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開催報告：専門講座「アフリカとラテンアメリカの比較に挑戦してみた」
No full textアジア経済研究所の研究部門には部に相当する3つの研究センターがおかれ、各研究センターには課に相当する複数の研究グループがおかれている。2025年4月に行われた組織改編では、研究センター間での業務負担の均等化と研究センター内での連携の強化を趣旨として、研究グループの再編が行われた。これにより、地域研究センターに従来おかれていたアフリカ研究グループとラテンアメリカ研究グループが統合され、新たに「アフリカ・ラテンアメリカ研究グループ」が設けられた。アフリカ研究者とラテンアメリカ研究者をひとつの研究グループにおく態勢は、アジア経済研究所の長い歴史のなかでもはじめてのことである。このような新しい機会を捉え、アフリカとラテンアメリカをともに視野に入れた研究の可能性を模索してはどうかという機運が生まれ、2025年9月に一般向けの公開の専門講座に結実した。本フォーラムでは、発案ととりまとめにあたった網中昭世と上谷直克が同講座の模様を紹介する。PJa/3/Af4articl
Precision Measurements in Quantum Chromodynamics
No full textIn this thesis, I present a series of theoretical developments and precision calculations in Quantum Chromodynamics (QCD) and collider physics, with a focus on collider observables sensitive to jet substructure. In particular, I propose a new family of observables called multi-point energy correlators and perform precision calculations of them using both fixed-order and effective field theory techniques. On the fixed-order side, I finish the analytic calculations of three-point correlator in super Yang-Mills theory, QCD and Higgs boson decays and extract the asymptotic behaviors in various kinematic limits. Along the way, I also develop analytic techniques to compute physical observables in quantum field theory.
The large logarithms arising from infrared divergences in the collinear limit and coplanar limits are resummed to all orders in perturbation theory through soft-collinear effective theory, improving its convergence and allowing for phenomenological applications. The collinear limit result for proton-proton collisions has been used to extract the value of strong coupling constant at the Large Hadron Collider.
In addition, I also study another collider observable called heavy jet mass at electron-positron collider and apply the effective field theory technique to resum the remaining logarithms in the trijet region, which are referred to as Sudakov Shoulders. Integrating with the existing resummation in the dijet limit, and a renormalon-based model for non-perturbative power corrections, I also perform a global fit with experimental data to extract the strong coupling constant.
To further improve the precision measurements at colliders, I derive a new factorization theorem for small-radius jet production, correcting the missing logarithms in the literature. To validate the factorization, I also develop a Monte Carlo program for calculating jet functions with a jet algorithm at two-loops.
Using this formula, I push the precision of inclusive jet production spectrum at hadron colliders further and significantly improve its agreement with experimental data.Physic
Embryonic plasticity through evolution and development in the acoel Hofstenia miamia
No full textThe single-celled zygote ultimately develops into an adult animal with many diverse terminal cell types and tissues. This totipotency is lost during subsequent cleavages as specification begins in the embryo. In Chapter 1, I reviewed and synthesized literature on the divergent modes of early cleavage across animals. Through this study, it was revealed that many phyla, including Xenacoelomorpha, are understudied in regard to early embryonic plasticity. To investigate the potentials of embryonic cells in xenacoelomorphs, I studied the acoel worm Hofstenia miamia, which enables investigations of embryos in the lab. Though H. miamia adults are known for their tremendous regenerative capacity, no prior study has examined the embryo’s capacity to compensate for missing cells. In Chapter 2, I conducted an exhaustive examination of the post-zygotic totipotency and embryonic plasticity of the early embryo, from the 2-cell stage to the 8-cell stage. I isolated blastomeres at the 2- and 4-cell stage and found that both 2-cell blastomeres and the 4-cell stage macromeres were competent to develop into complete adult worms, exhibiting post-zygotic totipotency. Examination of the progeny of the micromeres and macromeres at the 4- and 8-cell stage showed that micromeres are specified upon birth and cannot develop cell types beyond what they endogenously produce in the wild type embryo, whereas isolated macromeres can develop cell types beyond their endogenous fates. A dissociation and reconstitution assay found that all blastomeres at the 8-cell stage can expand their developmental potential and contribute to exogenous cell types in response to the perturbation, despite earlier specification. The H. miamia embryo exhibits extraordinary plasticity despite having an invariant cleavage program with early, fate-specifying cleavages. In Chapter 3, I attempted to uncover differentially expressed transcripts