91福利导航

Emrah Altindis (Biology)

The Altindis Lab focuses on two different projects: (i) determining the role of viral hormones in host-pathogen interactions and human disease and (ii) investigating the role of gut microbiota in Type 1 Diabetes and celiac disease. We will use this cluster for both of these projects.

James Anderson (Economics)

Climate change is expected to lower global agricultural production by making the world less arable. International trade will play a crucial role in easing global food stress by moving agricultural products from more cultivable lands to less. Yet, most countries currently protect their farmers with high tariffs, and it reduces efficiency in global trade. This project studies the welfare effects of global agricultural protectionism under climate change. Further, we analyze the effectiveness of global cooperation in trade policies to fight against global food stress under climate change.

Stefano Anzellotti (Psychology)

Our group uses the cluster for two main applications. First, we build and test computational models of how humans understand people, actions, and events, including deep neural network models as well as Bayesian models. Second, we develop novel data analysis methods and apply them to the study of cognition in healthy individuals as well as individuals with developmental disorders (such as autism).

Jean-Pierre Aubry (Center for Retirement Research)

Roughly half of all private sector workers in the U.S. participate in a tax-qualified retirement plan through their employer 鈥� with about 700,000 employers sponsoring 750,000 plans that held more than $13 trillion in assets for 145 million participants in 2022. Each retirement plan must file a Form 5500 report, which serves as an important compliance and disclosure tool for the U.S. Department of Labor (DOL). But, for those who study retirement policy, it also serves as the only publicly available source of detailed information on plans. The goal of this effort is to provide researchers with a more user-friendly version of 5500 going back to 1984. This will involve two tasks: 1) turning the DOL鈥檚 existing 5500 repository into research-friendly data; and 2) building a user-friendly portal that makes the data easily accessible to all.

Pierluigi Balduzzi (Finance)

The R-square of a cross-sectional second-pass OLS regression of test asset risk premia on factor loadings is factorized into two components. The first component captures the time-series fit of the model. The second component reflects the relative risk-return trade-off of the model. We show that popular factor models perform poorly mainly because they do not explain enough of the time-series variation of test asset returns.

Lucas Bao (Chemistry)

We apply quantum chemistry methods, kinetics theories, and dynamics simulations to study the problems that are related to energy, catalysis, environment, and sustainability. Our research goal is to apply and develop new theoretical methods to understand the underlying physical principles of atmospheric reactions, electrocatalysis, photocatalysis, and materials chemistry. We focus on predicting and understanding the kinetics and dynamical behaviors of the complex chemical systems based on first-principle computations. The theoretical methods that we use include but not limited to (a) electronic-structure methods: multireference wavefunction theory, density-functional theory, composite correlated wavefunction theory, etc.; (b) kinetics and dynamics: variational transition state theory, semiclassical quantum tunneling, ab-initio molecular dynamics, etc.

Simcha Barkai (Carroll School of Management)

The aim of this project is to understand the role of higher education in shaping attitudes towards economic policy. The project will provide two types of analysis. The first is analysis of new survey data collected from Boston College students and Alumni. The second focuses on observational data, such as data available through online profiles of individuals.

Susanto Basu (Economics)

Emerging market economies experience financial crises (sudden stops of capital inflows) that are characterized by large reversals of capital flows and sharp exchange rate depreciations. At the same time, they also face high levels of income and consumption inequality. This project investigates (i) how income inequality and non-homotheticity of preferences affect the frequency and severity of sudden stops, (ii) what happens to consumption inequality during sudden-stop crises, and (iii) whether regulators should consider the income redistribution when setting macroprudential policies. The questions are investigated by estimating non-linear economic models on micro consumption data from Peru.

Christopher Baum (Economics)

This project evaluates various behavioral risk factors in a public health context, including the effect of tobacco taxes and smoke-free legislation on mothers' smoking and babies' birthweight; the relation between smoke-free legislation and the incidence of childhood asthma; and the use of tobacco products by adolescents.

Mark Behn (Earth and Environmental Science)

Our research involves developing numerical models to study active tectonic and magmatic processes in marine, terrestrial, and polar environments. Deformation and mass transport depend critically on the rheologic properties (i.e., strength) of the crust and mantle. Thus, any quantitative study of active tectonics requires a thorough understanding of the Earth鈥檚 rheology. My research group develops numerical models to relate laboratory-based rheologic and petrologic models to the large-scale behavior of the Earth. We use finite-difference and finite-element based computational approaches in two- and three-dimensional simulations. Our models are applied to a range of problems, including faulting, mantle convection, and melting and melt migration in the Earth鈥檚 mantle, as well as to societally-relevant issues, such as the dynamic response of ice sheets to climate change, global geochemical cycling, and hazards associated with earthquakes and volcanic eruptions.

Jose Bento (Computer Science)

We will be working on distributed and parallel optimizations algorithms and their applications to machine learning and biology. The framework will be based on the popular alternating direction method of multipliers and their variants. We will use tools like spark, OpenMP and OpenMPI to develop these tools. In addition, we will develop a series of theoretical results to improve the robustness and accuracy of these algorithms.

Vincent Bogousslavsky (Carroll School of Management)

I study how measures of stock market liquidity vary over the trading day. The close of the stock market concentrates a much larger fraction of daily trading volume nowadays than twenty years ago. This change seems in part due to institutions that trade near the close because of indexing constraints. As a result, intraday liquidity patterns have changed over time. This project analyses a large data set of high-frequency liquidity measures to understand what drives variation in liquidity both over time and in the cross-section. A better understanding of stock market liquidity has important implications for market efficiency and regulation.

David Broido (Physics)

Our research group focuses on the study of heat transport in bulk and nanostructured semiconductor materials. Our goal is to develop an accurate theoretical approach that will allow us to provide guidance to experimental groups who perform measurements of these materials, as well as contributing to the development of new nanomaterials engineered for specific applications. We are employing state-of-the-art computational methods (for example, ab initio and adiabatic bond charge calculations of phonon dispersions, iterative solution of phonon Boltzmann equation) in this effort that require multiple fast cpu's and substantial memory.

Thomas Chemmanur (Carroll School of Management)

We study that social network has a first-order effect on the portfolio holdings and trades for mutual fund managers. We examine whether there is valuable information transmitted through the network or the effect is due to the herding behavior of professional money managers.

