This thesis investigates how temporal network structures can predict financial success in early-stage startups. Using investment data from Pitchbook, it constructs a dynamic graph of the North American Venture Capital (NAVC) ecosystem, capturing evolving relationships between investors and startups over time. From this network, node-level features such as temporal centrality and community embedding are computed to represent each startup’s structural identity. These features are used as inputs to train an Extreme Gradient Boosting (XG- Boost) supervised ML model, to predict a binary classification target of successful exits (IPO or acquisition) or failure within a fixed time window. Results show that models incorporating temporal network features consistently outperform baselines and results from similar problems, particularly on Precision@K metrics, which are practically relevant to VC decision-making. The findings demonstrate that interpretable, time-aware network metrics can meaningfully enhance startup evaluation frameworks. This work contributes to the intersection of finance, network science, and predictive modeling, offering new tools for data-driven early-stage investment.

NFL teams invest heavily in the scouting and evaluation of college players before the draft, however many high draft picks underperform while later round picks emerge as stars. This thesis investigates whether intangible traits, such as leadership, competitiveness, and work ethic can be quantified from scouting reports and used to better predict a player’s success in the NFL. Using a combination of zero-shot classification and sentiment analysis, trait-specific sentiment scores are computed across multiple positions. These scores are then incorporated alongside quantitative combine data and college career statistics for K-Means clustering to group players with similar profiles. For each position where the incorporation of intangible traits was more explanatory than using strictly quantitative statistics, six regression models were trained to predict a custom-defined career success metric based on positional performance and Approximate Value (AV). Cluster assignments were one-hot encoded to determine their predictive impact on career success. Clusters with high cluster coefficients and late average draft positions were identified as “undervalued,” while clusters with early average draft positions and lower coefficients were considered “overvalued.” Results indicated that qualitative clustering often yielded higher explanatory power than models based purely on quantitative features. In several cases, we observed higher cluster coefficients with a later average draft pick, suggesting that NFL teams may be systematically overlooking certain high-potential players. This thesis demonstrates the potential of utilizing Natural Language Processing and qualitative data in the evaluation of professional football scouting, and how it can prove more effective than the traditional quantitative approach.

Type I Diabetes is a chronic disease in which patients cannot make insulin or make very little insulin to regulate their blood glucose. It affects over 1.7 million adults in the United States. People with Type I Diabetes are reliant on taking insulin every day, and recently insulin pumps and specifically Automated Insulin Delivery (AID) systems have revolutionized diabetes care, making treatment easier and more effective. There are three components needed for an AID system: a Continuous Glucose Monitor which relates patient blood glucose, an Insulin Pump which infuses insulin into the body, and an algorithm which translates information from the first two components to an amount of insulin necessary to keep blood glucose in the target range. Our focus will be on the last component. First, this thesis will provide an overview of machine learning techniques for blood glucose prediction on the novel DiaTrend dataset (2023) which has not been extensively studied before (although research on machine learning models has been applied to previous datasets). Our work finds that adding complexity to our model only barely improves performance and does not justify longer run times and less interpretable results. Rather, we recommend a simple Autoregressive time series model which reaches similar impressive performance to the rest of our models while being simpler for healthcare providers to interpret. In the second part of the thesis, we propose two new AID algorithms which utilize our Autoregressive model: the Threshold Controller and IOB Controller. Rather than a PID or MPC approach, these algorithms rely on a set of simple heuristics similar to what an actual patient would use. We find that in a stressful scenario, these controllers are able to improve time in Target Range by up to 12% more than the leading Open Source OpenAPS oref0 algorithm, while providing safety by mitigating low blood glucose. This work lays the foundation for researchers and healthcare providers to implement new AID algorithms which utilize a combination of machine learning models and patient-based heuristics.

This thesis addresses the optimization of radiopharmaceutical transportation for distribution by developing a comprehensive variant of the Vehicle Routing Problem (VRP). Radiopharmaceuticals are highly time-sensitive due to their short half-lives, making timely delivery crucial for maintaining clinical efficacy in PET scan imaging. The proposed model incorporates several realistic extensions to the classical (capacitated) VRP model, and these extensions are heterogeneous fleets, time windows, pickup and delivery, and split deliveries. The combination of these features within a single, healthcare-specific VRP model tailored to radiopharmaceutical delivery represents a meaningful and novel advancement not covered by existing literature.

An exact mixed-integer programming model is implemented using Gurobi to explore the scalability limits of exact methods. A series of computational experiments on randomly generated networks of increasing size reveals that exact methods quickly become infeasible beyond 17–18 nodes due to exponential runtime growth. To address this limitation, an Adaptive Large Neighborhood Search (ALNS) heuristic is developed and tested. A real-world case study involving 44 medical imaging facilities in the Metro-Detroit area is then used to evaluate heuristic performance at scale. Results show that the ALNS consistently produces high-quality solutions in an efficient manner, achieving up to a 21.5% improvement over an initial feasible solution. The most effective operator combination emerged as random removal followed by savings insertion, with the "adaptive" portion of the algorithm quickly learning to prioritize these heuristics. These findings underscore the potential of algorithmic approaches in improving delivery reliability for time-sensitive healthcare supply chains.

Shari’ah-compliant investments grew 128% to reach $25.9 billion in 2022, highlighting rapid growth and increasing relevance of Islamic finance. Islamic finance prohibits interest (riba), excessive uncertainty (gharar), and speculation (maysir) while promoting fairness, transparency, and social responsibility. This research evaluates optimal portfolios constructed from 29 global Shari’ah screened indexes. Three optimization methods are applied: mean-variance optimization (MVO), multi-objective optimization (MOO), and a Shari’ah-based model proposed by Masri (2018). Performance is measured by return, volatility, Conditional Value-at-Risk (C-VaR), and Sharpe Ratio.

The empirical approach backtests across a 2-year span (2023–2024). The MVO portfolio outperforms all conventional benchmarks (Dow Jones, FTSE, and S&P) in absolute and risk-adjusted terms. In an extended 12-year backtest (2005–2014, 2023–2024), the MVO portfolio underperforms only the FTSE benchmark on a risk-adjusted basis due to higher volatility and Technology sector exposure. Notably, in the high-volatility years of 2008 and 2020, the MVO portfolio outperformed the benchmark average annual return by net margins of 8.1% and 9.4% respectively. This reinforces prior studies suggesting that Shari’ah screened portfolios may offer upside potential during market instability.

In the 2-year backtest, the MOO portfolio achieves the lowest volatility and downside risk with returns equal to the benchmark average. Its risk-adjusted performance makes it suitable for risk-averse investors. The Masri portfolio delivers higher returns than the benchmarks but with greater volatility, more extreme C-VaR, and lower Sharpe Ratio. This reflects a return-focused strategy grounded in Shari'ah principles. The optimization models show that Shari’ah-compliant portfolios maintain competitive performance at varying risk preferences.

With a broader investment universe and more quantitative portfolio construction methods than existing literature, this research demonstrates that Shari’ah-compliant portfolios exhibit competitive performance to conventional benchmarks. The results contribute to the growing study of Islamic finance and its integration into modern portfolio theory.