between the micromeres and macromeres of the early-stage H. miamia embryos. I assembled a new version of the H. miamia transcriptome, enriched for embryonic stages, adding an additional 6,839 transcripts to the previous transcriptome assembly. After remapping newly and previously collected data to the updated transcriptome, I was able to identify cell type marker genes that were expressed earlier during embryogenesis. Altogether, my thesis research provides a summation of previous experimental embryology experiments across animals, a characterization of the plasticity in the early embryo of the acoel H. miamia and identified putative mRNA maternal determinants in the early embryo.Biology, Organismic and Evolutionar
Leveraging Millimeter-Scale Multi-Material Manufacturing for Biomedical Devices
No full textThis thesis explores the use of precision laminate manufacturing to develop millimeter-scale biomedical devices that enable new capabilities in soft-tissue attachment, biological fluid sampling, and integrated sensing. Millimeter-scale devices are transforming minimally invasive medicine by allowing tools to be ingested, injected, or delivered through natural orifices, accessing previously unreachable areas of the body with minimal trauma. We present three proof-of-concept devices that demonstrate how laminate-based design unlocks new clinical functions. First, we draw inspiration from parasitic organisms such as Taenia sp. to replicate their mechanical anchoring strategies with rotating hook-like elements that latch into tissue with minimal damage. Second, we develop a modular gastrointestinal fluid-sampling capsule designed to collect microbiome-rich samples from hard-to-reach regions of the GI tract. The capsule architecture supports interchangeable modules for actuation, one-way fluid control, sample storage, and triggering. Third, we propose a set of customized sensors tailored to patient-specific anatomy and constraints. These devices are fabricated using multi-material micromanufacturing techniques that combine laser machining, lamination, and origami-inspired folding to integrate complex mechanisms at the millimeter scale. The modular design principles established here provide a foundation for future adaptive, patient-specific, and scalable biomedical tools.Engineering and Applied Sciences - Engineering Science
Disrupting Bipartite Trading Networks: Matching for Revenue Maximization
No full textOnline platforms and marketplaces have revolutionized everyday commerce, breaking down many of the physical and geographic barriers that previously prevented trade. Today, these online marketplaces facilitate trillions of dollars of trade and have disrupted a wide variety of markets like retail commerce (Amazon, eBay), transportation (Uber, Lyft), food delivery (Instacart, UberEats), and many others. However, the growing power of these platforms also comes at a cost, with regulatory bodies taking an increasing interest in the competitive practices of these platforms in the past years.
Motivated by the interest in these competitive practices, we study the incentives facing platforms when choosing how to match their users together – e.g., buyers and sellers on Amazon. We model the role of an online platform disrupting a network of n unit-demand buyers and m unit-supply sellers. Each seller can transact with a subset of the buyers whom she already knows outside of the platform, as well as with any additional buyers to whom she is introduced or matched by the platform. Given these constraints on trade, we model prices and transactions as being induced by a competitive equilibrium. The platform's revenue is proportional to the total price of all trades between platform-introduced buyers and sellers. We consider a revenue-maximizing platform and study the effect of the platform on social welfare (the sum of transacting buyers' values for the items they receive).
We show that even when the platform optimizes for revenue, the social welfare is at least an O(log(min{n,m}))-approximation to the ideal welfare, giving non-trivial guarantees for social welfare even in the presence of platform behavior. When the platform can significantly increase social welfare, i.e. when the existing market is inefficient, we give a polynomial-time algorithm that guarantees a logarithmic approximation of the optimal welfare as revenue, also attaining a logarithmic fraction of optimal social welfare in the process. In general, we show that the platform's revenue-maximization problem is computationally intractable, but we provide structural results for revenue-optimal matchings and isolate special cases in which the platform can efficiently compute them.
