Teleoperation offers a promising solution for enabling hands-on learning in remote education, particularly in environments requiring interaction with real-world equipment. However, such remote experiences can be costly or non-intuitive. To address these challenges, we present TeleopLab, a mobile device teleoperation system that allows students to control a robotic arm and operate lab equipment. TeleopLab comprises a robotic arm, an adaptive gripper, cameras, lab equipment for a diverse range of applications, a user interface accessible through smartphones, and video call software. We conducted a user study, focusing on task performance, students' perspectives toward the system, usability, and workload assessment. Our results demonstrate a 46.1% reduction in task completion time as users gained familiarity with the system. Quantitative feedback highlighted improvements in students' perspectives after using the system, while NASA TLX and SUS assessments indicated a manageable workload of 38.2 and a positive usability of 73.8. TeleopLab successfully bridges the gap between physical labs and remote education, offering a scalable and effective platform for remote STEM learning.

Microfinance, despite its significant potential for poverty reduction, is facing sustainability hardships due to high default rates. Although many methods in regular finance can estimate credit scores and default probabilities, these methods are not directly applicable to microfinance due to the following unique characteristics: a) under-explored (developing) areas such as rural Africa do not have sufficient prior loan data for microfinance institutions (MFIs) to establish a credit scoring system; b) microfinance applicants may have difficulty providing sufficient information for MFIs to accurately predict default probabilities; and c) many MFIs use group liability (instead of collateral) to secure repayment. Here, we present a novel control-theoretic model of microfinance that accounts for these characteristics. We construct an algorithm to learn microfinance decision policies that achieve financial inclusion, fairness, social welfare, and sustainability. We characterize the convergence conditions to Pareto-optimum and the convergence speeds. We demonstrate, in numerous real and synthetic datasets, that the proposed method accounts for the complexities induced by group liability to produce robust decisions before sufficient loans are given to establish credit scoring systems and for applicants whose default probability cannot be accurately estimated due to missing information. To the best of our knowledge, this paper is the first to connect microfinance and control theory. We envision that the connection will enable safe learning and control techniques to help modernize microfinance and alleviate poverty.