Xiao Chen (Physics)

My research focuses on non-equilibrium quantum dynamics. I use a numerical simulation to (1) better understand the quantum dynamics experiments in Nitrogen-vacancy center and cold atom systems, (2) A study the thermalization and entanglement dynamics in isolated many-body quantum system with or without conservation law, (3) find possible new phases in non-equilibrium dynamics and (4) design many-body quantum circuit models with possible applications in quantum computation.

Ki-Soon Choi (Carroll School of Management)

We plan on investigating whether mutual funds use disclosures to convince investors and mitigate fund outflows. The cluster will be used to scrape funds鈥� letters to shareholders and conduct textual analysis.

Donald Cox (Economics)

What impact did Lockdowns associated with COVID have on mental health? There is abundant evidence that isolation can have adverse mental health consequences. COVID-related isolation occurred on an unprecedented scale. We plan to use the Substance Abuse and Mental HealthServices (SAMHSA) dataset, for the years 2018 to 2021, to assess the mental health consequences of lockdown from COVID.

Rebekah Levine Coley (Education)

Extensive evidence highlights increased mental health concerns among adolescents, and decreased engagement in externalizing problems. But knowledge is limited concerning how these patterns vary across demographic groups and how they fit together. This project will use data from the YRBS and Monitoring the Future to track patterns of internalizing, interpersonal violence, substance use, and sexual risk behaviors among cohorts of adolescents from 1991 through 2021, and to assess cohort and demographic variability and policy correlates of such patterns.

Jeffrey DaCosta (Biology)

Evolutionary biology is currently undergoing tremendous growth with the emergence of next-generation sequencing technology and associated analyses, which provides unprecedented power to study evolution on a genomic scale, determine the genetic underpinnings of phenotypes, and examine those phenotypes in a phylogenetic framework. My research uses these tools to reconstruct the evolutionary history of species and populations (mostly birds), and advance our understanding of the generation and maintenance of biodiversity. My expertise in this field is transferred to undergraduates through advanced experience research labs and independent projects in which students gain exposure to the process of collecting and analyzing genome-scale data. These skills are general to modern genomics biology, and could also be useful for students seeking careers in fields such as evolutionary biology, conservation genetics, biotechnology, or biomedical research.

Sean Dougherty (Education)

In this project, we use publicly available data on the expansion of charter schools and other schools of choice, to understand dynamic changes in the enrollment of Catholic schools in the United States. In addition, we estimate the impact of the proliferation of new schools of choice in proximity to Catholic schools to predict declines in enrollment and the conditions under which Catholic schools close. Using over two decades of nationally representative school survey on student enrollment and educator characteristics, as well as local school data in a sampling of metro areas, we plan to show whether and how the proliferation of school choice options changed the characteristics and numbers of Catholic schools.

Ran Duchin (Carroll School of Management)

In this project, we study the role of politics in venture capital investment. Specifically, we study whether political partisanship and polarization affect the funding of high-growth entrepreneurial firms.

John Ebel (Earth and Environmental Sciences)

Although earthquake prediction remains an elusive goal, it is possible to forecast the general characteristics of future earthquakes at some level of detail. Seismologists use the term "earthquake forecast" to refer to a statement of the long-term probability of one or more earthquakes occurring in a region. Our research on earthquake forecasting is focused on discerning the level of detail that can be known about the spatial and temporal characteristics of future earthquake processes. We are investigating the extent to which the distribution of seismicity in a region delineates where future earthquakes are likely to occur, as well as the extent to which non-random patterns in the temporal distribution of seismicity might indicate increased probability of earthquakes occurring.

Farid Farrokhi (Economics)

International trade and climate agreements have historically evolved separately. However, trade agreements can increase emissions creating climate externality, while climate policies like carbon pricing may cause pecuniary terms-of-trade externalities. This study addresses two key questions: the empirical magnitude of the cross-externalities between trade and climate, and how carbon pricing policies can be integrated into existing trade agreements. Using a quantitative trade model that incorporates fossil fuel supply chains and input-output linkages, this project assesses the cross-externalities of trade policies and carbon pricing, and proposes a unified framework for integrating international agreements on climate and trade.

Maksym Fedorchuk (Mathematics)

I will be doing intersection-theory calculations on the moduli space of pointed rational curves, which is one of the principal objects in algebraic geometry. I plan to utilize Sage and polymake.

Jessica Finocchiaro (Computer Science)

This project aims to investigate the role training objectives play as one varies data size and ML model size simultaneously.

Hanno Foerster (Economics)

This project studies questions in labor and family economics. I develop and estimate dynamic economic models (e.g., life-cycle models or search and matching models) to study questions of high policy relevance. This involves working with data from various sources, including survey data as well as administrative data, such as Danish data from social security and tax records or German social security data. As part of this project I study the optimal design of child support and alimony policies. I also investigate what led to the abandonment of U.S. laws that regulated women鈥檚 work hours and occupational choices until the 1960s.

Lukas Freund (Economics)

My research seeks to understand, in broad terms, how the characteristics and behavior of workers and firms shape macroeconomic outcomes. To this end, I develop and quantitatively analyze models that feature rich, empirically grounded heterogeneity at the micro level, but which can aggregate up to and explain macro-level outcomes such as productivity and the distribution of earnings. I plan to use the cluster for the estimation of such models. In addition, recent work uses state-of-the-art natural language processing tools to leverage naturally occurring, unstructured text to empirically discipline micro-to-macro models of the kind just described.

Solomon Friedberg (Mathematics)

Automorphic forms are fundamental in modern number theory and representation theory, and are closely related to the spectral decomposition of certain homogeneous spaces. It is often challenging to compute this spectrum explicitly, but desirable to do so, as there are many distributional questions one can ask once one has the data. The goal of this project is to compute approximations to Maass forms attached to number fields and then to study them numerically. The initial goal is the computation of Bianchi modular forms. These objects are attached to congruence subgroups of the group of two by two determinant one matrices with entries in the ring of integers of a fixed imaginary quadratic field.

Vincent Fusaro (Social Work)

This project examines the effectiveness of eviction moratoria during the COVID-19 pandemic. Drawing data from a variety of sources and multiple levels of geography, we estimate models of the number of eviction filings at the Census tract level as a function of eviction moratoria policies, local economic and public health conditions, and population characteristics.