Finally, we prove significantly stronger bounds for revenue and social welfare in homogeneous-goods markets, where each seller is selling an identical item. We prove that revenue maximization aligns perfectly with welfare maximization in these markets; any revenue-optimal platform matching also maximizes overall social welfare. In inefficient homogeneous-goods markets, we give a constant-factor poly-time approximation algorithm for revenue that also maximizes social welfare.Applied Mathematic
Learning to Lead Many: Online Algorithms for Bayesian Stackelberg Games
No full textA \textit{Stackelberg Game} models a strategic interaction in which a leader takes an action that influences the behavior of the followers. Stackelberg games have been applied in various real-world domains, including security, transportation, computer networks, and supply chains. In many practical settings, the leader often faces uncertainty about key environmental parameters, requiring them to learn and adapt over time. We consider a \textit{Bayesian} Stackelberg Game, where followers have private types unknown to the leader. These types represent the followers' private information. The followers each have one of types, which may be either independent or correlated with other followers. The leader's objective is to learn about the environment (i.e. the distribution over follower types) in order to compute an optimal strategy. We formulate this as an \textit{online learning} problem, where the leader repeatedly plays the Stackelberg Game over rounds, with follower types drawn from an unknown but fixed distribution at each interaction. The leader's goal in this setting is to minimize their \textit{regret}, defined as the cumulative difference between the utility of the optimal fixed strategy and that of the strategy that they choose at each round. This thesis theoretically and empirically analyzes the regret of various learning algorithms for the leader. Under \textit{type feedback}, where the leader observes the followers' types after each round, we design learning algorithms that achieve an \textit{upper bound} of on expected regret. Under \textit{action} feedback, where the leader only observes the followers' actions, we design algorithms with at most regret. Furthermore, we establish a \textit{lower bound} of on the regret of this learning problem.Computer Scienc
Building Resilience Out of Air: Reimagining a Typology of Thin Shell Air-formed Shelters
No full textIn recent decades, a novel form of disaster architecture has emerged on grade-school campuses across the U.S. Midwest and Gulf Coast as thin-shell concrete monolithic domes. Constructed using pneumatic formwork, these FEMA-rated domes possess exceptional structural capacity to resist catastrophic loads, serving an everyday function for schools, most often as gymnasiums, while doubling as public shelters during high-wind events. This typology demonstrates a form of resilient architecture that is anticipatory rather than reactionary, permanent rather than temporary, and multipurpose rather than single purpose. However, structural optimization has overshadowed all other possibilities, producing spaces that privilege performance at the expense of experience.
Historically, pneumatic and surface structures pushed the limits of architectural imagination, yet these structures have fallen into banal repetition. Heavy, bunker-like, and spatially austere, contemporary disaster shelters are more often utilitarian than visionary. While windowless hollow forms are an effective approach as a brief reactionary response, anticipatory architecture has an untapped potential to create shelters that provide dignity and comfort to a community in and beyond crisis. This thesis explores the possibility of building resilience out of air by reimagining FEMA-rated dome shelters through the experimental spirit of Frei Otto, Dante Bini, and Heinz Isler. Through a physical form-finding process that employs strategically restrained pneumatic forms to create solid casts, it investigates the unrealized formal and spatial potential of pneumatic formwork construction. Maintaining the community-scale and construction logic of existing FEMA domes, this research challenges the typology’s structural and programmatic conventions, proposing imaginative shelters that unite daily use and disaster preparedness with the aim of fostering resilient communities.Department of Architectur
War in Wôbanak: Environmental Histories of the French and Indian Wars, 1675-1763
