Events

International airfreight forwarders are faced with the problem of consolidating shipments for effcient transportation by airline carriers. The use of standard unit loading devices (ULDs) is a solution adopted by the airfreight industry to speed up cargo loading, increase safety, and protect cargo. We study the airfreight consolidation problem from the forwarders perspective where a decision on the number of ULDs used and the assignment of shipments to ULDs is optimized. The cost of using a ULD consists of a fixed charge and depends on the weight of the cargo it contains.

It is generally well accepted that your position in the social network affects your ability to get information.  But how do the network positions of those with whom you interact, influence you?  This issue is explored using high dimensional network data. Drawing on theories of social influence and the generalized other, social network analytic and text analytic methods, and data science techniques for big data a series of complex socio-technical situation are assessed.

In this talk I will present an overview of my research on chronic care services.  I will then focus on the problem of care delivery for complex patients, with multiple comorbidities. In this project, we develop a Markov Decision Process framework to manage care for individual patients with multiple chronic conditions through a complex care hub. Complex care provision influences the evolution of Patient Activation Measure (PAM), an indicator for healthy behavior, which affects the evolution of health state of patients.

Supervisor

Jatin Nathwani

We will discuss a few examples from healthcare, where quantitative models embedded in decision-support tools can improve the quality of decision-making (for patients, physicians, or caregivers) and patient outcomes.

Dr. Prokopyev will be discussing ‘Sequential Interdiction with Incomplete Information and Learning’ on the June 19^th. Graduate students have an opportunity to meet with our distinguished speaker before the seminar.

We present a framework for a class of sequential decision-making problems in the context of max-min bi-level programming, where a leader and a follower repeatedly interact. At each period, the leader allocates resources to disrupt the performance of the follower (e.g., as in defender-attacker or interdiction problems), who in turn minimizes some cost function over a set of activities that depends on the leader’s decision.

The transportation of hazardous materials (hazmat) has drawn significant attention from various stakeholders due to the undesirable impacts on the environment and public health. Focusing on the connection between the traffic and the risk associated with the hazmat shipments, the present research aims to assist the regulator in designing a policy of dual tolls, imposed on both hazmat and non-hazmat shipments, to mitigate the hazmat risk in a road network.

Supervisor: Fatih Erenay

Automatic generation of text is an important topic in natural language processing with applications in tasks such as machine translation and text summarization. In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models - variational autoencoders (VAE) and variational encoder-decoders (VED).