Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 77-84). / Conventional imaging for health applications captures photons from the objects that are directly in the field of view of the camera. In this thesis, we develop computational frameworks to exploit scattered photons to image regions that are not directly visible to the camera. First, we will explore a new framework to model volumetric scattering with time-of- flight imaging to recover objects in scattering media with less need for calibrations. This technology can be applied to see under the skin. Second, we will exploit fluorescent tags, and quantum dots to image tagged objects around the corner for endoscopy with traditional cameras. We introduce a novel parametric approach to NLOS imaging for localizing tags around corners from radiometric measurements. The goals of the thesis are to develop novel approaches to model scattered light-transport and to demonstrate recovery of hidden objects, though scattering or around corners. The proposed technology can extend the scope of medical imaging. / by Tomohiro Maeda. / S.M. / S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 171-186). / Individuals form network connections based on homophily; individuals' networks also shape their actions. Pervasive behavioral data provides opportunities for a richer view of the decisions on networks. Yet, the increasing volume, complex structures, and dynamics of behavioral data stretch the limit of conventional methods. I develop mathematical modeling (e.g., machine learning, game theory, and network science) and large-scale behavioral data to study collective behaviors over social networks. My dissertation will tackle this area in four directions, revolving around the intricate linkage between individuals' characteristics, actions, and their networks. First, I empirically investigate how social influence spreads over networks using two massive cell phone data, and theoretically model how do individuals aggregate information from local neighbors. Second, I study how to leverage influential nodes for selective network interventions (e.g., marketing and political campaigns), by proposing a centrality measure going beyond network structures. Third, I build a geometric deep learning model to infer individual preferences and make personalized recommendations to utilize noisy network information and nodal features effectively. Last, given that the network is essential, I develop a framework to infer the network connections based on observed actions, when networks are unavailable. My thesis provides building blocks for further network-based machine learning problems integrating nodal heterogeneity and network structures. Moreover, the findings on human behaviors and frameworks developed in my thesis shed light on marketing campaigns and population management. / by Yan Leng. / Ph. D. / Ph.D. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, September, 2020 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 89-102). / To build effective HCI interventions on cognitive processes, we must build off of updated and inclusive cognitive models. Recent research in psychology distinguishes levels of consciousness into a tripartite model - conscious, unconscious, and meta-conscious. HCI technologies largely focus on the conscious pathway for computer-to-human interaction, requiring explicit user attention and action. In contrast, the other two pathways provide opportunities to create new interfaces that can alter emotion, cognition, and behavior without demands on attentional resources. In this thesis, we present a framework for creating technological interfaces that engage different cognitive processes, such as emotions. These direct interfaces connect to cognitive processes that are in our perception but outside our conscious control. Our goal is to provide a finer categorization of cognitive processes that can help classify HCI research related to activating non-conscious cognitive pathways. We present the design of two wearable devices, MoveU and Frisson that highlight the modulation of cognitive processes through body-based input. The contribution of this thesis is twofold: first, the tools developed in this work provide a platform for researchers to experiment by engineering cognitive processes. This could allow researchers to evaluate the causation rather than correlations in the manifestations of cognitive processes and ask new questions about links between our physiology and psychology. Second, the framework provides researchers and designers a new design space and highlights that awareness of consciousness levels can be a valuable design element and can help to expand the range of computer-to-human interface devices we build. / by Abhinandan Jain. / S.M. / S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, September, 2020 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 123-132). / The transport of particles of organic carbon from the ocean's surface to its bottom plays a key role in the global carbon cycle and carbon sequestration. Quantifying the rate of this Biological Carbon Pump - the size and velocity distribution of falling particles below the mixing layer, for example - is thus of considerable importance. The complexity of this Pump, however, together with systematic biases in available measurement methodologies and vast spatial and temporal undersampling, makes this quantification difficult. In this thesis I set out to design and build a low-cost underwater stereo-imaging system to remotely measure the flux of sinking particles in the mid-ocean. By recording time-lapsed images of marine snow falling