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Robots have become increasingly advanced recently, capable of performing challenging tasks such as taking elevators and cooking shrimp. Moreover, their ability to accomplish long-horizon tasks given simple natural language instructions is also made possible by large language models. However, with this increased functionality comes the risk that intelligent robots might unintentionally or intentionally harm people based on instructions from an operator. On the other hand, significant efforts have been made to restrain large language models from generating harmful content. Can these efforts be applied to robotics to ensure safe interactions between robots and humans, even as robots become more capable? This project aims to answer this question.
- Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| The global electric vehicle (EV) fleet is projected to reach 145 million units by 2030, posing new threats to the reliability of the power system. However, EVs can also play a key role as a source of demand-side flexibility to support the system in managing uncertainty resulting from the integration of renewable energy resources. The onsite coupling of photovoltaics (PVs), battery energy storage systems (BESS) and EV fleets with vehicle-to-grid (V2G) technology has shown promising performance in terms of demand-side flexibility provision. - Engineering and Technology
- Master Thesis
| The Traverso lab at Brigham and Women's Hospital, Harvard Medical School currently has several openings students interested in interdisciplinary research in translational engineering. We are pushing the frontier of medical technologies by bridging the gap between engineering and clinical medicine. - Engineering and Technology
- Internship, Master Thesis
| Buildings are significant energy consumers, primarily due to the operation of heating, ventilation, and air conditioning (HVAC) systems. Effective control of such systems is crucial for enhancing overall energy efficiency. Typically, traditional rule-based controllers are used due to their affordability and interpretability. However, as complexity increases, these controllers suffer from non-optimal performance and limited scalability. Recent advancements in Deep Reinforcement Learning (DRL) provide a data-driven alternative, demonstrating promising control performance without the need for explicit system modeling. Despite these advantages, conventional DRL approaches often fail to account for specific operational constraints present in HVAC systems. One critical constraint is the requirement for smooth control actions with a limited number of on-off switches, as frequent switching can lead to faster deterioration of the controlled systems. Therefore, it is imperative to develop data-driven control strategies that not only optimize energy consumption but also adhere to these operational constraints. This study, part of the Euthermo Project, aims to develop safe reinforcement learning algorithms for building climate control. - Engineering and Technology
- Master Thesis
| Energy consumption in buildings is a critical concern, primarily driven by the operation of heating, ventilation, and air conditioning (HVAC) systems, lighting, and other appliances. Efficient control of these systems is paramount for achieving significant energy savings and reducing environmental impact. Traditional rulebased controllers, while cost-effective and easy to implement, often fail to provide optimal performance and lack scalability as system complexity grows. Recent advancements in Deep Reinforcement Learning (DRL) offer a powerful, data-driven alternative. DRL has shown promising results in optimizing control performance without the need for explicit system modeling. However, the complexity of managing multiple interdependent control variables within a building remains a challenge. For instance, the heating control of individual rooms can influence each other, and shading controls can affect both heating and cooling demands. - Engineering and Technology
- Master Thesis
| In recent years, advancements in reinforcement learning have achieved remarkable success in quadruped locomotion tasks. Despite their similar structural designs, quadruped robots often require uniquely tailored reward functions for effective motion pattern development, limiting the transferability of learned behaviors across different models. This project proposes to bridge this gap by developing a unified, continuous latent representation of quadruped motions applicable across various robotic platforms. By mapping these motions onto a shared latent space, the project aims to create a versatile foundation that can be adapted to downstream tasks for specific robot configurations.
- Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis
| The remarkable agility of animals, characterized by their rapid, fluid movements and precise interaction with their environment, serves as an inspiration for advancements in legged robotics. Recent progress in the field has underscored the potential of learning-based methods for robot control. These methods streamline the development process by optimizing control mechanisms directly from sensory inputs to actuator outputs, often employing deep reinforcement learning (RL) algorithms. By training in simulated environments, these algorithms can develop locomotion skills that are subsequently transferred to physical robots. Although this approach has led to significant achievements in achieving robust locomotion, mimicking the wide range of agile capabilities observed in animals remains a significant challenge. Traditionally, manually crafted controllers have succeeded in replicating complex behaviors, but their development is labor-intensive and demands a high level of expertise in each specific skill. Reinforcement learning offers a promising alternative by potentially reducing the manual labor involved in controller development. However, crafting learning objectives that lead to the desired behaviors in robots also requires considerable expertise, specific to each skill.
- Information, Computing and Communication Sciences
- Master Thesis
| Traditional 2D diagnostic techniques have limitations in accurately assessing histologic features and cancer staging, leading often to a wrong treatment choice. This underlines the need for enhanced diagnostic methods to improve the accuracy and reproducibility of pathology reports. Recent advancements in computational diagnostic systems and 3D imaging technologies offer significant improvements in this regard. For instance, 3D diagnostics enable comprehensive visualization of tumor architecture and spatial relationships within tissue samples, leading to more detailed and accurate assessments.
Our study focuses on colorectal cancer and metastatic lymph nodes from various cancer types. Traditional diagnostic methods often struggle with quantifying and detecting key histologic features, whereas 3D diagnostic technologies provide improved accuracy and consistency in pathology assessments. This enhances staging precision and aids in personalized treatment strategies. - Diagnostic Applications, Image Processing, Medicine-general, Oncology and Carcinogenesis, Pathology
- Master Thesis, Semester Project
| Inspired by how humans learn, this project aims to explore the possibility of learning flight patterns, obstacle avoidance, and navigation strategies by simply watching drone flight videos available on YouTube. - Computer Vision
- Master Thesis, Semester Project
| We are currently looking for Master students in the field of Bioinformatics or Computational Biology for the analysis of cell-free DNA sequencing data. We have several topics which students can apply for depending on their previous expertise and proposed duration of the project (3 months or longer). - Medical Biochemistry: Nucleic Acids, Sequencing and Genomics
- Internship, Lab Practice, Master Thesis, Semester Project
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