Directory · Professors · 222 entries
AI professors at Boston University
The directory lists 222 BU faculty whose research develops or applies AI, grouped below by primary research area. Each name links to a profile with research interests, contact details, and — where we have sourced them — the labs and courses they are linked to.
Learning theory & data science
- Aaron MuellerA natural language processing researcher who studies how to understand, improve, and precisely control language models. His work applies mechanistic interpretability, causal analysis, and cognitive-science-inspired evaluation to questions of model robustness and efficiency, mentoring students who lead publications on topics such as concept unlearning and model steering.
- Adam SmithA theoretical computer scientist working on data privacy and cryptography and their connections to machine learning, statistics, and information theory. He co-invented differential privacy, a rigorous framework for analyzing privacy in statistical databases, work recognized with the 2017 Gödel Prize and now used in deployed systems.
- Aguêmon Yves AtchadéA computational statistician developing Markov chain Monte Carlo methods and studying the theoretical properties of Bayesian procedures. His work builds statistical machinery for high-dimensional inference and applies it to environmental prediction, spanning both the asymptotic theory of sampling algorithms and their practical use on large datasets.
- Ajay JoshiA computer engineering researcher who designs energy-efficient computing systems, spanning computer architecture, hardware security, and silicon photonics. His group develops heterogeneously integrated electronic-photonic accelerators intended to run AI workloads faster and more efficiently, work supported by a National Science Foundation award.
- Alan MarscherAn astrophysicist who studies blazars, active galactic nuclei that launch plasma jets moving near light speed. With collaborators and students in BU's blazar research group, he builds physical models of jet formation and particle acceleration and interprets multifrequency monitoring observations from radio through gamma-ray telescopes.
- Aldo PacchianoA machine learning theorist who studies how algorithms learn in adaptive environments, working on reinforcement learning, online learning, bandit problems, and algorithmic fairness. He designs methods with provable efficiency and safety guarantees and applies them to biomedical research questions.
- Alexander OlshevskyA control and optimization theorist whose work develops distributed algorithms that let networks of autonomous agents, such as vehicle formations and sensor networks, cooperate reliably. His recent research analyzes reinforcement learning methods, proving convergence guarantees and sample-complexity bounds for algorithms including temporal difference learning and actor-critic.
- Alice Cronin-GolombA clinical neuropsychologist whose work examines how perception and cognition change in aging and neurodegenerative disease, with emphasis on the non-motor symptoms of Parkinson's disease. Her Vision and Cognition Laboratory combines visual psychophysics, neuropsychological assessment, portable brain imaging, and smartphone-based symptom tracking in these populations.
- Alina EneA theoretical computer scientist whose work designs and analyzes algorithms for combinatorial optimization, with emphasis on submodular functions and graphs. Her research examines the mathematical structure of these problems and applies the resulting algorithms to machine learning and the foundations of data science.
- Allyson SgroA quantitative biologist whose work examines how cells communicate and coordinate collective behaviors such as tissue assembly, wound healing, and biofilm formation. Her laboratory combines optogenetics and microscopy with machine learning and mathematical modeling to build a predictive understanding of how multicellular systems organize themselves.
- Anand DevaiahA skull base surgeon and health technology researcher whose work applies data analytics, AI, and ML to health care, including unlocking data held in siloed clinical repositories and developing digital biomarkers. He collaborates with startups and established companies to move new medical technologies into practice.
- Anatoly TemkinA mathematician whose expertise centers on cryptography, with research interests in information security and curriculum design. He teaches undergraduate and graduate courses in discrete mathematics, cryptography, and algorithms, and received BU's Metcalf Award for Excellence in Teaching for his classroom work.
- Andrew EmiliA systems biologist whose work maps protein interaction networks, biochemical pathways, and macromolecular complexes on a large scale. His research develops proteomic, genomic, and bioinformatic technologies, using protein mass spectrometry and computational analysis to reveal how molecular associations function in health and disease.
- Andrew FitzpatrickA theoretical physicist whose work develops quantum field theory methods, with emphasis on effective field theories and scale-invariant systems. His current research uses the conformal bootstrap, which constrains theories through symmetry and consistency requirements, to study quantum gravity and dualities between seemingly different physical descriptions.
- Andrew LyasoffA mathematician whose work applies stochastic analysis to asset pricing and the study of continuous-time financial markets, including equilibrium in incomplete markets. His research develops computational methods for finance, such as spline cubatures for expectations of diffusion processes, and he wrote the MIT Press text Stochastic Methods in Asset Pricing.
- Andrew SabelhausA roboticist whose work builds control systems and AI for soft and flexible robots, combining computational intelligence with embodied intelligence. His Soft Robotics Control Lab develops algorithms for locomotion and manipulation that aim to make close physical contact between robots and humans verifiably safe.
- Andrew SellarsA technology lawyer whose scholarship examines how intellectual property and computer access laws apply to technology research and journalism. As founding director of BU's Technology Law Clinic, he has supervised law students representing student security researchers, journalists, and startups on privacy, cybersecurity, and media law questions.
- Anthony RoselliniA clinical psychologist whose work studies the onset and course of anxiety and mood disorders. His research applies machine learning to clinical and epidemiologic data to build prediction tools that identify people at risk for these conditions, with applications including depression and suicide risk in military populations.
- Archana VenkataramanA biomedical imaging and AI researcher whose work connects machine learning with clinical neuroscience. Her Neural Systems Analysis Laboratory develops algorithms for multimodal brain imaging data, including methods that localize seizure onset in epilepsy and models that characterize disorders such as autism and schizophrenia.
- Ari TrachtenbergAn algorithms and security researcher whose work develops methods for data synchronization across distributed systems, error-correcting codes, and cybersecurity for smartphones and networked devices. His synchronization research includes protocols that reconcile transaction pools in large-scale blockchain networks, and he directs BU's Red Hat Collaboratory.
- Ashok CutkoskyA machine learning theorist who designs optimization algorithms for training models, focusing on online learning, stochastic non-convex optimization, and parameter-free methods that remove manual hyperparameter tuning. He teaches BU courses on machine learning and optimization and advises PhD and undergraduate researchers.
- Ayse CoskunA computer systems researcher whose work spans design automation, large-scale computer systems, and applied machine learning, with a focus on energy efficiency. She develops modeling and optimization methods that manage performance and power in data centers and high-performance computing clusters.
- Azer BestavrosA computer scientist specializing in distributed systems and trustworthy computing, with contributions to internet content delivery, cloud resource management, and privacy-preserving data analytics. He served as founding director of BU's Hariri Institute and now leads the Faculty of Computing & Data Sciences as associate provost.
- Belinda BorrelliA clinical psychologist who develops and tests digital health interventions that motivate behavior change, particularly among people not yet motivated to change. Her work uses mobile platforms such as apps, text messaging, and virtual reality for smoking cessation, oral health, and adherence to chronic disease treatment.
- Benjamin LubinA researcher at the intersection of computer science, game theory, and economics who designs market mechanisms such as combinatorial auctions and exchanges. He also applies spectral graph theory, network science, and machine learning to analyze social networks and improve healthcare delivery, and directs a master's program in digital technology.
- Bin GuAn information systems researcher who examines how information technologies and AI can address information asymmetry and social inequity in business and society. His studies trace these dynamics across settings such as fintech, digital platforms, and online social media, connecting technology design to economic and social outcomes.