Jianmin Gao (Chemistry)

The Gao group in the Chemistry Department of Boston College seeks to develop novel peptide inhibitors for biomolecules previously deemed undruggable. In comparison to the traditional small molecules used in medicinal chemistry, peptides harbor larger molecular footprint and hence are able to effectively inhibit protein-protein and protein-nucleic acid interactions, which often display larger contact surfaces. The Gao group develops various bioconjugation chemistries and applies them to construct peptide libraries that incorporate diverse non-natural structure motifs. Screening of such peptide libraries has proven to be a powerful way to identify initial hits for inhibiting a biomolecule of interest. The initial hits typically display suboptimal potency and specificity, which calls for structural optimization. We propose to use computational modeling, including docking and molecular dynamic simulations, to elucidate the binding mode of the peptide hits. The structural models will be used to generate hypotheses to streamline the structural optimization of the peptide inhibitors.

Jason Geller (Psychology)

Analysis and data storage for work involving EEG, eye-tracking, fNIRs, TMS, and fMRI as part of the Human Neuroscience Lab Recharge Facility.

Siddhartan Govindasamy (Engineering)

Future generations of wireless and mobile communication networks are expected to use a very large number of antennas to help serve a large number of mobile users. These systems are referred to as Massive, Multiple-Input Multiple-Output (MIMO) wireless communications. System-wide performance of massive MIMO networks can be understood through a combination of analytical and simulation-based methods. In this work, we plan to simulate wireless networks with a very large number of antennas to improve our understanding of the tradeoffs between the increased complexity of such systems, and their ability to provide service to a large number of users.

Michael Graf (Physics)

Our group focus is on measurements of strongly correlated electron and magnetic systems at low temperatures. These works encompass materials that are at the forefront of modern condensed matter physics, and involve exotic low temperature phases and are a result of complex many-body interactions. One prominent technique in research is Muon Spin Resonance (MuSR/渭SR), used to probe the local distribution of magnetic fields. As a part of this technique, Density Functional Theory (DFT) methods are used to simulate the implantation of muons into samples. The cluster will perform CASTEP code in parallelization, allowing for the timely simulation of results.

Julia E Grigsby (Mathematics)

Generate and store training data and then train sequence-to-binary models like RNN's, LSTM's, and transformer models to solve the word problem in two families of finitely-presented groups that are relevant in mathematics: Coxeter and Artin groups.

Rob Gross (Mathematics)

Our research group is investigating new algorithms to find efficient lattice packings in moderately high dimensions听(20<n<99). We hope to improve on some known bounds by using parallel methods.

Michael Grubb (Economics)

My research focuses on topics in behavioral industrial organization. In ongoing work I use historical cellular phone and electricity billing data to investigate the potential effect of bill-shock regulation on consumers. Bill-shock regulation seeks to inform consumers by requiring firms to alert consumers when high usage triggers an increase in marginal price. Importantly, the work seeks to predict how firms would change prices in response to such regulation and takes these predicted price changes into account when evaluating the policy鈥檚 impact on consumers.

Pablo Guerron (Economics)

My research involves the development of fast and efficient methods to solve and estimate macroeconomic models. I am particularly interested in models that display high nonlinearities arising from borrowing constraints, default, and nonnormal shocks. The GPU cluster will be used to develop these methods. Equally important, the cluster will help to introduce students to modern computational techniques such as parallelization and programming, for example, in CUDA.

Mathias Hasler (Carroll School of Management)

This paper re-evaluates academic research on 92 cross-sectional stock return predictors. Researchers studying return predictability must make decisions about portfolio construction, for example, whether to rebalance annually or monthly. In the sample, the returns of predictor portfolios constructed with the precise research decisions made in the original papers are significantly larger than those constructed with a random combination of decisions made in the literature. Out of sample, half of this difference disappears. The effects exist only for predictors published in top-ranked journals. The results suggest that statistical biases from researchers' decisions explain a fifth of the return predictability in the literature.

Joshua Hartshorne (Psychology)

Our research is focused on learning the structure of the syntax and semantics of English verbs, specifically in verb argument structure and verb classes, using data from both existing lexical resources and large web-based experiments. We take a computational approach to analysis, using non-parametric Bayesian models and artificial neural networks. Note: In collaboration with Professor Prud'hommeaux.

Andrzej Herczynski (Physics)

This interdisciplinary project is an extension of a recent work on the new approach to scaling analysis of images, in particular abstract art. The goal is to refine scaling plots based on successively finer computational grids and further develop the idea of fractal contours, derivatives of these plots. Such contours provide a newly proposed tool for representing images, more precise and insightful than a single fractal index. We are planning to test the method on a set of complex synthetic examples and explore its utility for a systematic study of art.

Zoe Hilbert (Biology)

The Hilbert lab studies how fungal pathogens interact with different host species (from mammalian cells to amoebae and nematodes) and with their environments. We're particularly interested in how these interactions shape the evolution of both fungus and host cells. We use a combination of genetics, experimental evolution, sequencing and imaging techniques to investigate these questions. In addition, we use computational methods to analyze large sequencing data sets and phylogenetic tools to analyze the evolution of genes and proteins across species.

Catherine Hoar (Engineering)

Our work focuses on the detection and removal of emerging biological and chemical contaminants in water and wastewater. The cluster will be used for analysis of sequencing data related to (1) tracking these contaminants and (2) identifying microorganisms active in contaminant degradation and the associated metabolic mechanisms.

Jier Huang (Chemistry)

Our research focuses on the design of covalent organic framework (COF) materials for photocatalytic performance studies. We aim to utilize density functional theory (DFT) calculations to investigate the Gibbs free energy changes along the elementary reaction pathways during the photocatalytic processes of COFs. By combining theoretical calculations with experimental results, we seek to gain deeper insights into the mechanisms of photocatalytic reactions and provide innovative strategies for the design of novel COF-based photocatalysts.

David Hughes (Economics)

My work involves the development of new estimators to answer economic problems, as well as theoretical and data-based exploration of the properties of these estimators. Examples included methods for bias correction in models that allow for unobserved heterogeneity, and the application of nonparametric and machine learning techniques to instrumental variables problems. This typically involves large-scale simulations of new estimators under different data generating processes.

Megan Hunter (Carroll School of Management)

My projects involve exploring the pros/cons of firms making it harder for consumers to access information. In particular, I am working with a business to business firm. I will be exploring their clickstream data to see how their customers react when they hit an "identity wall". Certain technical papers by the firm require the consumer to fill out more information about themselves before they can access it.

Jessica Johnson & Indrani Saran (School of Social Work)

This study uses longitudinal Boston Public Schools (BPS) administrative data to assess the short-term and longer-term impacts of homelessness on youth academic outcomes. We are also comparing the academic trajectories of BPS students who participated in a novel homelessness prevention program to students who did not participate in the program to help identify the impacts of homelessness prevention services. Further investigations on the timing and number of services received by program participants will help provide additional evidence about the potential benefits of homelessness prevention programs.