through the imaging volume over day-to- week timescales, we can estimate both the particle size distributions and, via 3D particle tracking velocimetry, their velocity distributions too. This allows us to directly estimate the net flux. Making the system low-cost and compact enables largescale observations capable of resolving relevant length and time-scales over which this flux likely varies in the ocean. The hardware design is thus primarily constrained by the target depth, expected particle size distribution, expected sinking rates, deployment duration, and cost. The resulting prototype was then tested in the lab and, computationally, against simulated data in preparation for eventual deployment the Minion platform, a Lagrangian float designed to quantitatively explore the Biological Carbon Pump. An evaluation of the system's efficacy in estimating particle concentration and sinking rate, and ultimately estimate the particle flux, indicates a good match to our target specifications. / by Junsu Jang. / S.M. / S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, May, 2020 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 60-68). / On-orbit assembly missions typically involve humans-in-the-loop and use large custom-built robotic arms designed to service existing modules. The concept of on-orbit robotic assembly of modularized CubeSat components supports use cases such as rapidly placing failed nodes within a constellation of satellites and monitoring damaged assets in Low Earth Orbit. Despite the recent proliferation of small satellites, there is a lack of planned demonstrations of spacecraft manufactured through the on-orbit assembly as well as the servicing of small satellites in space. Key gaps limiting in-space assembly of small satellites are (1) the lack of standardization of electromechanical CubeSat components for compatibility with commercial robotic assembly hardware, and (2) testing and modifying commercial robotic assembly hardware suitable for small satellite assembly for space operation. Working towards on-orbit robotic assembly, we report on progress addressing both gaps. / Toward gap (1), the lack of standardization of CubeSat components for compatibility with commercial robotic assembly hardware, we have developed a ground-based robotic assembly of a 1U CubeSat using modular components and Commercial-Off-The-Shelf (COTS) robot arms without humans-in-the-loop. Two 16 in x 7 in x 7 in dexterous robot arms, weighing 2 kg each, are shown to work together to grasp and assemble CubeSat components into a 1U CubeSat. We assess performance for a subset of five commercial robotic arm sensors and find the force-torque (FT) sensor as the most efficient sensor for use at the end-effector and brushless motors as the best sensor for use at other joints. We report on the feasibility of sensing and grasping CubeSat components robotically, while using Inverse Kinematics to target, position and maneuver the robot arms. / Addressing gap (2) in this work, solutions for adapting power-efficient COTS robot arms to assemble highly-capable radiation-tolerant CubeSats are examined. We also analyze the systems engineering process for in-space CubeSat robotic assembly systems. Lessons learned on thermal and power considerations for overheated motors and positioning errors were also encountered and resolved. We find that COTS robot arms with sustained throughput and processing efficiency have the potential to be cost-effective for future space missions. / by Ezinne Uzo-Okoro. / S.M. / S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February, 2021 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 218-240). / Synergetic tensions have evolved the dichotomy between the physical and digital design domains into a symbiotic unity. New capabilities in digital fabrication give rise to sophisticated tools of computational design, while new affordances in computational design inspire innovation in digital fabrication. The role of design in this process is that of synthesis through mediation. As designers, we mediate between different principles and fields, and their synergies and conflicts generate new elements of design. The challenge to mediate in a universal language across domains becomes critical as a third domain encompassing biological entities grows more amenable to design. Biological systems offer reproduction, self-organization and growth -- among other features and benefits -- / which in turn enable previously unattainable properties to design systems. At the same time, their own modes of intelligence, expression, and agency demand a promising shift in design thinking. This thesis hypothesizes that the relations across design domains can be established through translational design computation, which is a framework that uses computational design as a language to mediate between physical, digital, and biological entities. We build this framework in two parts -- / Systems and Mediations. The first part, Systems, explores whether computational design can serve as a mediating language between the three entities. The second part, Mediations, examines how these mediations can occur. In Systems, we show that computational design can mediate between living and nonliving matter along the spectrum of biomimetic, biointegrated, and biosynthetic systems. As part of this, we demonstrate three systems of computational mediation: (i) programmable matter applies computational design to physical systems to enable biologically inspired design strategies, (ii) programmable templating applies computational design to the intersection of physical and biological systems to