- Bindu KalesanA clinical epidemiologist and biostatistician who studies the public health consequences of firearm violence, including injury survivorship, and long-term outcomes after cardiovascular disease and traumatic injury. Her methods include longitudinal cohort studies, meta-analysis, and spatiotemporal modeling of large clinical and population datasets.
- Björn ReinhardA nanomaterials chemist who develops plasmonic probes and functional optical materials for imaging, sensing, and photonics. His interdisciplinary group studies the interface between nanotechnology and biological systems, using engineered nanostructures to probe and control cellular processes, characterize nanoparticle-cell interactions, and profile tumor cells.
- Brian ClearyA computational biologist who applies compressed sensing and related algorithmic techniques to make genomic experiments more efficient. His group develops computational and laboratory methods for composite imaging transcriptomics, compressed genetic perturbation screens, and viral infection assays, recovering rich biological signal from far fewer measurements.
- Brian DePasqualeA computational neuroscientist whose research sits at the boundary of machine learning and theoretical neuroscience, developing models of how biological neural circuits give rise to computation and behavior. He teaches a foundations course on biomedical data analysis and machine learning.
- Brian KulisA machine learning researcher focused on large-scale optimization for problems such as metric learning, clustering, and content-based search, combining Bayesian methods with deep learning. His applications center on audio and visual data, and he teaches machine learning and deep learning courses while advising doctoral and undergraduate researchers.
- Carol NeidleA linguist specializing in American Sign Language (ASL) who collaborates with computer scientists on video-based sign language recognition. Her work has produced computational tools such as SignStream and large annotated ASL video corpora that support both linguistic analysis and computer vision research.
- Catherine Caldwell-HarrisA psycholinguist who studies how bilingual speakers process language and emotion, combining experimental measures such as skin conductance with cross-cultural surveys. Her current work also examines large language models from a psychological perspective, with undergraduate researchers participating through BU's Undergraduate Research Opportunities Program.
- Catherine EspaillatAn astrophysicist who studies planet formation, searching protoplanetary disks around young stars for the footprints that forming planets leave behind. She combines multiwavelength observations from X-ray to radio with computer simulations and radiative transfer modeling, and co-leads an international consortium analyzing Hubble Space Telescope data.
- Cathie Jo MartinA comparative political scientist who applies computational text analysis to centuries of British and Danish literature to trace the cultural origins of national education systems. Her work examines how these systems shape social inclusion and outcomes for low-skill youth, pairing literary corpora with political-economy scholarship.
- Chao ZhangA computational biologist who develops machine learning and statistical methods for next-generation sequencing data, including single-cell RNA sequencing. His research builds deep learning frameworks to uncover temporal changes in host-microbiome interactions and applies genomic analysis to stem cell biology, cancer, and neurodegenerative disease.
- Charalampos (Babis) TsourakakisAn algorithms researcher whose work spans large-scale graph mining and machine learning, including methods for finding dense subgraphs in massive networks. His research applies these techniques to problems such as anomaly detection, community detection, and web graph analysis, and he has built graph mining libraries adopted in industry.
- Charlene OngA neurointensivist who develops and validates machine learning and dynamic risk-prediction tools to guide treatment decisions for patients with severe brain injury, including ischemic and hemorrhagic stroke. Her research draws on physiologic biomarkers and multimodal clinical data to improve outcomes in neurocritical care.
- Christoph NolteA conservation scientist who combines remote sensing, property and sales data, causal inference, and predictive machine learning to measure the effects and costs of land conservation. His projects estimate what protection actually costs, evaluate biodiversity and climate policies, and value environmental amenities and risks.
- Christos CassandrasA control and systems theorist who studies discrete-event and hybrid systems, cooperative multi-agent control, and optimization for cyber-physical systems. His work applies these methods to smart cities and connected automated vehicles, including adaptive traffic-light control, smart parking, and safe coordination of autonomous vehicle networks.
- Chuanfei DongA computational plasma physicist who models interactions between stars and terrestrial planets in the solar system and beyond, along with magnetic reconnection, turbulence, and wave-particle interaction. His research combines high-performance simulation with physics-informed machine learning to study space plasmas and laser-plasma interaction.
- Clem KarlA computational imaging researcher who develops statistical methods for signal and image processing, estimation, and image reconstruction. His work extracts information from noisy, uncertain data in applications spanning medical tomography and magnetic resonance imaging, synthetic aperture radar target recognition, and geophysical and atmospheric sensing.
- Conor MackA strategy and innovation educator whose interests center on machine learning, large-scale data mining, and knowledge discovery in business applications such as business intelligence and e-commerce. Alongside teaching, he directs a venture studio and accelerator for early-stage companies building AI and Web3 technologies.
- Dan LiAn urban climate scientist who uses weather and climate models, fluid mechanics, and observational data to study how urbanization and climate change reshape city heat, water, and energy cycles. His research examines urban heat islands and heat waves to inform planning for hotter cities.
- Daniel FulfordA psychologist who studies motivation and social processes in psychopathology, using smartphone-based experience sampling and behavioral sensing to capture daily life. His research develops mobile interventions, including an app that combines social skills training with motivation support to improve social functioning in schizophrenia.
- Daniel SegrèA computational biologist who studies the structure, dynamics, and origin of metabolism from single cells to microbial ecosystems. His group combines mathematical models with metagenomic sequencing to predict metabolism-mediated microbial interactions, developing simulation frameworks with applications in human microbiome research, biogeochemical cycling, and synthetic ecology.
- Daniel SussmanA statistician who develops methods for network data, including node embeddings, graph matching, and causal inference under interference. His work examines trade-offs between computational constraints and statistical efficiency and applies network inference to brain connectomes, molecular and social networks, and knowledge graphs.
- David CampbellA theoretical physicist who studies nonlinear phenomena and complex systems, including intrinsic localized modes, novel electronic materials, and electron transport in semiconductor superlattices. His work uses analytical theory and computational simulation to explain how nonintegrable dynamical systems approach equilibrium or remain trapped in metastable states.
- Deepak KumarA clinician-scientist who develops technology-enabled movement interventions for adults with knee osteoarthritis, aiming for sustained improvements in pain and physical function. His research tests AI-powered wearable sensors that assess patients' movement patterns at home and in the clinic, supporting more accessible rehabilitation care.
- Diane Joseph-McCarthyA computational chemist whose work applies molecular modeling, structure-based design, and machine learning to early-stage drug discovery. Her research develops fragment-based lead discovery methods and combines cryo-electron microscopy structural data with AI to identify and optimize candidate therapeutics, including antibacterials.
- Dokyun LeeA business data scientist who combines text mining, machine learning, and causal inference to extract market and consumer insights from unstructured data. His research examines generative AI and algorithmic transparency in marketing and retail settings, and he teaches analytics courses and mentors doctoral students.
- Douglas DensmoreA computer engineer whose research builds design-automation tools for synthetic biology, adapting techniques from electronic design automation to living systems. His group develops programming languages, software tools, and microfluidic platforms for designing, assembling, and testing engineered biological circuits with predictable behavior.
- Dylan WalkerA computational social scientist whose work measures how information, behaviors, and products spread through online social networks. He combines randomized experiments on large platforms with causal analysis of observational data to study peer influence, misinformation, and how platform design shapes collective outcomes.
- Elaine NsoesieA computational epidemiologist whose research applies data science and machine learning to public health surveillance. She studies how data from social media, search engines, and mobile phones can track disease and inform policy, and how these tools can advance health equity.