Welkin Johnson (Biology)

Viruses have contributed to the evolution of all life, and we are only beginning to understand the magnitude of their contributions. In this project, we aim to elucidate the presence, abundance and potential activity of endogenous retroviral elements in vertebrate genomes, with a focus on primates and fish, with the goal of discovering host functions that originate from retroviral genome insertions.

Mohannad Ali Kadivar (Sociology)

Forty years after the Islamic Revolution in Iran, democracy in Postrevolutionary Iran in the theological state is still a topic of discussion. In this inquiry, we focus on the role of pro-government mobilizations in Postrevolutionary Iran to argue how and when the government in Iran mobilizes people. We focus our attention on organization and institutional procedures behind this kind of social mobilization. To this end, we gather data from government-sponsored news agencies and code their news into particular categories that represent how the government in Iran mobilizes people. Since the government owns a series of news agencies, we scrape them in order to find our key categories. Therefore, we limit our scope to recent years in order to get the most-updated data about the role of pro-government mobilizations in Postrevolutionary Iran.

Christopher Kenaley (Biology)

Our lab is interested in macroevolutionary questions that address patterns, tempo, and mode of evolution in the world's largest vertebrate group, the ray-finned fishes. The modern comparative methods we apply in this work often rely on analytical techniques that scale exponentially with the complexity of our data and, most of all, the number of taxa included in the analysis.

Elizabeth Kensinger (Psychology)

Our research examines the neural activity associated with memory processes. We are particularly interested in understanding how neural activity differs when information is successfully remembered versus when it is forgotten, and how the neural processes that correspond with accurate memory differ for emotionally meaningful experiences versus for more mundane ones. To examine these questions we use Matlab-based software in order to analyze the hemodynamic (blood-flow) responses throughout the brain as individuals are remembering events.

Shakeeb Khan (Economics)

The work involved will be using computer software packages such as Matlab to explore statistical properties of new inference procedures for dynamic nonlinear panel data models in econometrics, that pertains to my ongoing academic research. I will be hiring one or two PhD students to assist me on this project.

David Sunghyo Kim (Carroll School of Management)

In this project, we investigate whether ESG investors accept lower returns for nonpecuniary reasons by examining securities lending practices of ESG funds. Using granular, fund-level data from SEC filings that details lending on a stock-by-stock basis each quarter, we compare the extent of lending by ESG funds to that of their non-ESG counterparts.

Nam Wook Kim (Computer Science)

We investigate the capabilities of large language models in data visualization and human-computer interaction systems. Example projects include analyzing how students use LLM-based chatbots for education, building inclusive chart question-and-answering for blind and low-vision people, and leveraging LLMs for chart generation and data analysis.

Do Yoon Kim (Carroll School of Management)

I study the different roles of corporate and open source contributors in creating economic value. Towards this, I collect massive amounts of data consisting of source code, patents, and product introductions.

David Kurz (Earth and Environmental Sciences)

Our research seeks to expand knowledge of a Southeast Asian Suid species through evolutionary genetics. Our goal is to analyze genomic sequences of the species to estimate effective population size through time and identify potential sub-populations. This data will also be tied to spatial data to further understand how habitat fragmentation and a natural riverine feature impact this species.

Mariana Laverde (Economics)

This paper studies the limits of school choice policies in the presence of residential sorting. Using data from the Boston Public Schools choice system, I show that white prekindergarteners are assigned to higher-achieving schools than minority students, and that cross-race school achievement gaps under choice are no lower than would be generated by a neighborhood assignment rule. To understand why choice-based assignments do not reduce gaps in school achievement, I use data on applicants' rank-order choices to estimate preferences over schools, and consider a series of counterfactual assignments. I find that half of the gap in school achievement between white and Black or Hispanic students is explained by minorities' longer travel distance to high-performing schools. Differences in demand parameters explain a smaller fraction of the gap, while algorithm rules have no effect.

Youngeun Lee (Carroll School of Management)

Our project investigates the impact of firm product and pricing decisions on consumer purchases and health. We leverage multiple sources of data on store sales, product descriptions, and nutrients to understand and estimate the impact of these decisions on demand and welfare. Note: cooperate with Prof Megan K Hunter Antill.

Matteo Leombroni (Carroll School of Management)

We aim to study the effects of macroeconomic shocks on the asset allocation of mutual funds. Inflation shocks and interest rate shocks have significantly different effects on mutual funds. To do that we download mutual fund holdings from Morningstar for every mutual fund in each month.

Zhushan Li (Education)

Our study is about developing valid and reliable instruments for small samples via Bayesian approaches. We proposed a novel Bayesian method for establishing content and construct validity evidence for multi-unidimensional instruments through the integration of expert and participant data. Extensive simulation studies will be conducted under different conditions to evaluate the performance of the proposed method and determine the optimal number of experts in terms of efficiency. We will examine the performance of our proposed model fit evaluation methods in detecting different levels of misfit under various conditions through simulation studies.

Miao Liu (Carroll School of Management)

We are building state-of-the-art machine learning models to predict corporate earnings and examining if this is a superior approach to assess a firm鈥檚 fundamental value than other approaches in the literature. The ultimate goal is to understand the value of corporate financial reporting to investors.

Shih-Yuan Liu (Chemistry)

The research in the Liu group is focused on the development of boron(B)鈥搉itrogen(N)- containing heterocycles, specifically azaborines, for potential applications in biomedical research and materials science. Azaborines are structures resulting from the replacement of two carbon atoms in benzene with a boron and a nitrogen atom. Azaborines closely match the size and shape of ordinary benzene rings, but most of their other physical, chemical, and spectroscopic properties are significantly altered. Computational studies will be invaluable to our efforts in understanding the electronic structure and spectroscopic features of azaborines, and the mechanism of reactions they undergo.

Qiong Ma (Physics)

Our group focuses on electron transport properties in two-dimensional vdW materials. We would like to use first principles to theoretically calculate the electron band structures, which are essential to help us understand the experimental observations.

Thibaud Marcesse (Political Science)

This project aims to investigate the heterogeneity in outcomes under a large public policy 鈥� the National Rural Employment Guarantee Scheme or NREGS 鈥� through different approaches, such as a difference鈥攊n鈥攄ifferences analysis and conditional probability models. It is based on a large dataset that combines implementation data and Census data from the Census of India, along with data on the partisan identity of lawmakers at the state and national level. It is led by Professor Thibaud Marcesse in the Political Science department.

