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 list below is preliminary and subject to change.
Alfredo Canziani
NYU Courant Institute of Mathematical Sciences, USA
Alfredo Canziani is an Assistant Professor of Computer Science and a Deep Learning Research Scientist at NYU Courant Institute of Mathematical Sciences, under the supervision of professors Kyunghyun Cho and Yann LeCun. His research mainly focuses 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 Electrical Engineering 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 PyTorch.
David Gamba
SiMa.ai, USA
David is the Vice President of Sales and Business Development at SiMa.ai and has over 24 years of sales & marketing experience at large companies such as Xilinx (being acquired by AMD) and Altera (bought by Intel) and start-ups such as Aeluros (bought by Netlogic/Broadcom) and HotRail (bought by Conexant). David has led groups that have won the largest revenue generating design in FPGA history and won awards for the introduction of hard floating point into FPGAs, winning the only >$100M annual Data Center customer in FPGA history, the introduction of OpenCL compilers for FPGAs, winning the first every FPGA design for cryptocurrency mining, and sponsoring the very first Machine Learning in FPGAs seminar. David is a diversity champion and started Altera’s diversity program as well as New College Graduate programs at Xilinx and Altera. David has a BSEE from UCLA, an MSEE from UC Berkeley and an MBA from Stanford University.
Krzysztof Geras
NYU School of Medicine, USA
Krzysztof is an assistant professor at NYU School of Medicine and an affiliated faculty at NYU Center for Data Science. His main interests are in unsupervised learning with neural networks, model compression, transfer learning, evaluation of machine learning models and applications of these techniques to medical imaging. He previously did a postdoc at NYU with Kyunghyun Cho, a PhD at the University of Edinburgh with Charles Sutton and an MSc as a visiting student at the University of Edinburgh with Amos Storkey. His BSc is from the University of Warsaw. He also did industrial internships in Microsoft Research (Redmond, working with Rich Caruana and Abdel-rahman Mohamed), Amazon (Berlin, Ralf Herbrich’s group), Microsoft (Bellevue) and J.P. Morgan (London).
Xavier Giró
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). He 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
Kang-Hyun Jo
University of Ulsan, Korea
Prof. Jo, Kang-Hyun, Ph.D., is with the University of Ulsan as the professor in charge of Intelligent Systems Laboratory. He has served as the vice dean of e-Vehicle Graduate Institute and of College of Engineering, and currently the faculty dean of School of Electrical Engineering. He has cooperated with many universities and served as a director of societies like: ICROS, KMMS (Korea), SICE (Japan), and IEEE IES AdCom member and the secretary until 2019. He has been contributing as an (associate or guest) editor for a few renowned international journals like IJCAS, TCCI, IEEE IES TII, etc. Until now, he has published more than hundred technical papers with peer reviews. His research interests cover many practical applications of neural networks (including deep models) mainly in the area of video surveillance, human and object detection and classification from land and aerial vehicles, etc.
Dimitrios Iliadis
KERMIT research unit of Ghent University, Gent, Belgium
Dimitrios received his Bachelor’s and Master’s Degree in Computer Science from the School of Informatics of the Aristotle University of Thessaloniki. For his Bachelor’s and Master’s thesis he worked on the task of predicting interactions between potential drugs and protein targets using multi-label classification methods. He is currently a Ph.D. candidate at the KERMIT research unit of Ghent University, where he is conducting research in Multi-target prediction (MTP) methods. More specifically, he is developing an automated framework that performs algorithm selection for MTP. This realized by adopting a rule-based system for the algorithm selection step and a flexible neural network architecture that can be used for the several subfields of MTP.
Alexandr Kalinin
Shenzhen Research Institute of Big Data, China
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.
Lukasz Kidzinski
Stanford University, USA
Saliency.ai, 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.
Homepage: https://kidzinski.com/
Krzysztof Kwaśniewski,
Google, Poland
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.
Thomas Merritt
Amazon, UK
Thomas Merritt is an applied scientist at Amazon, based in Cambridge UK. Thomas received his PhD from the University of Edinburgh in 2016. The title of his thesis is: Overcoming the limitations of statistical parametric speech synthesis. Since graduating he has been working on text-to-speech research at Amazon, focusing on improvements to prosody and overall naturalness of synthesized speech.
Arushi Raghuvanshi
Infinitus Systems, USA
Arushi Raghuvanshi is a Senior Machine Learning Engineer at Infinitus Systems focused on production-level conversational interfaces. She has developed instrumental components of the core Natural Language Processing platform, drives the effort on active learning to improve models in production, and is leading new initiatives such as speaker identification. Prior to MindMeld, Arushi earned her Master’s degree in Computer Science with an Artificial Intelligence specialization from Stanford University. She also holds a Bachelor’s degree from Stanford in Computer Science with a secondary degree in Electrical Engineering. Her prior industry experience includes time working at Microsoft, Intel, Jaunt VR, and founding a startup backed by Pear Ventures and Lightspeed Ventures. Arushi has publications in leading conferences including EMNLP, IEEE WCCI, and IEEE ISMVL.