- Emily RyanA computational engineer whose research models reactive transport, fluid mechanics, and electrochemistry in energy systems. Her Computational Energy Laboratory develops multiphysics simulations to study advanced batteries, fuel cells, and carbon capture, including work on understanding and suppressing dendrite growth that limits battery life.
- Emma LejeuneA computational mechanics researcher whose work integrates machine learning with physics-based simulation to characterize and predict the mechanical behavior of heterogeneous materials and biological systems. Her group builds benchmark datasets and metamodels that let researchers evaluate ML approaches to problems such as brittle fracture.
- Erol PekozAn applied probabilist whose research develops approximation methods for probability distributions and models of networks, queueing, and reliability. He applies these tools to operations management and healthcare, including statistical methods for measuring care quality and comparing performance across healthcare providers.
- Eugene PinskyA computer scientist whose research designs computationally simple, explainable machine learning methods for prediction and classification. His published work spans financial modeling, time-series analysis, and clustering, and he has developed core data science courses and led student projects in applied machine learning.
- Evimaria TerziA computer scientist whose research develops algorithms for data mining and machine learning, with emphasis on social network analysis, recommendation systems, and clustering. Her recent work extends these algorithmic methods to problems in online learning, urban informatics, and large language models.
- Gabriel OckerA theoretical neuroscientist whose research studies how the structure of neuronal networks shapes their activity and function. His group builds mathematical models of neural circuits using dynamical systems, stochastic processes, and statistical physics, and tests model predictions against neural data from experimental collaborators.
- Gareth MorganA protein biochemist whose research examines why antibody light chains misfold and aggregate into amyloid fibrils in light-chain (AL) amyloidosis. His group combines biochemical experiments with computational structure prediction to understand light-chain instability and to develop small molecules that stabilize the proteins as potential therapies.
- Georgios ZervasA quantitative marketing researcher whose work sits at the intersection of data science and economics, using large-scale data to study online platforms and marketplaces. His research examines the sharing economy's impact on incumbent industries, online reputation and reviews, and how digital markets shape competition.
- Gerdus BenadeAn operations researcher working at the intersection of computer science and economics, studying group decision-making under uncertainty and the fair division of resources. His work applies optimization and computational social choice to civic processes such as voting, participatory budgeting, and political districting.
- Gerry TsoukalasA business technology researcher who examines how emerging technologies change the way firms operate, finance themselves, and design platforms. His work covers blockchain governance, token economics, and fintech platform design, with recent projects on AI in business and human-AI collaboration.
- Gianluca StringhiniA security researcher who applies data-driven methods to understand and mitigate malicious activity online. By collecting and analyzing large-scale datasets with machine learning, statistical analysis, and systems design, he studies threats such as malware, coordinated harassment, and influence operations, and builds techniques to counter them.
- Gustavo SchwenklerA financial economist who develops statistical and computational tools for measuring financial risk, spanning asset pricing, credit and systemic risk, and cryptocurrencies. His econometric work includes machine learning methods that extract networks of economically linked firms from news text to study how risk spreads.
- Hadi NiaA mechanobiology researcher who develops tools and models at the interface of physical sciences, biology, and immunology to study cancer and lung disease. His group examines mechanical forces in the tumor microenvironment and combines biophysical modeling with AI to detect lung cancer from computed tomography scans.
- Harold ParkA computational mechanics researcher who develops simulation techniques for material behavior from the atomic scale to the macroscale. His group studies topological mechanics, soft active materials, and phononic metamaterials, and integrates machine learning with atomistic simulation to search for materials with designed mechanical properties.
- Helen JenkinsA biostatistician whose work applies statistical modeling to infectious disease data, with a focus on tuberculosis. She develops estimates of pediatric tuberculosis incidence and mortality and uses spatial methods to examine geographic variation in drug-resistant disease, aiming to inform public health policy and decision-making.
- Ioannis PaschalidisAn optimization and control researcher who integrates stochastic modeling with machine learning to develop methods robust to noisy, incomplete data. His work applies these tools to computational medicine and biology, health analytics, and networked systems, and he directs the Hariri Institute, BU's computing research hub.
- Irena VodenskaA finance researcher who applies network theory and complexity science to model interconnected financial systems, banking dynamics, and systemic risk propagation. Her work develops early warning indicators for financial crises and uses deep learning for natural language processing to mine news for factors affecting markets.
- Iván Fernández-ValAn econometrician whose research develops methods for nonlinear panel data models, quantile regression, and distributional and causal analysis, with applications in labor economics. His recent work examines how machine learning methods can be applied to causal inference questions in empirical economics.
- Jacob BorA population health researcher who applies the tools of economics and data science to study HIV treatment and prevention in southern Africa and structural drivers of health disparities in the United States. His methods work advances causal inference designs, including regression discontinuity, to estimate causal effects without randomized trials.
- James ChapmanA materials informatics researcher who fuses physics-based simulation with machine learning to discover and design materials for energy generation and storage, corrosion resistance, and pollution mitigation. His group combines quantum-mechanical and multiscale modeling with learned models that rapidly predict properties of candidate materials under extreme conditions.
- James FeigenbaumAn economic historian and labor economist who studies inequality, intergenerational mobility, and health in the nineteenth- and twentieth-century United States. He develops machine learning methods for linking individuals across digitized historical census records, using the resulting data to trace the long-run effects of public policy and environmental shocks.
- James KatzA communication researcher who examines how emerging media technologies, especially AI and mobile communication, change social interaction and everyday life. Through books and studies of technology adoption, he explores what these tools reveal about human nature and organizations, and he directs a project on AI's implications for communication.
- James McDanielA structural acoustics researcher who studies how vibrations and sound interact with complex structures and materials, from acoustic metamaterials to viscoelastic damping. He combines theory, computation, experiment, and machine learning to model dynamic systems, with applications in sound absorption, target identification, and the built environment.
- Janet FreilichA legal scholar who studies patent law and innovation policy through large-scale empirical analysis, including a dataset of over two million patents. Her work examines how untested prophetic examples, inaccurate information, and AI-derived drug discoveries shape what patents disclose and how the patent system handles scientific evidence.
- Jennifer BeaneA computational biologist who develops and applies genomic methods to understand early lung cancer development. Her group analyzes bulk and single-cell RNA sequencing, spatial transcriptomics, and pathology images from the smoking-injured airway to find molecular markers that predict lung cancer risk and candidates for preventive therapy.
- John LiagourisA systems researcher who builds distributed data processing systems with strong security and privacy guarantees. He co-leads a lab developing platforms for secure collaborative analytics, including Secrecy, a system that lets multiple organizations run computations over combined data using cryptographic protocols without revealing their private inputs.
- Jonathan HugginsA statistician and machine learning researcher who develops scalable Bayesian inference methods with trustworthy uncertainty quantification, even when models are imperfect and inference is approximate. His group builds computational tools applied to large-scale ecological forecasting and to scientific discovery from high-throughput, multi-modal genomic data.
- Jonathan JayA public health researcher who studies urban gun violence, particularly youth exposure, using machine learning, satellite imagery, and spatial analysis to identify block-level risk factors. His work evaluates place-based interventions such as building demolitions and vacant-lot improvements as strategies for reducing firearm injuries and racial disparities.
- Joshua CampbellA computational biologist who develops Bayesian methods and software for analyzing single-cell and spatial omics data and mutational signatures in cancer. His lab applies these tools to precancerous lung lesions and prostate cancer disparities, generating multi-omic datasets from patient samples to uncover mechanisms of tumor development.
