2023
Project #1: Evaluating Model Poisoning Attacks in Federated Machine Learning Download Evaluating Model Poisoning Attacks in Federated Machine Learning
- Company/Inst.:IT
- Supervisor: Usama Zafar <usama.zafar@it.uu.se>, Salman Toor <salman.toor@it.uu.se>,
- Team :Claude Carlsson <claudecarlsson@outlook.com>, Edvin Germer <edvin.germer@gmail.com>, Emy Engström <emyeng450@gmail.com>
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Project #4: Computational modeling of avascular tumors: exploring the hallmarks of cancer in silico Download Computational modeling of avascular tumors: exploring the hallmarks of cancer in silico
- Company/Inst.:IT
- Supervisor: Stefan Engblom <stefane@it.uu.se>, Erik Blom <erik.blom@it.uu.se>,
- Team: Julia Fejes <juliafejes1@gmail.com>, Anton Ljungar <anton.ljungar@gmail.com>, Sofia Reineck <sofia@reineck.se>
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Project #5: Incorporating learned preconditioners in an open-source FEM solver Download Incorporating learned preconditioners in an open-source FEM solver
- Company/Inst.:IT
- Supervisor: Paul Häusner <paul.hausner@it.uu.se>, Jens Sjölund <jens.sjolund@it.uu.se>,
- Team: Martin Knebel <martin.knebel.5562@student.uu.se>, Mattias Persson <mattias.persson.3711@student.uu.se>, Erik Bjerned <erik.bjerned.0497@student.uu.se>
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Project #6: Learning preconditioners for interior point methods Download Learning preconditioners for interior point methods
- Company/Inst.:IT
- Supervisor: Paul Häusner <paul.hausner@it.uu.se>, Jens Sjölund <jens.sjolund@it.uu.se>,
- Team: Felix Svedbergh <Felix.Svedbergh.2066@student.uu.se>, Emil Edvardsson <Emil.Edvardsson.5732@student.uu.se>
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Project #7a: Neural representation of massive 3D data Download Neural representation of massive 3D data
- Company/Inst.:IT
- Supervisor: Jens Sjölund <jens.sjolund@it.uu.se>,
- Team: Linus Jacobsson <jacobssonlinus00@gmail.com>, David Näsström <david.nasstrom.2959@student.uu.se>
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Project #7b: Neural representation of massive 3D data Download Neural representation of massive 3D data
- Company/Inst.:IT
- Supervisor: Jens Sjölund <jens.sjolund@it.uu.se>,
- Team: Aoping Lyu <aoping.lyu.6830@student.uu.se>, Jingling Gao <jinglin.gao.1941@student.uu.se>
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Project #9: Architectured materials with tailored energy dissipation: Design with machine learning Download Architectured materials with tailored energy dissipation: Design with machine learning
- Company/Inst.: Materials science Links to an external site. Hitachi Links to an external site.
- Supervisor: Mahmoud Mousavi <mahmoud.mousavi@angstrom.uu.se>, Aliae Welander <aliae.welander@hitachienergy.com>,
- Team: Ture Hassler <ture.hassler@gmail.com>, Jacob Kovaleff Malmenstedt <j.malmenstedt@gmail.com>
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Project #10: High-dimensional option pricing using dimension reduction Download High-dimensional option pricing using dimension reduction
- Company/Inst.:IT
- Supervisor: Elisabeth Larsson <elisabeth.larsson@it.uu.se>,
- Team: Albin Sand <hansalbinsand@gmail.com>, Anton Larsson <a00larsson@gmail.com>, Thor Rönnegård Lindberg <thor.ronnegard@gmail.com>, Gustav Karlsson <gustav.cf.karlsson@gmail.com>
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Project #11: Quasi-Optimal Entanglement Characterisation in Quantum States Download Quasi-Optimal Entanglement Characterisation in Quantum States
- Company/Inst.:IT
and physics Links to an external site.
- Supervisor: Prashant Singh <prashant.singh@scilifelab.uu.se>, Vahid Azimi Mouslou <vahid.azimi-mousolou@physics.uu.se>, Per Mattsson <per.mattsson@it.uu.se>,
- Team: Stefanos Tsampanakis <stefanos.tsabanakis@gmail.com>, Ramin Modaresi <Raminmodaressi@gmail.com>, Niklas Kostrzewa <niklas.kostrzewa@gmx.de>
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Project #12: A Hybrid Finite Difference – Finite Element Method for Wave Equations Download A Hybrid Finite Difference – Finite Element Method for Wave Equations
- Company/Inst.:IT
- Supervisor: Martin Almquist <martin.almquist@it.uu.se>, Vidar Stiernström <vidar.stiernstrom@it.uu.se>,
- Team: Kristina Bergström <kristina.linnea.bergstrom@gmail.com>, Hannes Juujärvi <hannes.juujarvi.8165@student.uu.se>, Isak Kinnunen <isak.kinnunen.5473@student.uu.se>, Jakob Malmsten <jakob.malmsten.1640@student.uu.se>
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Project #13: Filtering and identification in Epidemics Download Filtering and identification in Epidemics
- Company/Inst.:IT
- Supervisor: Stefan Engblom <stefane@it.uu.se>,
- Team: Daniel Emanuelsson <daniel.emanuelsson.8275@student.uu.se>, Elsa Rimhagen <elsa.rimhagen.7989@student.uu.se>, David Lood <david.lood.3070@student.uu.se>
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Project #14: Analysis of weakly labeled digital cytology data Download Analysis of weakly labeled digital cytology data
- Company/Inst.:IT
- Supervisor: Nadezhda Koriakina <nadezhda.koriakina@it.uu.se>, Joakim Lindblad <joakim.lindblad@it.uu.se>, Natasa Sladoje <natasa.sladoje@it.uu.se>,
- Team: David Hovstadius <david@hovstadius.com>, Oskar Lernholt <oskar.lernholt@gmail.com>
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Project #15: Evaluating resilience to heat stress among dairy cows Download Evaluating resilience to heat stress among dairy cows
- Company/Inst.: SLU Links to an external site.