Katherine McAuliffe (Psychology)

Our research studies the forces that shape and sustain cooperation, with a particular focus on how cooperative behaviors develop in children between the ages of 5 and 12. Some of the questions that we investigate are: What are the psychological mechanisms that support cooperation in humans? How do cooperative abilities develop in children? Do cooperative abilities develop similarly across different cultures? Using a variety of methods that capture people's behavior in cooperative dilemmas, we hope to better understand how cooperative behavior is sustained in humans, how it develops in children, and how it evolves.

Paul McNelis (Economics)

This project makes use of recent advances in machine learning methods based on XGboost for forecasting inflation in five ASEAN countries: Philippines, Singapore, Malaysia, Indonesia and Thailand. We compare the forecasting of this approach relative to standard linear specifications. We make use of monthly data, which begin prior to the Global Financial Crisis of 2008 and which end after the COVID19 pandemic, so that the time series are framed by two crisis periods, the former used in estimation and the latter for evaluating forecasting performance. Monetary growth rates became dominant for forecasting inflation at the time of the COVID19 pandemic.

Carl McTague (Computer Science)

The goal is to develop software to efficiently compute with Hopf rings (elaborate algebraic gadgets having an addition, two products, and a coproduct). And to use it to perform intricate calculations to solve open problems in homotopy theory 鈥� for example, to determine the homotopy type of the string bordism spectrum MO<8> at the prime 3.

Michelle Meyer (Biology)

The biological roles of RNA, beyond encoding proteins, have expanded in the last decade to include a diversity of important gene regulatory functions in nearly all living things. At the same time, genome sequencing efforts have produced a wealth of data that can be mined to study the evolution of non-coding RNAs (ncRNAs), as well as identify previously unknown non-coding RNAs. We are particularly interested in RNA structures that bind proteins to control gene expression. The predominant methodology for the discovery of ncRNAs is comparative genomics. Using the massive amounts of sequence information generated by microbial sequencing projects, and various metagenomic projects(such as the Human Microbiome Project) we apply a variety of computational tools to discover new structured mRNA elements that are hypothesized to control gene expression. In particular, we use RNA structure alignment searches using programs built on stochastic context free grammars.

David Miele (Education)

This project aims to estimate the magnitude and significance of the associations between a number of psychological and educational constructs, including critical consciousness, motivation, well-being, and academic achievement. Our lab will be using the Linux cluster to conduct simulation-based power and sensitivity analyses for examining multilevel regression models. We also hope to use the cluster to conduct machine-learning analyses of participants' open-ended responses.

Yi Ming (Earth and Environmental Sciences)

My research group strives to elucidate the physical mechanisms governing Earth鈥檚 climate system, and to apply the fundamental understanding to practical issues of societal and policy importance. We will perform idealized and comprehensive climate model simulations on the cluster to study how climate change may affect precipitation patterns (e.g. droughts and floods) and extreme events (e.g. hurricanes, wildfires and winter storms), a topic with profound impacts on local populations and ecosystems.

Udayan Mohanty (Chemistry)

Electrostatic interactions between charges in solution and between the charges along the backbone of RNA play a delicate role in determining its overall stability. The importance of electrostatic interactions is apparent from the fact that magnesium influences the biological activity of tRNA and Tetrahymena group I intron, for example. We are studying the nature of interaction of magnesium ions with RNA bases. The approach combines Grand Monte Carlo simulations, Poisson-Boltzmann calculation and high-level ab initio theory to determine the binding free energy for magnesium ions with RNA bases. Another goal of the project is to use Monte Carlo simulations to quantitatively determine the effects of phosphate-phosphate repulsion on DNA stiffness in vitro. Finally, we will investigate by Brownian dynamic simulations the effects on the end-to-end contact probabilities for 200-bp DNAs of binding at different degrees of saturation for HMGB proteins.

Amin Mohebbi (Engineering)

The proposed research aims to conduct comprehensive large-scale climate modeling, with a specific emphasis on precipitation dynamics. This investigation will leverage the Weather Research and Forecasting (WRF) model, complemented by essential codes including netCDF Operator (NCO), Climate Data Operator (CDO), and NCAR Command Language (NCL). The precipitation patterns simulated by the WRF model for a specific geographic area will undergo thorough validation against both ground-based in-situ station data and satellite observations.

Babak Momeni (Biology)

Communities of interacting microbes are abundant in nature. They play important roles in ecosystems (e.g. by cycling carbon), in human health (e.g. by causing infections), and in industry (e.g. by degrading toxic waste). We build mathematical models to study how cell-level properties of members shape the overall functions of multispecies microbial communities.

Sara Moorman (Sociology)

I am using data from the National Social Life, Health, and Aging Project (NSHAP) to examine dyadic effects of marital quality on older adults' well-being, including whether there are any differences according to gender.

James P. Morken (Chemistry)

Research in the Morken group focuses on the development of new catalytic enantioselective processes and their application to natural products synthesis. Our current work in asymmetric catalysis focuses on the design and study of rhodium and palladium complexes for enantioselective allylation and dimetallation of alkenes. Computational studies (Gaussian and MacroModel) of reaction mechanisms and catalyst structures often complement experimental studies. Combined, the two approaches enhance our ability to design effective new reactions. Our most recent DFT studies on the Pd-catalyzed addition of organometallic reagents to enones, revealed an unprecedented and unexpected reaction mechanism, which is forming the basis for many of our research directions.

Eric Moss (Mathematics)

Automorphic forms are fundamental in modern number theory and representation theory, and are closely related to the spectral decomposition of certain homogeneous spaces. It is often challenging to compute this spectrum explicitly, so high-performance computational resources are required. Our work is to compute approximations to Maass forms attached to number fields and then to study them numerically, such as investigating various distributional statements from the Fourier coefficients.

Jia Niu (Chemistry)

Research in the Niu group focuses on the development of new tools for the investigation and regulation of key biochemical processes taking place on the surface of and within a cellular system. One part of the research is the design and synthesis of glycomimetic synthetic polymers with unique architectures for the engineering of cell surface glycome. Theoretical investigations carried out with Gaussian or Jaguar will be invaluable for the understanding of new polymerization processes.

