I am keen on helping businesses make better decisions by combining their data and machine learning and modeling expertise. I am an analytics consultant with a decade of industry experience. I also have a scientific background in systems and operations research (I am defending my Ph.D. thesis in June 2021).
Visit also my crowddynamics and evacuation research website crowddynamics.aalto.fi.
PhD in Systems and Operations Research, 2021
MSc in Systems and Operations Research, 2014
BSc in Systems and Operations Research, 2014
My research from 2014 to 2020. First, the focus is on mathematical and physical modeling of the dynamics of an evacuating crowd. Later, optimization methods and heuristics like stochastic optimization, portfolio optimization and genetic algorithms are applied to develop tools for evacuation planners and risk management.
My first research paper concerns the modeling of an evacuation through a bottleneck with a computationally light cellular automaton (CA) model. In it, the evacuating crowd is modeled as a multi-agent system, and the agents move in a discrete square grid according to probabilistic rules. Their decision-making is modeled with a game-theoretical model. The game is played locally and the strategy choice affects the movement of the agents.
We perform extensive numerical calculations with the model from (von Schantz & Ehtamo, 2014). We show that most collective effects observed in crowds evacuating through bottlenecks can be produced with the simple local-decision making model.
The model from (von Schantz & Ehtamo, 2014) is extended to include agents with different risk perceptions. In the numerical simulations with the model we show that agents evacute in irregular successions. The more threatening they perceive the situation, the longer the time lapse on average between two consecutively evacuated agents is.
The crowd dynamics in an exit congestion is studied with a physically realistic evacuation model. The same game-theoretical model used already in (von Schantz & Ehtamo, 2014) is coupled to it. The physics of an evacuating crowd are explained starting from the microscopic physical interactions and individual decision-making of the agents.
The previous publications considered the realistic and computationally efficient modeling crowd dynamics. In this manuscript, an optimization procedure for solving the minimum time evacuation plan using rescue guides is presented. The method is applied on an evacuation from a conference building, which gives an evacuation plan that decreases the evacuation time by 80%. A high-performance computing cluster is used to solve the problem.
Methods from investment portfolio optimization is used to solve the minimum time evacuation using rescue guides. With enough rescue guides the evacuation time in all scenarios can be optimized. If there is a restraint on the number of guides, there is a tradeoff between average- and worst case-performing evacuation plans.