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 2020 will be announced soon, after final confirmations.
Alphabetical list of speakers:
Norwegian University of Science and Technology (NTNU) & Oslo University Hospital (OUS), Norway
Ilangko Balasingham received the M.Sc. and Ph.D. degrees from the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), Trondheim, Norway in 1993 and 1998, respectively, both in signal processing. He performed his Master’s degree thesis at the Department of Electrical and Computer Engineering, University of California Santa Barbara, USA. From 1998 to 2002, he worked as Research Engineer developing image and video streaming solutions for mobile handheld devices at Fast Search & Transfer ASA, Oslo. Fast was a startup and was acquired by Microsoft Inc. and renamed as Microsoft Development Center Norway.
Since 2002 he has been with the Intervention Centre, Oslo University Hospital, where he is Head of Section for Medical ICT Research and Head of Wireless Biomedical Sensor Network Research Group. He is Professor of Medical Signal Processing and Communications at NTNU since 2006. His research interests include super robust short-range communications for both in-body and on-body sensors, body area sensor network, microwave short range sensing of vital signs, short range localization and tracking mobile sensors, signal-and image processing, and molecular and nano communication networks. He has authored or co-authored more than 90 journal papers, 170 full conference papers, 8 book chapters, 42 abstracts, and 16 articles in popular press and holds 6 issued patents and 10 disclosure of inventions. He has supervised 21 Postdocs, 21 PhDs, and 30 Masters. He was the General Chair of the 2012 Body Area Networks (BODYNETS) and 2019 IEEE International Symposium of Medical Information and Communication Technology (ISMICT) and TPC Chair of the ACM NANOCOM 2015. He serves as Area Editor of Elsevier Nano Communication Networks and Steering Committee Member of ACM NANOCOM. He is a Senior IEEE member.
Dr Peter Bloomfield
Digital Catapult, UK
Peter is the Senior Technology Policy & Research Manager at Digital Catapult in London.
He has worked with startups on Machine Intelligence Garage to provide them with the resources and expertise needed to scale their machine learning and commercial capabilities. He is also working on advanced technology public policy, government strategy. He specialises in artificial intelligence and advanced digital infrastructure to enable the convergence of different technologies for future solution provision.
Before working at the Digital Catapult, Peter was a Neuroscientist investigating problems including how different cell types integrate to circuits when learning new skills, brain changes associated with the onset of schizophrenia and novel ways to treat multiple sclerosis. He has worked as a scientific advisor on projects developing human-computer interface products for clinical symptom tracking and immersive gaming environments.
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.
Norwegian University of Science and Technology (NTNU), Trondheim, Norway
Noha El-Ganainy is currently affiliated as postdoctoral fellow at Norwegian university for science and technology NTNU funded by European research consortium for informatics and mathematics (ERCIM).
Noha received her Ph.D in Electrical Engineering and electro Physics from the Faculty of Engineering, Alexandria University, Egypt in 2010. From 2010 to 2017, she worked as an assistant professor at the Arab academy for science, technology, and maritime transport (AASTMT). She has also been teaching in different institutions in both Egypt and Norway including Alexandria institute of technology (AIT) in Egypt, University Graduate Center (UNIK) in Kjeller Norway, and Westerdals Oslo ACT in Oslo Norway.
Her current research work addresses machine learning with a special interest on algorithms learning and modeling different types of data in real time. Her previous research projects exploited the use of different signal processing techniques in wireless communication systems on the MAC layer including signal detection and estimation, cooperative and cognitive transmission/receiving, channel estimation and noise cancellation. She has also worked on image processing watermarking, stenography, change and motion detection.
She has published a number of scientific papers and articles in international journals and conferences. She has received the young scientists award from the union radio scientific international (URSI) in 2011. She has served as a reviewer, TPC, and track chair for a number of International journals and conferences including Radio science journal, Personal wireless communications, IEEE communication letters, optics letters, and IEEE vehicular technology conference VTC.
Universitat Politècnica de Catalunya, Spain
Xavier Giro-i-Nieto is an associate professor at the Universitat Politecnica de Catalunya (UPC) in Barcelona and visiting researcher at Barcelona Supercomputing Center (BSC). His obtained his doctoral degree from UPC in 2012 under the supervision of Prof. Ferran Marques (UPC) and Prof. Shih-Fu Chang (Columbia University). His research interests focus on deep learning applied to multimedia and reinforcement learning.
Home page: https://imatge.upc.edu/web/people/xavier-giro
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.
Stanford University, USA
Łukasz Kidziński is an AI researcher and entrepreneur. His most notable scientific work includes: algorithms for detecting neurological disorders from videos of patients, a machine learning library to analyze human movement using artificial intelligence, as well as statistical tools for functional data. He received academic support from National Science Foundation (Switzerland), National Institute of Health (USA), PASCAL (Germany), AWS, Google Cloud, and other institutions.
He turns academic ideas into commercial products. Deepart.io is the original deployment of the most recognizable deep learning algorithm Neural Style Transfer. Łukasz co-founded the company together with the authors of the algorithm. His recent work on predicting car accident risk from Google Street View images of houses was recognized globally by the media and became a basis for an insurtech AI startup. Most recently, his recent work in AI for healthcare gave rise to Saliency.ai — a medical computer vision platform for streamlining workflows in clinical research.
Fraunhofer Heinrich Hertz Institute, Germany
Sebastian received the Dr. rer. nat. (PhD) degree with distinction (“summa cum laude”) from the Berlin Institute of Technology in 2018. From 2007 to 2013 he studied computer science (B.Sc. and M.Sc.) at the Berlin Institute of Technology, with a focus on software engineering and machine learning. Currently, he is a tenured researcher at the machine learning group at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin. His research interests include computer vision, (efficient) machine learning and data analysis, data and algorithm visualization, and the interpretation, (meta-)analysis and rectification of machine learning system behavior.