No full textIn “War in Wôbanak: Environmental Histories of the French and Indian Wars, 1675-1763,” I argue that a century of conflict fought in northeastern North America can be explained by understanding different perceptions and relationships brought to bear on the natural world by members of the Wabanaki Confederacy, officials and soldiers of the British Empire, and English (descended) settler colonists. In Wôbanak, the Dawnland, the first place the sun rises each day in North America, stretching across what most maps now call Maine, Vermont, New Hampshire, Quebec, and the Canadian Maritimes, the people of the Wabanaki Confederacy, the Abenaki, Penobscot, Passamaquoddy, Maliseet, and Mi’kmaq made their home. And for the better part of one hundred years, they defended that homeland in the face of colonial and imperial expansion. While colonists and imperial officials insisted that the natural world could be commodified dominated, and extracted, Wabanaki people saw a space teeming with life and with relationships. By viewing the roles trees, non-human animals, agriculture and placemaking, and even pathogens played in these conflicts—and how differing ecologies shaped and were in turn shaped by them—these conflicts appear as environmental events. With the ascendance of the British Empire and the end of this story, their victory is a pyrrhic one as they are subsumed by settler colonists whose own environmental logic set the stage for contemporary environmental disaster.Histor
Essays on Data Science: Computational Measurement for Learning and Teaching
No full textTo study teaching and learning at a large scale, we must introduce new methods for the analysis of rich, unstructured data -- such as audio, video, and transcribed text -- from classrooms. In this dissertation, I develop and apply computational and statistical methods to measure teaching and learning processes captured in two unstructured sources: student writing and transcripts of teacher speech from classroom lessons.
My first essay introduces Coupled Likelihood Estimation (CLE), a method that improves the precision of parameter estimates in models of unstructured data features while requiring fewer expert-labeled observations. It combines information from limited samples of expert-labeled data with larger samples of data with machine-predicted labels. CLE leverages the geometric structure of the joint likelihood from both identifying (labeled) and non-identifying (unlabeled) data, constraining parameter estimates to, approximately, a surface defined by the unlabeled data's likelihood. Simulations demonstrate that CLE is unbiased, reduces root mean squared error, and yields narrower confidence intervals compared to existing methods, in some cases effectively achieving average efficiency gains equivalent to doubling the expert-labeled sample size. An application estimating the effect of an educational intervention on student writing quality illustrates CLE’s practical utility, producing estimates closer to an oracle benchmark using only 18\% of the expert-labeled data. By amplifying the value of limited labeled data, CLE lowers barriers to high-quality inference in resource-constrained domains such as healthcare, education, and policy evaluation. The method’s broad applicability, theoretical guarantees, and computational approach offer a pathway to cost-effective, reliable analyses in settings where researchers face high labeling costs.
My second essay leverages natural language processing techniques to study the use of mathematical vocabulary in elementary math classrooms. My collaborators and I develop a rules-based computational measure of mathematical vocabulary use. We find that teachers differ substantially in the amount of mathematical vocabulary they model for their students. Students of teachers in the 75th percentile were exposed to 28 more mathematical terms per lesson (4,480 per year) than students of a teacher in the 25th percentile. Observed characteristics explain very little of this variation in teachers' mathematical vocabulary use. Finally, students randomly assigned to teachers’ who used more mathematical vocabulary in previous years scored higher on standardized tests of mathematics. This implies that teachers who expose their students to more mathematical vocabulary are more effective teachers of mathematics. Across value-added studies, a teacher one standard deviation above the mean in effectiveness raises math scores by between .10 and .15 \citep{BacherHicks2023}; our estimate of the effect of being assigned to a teacher who uses one standard deviation more mathematical language accounts for roughly half of this variation, indicating that our measure is a powerful predictor of teacher effectiveness.