facilitate synergistic relationships, and (iii) programmable growth applies computational design to biological systems to give rise to material architectures. / In Mediations, we present dynamic, synergetic, and emergent strategies for how computational mediations can occur within cocreation systems. The living and nonliving parts of any cocreation system may interact to form synergies. Combined, these synergies produce complexes that give rise to new macro-level organizations -- products of the synergies of the parts and not simply of the parts themselves. Thus, the mediation between physical, digital, and biological entities needs to address the design of dynamic relations guiding synergetic behaviors, the design of the synergetic behaviors themselves or ultimately, the design of emergent self-expression of the system. Throughout this thesis, the framework is developed theoretically and applied in practice. It is documented in publications such as Making Data Matter and Hybrid Living Materials and projects such as Wanderers, Living Mushtari, the Vespers Series, Rottlace, Lazarus, Totems, Fiberbots, and Silk Pavilion II. / by Christoph Bader. / Ph. D. / Ph.D. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February, 2021 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 279-290). / Children learn best when knowledgeable adults support their learning process. Yet many learning technologies have not yet incorporated this vital social dimension. In response, we develop a technology-assisted coaching system, where a new adult collaborator--a coach--uses digital tools to support children and their families as they use children's literacy apps. This coaching system blends in-person and digital coaching in order to preserve the relational elements vital to coaching, while harnessing the power of digital technology to make information easily accessible to coaches, children, and families at their convenience. In our system, as children play with literacy apps, every tap and click of their play is streamed to their coach through our digital coaching platform. Using custom-built digital tools, coaches engage in four core coaching practices. / They analyze children's in-app activity, scaffold their learning, share progress with caregivers, and invite caregivers to engage in literacy learning experiences with their children. To develop this system, we iteratively designed, built, and evaluated it with approximately a hundred children and their families. We conducted in-depth study of two versions of the system's design through a randomized control trial (RCT) and a formative pilot study. From the RCT, we found that the coaching system increased caregivers' awareness of their children's in-app play and children's playtime with the literacy app. We also found that for families with lower formal education levels, the effect of the coaching system was greater across almost all outcome variables investigated. In both studies, we found coaches were able to use our digital coaching tools to effectively engage in the four core coaching practices, and that these tools helped increase coaches' efficiency. / Based on our findings, we discuss changes to the system's design to improve and scale this approach and provide design considerations for building digital coaching systems. Through the creation and in-depth study of a novel sociotechnical system for coaching children's literacy learning, this work contributes to the field of learning technology. We hope this work serves as a helpful guide to designers, developers, and policymakers as they create and scale-up these types of digital networks for children's learning. / by Juliana Toni Nazare. / Ph. D. / Ph.D. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February, 2021 / Cataloged from the official PDF version of thesis. / Includes bibliographical references (pages 57-58). / Ethical trade-off surveys have played a key role in building a data-driven understanding of human moral psychology. They have been conducted all over the world for decades, eliciting assessment of ethical dilemma outcomes from populations as diverse as those of rural, tribal settlements, and industrialized, information-age, cosmopolitan cities. While much data has been gathered through these surveys, attempts to compare what people across cultures consider ethically justifiable have been hindered by the fact that the surveys used have been reformulated for different cultures in the scenarios they depict, and in their framing. The objective of this thesis project is to build a survey tool with global reach and internationalized surveys, in order to collect survey data from around the world using consistent scenarios and framing. Building on the precedent and success of the Moral Machine tool for surveying people around the world regarding ethical dilemmas involving autonomous vehicles, I built and deployed a tool for conducting surveys with scenarios of the classic action/omission trolley problem, to collect ethical dilemma survey data internationally, in ten languages, for three variants of the trolley problem - one for remote action/omission with no double effect consideration, one for double effect consideration with direct action/omission, and one for double effect consideration with remote action/omission. Analyzing data from this experiment, I conclude that differences in preferences across the variants are confirmed across populations, and that they are universal across populations in order of preference. / by Sohan Savio Dsouza. / S.M. / S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February, 2021 / Cataloged from the official PDF