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.
The complete list of speakers for ISSDL 2019 will be announced soon, after final confirmations.
University of Wisconsin-Madison, USA
Sebastian Raschka is an Assistant Professor of Statistics at UW-Madison focusing on machine learning and deep learning research (http://www.stat.wisc.edu/~sraschka/ ). Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology. Among Sebastian’s other works is his book “Python Machine Learning,” which introduced people to the practical and theoretical aspects of machine learning around the globe with translations into German, Korean, Chinese, Japanese, Russian, Polish, and Italian.
- Keynote Lecture: Convolutional Neural Networks for Predicting and Hiding Personal Traits from Face Images
- Training Lecture: A tutorial on neural networks for ordinal regression
Adam Paszke is an author and maintainer of PyTorch. He has a few years of experience working with large organizations like Facebook AI Research and NVIDIA, where he developed high-performance computing libraries. Currently, he pursues two majors at the University of Warsaw — Computer Science and Mathematics. His academic work mostly focuses on structural graph theory and parametrized algorithms, but those don’t cover all his interests, which additionally include programming languages, automatic differentiation, functional programming and quantum computing.
- PyTorch 1.0: how to use and what’s the fuss about?
University of Essex, UK
Dongbing Gu is a Professor in the School of Computer Science and Electronic Engineering, University of Essex. He received the B.Sc. and M.Sc. degrees in automatic control from Beijing Institute of Technology, Beijing, China and the Ph.D. degree in robotics from University of Essex, UK. His research expertise is in the field of robotics, autonomous systems, navigation and control, mapping and localisation, cooperative control, and machine learning. He has published more than 200 papers in international conferences and journals. His research has been supported by UK Research Councils (EPSRC), InnovateUK, Royal Academy of Science, European Commission, and industry. He is an Editorial Board Member of Cognitive Computation, Frontiers in Robotics and AI: Multi-robot systems. He served the organisation committee and programme committee for many international conferences.
- Keynote Lecture: Visual SLAM from Geometry to Deep Learning
Rafał Scherer is an associate professor at the Institute of Computational Intelligence at the Czestochowa University of Technology. His research is focused on developing new methods in computational intelligence and data mining, ensemble methods in machine learning, content-based image indexing. He authored more about 100 research papers including a book on multiple classification techniques. He was a principal investigator or senior researcher in international projects. He is a vice chair of the IEEE Computational Intelligence Society Poland Chapter. He is also a co-editor of the Journal of Artificial Intelligence and Soft Computing Research (http://jaiscr.eu/).
- Keynote Lecture: Computer Network User Profiling by Machine Learning
Casey S. Greene
University of Pennsylvania, USA
Casey is an Associate Professor of Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania. His lab develops deep learning methods that integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such integrated data. Casey earned his Ph.D. for his study of gene-gene interactions in the field of computational genetics from Dartmouth College in 2009 and moved to the Lewis-Sigler Institute for Integrative Genomics at Princeton University where he worked as a postdoctoral fellow from 2009-2012. The overarching theme of his work has been the development and evaluation of methods that acknowledge the emergent complexity of biological systems.
- Keynote Lecture: The next challenges for deep learning in biology and medicine
- Training lecture: Continental Breakfast Included: how researcher degrees of freedom affect the evaluation of methods
University of Michigan, USA
Dr. Alexandr Kalinin is a PostDoctoral Research Fellow jointly at the University of Michigan and the Chinese University of Hong Kong, Shenzhen. He received his PhD in Bioinformatics at the University of Michigan in 2018. His PhD thesis focused on applications of statistical modeling, machine learning, and visual analytics to analyze morphological changes of cellular structures from 3D microscopic images. He holds BSc and MSc in Applied Math and Informatics from Novosibirsk State Technical University, Russia. In 2012-2013 Alexandr was a Fulbright Visiting Graduate Researcher at the University of California, Los Angeles, where he was designing and developing online statistical tools for interactive visual analytics and scientific data visualization. His current research is broadly focused on applications of machine learning and deep learning to the analysis of biomedical imaging data.
- Keynote lecture: Recent Advances in Biomedical Image Segmentation Using Deep Learning
- Training lecture: Medical Image Segmentation with Deep Learning in PyTorch
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.
- Keynote lecture: Deep learning – recent achievements and future perspective
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.
- Keynote lecture: Deep Learning for depth estimation and 3D reconstruction
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.
- Keynote lecture: Accelerating Machine Learning with RAPIDS and DGX-2
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.
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.
- Training lecture: Enhancing image resolution with Deep Learning
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.
