V ponedeljek, 4. avgusta 2025, bosta ob 16:00 uri izvedeni dve
predavanji v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 4. avgust 2025 ob 16.00 prek Zoom-a (https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09).

1. predavanje:
============

--------------------------------------
PREDAVATELJ: Matic POŽAR
--------------------------------------

Matic Požar is a 2nd year Data Science master's student at UP FAMNIT, writing his master thesis under the supervision of prof. Miklós Krész.

---------------------------------------------------------------------------------------------------------------------
NASLOV: Network-Aware Uplift Modeling through Influence Maximization Techniques
---------------------------------------------------------------------------------------------------------------------

POVZETEK:

Influence maximization is a fundamental problem in domains such as viral marketing, public health, and political campaigning, aiming to select a subset of users in a network to maximize the spread of influence. A central challenge in this task is the lack of knowledge about edge (infection) probabilities, which govern how influence propagates. We addresses this issue through the inverse infection problem: inferring edge probabilities from observed influence patterns. A novel diffusion and edge prediction model is proposed. Furthermore, we explore a novel application of this framework to uplift modeling, a technique used to identify individuals most likely to respond to interventions. By incorporating network effects into uplift modeling, our approach moves beyond the traditional assumption of user independence, enabling more realistic and effective targeting strategies in network settings. Leveraging recent advances in graph neural networks (GNNs), we propose a framework that captures complex network dependencies for accurate outcome estimation allowing for effective treatment allocation.

Seminar bo potekal v angleškem jeziku.

=============================================================================================================

2. predavanje:
============

---------------------------------------------
PREDAVATELJICA: Dina MITROVIĆ
---------------------------------------------

Dina Mitrović is a second-year Data Science master's student at UP FAMNIT and a participant in the pilot project GDI UP, currently writing her master's thesis under the supervision of Assist. Prof. Matjaž Hladnik, PhD.

--------------------------------------------------------------------------------------------------------------------------------------------------------------------
NASLOV: Analysis of full-length 16S rRNA gene and ITS regions amplicons sequenced with Oxford Nanopore Technologies
--------------------------------------------------------------------------------------------------------------------------------------------------------------------

POVZETEK:

This presentation introduces ongoing work aimed at proposing a standardized, open-source bioinformatics workflow for microbial community profiling using Oxford Nanopore Technologies (ONT) long-read amplicon sequencing. Unlike short-read methods, ONT enables full-length sequencing of 16S rRNA and ITS regions, offering the potential for improved species-level taxonomic resolution. However, reproducibility remains limited due to the lack of consistent analysis protocols. This research seeks to address that gap by evaluating existing bioinformatics tools and assembling a comprehensive pipeline that includes quality control, error correction, and taxonomic classification. Using sequencing data of mock microbial communities, this study will evaluate protocol performance and identify workflow steps that most influence classification accuracy. Key challenges to be addressed include sequencing error rates, primer and database biases, and tool compatibility. The ultimate goal is to improve accuracy, consistency, and reproducibility in long-read microbial profiling.

Seminar bo potekal v angleškem jeziku.

=============================================================================================================

Seminarja bosta izvedena online prek Zoom-a na sledeči povezavi:

https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09

Meeting ID: 297 328 207
Passcode: 123456789

Vabljeni!