iInstitute / Digital Humanities webinar: The Ethics of Datafication and AI by Geoffrey Rockwell

Tuesday, May 18th, 2021

Summary – We all want artificial intelligence to be responsible, trustworthy, and good… the question is how to get beyond principles and check lists. In this paper I will argue for the importance of the data used in training machines, especially when it comes to avoiding bias. Further, I will argue that there is a role for humanists and others who have been concerned with the datafication of the cultural record for some time. Not only have we traditionally been concerned with social, political and ethical issues, but we have developed practices around the curation of the cultural record. We need to ask about the ethics around big data and the creation of training sets. We need to advocate for an ethic of care and repair when it comes to digital archives that can have cascading impact.

About the speaker – Geoffrey Rockwell is a Professor of Philosophy and Digital Humanities, Director of the Kule Institute for Advanced Study and Associate Director of AI for Society signature area at the University of Alberta. He publishes on textual visualization, text analysis, ethics of technology and on digital humanities including a co-authored book Hermeneutica from MIT Press (2016). He is co-developer of Voyant Tools (, an award winning suite of text analysis tools. He is currently the President of the Canadian Society for Digital Humanities.

Welcome to iInstitute / DH seminar: On the “Art of Losing”—Some Notes on Digitization, Copying, and Cultural Heritage

Tuesday, February 16th, 2021

When? 4 March, 9am

On the “Art of Losing”—Some Notes on Digitization, Copying, and Cultural Heritage
Copying is a creative “art of losing” that sustains culture and lends substance to heritage. This talk will aim to unpack the meaning of this statement and unravel some of the many paradoxes inherent in copying what has been inherited as culture using digital technologies. How is it that cultural reproduction and representation always entail loss while also always perpetuate things and ideas valued as culture and as heritage? What kinds of loss do digital technologies ensure? In what ways do new digital technologies sustain culture and enable heritage to persist? Attempting to unravel some of the conceptual and practical knots that formulate riddles like these, the first half of the talk will investigate a few key terms—copying, culture, and heritage. It will also survey a few of the important technologies used to copy texts in East Asia and on the Korean peninsula—brushes, bamboo slips, paper, woodblocks, new and old forms of movable metal type, photography and various forms of lithography, digital imaging, encoding schema, and forms of machine learning. This brief survey will help to situate a discussion in the second half of the talk about the current state of our creative “arts of loss” as they concern creating digital copies of cultural heritage objects. To suggest the current state of our arts, two research projects will be introduced. The first is an effort by nuns at the Taiwanese Buddhist Temple Fo Guang Shan to create an accurate digital transcription of every historical instantiation of the massive Buddhist canon. Their aim is to help ensure Buddhist heritage. The second is an effort by the National Library of Korea to make more of Korea’s textual heritage available to its patrons as digital transcriptions by using deep learning to overcome long-standing difficulties associated with the automated transcription of Korean texts. The American poet Elizabeth Bishop suggests in her poem “One Art” that “the art of losing is not hard to master.” This talk will suggest that Bishop’s poetic insight is helpful for thinking about digitization and cultural heritage. Loss is inevitable when reproducing cultural heritage by means of digital technologies. These losses are not necessarily a disaster. Each copy makes what has been inherited available again to new places and times. But how we practice this “art of losing” is important to consider since how we copy with our digital tools formulates what is inherited as cultural heritage.

About Wayne de Fremery , he is an associate professor in the School of Media, Arts, and Science at Sogang University in Seoul and Director of the Korea Text Initiative at the Cambridge Institute for the Study of Korea in Cambridge, Massachusetts ( He also currently represents the Korean National Body at ISO as Convener of a working group on document description, processing languages, and semantic metadata (ISO/IEC JTC 1/SC 34 WG 9). Wayne’s research integrates approaches from literary studies, bibliography, and design, as well as information science and artificial intelligence. Recent articles and book chapters by Wayne have appeared in The Materiality of Reading (Aarhus University Press, 2020), The Wiley-Blackwell Companion to World Literature (Ken Seigneurie ed., 2020), and Library Hi-Tech (2020). Wayne’s bibliographical study of Chindallaekkot (Azaleas), a canonical book of modern Korean poetry, appeared in 2014 from Somyŏng Publishing. In 2011, his book-length translation of poetry by Jeongrye Choi, Instances, appeared from Parlor Press. Books designed and produced by Wayne have appeared from the Korea Institute at Harvard University, the University of Washington Press, and Tamal Vista Publications, an award-winning press he ran before joining the faculty of Sogang University. Some of his recent research projects have concerned the use of deep learning to improve Korean optical character recognition (funded by the National Library of Korea), technology and literary translation (paper forthcoming from Translation Review), and “copy theory” (paper under review). Wayne’s degrees are from Whitman College, Seoul National University, and Harvard.

Meet Speaker Anna-Lena Axelsson from The Forest Data Lab

Friday, November 27th, 2020

During the Big Data Conference on December 3-4, 2020 we will have several interesting invited speakers, one of them is Anna-Lena Axelsson from The Forest Data Lab .

