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--- aira:start [2024/04/12 10:12] – [Schedule Summer 2024] sbk
+++ aira:start [2024/04/12 10:19] – [Schedule Summer 2024] sbk
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 ===== Schedule Summer 2024 =====
-  * **[DOCTORAL TRACK] 2024.04.18**  Farnoud Ghasemi [[#20240412| Performance Optimization of the Platforms in Two-sided Mobility Market]] and Michał Bujak [[#20240412| Optimising network efficiency in the epidemic scenario]]
+  * **[DOCTORAL TRACK] 2024.04.18**
+    * Farnoud Ghasemi [[#20240412| Performance Optimization of the Platforms in Two-sided Mobility Market]] and
+    * Michał Bujak [[#20240412| Optimising network efficiency in the epidemic scenario]]
       * Meeting link: [[|MS Teams]]
       * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
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 ===== Presentation details =====
+==== 2024-04-18 ====
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+{{ :aira:farnoud-ghasemi-foto.png?width=200| }}
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+**Speaker**: Farnoud Ghasemi, PhD Candidate @ Jagiellonian University
+**Title:** Performance Optimization of the Platforms in Two-sided Mobility Market
+**Abstract:**
+The presentation will focus on analyzing two-sided mobility markets involving platforms such as Uber and Lyft with agent-based modeling. The MoMaS framework will be introduced, which models two-sided mobility markets as complex systems with intricate, non-linear interactions among the involved parties (including travelers, drivers, and platforms). Eventually, the integration of Reinforcement Learning into the proposed framework will be discussed explaining how RL-based platform strategies can improve platform performance.
+**Biogram:**
+Farnoud is currently a PhD student within the Faculty of Mathematics and Computer Science at the Jagiellonian University. His PhD research under the supervision of Dr. Rafal Kucharski, focuses on studying behavioural dynamics of two-sided mobility using agent-based microsimulation. He received his Bachelor’s degree in Civil Engineering at the University of Tabriz and he completed his MSc degree in Transport Systems at the Sapienza University of Rome.
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+{{ :aira:michal-bujak-foto.jpg?width=200| }}
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+**Speaker**: Michał Bujak, PhD Candidate @ Jagiellonian University
+**Title:** Optimising network efficiency in the epidemic scenario
+**Abstract:**
+We consider the problem of reducing virus spreading in the system network (graph) while keeping the utility of the whole system at the maximal level. To balance the above two opposite goals, we propose Deep Epidemic Efficiency Network (DEEN), an unsupervised clustering method, which optimises graph efficiency in an epidemic scenario using Graph Convolutional Neural Networks and a novel loss function. Given the desired virus transmission, it constructs a graph for which the predefined transmission rate is not exceeded and utility function is maximised. We show that proposed method successfully solves three real-life problems: ride-pooling service in New York City, economic exchange between regions in Poland, and information sharing via peer-to-peer network.
+In particular, by dividing 150 New York taxi travellers into four groups our method increases epidemic threshold more than twofold at the cost of reducing utility only by 13%, significantly outperforming benchmark methods. The model can be instrumental in future pandemic outbreaks when we need to balance between maintaining efficiency and preventing the spread of the virus.
+**Biogram:**
+A third-year phd student of technical computer science at the Jagiellonian University. He has a background in applied mathematics with a focus on the probability theory. Currently, he is a part of a team working on transportation problems in the theoretical framework. His main research area is network science (both analytical and AI-based approaches). Out of the academia, he has experience in quantitative analysis for the major global investment banks.
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 ==== 2024-04-04 ====