- Juan Fuxman BassA systems biologist who studies how human gene regulatory networks control immune responses, cell differentiation, and viral pathogenesis. His lab integrates high-throughput screening, including an enhanced yeast one-hybrid platform testing over a thousand transcription factors, with bioinformatics analysis to map which factors regulate which genes.
- Julie DahlstromA legal scholar whose research examines how human trafficking law in the United States has evolved and how litigators mobilize it, including its expanding reach across federal and state statutes. She directs a clinical program in which law students represent immigrants and trafficking survivors in real cases.
- Kaija SchildeA political scientist who studies the political economy of security, asking why states outsource security to firms and markets. Her work examines defense industries, military spending, and the relationship between spending, innovation, and capabilities, with a focus on the European Union and transatlantic security institutions.
- Kamal SenA computational neuroscientist who studies how the brain encodes and separates natural sounds, focusing on the cortical basis of auditory scene analysis. His lab combines neural recordings with information theory and signal processing, and develops brain-inspired algorithms for the cocktail party problem to improve hearing assistive devices.
- Katya RavidA molecular biologist who studies how bone marrow megakaryocytes develop into platelets, including the cell cycle control behind polyploidization, and how adenosine receptors shape vascular regeneration in injury and atherosclerosis. She also directs a university office fostering interdisciplinary biomedical research collaborations across BU's campuses.
- Keith BrownA materials researcher who develops self-driving laboratories that combine robotics, additive manufacturing, and machine learning to increase the pace and scale of experimentation. His group studies polymers and smart fluids to learn how useful properties emerge from composition, processing, and hierarchical structure.
- Kevin GoldA computer scientist and educator who teaches introductory data science, machine learning, and AI, and studies how large language models can support learning without short-circuiting it. His background spans industry and research roles at Google, MIT Lincoln Laboratory, and Epson, along with interests in modeling human behavior.
- Kia TeymourianA computer scientist who studies data stream and complex event processing, big data systems, and natural language processing. Recent work develops machine-learning models that predict heat transfer and air-pollutant dispersion over urban environments, and he teaches courses in data analysis and visualization.
- Kirill KorolevA biophysicist who uses mathematical modeling to understand evolution and population dynamics in microbial communities, expanding populations, and cancer progression. His work draws on statistical physics and stochastic processes, combining analytical theory with computational analysis to ask how cooperation, competition, and spatial structure shape ecosystems.
- Konstantinos SpiliopoulosAn applied mathematician whose research develops algorithmic and computational methods for machine learning, including mean-field analysis of neural networks and uncertainty quantification in deep learning. He also studies stochastic processes, multiscale methods, and large deviations, with applications in financial mathematics and statistical network modeling.
- Krzysztof OnakAn algorithms researcher focused on computing over massive datasets with limited resources, covering sublinear-time algorithms, streaming and sketching, and massively parallel computation. Recent projects address fairness in clustering, graph pattern mining, and estimating properties of probability distributions. His earlier industrial work covered resource allocation and scheduling.
- Lauren WheelockAn optimization researcher whose work spans algorithms for matrix completion and natural language processing, machine learning for biological sequence engineering, and educational technology. She teaches mathematical foundations alongside the practice of modeling: designing, applying, and interpreting models in ambiguous settings.
- Laurina ZhangAn economist of innovation who examines how technology and information access shape innovative, entrepreneurial, and creative outcomes, particularly for disadvantaged groups. Her empirical research studies the organizational and policy levers that affect innovation and inequality, spanning questions of digitization and democratized participation in creative work.
- Lei TianA computational imaging researcher who jointly designs optics and algorithms, applying deep learning to biomedical microscopy and neurophotonics. His group builds methods such as differential phase contrast microscopy, Fourier ptychography, and imaging through scattering media, using neural networks to reconstruct and digitally label large-scale microscopy data.
- Lou ChitkushevA computer scientist whose research spans health informatics, network security, and data assurance across healthcare, biomedical, and financial information systems. He co-founded BU's center for reliable information systems and cybersecurity, founded its health informatics program, and teaches medical informatics, computer networks, and network security.
- Lucy HutyraAn urban ecologist who studies the carbon cycle in cities, asking how urbanization alters vegetation, greenhouse gas emissions, and carbon uptake. Her group combines remote sensing, atmospheric observations, and ecological models on large NASA projects to quantify urban emissions and inform sustainability planning.
- Ludovic TrinquartA biostatistician and clinical trial methodologist whose work improves the design, analysis, and interpretation of randomized trials. His research develops methods for time-to-event outcomes, meta-analysis, and patient-centered measures of treatment effect, collaborating with clinical investigators across oncology, cardiology, and population health.
- Luis CarvalhoA statistician who develops Bayesian methods for statistical inference in structured, high-dimensional discrete spaces, together with computational statistics and network modeling. Applications include land-cover classification from satellite imagery, gene-interaction analysis in genome-wide association studies, and community detection in social networks.
- Maggie MulvihillAn investigative journalist who applies computational and data-driven reporting methods to government accountability stories. She developed BU journalism's first data journalism course and has trained hundreds of journalists in data storytelling; students in her courses can practice data analysis and data-driven storytelling for investigative work.
- Marc HowardA theoretical cognitive neuroscientist who develops mathematical models of memory and evaluates them against behavioral and neurophysiological data. His lab studies how the brain represents time and space to support episodic memory, bridging cognition and systems neuroscience through mathematical, computational, and behavioral tools.
- Marc LenburgA computational biologist who uses genomic approaches to understand how tobacco smoke alters airway biology and drives diseases such as lung cancer and chronic obstructive pulmonary disease. His work translates gene-expression findings into diagnostics, including a nasal swab genomic test for assessing suspicious lung nodules.
- Marc RysmanAn empirical industrial organization economist who studies network effects, platform markets, and standardization, with attention to the antitrust and regulatory questions they raise. His research spans payment cards, telecommunications, and consumer electronics, and his teaching covers industrial organization, econometrics, and antitrust policy.
- Marco GaboardiA programming-languages researcher who builds formal methods and type systems for verifying differential privacy, producing mechanized proofs that data analyses protect individual records while staying accurate. He mentors PhD students and postdoctoral researchers and teaches courses on formal methods in security and privacy.
- Marianne BaxterA macroeconomist whose research examines economic fluctuations, the international transmission of business cycles, and pricing in international settings. Her empirical and theoretical work includes estimating exchange rate pass-through, analyzing price-setting behavior in monopolistic international markets, and testing asset pricing models with panel data.
- Mark CrovellaA computer scientist who applies data mining, statistics, and performance evaluation to the understanding and design of networks and networked computer systems. His work spans Internet measurement, traffic analysis, and anomaly detection, and extends network-analysis methods to social and biological networks.
- Mark KonA mathematician whose research develops machine learning theory, statistical learning, and quantum probability and information, with applications in bioinformatics and computational neuroscience. He co-leads BU's Stochastic Machine Learning Group, where graduate and undergraduate students work on theoretical and applied problems in data analysis and prediction.
- Mark KramerA mathematical neuroscientist who builds biophysical models of neural activity and develops data analysis methods for brain recordings. His work characterizes the brain patterns seen during epileptic seizures, aiming to help clinicians identify which brain regions and mechanisms to target when treating epilepsy.
- Marshall Van AlstyneAn information economist who studies how networks and platforms create value, developing theory of two-sided markets and platform business models that has been applied in antitrust analysis. He also designs market mechanisms, such as truth warrants, to combat misinformation and improve information quality online.
