The Conference 2019
GAIA organises a one-day conference for people with an interest in artificial intelligence and data science with the focus on what is going on within the field in Gothenburg.
The aim is to create an environment for learning, networking, and knowledge-sharing among individuals, companies, organisations, and academia with a common interest. The conference focuses on applied machine learning and data science and introduces talks of diverse content given by enthusiastic people from the field, many with local connections.
Our first conference attracted over 400 people and was sold out. The second GAIA conference will take place on April 9th 2019 at Lindholmen Conference Centre!
Tickets are available now!
Ivana Bartoletti is the founder of Women in AI, a global organisation empowering women working in the field. She is an International public speaker and commentator on privacy, digital rights, data ethics, the IoT and the governance of Artificial Intelligence. She will talk about what ethics is, what the main issues are and why it matters to us as machine learning engineers and data scientists - and what we can do (beyond fixing algorithms)
A mathematician with a PhD in statistics from Chalmers University of Technology, Anton has been working with dynamic pricing for the last three and a half years. He is currently involved in developing a dynamic pricing and revenue forecasting engine for Atomize, an automatic revenue management platform for the hospitality industry, a 2016 startup based in Gothenburg. Anton is going to talk about some of the challenges unique to the hospitality industry's pricing problem, and applications of stochastic processes in modelling and forecasting demand.
Daniel Persson is a theoretical physicist and mathematician with a background from research in String Theory. Although his primary field is mathematics he is also researching together with a WASP PhD student how one can use theoretical physics to understanding modern machine learning algorithms, in particular neural networks. Ever wondered what Quantum Deep Learning is? Go find out!
Danila has a background in linguistics and studied language technology in Gothenburg. She currently works as a linguistics resource developer at Recorded Future. At Recorded Future, they struggle with the creation of good datasets for doing machine learning on vast amounts of text, just like many other companies. Danila will talk about her experience with dataset at Recorded Future, fighting issues like imbalanced classes, overfitting, in the area of a terrorist event classifier.
Benny has a PhD in mathematics with several years of research experience. Currently, I work as a data scientist at Combient while being a researcher at Uppsala University. He will talk, together with Nina, about building a recommender system for wound treatment of chronic wounds for Mölnlycke Healthcare.
Ellinor Rånge is a junior machine learning engineer and data scientist at Ericsson. She will talk about the transition from studying machine learning at university to what it is working in a real data science team, with all the real-world complexities that entail: data engineering, monitoring, data quality, and devops. And, of course, about the machine learning models they try to get to production. Ellinor has also been active as a student ambassador, spreading the ML, data science gospel at the university.
Nina have a PhD in physics and many years of experience in scientific research with a focus on observational astronomy. Since two years, she works as a data scientist at Combient. She will talk, together with Benny, about building a recommender system for wound treatment of chronic wounds for Mölnlycke Healthcare.
The machine learning meetup group that eventually grew into GAIA, and the GAIA conference, was founded back in 2014 by Karina when working as a machine learning specialist at Dreamler. After that, she left the best coast for Spotify, first in Stockholm and now in New York where she works as a research scientist. At the conference, she will talk about her work there building an experimentation platform that can be scaled for all the varied needs of Spotify.
Nils Svangård is a serial entrepreneur in Gothenburg that has been founding or joining startups working with machine learning for more than ten years. He was, for example, doing predictive analytics and computer vision long before the new wave of deep learning started. Currently, he works for the medtech startup Noomi as head of AI, and he is responsible for the smartness of their cutting edge and unique remote caring product.
No AI startup in Sweden might be more talked about at the moment than Peltarion. Trying to take the highly technical field of neural networks to the masses by creating a platform for operationalising and simplifying the use of AI, Peltarion is breaking new ground. Agrin Hilmkil, with Chalmers as alma mater, is part of an excellent team of AI research scientists there, pushing the boundaries of what is done with machine learning in Sweden. Currently Agrin is exploring how to use state of the art machine learning to understand sound.
Originally a mathematician with a PhD in complex analysis, Aron Lagerberg is a data scientist working with neural networks at the Contract Analytics company Seal Software. As parts of one of the best machine learning teams in town, he and his teammates try to use state of the art deep language models to understand legal documents, and to extract the essential information from those documents.
Jesper is a senior engineer from Ericsson, with a career spanning software design, architecture, research and now as machine learning engineer. At Ericsson, he has been busy working on using machine learning to improve processes, in areas such as fault prediction and statistical analysis of code complexity. Most recently he has been using reinforcement learning in production specifically for auto-scaling cloud resources and these experiences will be used as a basis for the talk at the conference.
The only speaker on the list of this year’s conference, who also appeared last year, is the talented Marko Cotra. His talk last year was very popular, and we are glad that he wanted to join us again, using his pedagogical skills to help us understand machine learning models, and their pros and cons. Marko comes from the machine learning startup Annotell, helping companies creating and managing good training data.
Björn's interest in AI drove him to start programming and subsequently to study software engineering. His thesis work involved using machine learning techniques to let an autonomous truck detect vehicles from video feeds. At WAVR, he works with applying machine learning and sensor fusion to human motion tracking.
Thomas has a long history of leading innovation-driven projects heavy on R&D. He has a background in computational geometry and has been involved in Burj al Khalifa, Campus des Wissens to name a few. He has taught at Stanford, Chalmers, KTH and LTH. Prior to WAVR, he founded Dreamler, where he worked as CTO.
Johanna is a safety bioinformatician in the Data Science and AI group within Drug Safety and Metabolism at AstraZeneca. Drug design is a multiparameter optimization problem that requires a fine balance between potency, ADME and safety. Data science and artificial intelligence are seen as a potential method to both improve the safety profile of new drugs as well as reduce the costs and time to bring these to the clinic and in her talk, Johanna will exemplify its application within drug safety.
Jesper has a PhD in medicine from Karolinska Institute, his research was focused on systems biology approaches for coronary artery disease. He joined AstraZeneca in 2011 and is now a Principal Biomedical Informatics Scientist in the Advanced Analytic Centre. In this role, he has the opportunity to both support clinical drug projects directly and work on projects with more strategic focus. One of Jesper’s current focus areas includes the application of wearable sensors in clinical trials including how analytical techniques which can be applied to understand biological phenomenon and link to established endpoints.
Emma Evertsson is a computational chemist with a PhD in theoretical chemistry from Lund University. At AstraZeneca, she is involved in drug design for the treatment of respiratory diseases. In addition, she is responsible for the computational platform providing predicted property values for real and virtual molecules from machine learning models. This platform is intended to reduce the number of experiments and to accelerate the drug development process.
Eskil was trained in engineering physics and wireless communication at Linköping University, National Taiwan University and Ericsson before moving to Gothenburg to deep dive into the world of AI. He is now pursuing an industrial PhD in deep learning and computer vision at Zenuity and Chalmers, with a focus on 3D scene understanding for automotive applications. The one question occupying Eskil's mind is: How far can you push the limits of camera-only perception systems?
Additional speakers are coming soon.