Anna-Lena Axelsson will talk about The National Forest Data Lab, an open platform that promotes co-creation and data-driven innovation within the forest sector. The platform builds upon existing data, infrastructure and collaboration between two strong players within management, analysis and curation of forest related data; the Swedish Forest Agency and the Swedish University of Agricultural Sciences (SLU). The main users and collaborators are companies and public authorities but also academia and research institutes. The presentation will focus on a number of use cases that demonstrate the value of open data and services for data-driven innovation in the forest sector. The Lab also arrange seminars, networking and training events. Currently The Forest Data Lab participate in the new version of Hack for Sweden 365, which is an innovation competition related to public open data.

Anna-Lena Axelsson. Foto: Linnea Lutto

Foto: Linnea Lutto

Anna-Lena Axelsson works with development of external collaboration and coordinates the forest environmental monitoring and assessment program at the Swedish University of Agricultural Sciences. She is one of the initiators of the National Forest Data Lab.

Don’t miss out on the opportunity to listen to him and take part of the conference by signing up here by December 1st.

Meet Speaker – Johan Thor from Södra at the Big Data Conference

Thursday, November 26th, 2020

During the Big Data Conference on December 3-4, 2020 we will have several interesting invited speakers, one of them is Johan Thor, Head of data science at Södra.

Johan Thor will talk about how the small European bark beetle, less than half a centimeter long, gets visible from space via satellite images, or rather; its effects. The bark beetle infests and kills large amounts of spruces, not only in Sweden, to large economic amounts. Today, we have limited possible actions to take in order to prevent further infestation and he will describe how Södra teamed up with a Dutch startup in order to explore a new way to fight back!

Johan Thor, Head of data science at Södra

More information about Johan Thor is currently working as Head of data science at Södra. He has been for Södra for over 14 years where he have had several different focuses and positions. He has a master of science in applied physics from The Institute of Technology at Linköping University. At Södra Johan is involved in collaborations with academia, consultants and other partners and is a great inspiration for the students he meets.

Don’t miss out on the opportunity to listen to him and take part of the conference by signing up here by December 1st.

Meet Keynote: Erik Willén – Big Data applications in Forestry

Friday, November 20th, 2020

During the Big Data Conference on December 3-4, 2020 we will have several interesting Keynote speakers, one of them is Erik Willén, who is Process Manager at Skogforsk, in Uppsala, Sweden

During the conference he will speak about how Swedish forestry is using and producing vast amount of data for planning, during operations and transportation to the industry. The presentation focus on the enablers for digitalization in Swedish forestry and their current status. The most important data collection and processing as well as several applications in operational use and in applied R&D will be presented.

Erik Willén, SKogforsk

Don’t miss out on the opportunity to listen to him and take part of the conference by signing up here by December 1st.

Meet Keynote: Håkan Olsson – Remote Sensing provides Big Data for assessment of our forest resources

Thursday, November 19th, 2020

During the Big Data Conference on December 3-4, 2020 we will have several interesting Keynote speakers, one of them is Håkan Olsson is professor in forest remote sensing at the Swedish University of Agricultural sciences. He leads the data acquisition work package in the research programme Mistra Digital Forest. He is also a member in the steering group for the ongoing national laser scanning for forest resource assessment, and a member of the national council for geodata (Geodatarådet).

Håkan Olsson

During his talk he focus on that there is an increasing stream of remote sensing data that together with digital techniques can be used for assessment of forest resources. Satellites provides frequent images that can be compared and used for forest damage assessment. Airborne laser scanners provide 3D point clouds that in combination with field surveyed reference data are used operationally for producing nationwide and accurate maps with data about the forest on raster cell level. The sensors develops rapidly and provides data with higher resolution, making assessments of single trees realistic. Among the current research frontiers are: combining single tree data from airborne sensors with stem shape data from sensors carried by man or vehicles; automated classification of tree species; assessment of forest growth from repeated sensor data acquisitions. The ultimate goal is to assimilate all new data into a continuously updated model of the forest resources.

Don’t miss out on the opportunity to listen to him and take part of the conference by signing up here by December 1st.

Webinar DISA-DSM November 10th

Friday, November 6th, 2020

The DISA-DSM group welcomes you to a webinar with Yaozhong Hu   (University Alberta, Edmonton, Canada) 10 November at 13:00 Stockholm time

Title: Numerical methods for stochastic Volterra integral equations with weakly singular kernels

Abstract: In this talk, we  will introduce  stochastic Volterra integral equations with weakly singular kernels and study the existence, uniqueness and Holder continuity of the solution. Then, we propose a  theta-Euler-Maruyama  scheme  and a Milstein scheme to solve  the equations numerically  and  we obtain the strong  rates of convergence for both schemes in L^{p} norm for any p\geq 1. For the theta-Euler-Maruyama  scheme the rate is min {1-alpha, 1\2-beta} and for the Milstein scheme the rate is min{1-alpha,1-2beta} when alpha\neq \frac 12, where 0<alpha<1, 0< beta<1\2. These  results on the rates of convergence are significantly different from that of the similar schemes for the stochastic Volterra integral equations with regular  kernels. The difficulty to obtain our results is the lack of Ito formula for the equations. To get around of this difficulty we use instead the Taylor formula and then carry a sophisticated  analysis on the equation the solution satisfies.