Home page: http://iphome.hhi.de/lapuschkin/
University of Buckingham, UK
Tuan T. Nguyen has a PhD in Computer Science from the University of Nottingham, an MSc in Computer Science from the University of the West of England, and a BSc in Information Technology from the University of Science, Vietnam. Prior to join the Department of Applied Computing as a Lecturer, Tuan was a Research Fellow, Teaching Assistant and UG & PG supervisor at the School of Computer Science, University of Nottingham. During that time, he was involved in a numbers of projects including iBit, iPlant UK and Root hydrotropism. He also had several years working in the software industry before returning to academia.
His research interests include Computer Vision, Image Processing & Analysis and Machine Learning.
Jacobo Salvador Ortiz
Oslo University Hospital (OUS), Norway
Jacobo S. Ortiz is a research and development engineer at the intervention center at Oslo University Hospital where he’s currently developing an AI real-time detection application and a graphical user interface to assist clinicians in the detection of lesions such as polyps in the gastrointestinal tract in order to reduce the incidence of colorectal cancer, the most common form of cancer in Norway. Apart from researching and developing medical applications that use AI to improve diagnosis and treatment, Jacobo and a small group of researchers at OUS and NTNU are developing in partnership with Telenor a project consisting in remote real-time polyp detection with help of a wireless pill camera. This will allow high definition transmission and automatic analysis of GI tract images by AI in remote locations, in real-time utilizing 5G technology.
Jacobo specializes in deep learning and his research mainly focuses on machine learning for autonomous diagnosis and treatment design while his field of interest includes NLP and deep learning for genomics. Previously, Jacobo worked with blockchain, machine vision and unsupervised learning techniques for fraud detection and pattern recognition in financial applications at diverse startups and as a financial engineer at the Norwegian Investment Fund for developing countries.
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.
The National Research Council, Italy
Fabrizio is a Director of Research, and leader of the Human Language Technologies group, in the Networked Multimedia Information Access Laboratory at the Institute for the Science and Technologies of Information of the Italian National Council of Research. The research interests of the group include text classification, information extraction, quantification, sentiment classification, cross-lingual and cross-domain text classification, technology-assisted review, authorship analysis, and their applications.
Home page: http://nmis.isti.cnr.it/sebastiani/
Google Scholar: https://scholar.google.com/citations?user=WZBcZV4AAAAJ&hl=en
University of Portsmouth, UK
Hui Yu is a Professor 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.
NavAlgo Ltd, Poland
I’m an assistant professor at the University of Wroclaw and Head of AI ad NavAlgo. I work on applications of Neural Networks and Deep Learning, for speech processing, NLP, and logistics.
Holds Ph.D. title from University of Louisville (USA), Department of Electrical and Computer Engineering and M.Sc. from Wroclaw University of Technology in Poland. Worked for Google Brain in Mountain View, California.
Gdansk University of Technology, Poland
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.
Adam Gabryś is a Software Development Engineer working on quality and expressiveness improvements to Amazon Neural Text-To-Speech voices. He graduated in 2017 from AGH University of Science and Technology in Cracow with a Bachelor of Applied Computer Science. During his time at the University, he focused on Machine Learning for time series prediction and high dimensional data visualization. Since then Adam has been working in Amazon Gdansk office. He has experience in Software Development, Digital Signal Processing, Deep Learning, and Data Science. To reboot himself, Adam likes going bouldering, rock climbing, and hiking.
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.
Software engineer at Google, since 2017 has been developing both the internal and external Google Clouds. Presently focuses on robust, easily configurable and auto-renewed managed SSL certificates for Google Cloud Platform. He has been driving Managed Certificates for Google Kubernetes Engine (https://github.com/GoogleCloudPlatform/gke-managed-certs).
Previously Krzysztof was developing a business and market intelligence platform, IHS Connect, at IHS Markit. Thrilled to give a talk at Politechnika Gdańska, his Alma Mater.
AI Factory, Poland
Managing director in AI Factory who loves to do cool things on the edge between two worlds with totally different dynamics: business and technology. In practice it means helping industrial and retail companies to automate mundane and boring tasks, which humans do not like to do by using Image Recognition technologies. One of his latest projects are – Automatic Shop Shelf Audit – counting products exposed on shelfs in shops – AI Cube – an amazing device, which helps to reduce fraud in pharmaceutical industry. He is also closely cooperating with team delivering a very interesting medical project https://brainscan.ai/
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/).
Gdansk Univ. of Technology, Poland
Alicja graduated with distinction from Gdansk University of Technology, receiving Bachelor’s and Master’s Degree in Biomedical Engineering – Computer Science in Medicine. Her Master Thesis was conducted in cooperation with Norwegian University of Science and Technology in the area of signal and image processing, where she developed a platform for contactless monitoring of elderly people. This work has been frequently awarded in different contests, e.g. best diploma in the area of bioinformatics. As a Ph.D. candidate at Gdansk University of Technology, she is conducting the research in image processing using machine learning algorithms for remote healthcare. She also conducts a joint research in the field of neural networks with University of Texas, San Antonio. She has 8 years professional experience in computer vision gained while developing software for processing images from various sources (e.g. webcams, medical data, security cameras, etc.). For the past 3 years, she has been working on deep learning algorithms for servers monitoring, autonomous cars, smart home, and healthcare at Intel Corporation.
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.
Maciej 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 in the distributed environment. His work focuses on leveraging hardware accelerators for improving deep learning workloads in constrained and mission-critical environments. Many of his solutions have been published in journals and presented during IEEE conferences, and has received best paper award and best young professional paper award.
We are still waiting for confirmations from many speakers, so please stay tuned for further updates!