- Martin FiszbeinAn economist whose research examines economic growth and development, economic history, and urban economics, with a focus on historical political economy and historical development. His work uses evidence from history to study how economies grow over the long run, and he is a National Bureau of Economic Research associate.
- Martin HerbordtA computer engineer who designs high-performance computing systems using hardware accelerators, particularly field-programmable gate arrays and graphics processing units. His research applies these architectures to problems in bioinformatics and computational biology, and he teaches courses in computer organization, computer architecture, and high-performance programming.
- Mary DunlopA biomedical engineer who combines synthetic biology, feedback control, and deep learning to study and engineer microbial systems. Her lab applies optogenetics and single-cell microscopy to problems including antibiotic resistance and metabolic engineering, and she has received BU awards for teaching and postdoc mentoring.
- Mohammad Soltanieh HaA data scientist with a computational physics background whose research explores embodied AI, interactive avatars, and socially impactful applications of large language models. He teaches courses spanning big data analytics, cloud computing, and Python and R programming, and advocates for integrating data science into undergraduate curricula.
- Nachiketa SahooA machine-learning and information-systems researcher who studies how user preferences and decision processes can be learned from large-scale activity data to improve personalized recommendations. His work applies recommender systems and statistical models to e-commerce, digital media, and philanthropic giving, and he helped design the school's business analytics master's program.
- Nicholas CrosslandA pathologist who investigates how highly pathogenic microbes interact with their hosts, combining classical pathology with spatial transcriptomics, quantitative image analysis, and deep learning-based spatial immunoprofiling. His lab evaluates medical countermeasures against infectious diseases such as tuberculosis and filovirus infection, and he mentors master's and PhD students.
- Nicholas WagnerA developmental psychologist who examines how early experiences and self-regulation shape children's social and emotional development from infancy to early adolescence. His lab measures development at behavioral, experiential, and biological levels, studying outcomes from empathy to callous-unemotional traits, and undergraduate and graduate students assist with its research.
- Noora LoriA political scientist whose research examines citizenship and forced migration in the Middle East, showing how states can hold residents in prolonged temporary status without citizenship rights. Her book Offshore Citizens studies the United Arab Emirates, and she directs the Initiative on Forced Migration and Human Trafficking.
- Orkun BaycikAn operations researcher who applies machine learning, optimization, and prescriptive analytics to modeling and disrupting illegal supply chains, digital transformation of operations, and data analytics for nonprofits. He teaches business analytics courses, including spreadsheet optimization, simulation, and causal methods for business experiments.
- Pankaj MehtaA theoretical physicist working at the interface of statistical physics, machine learning, and biology. He studies how cell fates emerge from biomolecular interactions, ecological principles in microbial communities, and the theory of learning, and develops new ML methods for analyzing biological data.
- Paola SebastianiA biostatistician who develops Bayesian modeling and machine-learning methods for genetic and genomic data, dissecting the biological basis of complex traits. Her work includes network models predicting complications of sickle cell anemia and long-running statistical studies of human longevity, including the New England Centenarian Study.
- Patricia FabianAn environmental health researcher who studies how the built environment shapes health disparities, combining systems science models, geographic information system mapping, and high-resolution sensor data. Her projects link housing conditions, indoor air quality, and energy use to health, informing climate resilience efforts in Boston schools and frontline communities.
- Paul CarlileAn organizational scholar who examines the knowledge boundaries that separate people in different expertise domains and how they can be crossed to enhance collaboration and innovation. His work spans product development in industries such as automotive, software, and pharmaceuticals, along with studies of data-driven practices in healthcare organizations.
- Peixi LiaoA prosthodontics researcher focused on digital dentistry, developing data-driven diagnostic approaches and virtual simulation technologies for restorative treatment planning. He has founded and taught courses on digital dentistry in prosthodontics and restorative science, and serves as president of the International Association for Dental Research's Prosthodontics Group.
- Peter BlakeA developmental psychologist who studies how children come to understand the social world, running cognitive and behavioral experiments with children ages two to twelve on fairness, cooperation, and ownership. He collaborates with game theorists and anthropologists and compares social development across cultures to identify common patterns.
- Qiang CuiA theoretical and computational chemist who develops and applies multiscale simulation methods, including quantum mechanics/molecular mechanics and coarse-grained models, to chemical and biological problems. His group studies enzyme catalysis, biological membrane remodeling, and the nano-bio interface, with an emphasis on processes spanning multiple length and time scales.
- Reza RawassizadehA computer scientist who builds resource-efficient machine learning algorithms for battery-powered devices such as smartwatches, fitness trackers, and small robots. His work enables models to run entirely on-device, without cloud infrastructure or network connections, supporting applications in digital health and ubiquitous computing.
- Rhoda AuA neuropsychologist who develops digital biomarkers for cognitive aging, applying voice analysis and other sensor technologies to detect early signs of dementia. As a principal investigator of the Framingham Heart Study Brain Aging Program, she works to build scalable, technology-agnostic platforms for monitoring brain health across populations.
- Roscoe GilesA computational scientist whose research applies high-performance and parallel computing to physics and materials problems, from quantum field theory to simulations of magnetic materials. He currently focuses on helping AI and ML research and education communities gain improved access to large-scale high-performance computing systems.
- Samuel BazziA development economist whose research examines migration, culture, and political economy, asking how individuals and nations adapt to diversity in a globalized world. He applies econometric methods to study labor mobility, the cultural effects of migration, and the links between economic shocks and conflict.
- Sarah BargalA machine learning and computer vision researcher whose work focuses on making AI systems explainable and accountable to humans and society. Her research spans image and video understanding and the interpretability of deep neural networks, and she has co-directed the AI4ALL program at BU.
- Sarah FrederickA scholar of modern Japanese literature whose work examines mass media, gender, and print culture, with a focus on interwar women's magazines. Her digital humanities projects apply geographic information system mapping to literary texts, including a collaborative effort charting writers' travels through Kyoto.
- Scott HirstA corporate law scholar whose empirical research examines corporate governance, shareholder voting, and the influence of index funds and institutional investors. He combines quantitative evidence with concepts from finance, accounting, and economics to explain how corporations and investors make decisions and to inform policy making.
- Scott LadenheimAn applied mathematician working on deep learning and the iterative numerical methods that underpin large-scale scientific computing. Before joining the faculty he supported high-performance machine learning workloads at BU Research Computing Services, and his earlier research spanned applied mathematics, scientific computing, and quantum computing.
- Selim ÜnlüA photonics researcher who develops optical imaging and biosensing techniques, including interferometric reflectance sensors that detect individual viruses, nanoparticles, and single molecules without labels. His work applies high-resolution microscopy to biological diagnostics and to imaging semiconductor devices and integrated circuits.
- Shanshan SheehyAn epidemiologist whose research examines cardiometabolic health, pregnancy complications, and health disparities, largely through prospective cohort studies such as the Black Women's Health Study. She analyzes genetic, clinical, and survey data to identify risk factors for cardiovascular disease, stroke, and Alzheimer's disease among African American women.
- Shariq MohammedA biostatistician who develops methods combining hierarchical Bayesian modeling, variable selection, and spatial statistics for complex biomedical data. His group applies these tools to imaging, spatial-omics, and digital health datasets, addressing questions in oncology and in neurodegenerative conditions such as Alzheimer's disease and dementia.
- Sheryl GraceAn aerospace engineering researcher who studies unsteady aerodynamics and aeroacoustics, the physics of noise generated by airflow. Her group develops and benchmarks computational fluid dynamics approaches for predicting fan noise in aircraft engines and examines inverse methods that identify noise sources from measured acoustic data.