- Supervisor: Tomas Klingström <tomas.klingstrom@slu.se>, Lena-Mari Tamminen <lena.mari.tamminen@slu.se>, Martin Johnsson <martin.johnsson@slu.se>,
- Team: Joakim Svensson <joakim.svensson7535@gmail.com>, Axel Englund <axel.e.englund@gmail.com>
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Project #16: Detecting the sequential use of resting beds by cows after milking Download Detecting the sequential use of resting beds by cows after milking
- Company/Inst.: DU Links to an external site.
- Supervisor: Lars Rönnegård <lrn@du.se>,
- Team: Johanna Ring <johanna.ring1998@gmail.com>, Emil Skytt <emil.skytt.6442@student.uu.se>
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Project #17: Detecting the location of cows before entering the milking parlor Download Detecting the location of cows before entering the milking parlor
- Company/Inst.: DU Links to an external site.
- Supervisor: Lars Rönnegård <lrn@du.se>,
- Team: Axel Danielsson <axel.d.1997@gmail.com>, Folke Gustafsson <Emilfolke.Gustafsson.8846@student.uu.se>
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Project #18: Detecting spikes in positioning data applied to cow movement Download Detecting spikes in positioning data applied to cow movement
- Company/Inst.: DU Links to an external site.
- Supervisor: Lars Rönnegård <lrn@du.se>,
- Team: Sajad Sharhani <sharhani.sajad@gmail.com>, Rabia Bashir <rabia.bashir.9649@student.uu.se>
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Project #19: Machine Learning Accelerated Solution of Inverse Problems in Photonics Download Machine Learning Accelerated Solution of Inverse Problems in Photonics
- Company/Inst.:IT
vub Links to an external site.
- Supervisor: Prashant Singh <prashant.singh@scilifelab.uu.se>, Francesco Ferranti <francesco.ferranti@vub.be>,
- Team: Gustav Fredrikson <gustavfredrikson98@gmail.com>, Fredrik Gillgren <fredrik.l.d.gillgren@gmail.com>, Johan Rensfeldt <johan.rensfeldt@gmail.com>
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Project #20: Modelling the physical and chemical behaviour of magma and associated geothermal systems in COMSOL Download Modelling the physical and chemical behaviour of magma and associated geothermal systems in COMSOL
- Company/Inst.: geo Links to an external site.
- Supervisor: Steffi Burchardt <steffi.burchardt@geo.uu.se>, Christoph Hieronymus <christoph.hieronymus@geo.uu.se>,
- Team: Erik Lindvall <erli2321@student.uu.se>, Emil Hansson <emilhansson96@gmail.com>
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Project #21: Modelling the propagation of fractures, such as magmatic dykes and hydrofractures - X-FEM Download Modelling the propagation of fractures, such as magmatic dykes and hydrofractures - X-FEM
- Company/Inst.: geo Links to an external site.
- Supervisor: Steffi Burchardt <steffi.burchardt@geo.uu.se>, Christoph Hieronymus <christoph.hieronymus@geo.uu.se>,
- Team: Simon Mojtahedi <simon_m11@hotmail.com>, Gustaf Karlsen <gustafkarlsen@hotmail.com>
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Project #23: Network usage at home: the electricity footprint
Usage of Internet-connected services and devices causes about 9% of total global electricity demand. We have regional estimates, for example in Europe video streaming is about 70% of all Internet traffic. However, we know much less about what services are used and when in homes. We are developing a methodology (link below) to quickly gather 24-hour samples of Internet usage from homes using NetFlow. It logs the IP endpoints, time and duration of the connection, and total data sent/received.
Usage of a given service (e.g. Netflix or Facebook) involves many different IP endpoints (advertising, tracking, analytics, and metadata/control channels). This project will work with a NetFlow dataset, to automatically group and classify the connections logged. Subclassification would also be of interest, for example "1 GB was attributable to a particular social networking service in the 24 hour sample, of which 400 MB was video and 200 MB was advertising."
https://www.it.uu.se/research/human-machine-interaction/digital-ecologies-lab/internet-microscope
For a description of the issues and challenges of attributing (based on reverse DNS lookup) and grouping NetFlow logs, have a look at sections 3.2.1.2 and 3.2.2 of this PhD thesis by Kelly Widdicks:
https://eprints.lancs.ac.uk/id/eprint/143606/1/2019widdicksphd.pdf
For an example of how well-analysed logs can lead to research outcomes, have a look at the paper by Widdicks et al. presented at the CHI conference in 2019:
https://doi.org/10.1145/3290605.3300696
- Company/Inst.:IT
- Supervisor: Mike Hazas <mike.hazas@it.uu.se>, Martin Stojanov <martin.stojanov@it.uu.se>,
- Team: Jesper Edström <Jesper.j.edstrom@gmail.com>, Linus Lindell <Linus.lindell00@gmail.com>, Samuel Medlock <samuel.student.ne@gmail.com>, Markus Norling <markus.norling@outlook.com>
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Project #24: Deep learning based image segmentation for enhanced seed phenotyping Download Deep learning based image segmentation for enhanced seed phenotyping
- Company/Inst.:IT
- Supervisor: Filip Thor <filip.thor@it.uu.se>, Carl Nettelblad <carl.nettelblad@it.uu.se>
- Team: Axel Wohlin <axelwohlin@gmail.com>, Oliver Groth <carlolivergroth@gmail.com>
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