- Training lecture: Accelerating your Deep Learning on CPU
After over 18 years as software developer he started new stage in career as a software manager.Currently he works for Intel AI and he is leading a team focused on performance validation. His team produces tools and solutions to produce as reliable data as possible. To deliver it Benchmarking Team collects wide range knowledge about fundamentals of deep learning – how it works and why. Michal in his career took many different roles: from junior developer up to architect and tech lead in many areas: enterprise solutions for healthcare management, huge databases in telco domain, web applications for remote learning for kids. His passion is to optimize things – both code and processes behind software development.He strongly believe in SOLID and Clean Code concepts. He is involved in mentoring programs and as a speaker in many software conferences to promote his ideas and share knowledge with less experienced developers and students.
- Training lecture: How to make it faster and more accurate – practical attempt to inference.
AGH, Poland, NVIDIA Ambassador
Dr Paweł Morkisz, Assistant Professor at Faculty of Applied Mathematics AGH University of Science and Technology and CTO at Reliability Solutions. Certified NVIDIA Deep Learning Institute Ambassador. His research interest is in numerical methods and applied mathematics, including approximating of stochastic differential equations, optimization algorithms, and usage of AI for real world applications. Experienced team leader for data science solutions.
- Training lecture: Fundamentals of Deep Learning Computer Vision
Daniel Pressel is a Senior Director of AI Technology at Interactions, a startup specializing in Intelligent Virtual Assistants for customer care and engagement. There, he leads a team of researchers focused on Machine Learning for Natural Language Processing and Conversational AI. He is obsessed with Machine Learning and has been involved in its application to enable innovative startups since 2008. He previously worked as the Chief Science Officer at Digital Roots which was acquired by Interactions in 2017.
- Keynote lecture: Transfer Learning Techniques, Architectures and Applications for NLP
- Training lecture: Applied Transfer Learning for NLP
Roberto is a Senior Speech Scientist in Amazon Cambridge working on Alexa’s Voice. He has over 15 years in the speech field and Machine Learning, particularly focused on text-to-speech and emotional speech modeling. He was previously a Senior Researcher and Assistant Professor at Universidad Politécnica de Madrid in Spain. His main interests are related to innovation in Human Machine Interfaces, Speech Technology and Affective Computing.
- Training lecture: Alexa’s Voice: Neural TTS Technology in Action.
Piotr is an independent data science consultant and workshop instructor focusing on machine learning, deep learning, and data visualization. He holds a PhD in quantum physics from ICFO, Barcelona. Piotr gave corporate training to companies such as Samsung Research, Intel and BCG. He worked on numerous projects with deepsense.ai and collaborates with RaRe Technologies on natural language processing
Piotr has lectured at Imperial College London and given talks at Caltech and the Bay Area D3.js User Group, among other places. Piotr is the author of a popular blog post series introducing readers to data science, word2vec and deep learning. In his free time, he develops the Quantum Game with Photons and is a volunteer teacher of gifted high-school students. He created and manages the Data Science PL Facebook group – the biggest such community in Poland.
- Keynote lecture: Interactive machine learning in your browser
- Training lecture: Activation functions, hidden layers and cost function
Daniel Korzekwa is an Applied Science Manager leading a team of machine learning scientists and engineers based in Gdansk/Cambridge, working on Amazon Neural Text-To-Speech expressive voices. He received his MSc. in Computer Science from Silesian University of Technology in Poland in 2003 and then for several years he has been working in industry across Telecom, Oil&Gas, Betting Exchange and Speech Synthesis. He has a deep theoretical and practical knowledge of Machine Learning and Software Development, with expertise in areas such as Deep Learning, Probabilistic ML, Distributed Processing and Programming. To give his brain a rest, Daniel spends time with his wife and two sons, goes for running, plays chess and reads books.
- Training lecture: How to improve people lives with deep learning and automated assessment of speech
Tomasz Stachlewski is a Principal 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, IBM and at Accenture.
Training lecture: Machine learning at Amazon Web Services cloud
Michal Karzynski has a research background in the areas of molecular biology and bioinformatics. He currently works as a Deep Learning Software Engineer at Intel. Michal held various roles including research scientist, software developer, systems architect, project manager and book author. He is currently the technical lead of a team working on adding support for ONNX, the Open Neural Network Exchange APIs to nGraph – Intel’s neural network compiler.He got his M.Sci. from the University of Gdansk and conducted graduate research at the European Molecular Biology Laboratory in Heidelberg.
- Training lecture: Neuroevolution
The list of speakers may change for reasons beyond the control of the organizers.