- Simone GillA movement scientist who studies how walking and motor function change across the lifespan, focusing on how obesity affects gait and fall risk. Her Motor Development Laboratory analyzes movement with three-dimensional motion capture, wearable sensors, and digital pressure mats, including studies of patients before and after bariatric surgery.
- Sophie HaoA computational linguist whose research develops interpretability methods for natural language processing, working toward a science of deep neural language models. Her group studies how models represent linguistic structure, using probing, feature attribution, and formal analysis of neural architectures, with attention to bias and fairness.
- Stephen GrossbergA theoretical neuroscientist who models how the brain learns to attend, categorize, and predict events in a changing world. He co-developed Adaptive Resonance Theory, a family of neural network models that learn incrementally without erasing prior memories and are applied to pattern recognition and prediction.
- Suresh KalathurA computer scientist focused on data analytics and analytics education who directs graduate analytics programs and helped establish a master's degree in applied data analytics. He teaches courses in analytics, data visualization, and server-side web development, and builds classroom tools that give students real-time feedback during lectures.
- Taylor BoasA comparative political scientist who studies electoral politics, public opinion, and political behavior in Latin America, with attention to religion, accountability, and mass media. He tests how voters respond to information using survey and field experiments, pre-registered designs, and cross-country meta-analyses, and teaches quantitative research methods.
- Terry EllisA neurorehabilitation researcher who studies walking impairments in people with Parkinson disease and develops interventions to improve real-world mobility. Her work applies wearable sensors, digital health technology, and soft robotic exosuits to measure gait and reduce freezing episodes, and she directs BU's Center for Neurorehabilitation.
- Tesary LinA quantitative marketing researcher who examines how data and privacy shape relationships between firms and consumers. Her work studies consumer privacy preferences and how privacy regulation, choice architecture, and algorithms affect digital markets and consumer welfare, informing analytics tools adapted to the modern information environment.
- Thomas ByrneA social welfare policy researcher who studies the causes, consequences, and policy solutions of homelessness and housing insecurity. His work applies quantitative and quasi-experimental methods to administrative data, examining how housing markets, prevention programs, and housing assistance shape homelessness rates and health outcomes, including among veterans.
- Thomas KeplerA computational immunologist who develops mathematical and statistical tools to study antibody evolution and B-cell responses. His laboratory combines these methods with systems-level experiments to guide vaccine design, including computational selection of immunogens for candidate vaccines against HIV, influenza, and anthrax.
- Tianyu WangA computational imaging and photonics researcher who harnesses optical physics to build more efficient sensors and processors. His laboratory studies information processing in physical systems, developing photonic hardware for artificial neural networks and quantum-inspired light sources for deep-tissue imaging, with applications in sensing, communication, and AI computing.
- Timothy BrownA clinical psychologist whose work centers on the classification of anxiety and mood disorders, temperament, and psychopathology, alongside research methodology and psychometrics. He directs research at BU's Center for Anxiety and Related Disorders and teaches multivariate statistics, including structural equation and latent growth modeling.
- Ting Fang Alvin AngA physician-researcher who studies Alzheimer disease and traumatic brain injury through the Framingham Heart Study. His work manages and analyzes large longitudinal cohort data, applying conventional statistical and machine-learning techniques to improve understanding, clinical guidelines, and practice around neurocognitive disorders.
- Tugba EfendigilA supply chain researcher who studies digital transformation and circular supply chains, designing forward and reverse flows that support agile, resilient operations. Her work applies technology and analytics to supply chain optimization, drawing on prior research experience at MIT and in industry.
- Venkatesh SaligramaA machine learning researcher who studies how models can train and predict under practical constraints such as limited computation, sparse labels, and distributed devices. His methods span federated learning, zero-shot and few-shot recognition, and bias mitigation, and he advises doctoral students working on these problems.
- Vijaya KolachalamaA machine-learning and precision-medicine researcher who develops software frameworks that assist clinicians in real-world settings. His laboratory works on biomedical machine vision, multimodal representation learning, and domain generalization, including AI tools that support diagnosis of Alzheimer disease and related dementias.
- Vipul ChitaliaA physician-scientist who studies vascular pathologies in cancer and renal failure, focusing on post-translational protein modifications such as polyubiquitination. His laboratory combines cellular and molecular biology, animal models, and computational and machine-learning methods, validating findings in humanized models and large human databases.
- Wen LiA space physicist who studies plasma waves, Earth's radiation belts, and energetic particle precipitation in planetary magnetospheres, including Jupiter's. Her research combines satellite observations with physics-based modeling and machine-learning approaches to quantitatively assess radiation-belt electron dynamics and solar wind-magnetosphere coupling.
- Wenchao LiA dependable-computing researcher whose work sits at the intersection of formal methods and machine learning, focusing on neuro-symbolic reasoning and the safety and trustworthiness of AI-driven autonomous systems. His laboratory develops computational proof techniques with applications from design automation to multi-robot systems and self-driving cars.
- William AdamsA pediatrician and clinical informatics researcher who develops and evaluates health information technology for children and families. His projects build electronic health record tools, patient-facing systems, and interactive voice-response interfaces aimed at improving care quality, medication safety, and health equity in urban pediatric populations.
- Xiaoling ZhangA bioinformatician and statistical geneticist who searches for genetic risk factors of complex diseases and the functional mechanisms behind them. Her lab integrates genome-wide DNA and RNA sequencing with statistical modeling, systems biology, and machine learning, focusing on Alzheimer's disease and its genetic overlap with cardiometabolic disorders.
- Xin ZhangAn engineer whose research develops metamaterials and microelectromechanical systems for photonic, acoustic, and medical-imaging applications. Her Laboratory for Microsystems Technology builds structures that improve magnetic resonance imaging signal quality and enable air-permeable sound silencing, operating as a student-centered, interdisciplinary research and education program.
- Xuezhou ZhangA machine-learning theorist who designs interactive learning algorithms, spanning online learning, reinforcement learning, and algorithmic game theory. His work builds reinforcement-learning methods that are sample efficient and robust to noisy or adversarial feedback, with applications including language-model-assisted drug repurposing from scientific literature.
- Yuhei MiyauchiAn economist who studies how networks of production, demand, and human mobility shape economic activity within cities and across regions. He combines models from trade, urban, and macroeconomics with granular data sources, including firm-to-firm transaction records and smartphone location data, to inform policy design.
- Yuting ZhangA computer systems researcher focused on mobile security and computing, and on cybersecurity education. Her Mobile Computing and Security Lab supports research and teaching in mobile platform enhancement, application analysis, and mobile sensing, and she has developed courses in mobile forensics and secure software development.
- Ziba CranmerAn innovation leader and founding director of BU Spark!, the Faculty of Computing & Data Sciences' experiential lab for student technology projects. She serves on the Innovate@BU steering committee and co-teaches the Spark! Technology Innovation Fellowship. Earlier, she led a national initiative supporting public-sector innovators combating human trafficking through technology.
Language & generative AI
- Asad MalikA software engineer at the Software & Application Innovation Lab who serves as principal AI architect for PREAA, the lab's open-source generative-AI platform for researchers. He is also BU Spark!'s innovation engineer, co-teaches the Spark! Technology Innovation Fellowship, and leads technical workshops and consultations for students.