Jaromir Nosal (Economics)

Market power in transportation distorts trade policy transmission. Using Chilean customs data, we document high concentration and price dispersion due to bilateral negotiations between transport carriers and importers. Estimating a trade model with two-sided market power, we find that while carriers charge high markups, importers retain significant bargaining power. Embedding this framework into a trade model, we show that importers鈥� market power reduces the welfare costs of tariffs by 40%. Additionally, carbon policies like the EU ETS have negligible effects on aggregate welfare, as importers absorb much of the cost impact.

Shufen Pan (Engineering)

Geospatial AI-MAP is a research initiative of the GeoAI Lab that advances Geospatial Artificial Intelligence for environmental Monitoring, Assessment, and Prediction (MAP). Integrating AI with Earth observation, environmental sensing, process-based modeling, and human-centered engineering, the lab develops scalable, data-driven solutions to address the impacts of rapid global change. With a focus on sustainability and resilience, the lab empowers policymakers, scientists, engineers, and resource managers to make informed decisions for the effective stewardship of land, water, and ecosystems.

Ritika Pandey (School of Social Work)

In this project, we will use a large language model to segment use of force text into different stages. Next we will train a sequence to sequence a large language model to predict the second stage based on the initial stage. The goal of this project is to learn about the factors leading to decisions on when and what type of force officers use.

Emily Prud'hommeaux (Computer Science)

Our research focuses on developing computational algorithms to automatically identify areas of pragmatic difficulty in the discourse of young adults with autism spectrum disorder. We will also be training deep learning automatic speech recognition models to generate transcripts of speech recordings.

Bryan Ranger (Engineering)

Our research focuses on developing computational algorithms for biomedical image and signal analysis. Current projects include interpretation of ultrasound images and analysis of sensor data from portable imaging systems.

Sam Ransbotham (Carroll School of Management)

Our research introduces a new disaggregated formulation of the Generalized Assignment Problem. Based on the reformulation, we are able to introduce unique strong inequalities. We test the strength of this formulation on a set of standard benchmark problems and show the new formulation to be significantly stronger than other known formulations.

Myra Reynoso (Education)

This project will utilize six years of NWEA MAP data to understand differences in student outcomes across school sectors (public, Catholic, and charter). Specifically, looking at how k-8 achievement test scores across sectors compare cross-sectionally over the past 6 years in large cities; how do growth in test scores among urban schools compare across sectors, and how was the expected academic growth of students impacted by the COVID-19 pandemic across school sectors.

Maureen Ritchey (Psychology)

Research in my lab is focused on the neuroscience of human memory and emotion. We use functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to measure brain activity, which we then relate to performance on cognitive tasks. This work involves the processing and analysis of very large datasets, which we primarily do with Matlab and R-based tools.

Michael Russell (Education)

This research simulates clustered educational datasets to examine how three methods of modeling intersectional identities handle the complexity of demographic data scenarios. In this simulation study, I varied the number of demographic categories, the proportion within each identity indicator, the within-intersectional group variance, and the overall sample size to create 81 realistic scenarios education researchers encounter when working with demographic data.

Matthew S. Rutledge (Economics)

In the United States food insecurity is prevalent among many low-income college students, impelling many to enroll in the U.S. Department of Agriculture鈥檚 (USDA) Supplemental Nutrition Program (SNAP), colloquially known as Food Stamps. Administered by the Food and Nutrition Service (FNS), it is the nation鈥檚 third-largest federal anti-poverty program. SNAP is designed to alleviate poverty among college students through the direct allocation of funds to vulnerable students in the form of electronic bank transfers (EBTs). Despite its efforts, there are eligible individuals who do not take up the program for a variety of factors including lack of eligibility awareness, lack of public information, social stigmas, etc. This is often referred to as the 鈥淪NAP Gap.鈥� Using data collected between 2014 and 2021 in the U.S. Census Bureau鈥檚 Survey of Income and Program Participation (SIPP), this paper attempts to calculate the SNAP Gap among college students and estimate the leading factors that contribute to students鈥� non enrollment. It also examines how belonging to the SNAP Gap affects one鈥檚 food insecurity using a binary regression, controlling for self-selection into SNAP.

Ehri Ryu (Psychology)

Our research involves the development and evaluation of statistical methods for analyzing multivariate multilevel data. The particular projects include model fit assessment of multilevel structural equation models, measurement equivalence in confirmatory factor analysis framework, multilevel multi-group comparison, and statistical inference about indirect effects. The cluster will be used to conduct simulation studies to empirically evaluate the performance of statistical methods.

Erika Sabbath (Social Work)

The Boston Hospital Workers Health Study (BHWHS) is a partnership between the 91福利导航- and Harvard-based study team and a large health system in Massachusetts. BHWHS consists of multiple longitudinal sources of administrative data from the employer, linked at the individual worker level with survey data on emerging and established occupational exposures and experiences. The goal of BHWHS is to improve the health of the entire hospital workforce and reduce within-workforce disparities by identifying the roots of those disparities in the conditions of work.

Geoffrey Sanzenbacher (Economics)

Despite the potential influence of secondary protections on access to new treatments, generic entry and formulations, and ultimately prices, the use of these patents and exclusivities is understudied. Little is known about how these protections are used, how these uses vary across the life-cycle of a product, and whether certain exclusivities 鈥� like the powerful pediatric extension 鈥� actually serve their desired end. This series of three papers will use a comprehensive dataset of drugs that were approved by the FDA between 1984 and 2016 to: 1) describe the use of secondary protections; 2) identify varying patterns of how the protections are used across products and over a drug鈥檚 lifecycle (e.g., securing exclusivities at the end of a patent versus throughout a drug鈥檚 life); and 3) determine whether drugs receiving pediatric extensions help meet pediatric need (or simply extend the period of monopoly pricing for an additional six months).

Edson Severnini (Economics)

Rising temperatures have been shown to influence human behavior, increasing aggression and crime. For example, Heilmann, Kahn, and Tang (2023) find that crime rates in Los Angeles rise by up to 1.9% on hotter days, especially in low-income areas, driven largely by non-economic crimes of passion. Gun violence may be one manifestation of this pattern, imposing significant social costs. In the U.S., exposure to school shootings has been linked to lasting negative effects on students鈥� attendance, education, and earnings (Cabral et al., 2024). Our project examines whether high temperatures affect shootings in Brazil, where limited state capacity may weaken law enforcement.

Scott Slotnick (Psychology)

In this project, we aim to determine the functional connectivity of the anterior hippocampus and the posterior hippocampus using resting-state functional magnetic resonance imaging (fMRI) data collected as part of the Human Connectome Project. We will employ fMRI data from over 800 participants using state-of-the art pre-processing and analysis protocols. We will distinguish between the hypothesis that the anterior and posterior hippocampus will be functionally connected to the attention network and default network, respectively (supporting a memory-based account), or the opposite pattern of functional connectivity (supporting a signal-to-noise account).

Caroline Smith (Psychology)

The Smith Lab investigates the impact of gestational exposure to air pollution and maternal stress on fetal development. In this project, we are using single-nucleus RNA sequencing to elucidate the impact of these exposures on cell-type specific gene expression profiles in the placenta and fetal brain.

Rishi Sonthalia (Mathematics)

This research project has two main focuses. The first is the use of geometry to improve machine learning models. In particular, we are interested in using curvature to improve the performance of Graph Neural Networks. The second focus is statistical learning theory. Here we prove theorems about the generalization error. To validate these theorems, we train models and empirically verify our results. We are currently focused on the interaction of different regularization and the generalization error.

Sebastian Steffen (Carroll School of Management)

My research focuses on the future of work and on how information and automation technologies transform businesses and society. While technologies have the potential to complement workers, raise productivity, and improve society, they also pose the risk of uncertainty, obsoletion, and technological displacement. Several of my projects leverage data on online job postings, as an expression of skill demands, to understand (i) the value of human capital, (ii) the impact of technologies on occupations and firms, (ii) the strategic hiring responses made by firms in response to unexpected shocks, and (iii) how technologies diffuse through the economy.

Richard Sweeney (Economics)

A growing literature demonstrates that the most valuable piece of information for identification and estimation of heterogeneous consumer preferences is second-choice data. A common way to measure second-choice data is via surveys or conjoints, which may capture stated rather than revealed preferences. We develop a method to recover second choices from aggregate data either via field experiments or observationally that is consistent with consumers making discrete choices under a minimal set of restrictions from consumer theory. We illustrate our method in an experiment where we randomly remove products from vending machines.

Fazel Tafti (Physics)

We use two computational programs, WIEN2k and CASTEP, to compute the electronic structure of intermetallic solids. Areas of interest are topological semimetals, itinerant magnets, superconductors, van der Waals systems, and magnetic semiconductors. For magnetic materials, we analyze the ground state spin structure to identify nearly degenerate magnetic structures. For metallic systems, we compute the Fermi surface geometry and compare our computational results to quantum oscillation experiments. For semiconductors, we analyze the direct and indirect gaps and compare the calculated values to experimental values from spectroscopic measurements. These calculations provide invaluable insights into the design and engineering of quantum materials synthesized in our group.

Hanqin Tian (Earth and Environmental Sciences)

Our research group has been pursuing a data-driven systems approach to a predictive understanding of complex interactions among climate, ecosystems, and society in the context of coupled natural-human systems. By using emerging technology and theory in earth system modeling, satellite observations, high-performance computing, Big Data, and AI, we have worked on a range of topics in climate change and global sustainability, including Harnessing AI and Big Data to quantify and predict carbon and nitrogen cycles and greenhouse gas emissions; Harnessing AI and Big Data to understand and predict Extremes, Thresholds, and Tipping Points; Harnessing AI and Big Data to predict land-coastal interactions for achieving coastal resilience; and Harnessing AI and Big Data to understand and predict food and water security.

Robert Ulbricht (Economics)

This paper studies the dynamics of skill mismatch over the business cycle. We build a tractable directed search model, in which workers differ in skills along multiple dimensions and sort into jobs with heterogeneous skill requirements along those dimensions. Skill mismatch arises due to information and labor market frictions. Estimated to the U.S., the model replicates salient business cyclic properties of mismatch. We show that job transitions in and out of bottom job rungs, combined with career mobility of workers, are important to account for the empirical behavior of mismatch. The model suggests significant welfare costs associated with mismatch due to learning frictions.

Rosen Valchev (Economics)

This project seeks to understand the origins of nominal price stickiness, which is both a pervasive feature of the data, and a crucial ingredient in modern macroeconomic theoretical models Yet, standard models of price stickiness are at odds with certain robust empirical facts from micro price datasets. To address this, we explore a new, parsimonious theory of price rigidity, built around the idea of demand uncertainty, that is consistent with a number of salient micro facts. In the model, firms face Knightian uncertainty about their competitive environment. They learn non-parametrically about the underlying, uncertain demand and make robust pricing decisions. The non-parametric learning leads to kinks in the expected profit function at previously observed prices, which generate price stickiness and a discrete price distribution. In addition, we show that when the ambiguity-averse firm worries that aggregate inflation is an ambiguous signal of the prices of its direct competitors in the short run, the rigidity becomes explicitly nominal in nature.

Joanna Venator (Economics)

My research focuses on household location choice, family structure, and gender differences in earnings. For example, in ongoing research, I explore how dual earner couples make location choices and the impacts of those location choices on men and women's earnings. I show that women are more likely to be trailing spouses and explore how different factors, such as access to unemployment insurance or choice of occupation, impact these outcomes. I use large micro-level data sets, reduced form econometric methods, and structural models to answer these questions.

Matthias Von Davier (Education)

The TIMSS & PIRLS International Study Center at the Education conducts two major ongoing programs of international assessment of student achievement: TIMSS (Trends in International Mathematics and Science Study), which involves more than 60 countries and assesses fourth- and eighth-grade student achievement in mathematics and science, and PIRLS (Progress in International Reading Literacy Study) which involves more than 40 countries and assess fourth-grade students' reading achievement, as well as additional special studies. Current research at the center is investigating how machine learning methods can be used to improve assessment development as well as the analysis and reporting of data collected on students taking the assessments. The center conducts research on automated test assembly, automated item generation using deep learning methods, and variable selection studies using machine learning methods. Other studies include examining the potential application of pre-trained language models to item development and item banking, using artificial neural networks to classify items as well as text-based and graphical responses. Additional research studies may include Bayesian estimation of latent variable models as well as parallel programming to speed up the estimation of statistical models for large-scale data analysis.

Dunwei Wang (Chemistry)

The Wang group's research is centered around artificial photosynthesis to mitigate problems caused by the usage of fossil fuels. We will use the cluster to understand the nature of charge transfer between catalyst and photoelectrodes. The understanding is an important piece of our effort to mimic photosynthesis in the lab, which will pave the way toward a green, sustainable solution to our energy needs.

