See all upcoming seminars in LäsIT and seminar web pages at the homepage for the PhD studentseminars, TDB, Vi2, Theory and Applications Seminars (TAS) @ UpMARC., Department of Mathematics and The Stockholm Logic Seminar.
|Prof. Emeritus John Heywood, Trinity College, Dublin: Toward the Greater S-T-E-M Concerning Luck and the Development of Ideas |
Location: 4308, Time: 10:00-10:30
A public lecture in celebration of the inauguration of Åsa Cajander, Mats Daniels and Arnold Pears from the UpCERG group to Professors’ in the University of Uppsala.
|Dag Wolters, Founder of Hello World!: Hello World! Insights from running a Computing Camp.|
Location: 4308, Time: 10:30-11:30
|InfoLab fika meeting|
Monday 19 Nov
| : Scale-free networks well done|
Location: 19120, Time: 15:00-16:00
Every Monday we meet in room 120, House 19 to discuss various topics related to the analysis of human-generated information. This time we will discuss the paper:
Scale-free networks well done (https://arxiv.org/abs/1811.02071)
Participants are expected to read the paper in advance. This is also part of the reading course on network science (if you are a PhD student and want credits, more info here: http://www.it.uu.se/research/group/infolab/netsciphd/netscireadings2018 ). Each meeting starts at 15:00 (please have coffee/tea ready: they can be made in the local kitchen, and espresso can be bought at the lab) and ends at 16:00. The meetings are informal, open and no registration is needed, but if you tell us in advance there are higher chances to get some cookies or cakes. A list of topics is maintained at <http://www.it.uu.se/research/group/infolab/fika>.
|Lennart Ljung, Linköping University: Data Science: From System Identification to (Deep) Learning and Big Data|
Location: ITC 2347, Time: 13:15
|Equal Opportunities Group - Monthly Meeting|
|Equal Opportunities Group: TBA|
Location: ITC 4307, Time: 13:30-14:30
ON THE AGENDA: TBA
|Marc Brockschmidt, Microsoft Research Cambridge: Learning from Programs with Graphs|
Location: 1113, Time: 15:15
Learning from large corpora of source code ("Big Code") has seen increasing interest over the past few years. A first wave of work has focused on leveraging off-the-shelf methods from other machine learning fields such as natural language processing. While these techniques have succeeded in showing the feasibility of learning from code, and led to some initial practical solutions, they often forego explicit use of known program semantics. In a range of recent work, we have tried to solve this issue by integrating deep learning techniques with program analysis methods in graphs. Graphs are a convenient, general formalism to model entities and their relationships, and are seeing increasing interest from machine learning researchers as well. In this talk, I present two applications of graph-based learning to understanding and generating programs and discuss a range of future work building on the success of this work.
|Disputation | Dissertation|
|Saleh Rezaeiravesh : Application of Uncertainty Quantification Techniques to Studies of Wall-Bounded Turbulent |
Location: ITC 2446, Time: 10:15
will present and defend his PhD thesis Application of Uncertainty Quantification Techniques to Studies of Wall-Bounded Turbulent.
|Mulari Annavara: Distributed machine learning at the edge|
Location: ITC 1113, Time: 14:15
|Dr. Manon Kok: Magnetic Field SLAM|
Location: 2244, Time: 13:15
In this talk I will discuss our recent work on scalable magnetic field SLAM in 3D using Gaussian process maps. We use local anomalies in the magnetic field as a source of position information. These anomalies are for instance due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We build local magnetic field maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao–Blackwellised particle filter. We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic field SLAM algorithm in terms of both computational complexity and in terms of map storage.
|Pierre Flener: Solving Combinatorial Problems without Knowing How to Solve Them|
Location: ICT 1111, Time: 14:15-15:00
Solving technologies for combinatorial problems abound: mixed-integer programming
(MIP), Boolean satisfiability (SAT), satisfiability modulo theories (SMT), constraint
programming (CP), local search, etc, and hybrids. No technology dominates the others or
shares a modelling language with them. Unbeknownst to many, it has become possible to
model the constraints (and objective function) of a combinatorial problem upon learning
a single fully declarative high-level modelling language and, upon experiments with
solvers of different technologies, to choose a winning technology and solver, without
knowing (in depth) how the solvers work.
Pierre Flener is a professor of Computing Science. A short bio is here
|Pedagogic Lunch Seminar|
| : IOOPM: a learning centered course|
Location: ITC 4308, Time: 12:15-14:00
You are kindly invited to the IT Pedagogic Lunch Seminar
IOOPM: a learning centered course
Place: ITC 4308
You can find more and updated information in Medarbetarportalen.
If you’d like us to arrange lunch for you, you should sign up here at least two days before the seminar.
See also the list of all upcoming seminars.
Internal seminars. Lecturers may be either internal or external.