- Chris McVeyA writing studies scholar and teacher whose work examines AI literacy, ethics, and student practices with generative AI. He teaches courses on AI and writing, studies student beliefs about generative AI tools, and participates in national curriculum initiatives on AI in higher education.
- Chris SeferlisAn enterprise data practitioner who teaches in the online Master's in Data Science program, drawing on work in cloud architecture, data warehousing, and business intelligence. He co-authored a 2023 practical guide to Azure Cognitive Services and OpenAI aimed at technology leaders deploying AI systems.
- Derry WijayaA natural language processing researcher who develops machine learning methods for machine translation, with emphasis on using well-annotated languages to improve translation of low-resource ones. Her work also examines verb semantics across languages and time and builds techniques for populating knowledge bases from text.
- Ian WestmacottA software engineering leader who directs the Hariri Institute's Software & Application Innovation Lab. Before BU, he led multidisciplinary engineering teams at Symbotic, Veo Robotics, and Amazon. His work spans applied machine learning, computer vision, and robotics, and he holds more than 30 patents in video analysis and security technologies.
- John ByersA computer scientist who studies algorithmic and data-analytic problems in computer networking and Internet platforms. He collaborates on interpretability research into attention in transformer models. As executive director of BU's AI Development Accelerator (AIDA), he advises the provost on AI and leads generative-AI integration across academic programs and administration.
- Najoung KimA computational linguist who studies how humans and machines generalize, represent meaning, and evaluate language. Her work combines computational and experimental methods to examine semantics, pragmatics, language-model behavior, and the relationship between linguistic cognition and machine representations across varied tasks and datasets.
- Naomi SaphraA natural language processing researcher who studies how language models change during training, connecting optimization behavior to the generalizations models end up making. Her work draws on linguistics, deep learning theory, and interpretability, and extends to applying interpretability methods to models used for scientific discovery.
- Pary FassihiA writing instructor whose work examines how generative AI tools can support academic writing instruction and AI literacy. She teaches AI-intensive writing courses, develops flipped-learning modules and other digital course materials, and serves on the Provost's AI Task Force, exploring generative AI activities in the classroom.
- Wayne SnyderA computer scientist whose doctoral and early research addressed computational logic and algebraic reasoning within artificial intelligence, including unification and equational theories. He now teaches in the online Master's in Data Science program and has developed courses in computational audio and natural language processing.
Vision & imaging
- Boqing GongA computer vision researcher who studies visual recognition and video understanding, with attention to the safety, generalization, and efficiency of AI models. His methods include domain adaptation and transfer learning, long-tailed recognition, adversarial robustness, and self-supervised learning, grounded in the mathematical structure of each problem.
- Bryan PlummerA multimodal machine learning researcher who develops methods for combining vision and language, making neural networks efficient, and keeping models robust to label noise and domain shifts. His work spans explainable and fair AI, multimodal benchmarks, and self-supervised learning for medical imaging, with several completed doctoral advisees.
- Deepti GhadiyaramA computer vision researcher who studies how to make vision and generative models safe, interpretable, and robust. Her group examines the inner workings of generative and multimodal large language models and applies them to problems including healthcare, while she teaches vision courses and mentors doctoral, master's, and undergraduate researchers.
- Eshed Ohn-BarA computer vision and robotics researcher who develops machine learning for embodied, assistive, and autonomous systems. His lab builds perception, planning, and interaction systems for safety-critical settings such as autonomous driving and navigation assistance for blind and visually impaired pedestrians, tested with community partners.
- Janusz KonradA video and image processing researcher whose work spans computer vision, visual sensor networks, and 3D imaging. His lab develops multi-camera AI systems that estimate occupancy in large indoor spaces from overhead fisheye cameras, supporting applications in energy-efficient building management and emergency response.
- Kate SaenkoA computer vision researcher who develops adaptive machine learning methods that help deep models generalize across changing data distributions. Her group studies domain adaptation, vision-and-language tasks such as captioning and visual question answering, and bias in multimodal models, training graduate researchers across these areas.
- Prakash IshwarA statistical signal processing and machine learning researcher who develops model-based and data-driven tools for learning and inference problems. His work spans information theory, information-theoretic security, and visual information analysis, with applications that include people detection and activity recognition in video.
- Stan SclaroffA computer vision researcher whose work covers tracking, video-based analysis of human motion and gesture, and deformable shape matching and recognition. He founded BU's Image and Video Computing group and developed ImageRover, one of the first content-based image retrieval systems for the web.
- Swathi KiranA speech-language neuroscientist who studies how the brain recovers language after stroke, focusing on aphasia rehabilitation and bilingual language recovery. Her work combines neuroimaging, behavioral treatment studies, and computational modeling to predict rehabilitation outcomes, and she co-founded a digital therapy platform that uses machine learning to personalize exercises.
- Thomas GardosA computer vision and signal processing researcher who spent three decades at Intel developing deep learning applications and algorithm specifications for ultra-low-power vision chips and contributing to international video compression standards. He directs the MS in Data Science program and previously served as Intel's researcher-in-residence at the MIT Media Lab.
- Wei-Lun ChaoA machine-learning and computer-vision researcher who studies learning from imperfect data, including limited, noisy, and distribution-shifting sources. His work develops methods for federated learning and robust visual recognition that keep models reliable under such conditions, with applications spanning autonomous driving, biodiversity monitoring, and healthcare.
Robotics & autonomous systems
- Alyssa PiersonA robotics researcher whose work studies trust and cooperation in multi-agent systems, distributed robot control, and socially aware autonomy. She designs control algorithms that let teams of robots share tasks and interact safely with humans and each other in complex, dynamic environments.
- Hongwei XiA programming languages researcher who applies advanced type theory to language design and implementation. He is the principal designer of ATS, a language that combines programming with theorem proving through dependent and linear types, supporting the construction of safe and reliable software systems.
- Hua WangA control systems researcher who studies nonlinear dynamics and the control of systems exhibiting complex behavior such as bifurcations. His research interests span nonlinear and intelligent control methods, including fuzzy control, with applications to robotics and to biomedical and energy systems.
- John BaillieulA control theorist and roboticist who studies the mathematics of controlling mechanical systems, from nonlinear and networked control to communication-constrained robotics. His recent work develops neuromimetic control, analyzing how animals perceive and move in order to design perception and decision-making algorithms for autonomous vehicles and robots.
- Kenn SebestaA robotics engineer with a controls background who directs BU's hands-on robotics teaching center, where students design, build, and test real robotic systems. Through courses and a tiered microcredential program, students can practice skills in automation, autonomous systems, and robot design.
- Roberto TronA robotics researcher whose work combines control theory, computer vision, and machine learning for teams of robotic agents and human-robot interaction. He studies safety and performance of multi-robot systems using tools from Riemannian geometry and optimization, and develops distributed sensing platforms that model human motion to support aging in place.
- Sean B. AnderssonA systems and control researcher whose work spans robotics and nanobioscience. He develops feedback control and estimation methods for real-time single-particle tracking microscopy, which keep individual nanometer-scale particles inside a microscope's detection volume, and applies related control and estimation techniques to problems in robotics.
- Sheila RussoA surgical robotics researcher who designs miniaturized soft robots for minimally invasive procedures. Her group combines soft materials, sensing and actuation, and meso- and micro-scale manufacturing to build flexible surgical instruments, including robotic systems for diagnosing and treating lung cancer inside the body.
- Shengzhi ZhangA computer security researcher whose work examines how attackers compromise modern computing platforms and how to defend them. He studies AI security alongside the security of Internet of Things devices, automobiles, and operating systems, analyzing vulnerabilities in these systems and developing practical protections.
- Thomas LittleA networking and communications researcher who develops technologies for smart, connected systems. His work spans optical wireless and visible-light communication, vehicular networks, and pervasive computing, and he directs BU's Multimedia Communications Laboratory, which builds enabling technologies and applications for interactive multimedia systems.
- Tommaso RanzaniA soft-robotics researcher who designs bioinspired robots that operate in complex environments, from minimally invasive surgery to underwater exploration. His Morphable Biorobotics Lab develops soft manipulators and wearable devices, including a soft robot that guides interventional tools inside the beating heart with an expandable stabilization mechanism.
Health & life sciences
- Huimin ChengA biostatistician who develops machine learning and AI methods grounded in statistical network analysis. Her research spans graph deep learning, causal inference, and network modeling techniques such as graphon-based methods, with applications to trustworthy large language models, knowledge graphs, and health data.
- Kayhan BatmanghelichA medical AI researcher who addresses explainability, data efficiency, multimodal fusion, and causality in machine learning for healthcare. His lab applies vision-language and diffusion models to problems such as lung disease and cancer imaging, and he teaches deep learning and machine learning courses while supervising graduate researchers.
- Simon KasifA computational biology researcher who connects AI and biomedical science in both directions, seeking methods that advance each field. His work applies machine learning, network biology, and algorithm design to gene function prediction, genomic systems biology, and studies of metabolic disease and aging, alongside questions of responsible AI use.
- Mark BunA theoretical computer scientist who studies algorithmic data privacy, applying complexity theory to questions about differential privacy and its connections to machine learning. His work examines the computational complexity of private learning, relationships between privacy and online prediction, and polynomial approximations to Boolean functions.
- Mayank VariaA cryptographer who builds privacy-preserving data analysis systems and studies their social and legal dimensions. His deployments have measured gender wage gaps, tracked minority business subcontracting, and helped identify patterns across sexual assault reports. He directs the Hub for Civic Tech Impact and advises national data policy committees.
Physical sciences & environment
- Elizabeth BarnesA climate scientist whose research develops explainable AI methods to study how the Earth system varies, becomes predictable, and changes over time. Her group designs deep-learning tools that mirror scientific reasoning, applying them to questions about climate variability, prediction, and human-Earth system futures.
- Michael DietzeAn ecological forecasting researcher whose lab builds iterative prediction systems that update continuously as new observations arrive, combining data assimilation, statistical modeling, and ecoinformatics. Applications span the terrestrial carbon cycle, vegetation phenology, and disease ecology. He chairs the Ecological Forecasting Initiative and leads the PEcAn project.
- Siddharth Mishra-SharmaA researcher at the intersection of machine learning and fundamental physics who develops neural methods for simulation-based inference, scalable generative modeling, and symmetry-preserving data processing. He applies these to cosmology, astrophysics, and particle physics datasets, balancing data-driven scale against physics-informed structure.
- Sucharita GopalA geographer who applies neural networks and machine learning to remote sensing, geographic information systems, and spatial analysis. Her projects pair these methods with policy questions, including marine spatial planning in Massachusetts, malaria risk mapping in Ethiopia, and mapping health service delivery in Zambia.
Society, ethics & education
- Ayse LokmanogluA computational social scientist who examines how supremacist ideologies, disinformation, and conspiracy theories spread online through state and non-state actors. She applies automated text and visual analysis, topic modeling, and network analysis to large-scale social media data, with attention to race, gender, and religion.
- Chang XiaoA human-computer interaction researcher working at the intersections of human-AI interaction, computer graphics, and augmented and virtual reality. He develops interactive systems that use generative models and visual computing to augment human creativity, communication, and physical interaction with digital content.
- Chris Chao SuA media researcher who uses computational methods to study how audiences form in a fragmented digital environment. His work analyzes mobile and social media consumption, the platform affordances that shape it, and the relationship between social media use and political behavior.
- Daniel MunroA philosopher of mind and epistemologist whose work examines the nature and epistemic value of imagination, including storytelling and pretend play. He applies this theorizing to conspiracy thinking, religious cognition, and the behavior of artificial agents such as large language models.
- Elena ForzaniA literacy researcher who studies how elementary and secondary students read and evaluate online information, including judgments about credibility. She uses multiple and mixed methods to examine cognitive, metacognitive, and motivational dimensions of digital reading, informing the design of more equitable literacy instruction and assessment.
- Emily WhitingA computer graphics researcher who studies computational fabrication, examining how computation supports creative design of physical objects. Her group develops methods such as differentiable topology optimization and interlocking-structure design, drawing on engineering mechanics, and she teaches computational fabrication while advising doctoral researchers.
- Emma WilesAn economist whose research examines how AI reshapes labor markets, focusing on search, matching, and hiring. She runs randomized experiments in the lab and field and analyzes large-scale platform data to study questions such as algorithmic writing assistance for job seekers and generative AI's effects on knowledge workers.
- James GradyA designer and associate professor of graphic design in the School of Visual Arts who serves as BU Spark!'s creative director. He co-teaches the Spark! Technology Innovation Fellowship. Before BU, he co-taught information design at MIT and led design projects for clients including Google, Nike, and the World Bank.
- Jennifer GreenA child clinical psychologist whose research supports student mental health in schools, examining teacher identification of students with mental health needs, disparities in service access, and youth bullying. She develops survey measures of school bullying and evaluates prevention and mental health promotion programs in partnership with local districts.
- Joshua PetersonA computational cognitive scientist who uses large behavioral datasets and machine learning to predict human judgment, covering risky decision-making, face perception, and visual object classification. Recent work tests long-standing behavioral economics theories against models fit to millions of human choices.
- Michael ChangA learning scientist and computer scientist who studies how AI can support teaching and learning beyond the dominant instructional practices of schooling. His work centers on the collaborative design of AI tools for education, and he serves as an assistant director of the Earl Center for Learning & Innovation.
- Naomi CaselliA cognitive scientist whose research examines how deaf children acquire American Sign Language and how early language deprivation affects vocabulary development. She builds resources such as the ASL-LEX lexicon database and directs BU's AI and Education Initiative and the Center on Sign Language and Deaf Education.
- Neha GondalA computational sociologist who studies how interpersonal and organizational networks reproduce cultural boundaries and status distinctions. She uses exponential random graph models, agent-based simulation, and machine learning across cases ranging from Renaissance Florentine moneylending to contemporary academic and public health inequalities.
- Ngozi OkidegbeA legal scholar who examines how predictive technologies used in the criminal justice system affect racially marginalized communities. Her articles analyze the data underlying pretrial risk assessment tools and ask who holds decision-making power over how such algorithms are built and adopted.
- Ola Ozernov-PalchikA cognitive neuroscientist who examines the brain and cognitive mechanisms of language and literacy development, with a focus on reading comprehension and dyslexia. She applies tools from neuroimaging and machine learning to predict reading difficulties and to build evidence-based, personalized approaches to early literacy assessment and intervention.
- Seth VillegasA philosopher specializing in the ethics of emerging technology, with research on transhumanism, life-extension technologies, and AI companions designed to mimic deceased family members. He teaches responsible and ethical data science and AI in BU's online master's programs and hosts the DigEthix ethics podcast.
- Wesley WildmanA philosopher of religion who studies complex human social systems using computational modeling alongside the cognitive and cultural sciences. He teaches ethics of emerging technologies, working through case analyses of how data science and computational tools interact with society and public policy.