Sebastian Raschka
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.
Michal Trzesiok
IBM, Poland
Michał Trzęsiok is a Data & AI technical specialist at IBM focusing on Machine Learning, Data Science, predictive analytics, and mathematical modelling. For almost twenty years he was working as a researcher and academic teacher in the Department of Economic and Financial Analysis at the University of Economics in Katowice, where he received his Ph.D. degree in econometrics and statistics. In IBM he is enjoying working with clients on real world data to help them embracing the AI capabilities, with focus on model deployment, monitoring, bias mitigation. Privately he is very passionate to popularize science.
Gang Wang
Center of Brain Sciences, Beijing Institute of Basic Medical Sciences, Beijing, China
Dr. Gang Wang received the Ph.D. degree from Ghent University, Belgium in 2019 under the supervision of Prof. Bernard De Baets. He received the best student paper nomination prizes at the EUSFLAT2017 and BNAIC2019. He won the first prize in the IDS2016 image denoising competition. He is the first author of more than 15 papers published in conference proceedings (e.g., ICCV) and international journals (e.g., IEEE-TIP). He is a member of IEEE and CSCS. Besides, he serves as a reviewer for more than 10 international journals (e.g., IEEE-TMM). He is currently an Assistant Professor with the Center of Brain Sciences, Beijing Institute of Basic Medical Sciences, Beijing, China. His research interests include computer vision, machine learning and brain-inspired vision computing.
Marcel Nassar
Intel AI Labs
San Diego, USA
A multi-disciplinary research scientist with expertise spanning machine learning, statistical modeling, and signal processing with a passion to develop reusable libraries and solve hard problems with highly motivated teammates. Marcel has 12 years experience in machine learning (ML) and applied machine learning to a variety of problem domains: signal detection, information decoding, drug cocktail design, Face ID, pedestrian detection, LIDAR super-resolution – versed in different machine learning algorithms: Deep Learning (Graph Neural Networks, Computer Vision), Probabilistic Graphical Models, Sparse Bayesian Learning. He also has an established academic record in top conferences & workshops, as well as multiple industry patents and standards. In addition, he is experienced with system-level hardware design.
Arun Das
University of Texas at San Antonio,
San Antonio, USA
Arun Das is currently a Ph.D. candidate at UT San Antonio, TX, USA where he focuses on explainable AI and self-supervised algorithms for healthcare and neuroscience domains. He earned his MS in Computer Engineering from UT San Antonio, TX, USA and his B.Tech. from Cochin University of Science and Technology, Cochin, India. Arun is a graduate student member of the IEEE Lone Star section and the IEEE Eta Kappa Nu honor society.
Tomasz Kocejko
Gdansk University of Technology, Poland
Tomasz Kocejko, Ph.D. received the M.Sc. degree in electronics and the Ph.D. degree in medical informatics from the Gdansk University of Technology. He is a training program coordinator in Digital Innovation Hub dih4.ai and assistant professor at Gdansk University of Technology, Faculty of Electronics, Telecommunication and Informatics. His research is mainly focused on image processing, human-computer interface and interaction, machine learning and biomedical engineering. He is involved into active aging programs organized by the faculty and the situation of disabled students acting as a dean’s plenipotentiary to this matters. Speaker, consultant and propagator of AI oriented healthcare and ambient assisted living. His passion for HCI allowed him to develop all sort of assistive technologies including the eye tracking interface for people with Amyotrophic Lateral Sclerosis (ALS), interactive cube for kids with cerebral pulse, EMG based extension for Scratch or prototype of gaze controlled prosthetic arm.
Alicja Kwasniewska,
SiMa.ai, USA
Alicja Kwasniewska (Ph.D. in AI for Biomedical Engineering) is a software architect at SiMa.ai specializing in Artificial Intelligence applications for embedded edge. Her expertise in the areas of Computer Science and Deep Learning for Biomedical Engineering are foundational as she works to develop solutions for autonomous driving, smart home and remote healthcare applications. She is also a co-founder of the International Summer School on Deep Learning educating future generations on the fundamentals of deep learning methods. She frequently presents her work at international conferences and has 20+ publications in the field, which were awarded with best paper and best young professional paper awards. In her PhD dissertation she proposed AI solutions aimed at improving the accuracy of the contactless vital signs estimation from low resolution thermal sequences.
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.
Maciej Szankin
Intel AI Labs, USA
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.