version of thesis. / Includes bibliographical references (pages 49-59). / Negative emissions technologies that can remove carbon dioxide from the atmosphere are a critical tool to limit global temperature rise and ocean acidification. Bioengineering capabilities have not been sufficiently assessed or utilized for the development of negative emissions technologies. Bioengineering holds the potential to improve the efficiency of some existing technologies and to create new methods of carbon removal. I review existing technologies to assess how bioengineering could improve them, focusing on technologies that could achieve at least 1 Gt of CO₂ removal per year. I also investigate and describe potential new methods of carbon removal that leverage bioengineering. Key questions for additional research are identified, as are key engineering targets for the development of improved negative emissions technologies. This evaluation of potential high-impact R&D work is intended to provide an initial roadmap for the development of bioengineered negative emissions technologies that are scalable, sustainable, and can remove gigatons of CO₂ from the atmosphere. / by Sarah Mary Haiken Sclarsic. / S.M. / S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, September, 2019 / Cataloged from the official PDF of thesis. "The Table of Contents does not accurately represent the page numbering"--Disclaimer page. / Includes bibliographical references (pages 117-126). / How can we build machines that collaborate and learn more seamlessly with humans, and with each other? How do we create fairer societies? How do we minimize the impact of information manipulation campaigns, and fight back? How do we build machine learning algorithms that are more sample efficient when learning from each other's sparse data, and under time constraints? At the root of these questions is a simple one: how do agents, human or machines, learn from each other, and can we improve it and apply it to new domains? The cognitive and social sciences have provided innumerable insights into how people learn from data using both passive observation and experimental intervention. Similarly, the statistics and machine learning communities have formalized learning as a rigorous and testable computational process. / There is a growing movement to apply insights from the cognitive and social sciences to improving machine learning, as well as opportunities to use machine learning as a sandbox to test, simulate and expand ideas from the cognitive and social sciences. A less researched and fertile part of this intersection is the modeling of social learning: past work has been more focused on how agents can learn from the 'environment', and there is less work that borrows from both communities to look into how agents learn from each other. This thesis presents novel contributions into the nature and usefulness of social learning as an inductive bias for reinforced learning. / I start by presenting the results from two large-scale online human experiments: first, I observe Dunbar cognitive limits that shape and limit social learning in two different social trading platforms, with the additional contribution that synthetic financial bots that transcend human limitations can obtain higher profits even when using naive trading strategies. Second, I devise a novel online experiment to observe how people, at the individual level, update their belief of future financial asset prices (e.g. S&P 500 and Oil prices) from social information. I model such social learning using Bayesian models of cognition, and observe that people make strong distributional assumptions on the social data they observe (e.g. assuming that the likelihood data is unimodal). / I were fortunate to collect one round of predictions during the Brexit market instability, and find that social learning leads to higher performance than when learning from the underlying price history (the environment) during such volatile times. Having observed the cognitive limits and biases people exhibit when learning from other agents, I present an motivational example of the strength of inductive biases in reinforcement learning: I implement a learning model with a relational inductive bias that pre-processes the environment state into a set of relationships between entities in the world. I observe strong improvements in performance and sample efficiency, and even observe the learned relationships to be strongly interpretable. / Finally, given that most modern deep reinforcement learning algorithms are distributed (in that they have separate learning agents), I investigate the hypothesis that viewing deep reinforcement learning as a social learning distributed search problem could lead to strong improvements. I do so by creating a fully decentralized, sparsely-communicating and scalable learning algorithm, and observe strong learning improvements with lower communication bandwidth usage (between learning agents) when using communication topologies that naturally evolved due to social learning in humans. Additionally, I provide a theoretical upper bound (that agrees with our empirical results) regarding which communication topologies lead to the largest learning performance improvement. / Given a future increasingly filled with decentralized autonomous machine learning systems that interact with humans, there is an increasing need to understand social learning to build resilient, scalable and effective learning systems, and this thesis provides insights into how to build such systems. / by Dhaval D.K. Adjodah. / Ph. D. / Ph.D. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences