In this work, the problem of multi-object tracking (MOT) is studied, particularly the challenges that arise from object occlusions. A solution based on a principled approximate dynamic programming approach called ADPTrack is presented. ADPTrack relies on existing MOT solutions and directly improves them. When matching tracks to objects at a particular frame, the proposed approach simulates executions of these existing solutions into future frames to obtain approximate track extensions, from which a comparison of past and future appearance feature information is leveraged to improve overall robustness to occlusion-based error. The proposed solution when applied to the renowned MOT17 dataset empirically demonstrates a 0.7% improvement in the association accuracy (IDF1 metric) over a state-of-the-art baseline that it builds upon while obtaining minor improvements with respect to all other metrics. Moreover, it is shown that this improvement is even more pronounced in scenarios where the camera maintains a fixed position. This implies that the proposed method is effective in addressing MOT issues pertaining to object occlusions.

Efficiently solving global optimization problems remains a pervasive challenge across diverse domains, characterized by complex, high-dimensional search spaces with non-convexity and noise. Most of the approaches in the Bayesian optimization literature have highlighted the computational complexity involved when scaling to high dimensions. Non myopic approximations over a finite horizon has been adopted in recent years by modeling the problem as a partially observable Markov Decision Process (MDP). Another promising direction is the partitioning of the input domain into sub regions facilitating local modeling of the input space. This localized approach helps prioritize regions of interest, which is particularly crucial in high dimensions. However, very few literature exist which leverage agent based modeling of the problem domain along with the aforementioned methodologies. This work explores the synergistic integration of Bayesian Optimization and Reinforcement Learning by proposing a Multi Agent Rollout formulation of the global optimization problem. Multi Agent Bayesian Optimization (MABO) partitions the input domain among a finite set of agents enabling distributed modeling of the input space. In addition to selecting candidate samples from their respective sub regions, these agents also influence each other in partitioning the sub regions. Consequently, a portion of the function is optimized by these agents which prioritize candidate samples that don't undermine exploration in favor of a single step greedy exploitation. This work highlights the efficacy of the algorithm on a range of complex synthetic test functions.

This thesis is developed in the context of biomanufacturing of modern products that have the following properties: require short design to manufacturing time, they have high variability due to a high desired level of patient personalization, and, as a result, may be manufactured in low volumes. This area at the intersection of therapeutics and biomanufacturing has become increasingly important: (i) a huge push toward the design of new RNA nanoparticles has revolutionized the science of vaccines due to the COVID-19 pandemic; (ii) while the technology to produce personalized cancer medications is available, efficient design and operation of manufacturing systems is not yet agreed upon. In this work, the focus is on operations research methodologies that can support faster design of novel products, specifically RNA; and methods for the enabling of personalization in biomanufacturing, and will specifically look at the problem of cancer therapy manufacturing. Across both areas, methods are presented attempting to embed pre-existing knowledge (e.g., constraints characterizing good molecules, comparison between structures) as well as learn problem structure (e.g., the landscape of the rewards function while synthesizing the control for a single use bioreactor). This thesis produced three key outcomes: (i) ExpertRNA for the prediction of the structure of an RNA molecule given a sequence. RNA structure is fundamental in determining its function. Therefore, having efficient tools for such prediction can make all the difference for a scientist trying to understand optimal molecule configuration. For the first time, the algorithm allows expert evaluation in the loop to judge the partial predictions that the tool produces; (ii) BioMAN, a discrete event simulation tool for the study of single-use biomanufacturing of personalized cancer therapies. The discrete event simulation engine was designed tailored to handle the efficient scheduling of many parallel events which is cause by the presence of single use resources. This is the first simulator of this type for individual therapies; (iii) Part-MCTS, a novel sequential decision-making algorithm to support the control of single use systems. This tool integrates for the first-time simulation, monte-carlo tree search and optimal computing budget allocation for managing the computational effort.

Autonomous systems should satisfy a set of requirements that guarantee their safety, efficiency, and reliability when working under uncertain circumstances. These requirements can have financial, or legal implications or they can describe what is assigned to autonomous systems.As a result, the system controller needs to be designed in order to comply with these - potentially complicated - requirements, and the closed-loop system needs to be tested and verified against these requirements. However, when the complexity of the system and its requirements increases, designing a requirement-based controller for the system and analyzing the closed-loop system against the requirement becomes very challenging. In this case, existing design and test methodologies based on trial-and-error would fail, and hence disciplined scientific approaches should be considered. To address some of these challenges, in this dissertation, I present different methods that facilitate efficient testing, and control design based on requirements: 1. Gradient-based methods for improved optimization-based testing, 2. Requirement-based learning for the design of neural-network controllers, 3. Methods based on barrier functions for designing control inputs that ensure the satisfaction of safety constraints.