Ziqiang Wang (Physics)

Our research group studies the fundamental physics of strongly correlated electronic materials with a special focus on that of the high temperature superconductors. Understanding the unconventional, complex, and emergent physical properties in these materials represents both the challenge and the vitality of condensed matter physics. The strong many-body correlations in these systems render the problem nonperturbative and the investigation of the possible electronic states of matter and the low energy excitations defies conventional perturbation approaches that use noninteracting electrons as a starting point. As a result, it is very difficult to study these materials by purely analytical means and numerical computations have played and continue to play a key role in this rapidly developing field. The computational component of the research projects in our group involves exact diagonalization, variational and quantum Monte Carlo simulations, and manipulations of large random matrices.

Donglai Wei (Computer Science)

Our research focuses on developing computational algorithms for biomedical image analysis and natural video understanding. Current projects include brain map reconstruction, biomedical predictive modeling, and video instance segmentation.

Eranthie Weerapana (Chemistry)

Our lab utilizes mass spectrometry to identify and quantify proteins from complex mixtures. We apply chemical probes to specifically enrich subsets of proteins based on activity or posttranslational modification-state for analysis by mass spectrometry using an LTQ-Orbitrap instrument. We are particularly interested in the functional significance of protein oxidation and glycosylation and seek to develop novel chemical proteomic technologies for quantitatively profiling these protein modifications. The 91福利导航 research cluster will be used for data analysis software programs that correlate tandem mass spectra of peptides with amino acid sequences from protein databases. One such search algorithm, known as SEQUEST, cross correlates the observed tandem mass spectrum to theoretical spectra to identify the best candidate sequence match. Using a combination of chemistry, biology, mass spectrometry and bioinformatics, we hope to identify novel dysregulated protein activities implicated in a variety of patho physiological states.

Milena Wittwer (Carroll School of Management)

In traditional over-the-counter (OTC) markets, investors trade bilaterally through intermediaries referred to as dealers. An important regulatory question is whether to centralize OTC markets by shifting trades onto centralized platforms. We address this question in the context of the liquid Canadian government bond market. We document that dealers charge markups even in this market and show that there is a price gap between large investors who have access to a centralized platform and small investors who do not. We specify a model to quantify how much of this price gap is due to platform access and assess welfare effects. The model predicts that not all investors would use the platform even if platform access were universal. Nevertheless, the price gap would close by 32%--47%. Welfare would increase by 9%--30% because more trades are conducted by dealers who have high values to trade.

Zhijie Xiao (Economics)

Bootstrapping has attracted a lot of research attention in the last twenty years. It provides a convenient way of estimating the distribution of an estimator or test statistic by resampling the original data. In this project, we consider a prewhitened block bootstrap (PBB) method. The prewhitened block bootstrap combines the ideas of the parametric residual-based bootstrap and the nonparametric blockwise bootstrap. The stated idea of prewhitened block bootstrap is as follows: First, one prewhitens (prefilters) the original data to obtain a less dependent series; then block bootstrap is applied to the prewhitened (filtered) data, which has less dependence; finally the (block) bootstrapped data is recolored to produce a bootstrapped data set for the original series, and bootstrap estimation and inference procedures can be constructed based on the recolored data. Bootstrap is a very computational intensive method, high power computers are needed for this research project.

Nancy R. Xu (Carroll School of Management)

My current work focuses on the identification of the dynamics of risk aversion (price of risk) and economic uncertainties (amount of risk) and their effects on both domestic and international asset markets. My work requires complex estimation on the dynamics of economic and financial data. For example, in one of my projects, we study the joint dynamics of growth rates across 180 countries over the past 50 years by exploiting multivariate gamma shocks. In another work, I estimate and extract a global risk aversion measure by incorporation of a large information set of financial and economic information around the world.

Hanyi Yi (Carroll School of Management)

This project studies whether and how shocks to the supply of municipal credit affect both the quantity and quality of local public goods provision, and through this channel, resident migration. I use a difference-in-differences approach based on a policy that unintendedly decreased bank investment in municipal bonds, and an amendment to the policy that partially reversed its effects. I assemble a massive dataset that consists of government-level data on spending and investment, county-level data on the quality of public goods, and individual-level data on geographic mobility.

Liane Young (Psychology)

Our research group studies the cognitive and neural basis of human moral judgment. Our current research focuses on the role of theory of mind, mind attribution, and emotions in moral judgment and behavior, as well as individual and cultural differences in moral cognition. We employ methods of social psychology and cognitive neuroscience, including functional magnetic resonance imaging (fMRI).

Yuan Yuan (Computer Science)

FLAC leverages Cycle-Consistent Generative Adversarial Networks (CycleGAN) to translate images between 35mm and 200mm focal lengths. By training on unpaired data, the model uses cycle consistency loss to ensure integrity and coherence in translated images. This approach enhances datasets and improves model performance in tasks like depth estimation and scene understanding, with applications in autonomous driving, aerial photography, and professional photography. FLAC provides a robust tool for synthesizing realistic images across different focal lengths, addressing the challenges posed by limited paired training data.

Peter Zhang (Chemistry)

Research in the Zhang group focuses on the development of metalloradical catalysis (MRC) as a new concept to develop general approaches for controlling reactivity and selectivity of radical reactions. The current projects involve the design of Co(II)-based metalloradical catalysts for enantioselective olefin aziridination/cyclopropanation and C-H alkylation/amination reactions. The Linux Cluster is used for performing molecular modeling of the catalytic radical processes. Theoretical investigations carried out with Gaussian and/or Jaguar will be invaluable to the design and optimization of these catalytic radical processes.

Huiqing (Jane) Zhou (Chemistry)

The Zhou Lab is interested in understanding the recognition mechanism between RNA chemical modification reader protein and the modified RNA, and in engineering designer reader proteins to recognize chemically modified RNA. The cluster will be used to predict and assess binding energetics between modified RNA and protein residues.

Ziyan Zhu (Physics)

The central theme of our group鈥檚 research is the design and characterization of large and complex physical systems across various scales, from atomistic to mesoscopic to macroscopic. Our research focuses on two-dimensional quantum materials lacking periodicity, employing a hierarchy of numerical models, including first-principles density functional theory, low-energy continuum models, many-body models, and machine learning. Additionally, we apply concepts from condensed matter physics, such as topology and band theory, to phenomena in the ocean and fusion plasmas.

We expect users to follow common policies for the use of shared computers, including:

• No shared accounts. The standard UNIX group structure can easily accommodate sharing of files within a group. If you have special needs, please contact us. We should be able to accomodate these needs.
• Boston College's听Computing Policies and Guidelines proper use policies apply to accounts on the cluster.
  听