In my third essay, I develop Contextual Value Separation (CVS), a general method for identifying words used differently between pre-specified subsets of documents in large text corpora. CVS achieves this by combining contextual embeddings with machine learning classifiers, permutation testing, and statistical adjustments for multiple comparisons. Whereas current methods identify words that predict membership within a given class of documents, CVS reveals cases where separate classes of the documents use the same word in differing ways. For example, experienced and novice math teachers may use a mathematical vocabulary term with similar frequency but in markedly different ways or contexts. This approach can search over a specified set of target words or over the entire vocabulary of the corpus. For each target word, CVS infers how consistently its contextual embeddings differ by subset. Because vocabularies are large, the method includes multiple testing correction to control the false-discovery rate, typically yielding a small set of words whose usage varies between the document classes. After identifying the words whose usage most consistently differs, example usages from each subset are extracted for qualitative examination. CVS easily extends to other forms of unstructured data represented by embeddings, such as video and audio. The method can be used as an exploratory tool for hypothesis generation, to test a priori hypotheses, or to detect treatment effects on textual outcomes in experimental settings. To demonstrate the method, I analyze a set of transcripts from upper elementary mathematics lessons and identify two ways that teachers with larger impacts on math scores use mathematical vocabulary differently: more use of the mathematical meanings of polysemous terms and more requests that students engage with questions related to the terms. The method can be easily extended to other forms of unstructured data can be encoded into vectors, e.g., audio and video.
As a collection, these three essays reveal the promise of computational methods for enabling the analysis of text data (and other rich, unstructured data sources). They contribute several novel findings in the field of education regarding mathematical vocabulary and effective teaching. From a statistical point of view, CLE introduces a new way to leverage large amounts of machine labeled data, which, in addition to its value for educational research, can lower the cost of research in several domains, such as phenotyping electronic health records.Educatio
Mapping determinants of protein specificity with high-throughput mutagenesis and probabilistic modeling
No full textProteins evolve by iteratively varying their sequences to traverse fitness landscapes for different functions, driving the emergence of novelty at larger scales. Understanding the design principles of proteins requires a quantitative understanding of the map between a given protein sequence and an array of different fitness landscapes—for example, for different substrates or ligands. Scientists have studied this question using patterns found in natural sequences and by collecting their own maps of how variation in protein sequence affects different cellular functions. In this thesis, I will begin by broadly reviewing the problem of understanding and predicting protein sequence-function landscapes and then introduce the particular system most of interest to this thesis: membrane transporter proteins. Transporters’ sequences must encode functional rules for highly tuned specificities across chemical space, as well as for carefully energetically balanced transitions between structurally distinct conformations.
First, in Chapter 2, I briefly discuss my work on the evolutionary history of one such family of transporters, the Natural resistance associated macrophage transporters (or Nramps). In Chapter 3, I then use high-throughput mutagenesis of a library guided by structure and evolution to systematically dissect the determinants of metal import and specificity in a model homolog of this family, DraNramp. I find that a key set of core residues in the first and second shells is essential to allow for import of the typically excluded substrate of Mg2+. A wide range of surrounding residues throughout the protein’s core additionally act as hotspots for modulating both epistatic interactions between mutations within one fitness landscape and specificity modulation between fitness landscapes. I then propose a theoretical model in which residues modulating the protein’s conformational equilibrium could underlie both effects.
In Chapter 4, I build a new database of results from multiplexed functional assays of specificity, including my own, to ask if and how probabilistic models trained on natural sequence information can be used to guide our search for protein variants that alter substrate specificity. I find that many popular machine learning-based approaches systematically bias their predictions against variants that alter specificity by conditioning on local sequence context. To address this, I propose a simple weighted difference between models that can guide the sampling of sequence libraries to enrich for variants with altered specificity. Finally, in Chapter 5, I discuss work done collaboratively with Jacob Licht and Rachelle Gaudet in which we systematically analyzed the commonalities and differences between conformational transitions in the superfamily of membrane transporters containing DraNramp, identifying a common core “rocking” mechanism with additional protein-specific variations, which we categorize.
In sum, I have done several projects analyzing protein specificity and evolution from several angles: experimental mutagenesis, machine learning, and structural analysis. These results demonstrate advances in our ability to predict and understand the determinants of protein specificity but additionally suggest that a properly predictive understanding of the design rules of protein specificity remains out of reach. In Chapter 6, I discuss prospects for this field.Biophysic