Due to the ongoing Corona virus restrictions IDA 2020 will be held as a virtual conference online on the original dates. As a result it will be free to all! More information will be posted here nearer the time: IDA2020
Congratulations to Toyah Overton & Biraja Ghoshal for their papers accepted to IDA 2020 this year:
Biraja Ghoshal, Cecilia Lindskog, Allan Tucker, “Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics”
Toyah Overton, Allan Tucker, “DO-U-Net for Segmentation and Counting”
Yani Xue, Miqing Li, Xiaohui Liu, “Angle-based Crowding Degree Estimation for Many-Objective Optimization”
Congratulations to Alina Miron and Leila Yousefi who have been succesful in theie application for the Turing Data Study Group this week:
IDA meeting held at WLFB 207/208 (2nd floor of Wilfred Brown) at 3:00PM
Leila Yousefi: The Prevalence of Errors in Machine Learning Experiments (slides can be found here)
Marco Ortu: The Butterfly “Affect”: Impact of Development Practices on Cryptocurrency Prices (slides can be found here)
Gabriel Scali: Constraint Satisfaction Problems and Constraint Programming (slides can be found here)
Congratulations again to Nicky Nicolson, who recently defended her thesis with no changes, and has now been awarded the GBIF Young Researcher award.
The award jury, led by GBIF science committee vice chair Anders G. Finstad of the Norwegian University of Science and Technology (NTNU), lauded Nicolson for her “highly original and innovative” approaches and her successful “use of data from GBIF to combine geographically distant collections using only minimal information on the specimen.”
She is the first U.K. national to win the award since Amy McDougal earned the honour in 2010, the programme’s first year. She is also the third U.K.-based winnner, preceded by McDougal and Juan Escamilla Mólgora, a Mexican PhD candidate at Lancaster University and 2016 award recipient.
For more information click here
Congratulations to Nicky Nicolson who successfully defended her thesis on Friday 5th Oct with no changes! Nicky leads an IDA group at the Royal Botanical Gardens at Kew.
Jaakko Hollmén, Stockholm University, Department of Computer and Systems Sciences, Sweden
Jaakko Hollmén is a faculty member at Department of Computer and Systems Sciences at Stokcholm University in Sweden (since September 2019). Prior to joining Stokcholm university, he was a faculty member at the Department of Computer Science at Aalto University in Finland. His research interests include theory and practice of machine learning and data mining, in particular in the context of health, medicine and environmental sciences. He has been involved in the organization of many IDA conferences for the past ten years. He is also the secretary of the IDA council.
Title of Talk: Diagnostic prediction in neonatal intensive care units
Abstract: Preterm infants, born before 37 weeks of gestation, are subject to many developmental issues and health problems. Very Low Birth Weight (VLBW) infants, with a birth weight under 1500 g, are the most afflicted in this group. These infants require treatment in the neonatal intensive care unit before they are mature enough for hospital discharge. The neonatal intensive care unit is a data-intensive environment, where multi-channel physiological data is gathered from patients using a number of sensors to construct a comprehensive picture of the patients’ vital signs. We have looked into the problem how to predict neonatal in-hospital mortality and morbidities. We have used time series data collected from Very Low Birth Weight infants treated in the neonatal intensive care unit of Helsinki University Hospital between 1999 and 2013. Our results show that machine learning models based on time series data alone have predictive power comparable with standard medical scores, and combining the two results in improved predictive ability. We have also studied the effect of observer bias on recording vital sign measurements in the neonatal intensive care unit, as well as conducted a retrospective cohort study on trends in the growth of Extremely Low Birth Weight (birth weight under 1000 g) infants during intensive care.