Sandor Szedmak

Phone+358 50 3044380

Aalto SCI Computer Science Konemiehentie 2

Research activity per year

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

Education/Academic qualification

Doctoral degree, Natural Sciences, Rutgers University-Camden

Award Date: 8 Oct 2002

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Dive into the research topics where Sandor Szedmak is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

1 Similar Profiles

14 Article
6 Conference article in proceedings
1 Chapter
1 Abstract

Scaling up drug combination surface prediction
Huusari, R., Wang, T., Szedmak, S., Dias, D., Aittokallio, T. & Rousu, J., Mar 2025, In: Briefings in Bioinformatics. 26, 2, p. 1-11 11 p., bbaf099.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
2 Downloads (Pure)
Protein function prediction through multi-view multi-label latent tensor reconstruction
Armah-Sekum, R. E., Szedmak, S. & Rousu, J., 2 May 2024, In: BMC Bioinformatics. 25, 1, p. 1-21 21 p., 174.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
4 Citations (Scopus)

35 Downloads (Pure)
Scalable variable selection for two-view learning tasks with projection operators
Szedmak, S., Huusari, R., Duong Le, T. H. & Rousu, J., Jun 2024, In: Machine Learning. 113, 6, p. 3525–3544
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
7 Downloads (Pure)
Personalized Learning Systems for Computer Science Students : Analyzing and Predicting Learning Behaviors Using Programming Error Data
Kamberovic, M., Krivic, S., Delic, A., Szedmak, S. & Ljubovic, V., 26 Jun 2023, p. 86-91. 6 p.
Research output: Contribution to conference › Abstract › Scientific › peer-review
1 Citation (Scopus)
Strain design optimization using reinforcement learning
Sabzevari, M., Szedmak, S., Penttilä, M., Jouhten, P. & Rousu, J., Jun 2022, In: PLoS computational biology. 18, 6, p. 1-18 18 p., e1010177.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
19 Citations (Scopus)

198 Downloads (Pure)

Drug combination data used for comboLTR in "Modeling drug combination effects via latent tensor reconstruction"
Wang, T. (Creator), Szedmak, S. (Creator), Wang, H. (Creator), Aittokallio, T. (Creator), Pahikkala, T. (Creator), Cichonska, A. (Creator) & Rousu, J. (Creator), Zenodo, 2021
DOI: 10.5281/zenodo.4625083, https://zenodo.org/record/4625084
Dataset
Fitted BRAID and Hill functions, and cross-validation splits, for scaled-up comboKR in "Scaling up drug combination surface prediction"
Huusari, R. (Creator), Wang, T. (Creator), Szedmak, S. (Creator), Dias, D. (Contributor), Aittokallio, T. (Contributor) & Rousu, J. (Contributor), Zenodo, 17 Dec 2024
DOI: 10.5281/zenodo.14509937, https://doi.org/10.5281/zenodo.14509938
Dataset
Protein function prediction through multi-view multi-label latent tensor reconstruction
Armah-Sekum, R. E. (Creator), Szedmak, S. (Creator) & Rousu, J. (Creator), figshare, 3 May 2024
DOI: 10.6084/m9.figshare.c.7215643.v1, https://springernature.figshare.com/collections/Protein_function_prediction_through_multi-view_multi-label_latent_tensor_reconstruction/7215643/1
Dataset

2 Reviewer of conference article
1 Reviewer of scientific journal

International Joint Conference on Artificial Intelligence (Event)
Szedmak, S. (Reviewer)
12 Mar 2021
Activity: Publication peer-review and editorial work types › Reviewer of conference article
Machine Learning (Journal)
Szedmak, S. (Reviewer)
13 May 2021
Activity: Publication peer-review and editorial work types › Reviewer of scientific journal
NeurIPS Meetup (Event)
Szedmak, S. (Reviewer)
27 Feb 2021
Activity: Publication peer-review and editorial work types › Reviewer of conference article

Machine learning methods for structural elucidation in untargeted metabolomics
Rousu, J. & Szedmak, S.
13/12/2022
1 item of Media coverage
Press/Media: Media appearance
AI predicts which drug combinations kill cancer cells
Rousu, J. & Szedmak, S.
01/12/2020 → 04/12/2020
15 items of Media coverage
Press/Media: Media appearance
Machine Learning Model Predicts Which Drug Combinations Kill Cancer Cells
Rousu, J. & Szedmak, S.
02/12/2020
2 items of Media coverage
Press/Media: Media appearance

Sandor Szedmak

Personal profile

Expertise related to UN Sustainable Development Goals

Education/Academic qualification

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

Scaling up drug combination surface prediction

Protein function prediction through multi-view multi-label latent tensor reconstruction

Scalable variable selection for two-view learning tasks with projection operators

Personalized Learning Systems for Computer Science Students : Analyzing and Predicting Learning Behaviors Using Programming Error Data

Strain design optimization using reinforcement learning

Drug combination data used for comboLTR in "Modeling drug combination effects via latent tensor reconstruction"

Fitted BRAID and Hill functions, and cross-validation splits, for scaled-up comboKR in "Scaling up drug combination surface prediction"

Protein function prediction through multi-view multi-label latent tensor reconstruction

International Joint Conference on Artificial Intelligence (Event)

Machine Learning (Journal)

NeurIPS Meetup (Event)

Machine learning methods for structural elucidation in untargeted metabolomics

AI predicts which drug combinations kill cancer cells

Machine Learning Model Predicts Which Drug Combinations Kill Cancer Cells

Sandor Szedmak

Personal profile

Expertise related to UN Sustainable Development Goals

Education/Academic qualification

Fingerprint

Collaborations and top research areas from the last five years

Research output

Datasets

Activities

Press/Media