reinforcement learning methods. He has been taking part in many machine learning projects at GUT and industry, including: speech command recognition system, marker-based augmented reality, medical image classification. He is currently finishing his Ph.D. thesis, where he investigated various text representations and classification algorithms for large scale multi-label text classification.

Xiaoxu Cai

Xiaoxu Cai is a PhD candidate in the School of Creative Technologies at the University of Portsmouth. She received her Bachelor’s (2013) and Master’s (2016) Degree in Computer Science and Technology from Ocean University of China. Her research area includes deep learning, machine learning and their applications in image processing and face analysis. She is currently working on 3d face reconstruction using deep learning.

Krzysztof Biniaś

Krzysztof Biniaś is a Machine Learning engineer in Intel Artificial Intelligence Product Group (AIPG). He is responsible for enabling and optimizing AI solutions into Intel platforms. Prior to joining Intel he worked as Software Developer at British Maritime Technology. He got his M.Eng. from Gdansk University of Technology.

Jacek Czaja

Jacek Czaja is a Machine Learning engineer in Intel Artificial Intelligence Product Group (AIPG) solutions enablement team. He is responsible for enabling and optimizing AI solutions into Intel platforms. He is a computer scientist with a passion for applicable machine learning. Prior to joining Intel he worked as Developer Technology engineer at Imagination Technologies. He got his M.Eng. from Gdansk University of Technology.

Iwona Sobieraj

Iwona received her Bachelor’s (2010) and Master’s (2011) degrees in Telecommunications from Warsaw University of Technology. After graduating, she worked for 3 years as a software engineer at Samsung R&D Centre where she developed machine translation systems. She is currently pursuing a PhD at the University of Surrey, where her main research interests are in machine listening – combining machine learning and digital signal processing to extract meaningful information from sounds. Her recent work focuses on deep learning for audio event detection.


The list of speakers may change for reasons beyond the control of the organizers.