For more information and registration contact Nacira Agram –

Upcoming seminars:

  • 17 November  at 13:00 – Paolo Di Tella (University of Technology Dresden, Germany) Title: On enlarged filtrations of point processes
  • 24 November  at 12:00 – David SISKA (University of Edinburg, UK)
  • 24 November  at 13:00 WEINAN E  (Princeton University, USA) Title: An Overview of Deep Learning Based Algorithms for high dimensional PDEs Abstract: I will give an overview of deep learning-based algorithms for PDEs. Topics to be covered includes: (1) The Deep BSDE method. (2) Applications to control theory. (3) Theoretical advances.
  • 30 November at 15:00 Jiequn Han (Princeton University, USA)
  •  8 December at 13:00 Stockholm time Mathieu Lauriere (Princeton University, USA)

Seminar November 3rd Solving mean-field PDE with symmetric neural networks

Thursday, October 29th, 2020

Welcome to the next talk related to Webinar DISA-DSM: stochastic analysis, statistics and machine learning, which is held by Huyên Pham (University Paris 7 Diderot, France)

When? 3 November at 13:00 Stockholm time.
Online via zoom: Contact Nacira Agram,, to get the link

Title: Solving mean-field PDE with symmetric neural networks
Abstract: We propose numerical methods for solving non-linear partial differential equations (PDEs) in the Wasserstein space of probability measures, which arise notably in the optimal control of McKean-Vlasov dynamics.
The method relies first on the approximation of the PDE in infinite dimension by a backward stochastic differential equation (BSDE) with a forward system of N interacting particles. We provide the rate of convergence of this finite-dimensional approximation for the solution to the PDE and its Lions-derivative. Next, by exploiting the symmetry of the particles system, we design a machine learning algorithm based on certain types of neural networks, named PointNet and DeepSet, for computing simultaneously the pair solution to the BSDE by backward induction through sequential minimization of loss functions. We illustrate the efficiency of the PointNet/DeepSet networks compared to classical feedforward ones, and provide some numerical results of our algorithm for the examples of a mean-field systemic risk and a mean-variance problem.
Based on joint work with M. Germain (LPSM, EDF) and X Warin (EDF).

We will book a room at LNU for those who wants to attend physically the seminar. Because of space restrictions due to Covid-19, please let me know if you want to do that.

A warm welcome!

Webinar DISA-DSM: stochastic analysis, statistics and machine learning

Monday, October 26th, 2020

Our new DISA-group Deterministic and Stochastic Modelling (DSM) invites you to a seminar on Tuesday 27th at 13.00, this seminar is a part of a seminar series so keep an eye out for more information.

Title: Rare events simulation: least-squares Monte Carlo method vs deep learning based shooting method

Speaker: Omar Kebiri (University B-TU Cottbus-Senftenberg, Germany)

Abstract: When computing small probabilities associated with rare events by Monte Carlo it so happens that the variance of the estimator is of the same order as the quantity of interest. Importance sampling is a means to reduce the variance of the Monte Carlo estimator by sampling from an alternative probability distribution under which the rare event is no longer rare. Determine the optimal (i.e. zero variance) changes of measure leads to a stochastic optimal control problem. The control problem can be solved by a stochastic approximation algorithm, using the Feynman-Kac representation of the associated dynamic programming equations which leads to an FBSDE, and we discuss numerical aspects for high-dimensional problems along with simple toy examples using two methods: least-squares Monte Carlo method and deep learning based shooting method.
Joint work with Carsten Hartmann, Lara Neureither, and Lorenz Richter.

Practical information: We will book a room at LNU for those who wants to attend physically the seminar. Because of space restrictions due to Covid-19, please let me know if you want to do that, otherwise a link will be provided. Contact Nacira Agram – for more information.

Call for presentations, Big Data Conference 2020

Friday, October 23rd, 2020

A fast-forward (FF) + virtual poster (VP) sessions will be organized as part of the Big Data Conference 2020. In the FF presentations, each participant gets to show a 3-minute video to briefly summarize her/his research. Directly after the FF,  participants will be redirected to breakout rooms where it will be possible to present their VP and interact with the interested public.

The FF+VP presentations can focus on either ongoing research or new ideas:

1) Ongoing research will focus on research recently published or at an advanced stage of elaboration. The main goal here is to present research results of general interest for the public of the conference and eventually receive feedback on ongoing work.

2) New ideas will focus on future research, plans, or simply new ideas. The goal here is to share with the public their own plans, receive feedback, find partners and possibly find synergies to develop future research together.


To submit to the FF+VP session, participants should submit a 500-word abstract briefly presenting the research by November 9th, 2020 to Diana Unander,  Each participant can submit at most two abstracts.

Acceptance information will be sent out by November 13th, 2020.

If accepted, a video (in videos in 720p and mp4 format) of maximum 3 minutes should be sent by November 24th, 2020.

For more information or questions, please contact: