جست‌وجوی استاد و پژوهشگر

Alan Lindsayمشاهده پروفایل

دانشیار

• Applied Partial Differential Equations
• Computational Mathematics
• Mathematical and Computational Biology
• +۳ مورد دیگر

Alan Lindsay is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. He holds a Ph.D. from the University of British Columbia (2010) and a B.S. from the University of Edinburgh (2005). His research focuses on computational and analytical methods for partial differential equations (PDEs) modeling physical and biological systems, including Micro-Electromechanical Systems (MEMS), mathematical ecology, imaging, and inverse problems. His email is a.lindsay@nd.edu, and he is based in Crowley Hall. Education: Ph.D., Applied Mathematics, University of British Columbia, 2010 B.S., Mathematics, University of Edinburgh, 2005 Research Interests: Applied Partial Differential Equations Numerical Methods for PDEs Mathematical Biology and Biophysics Scientific Computing and Simulation MEMS and Micro-Electromechanical Systems Mathematical Modeling of Biological Processes Recent Research Trends: Lindsay’s work emphasizes computational techniques like boundary integral methods, kinetic Monte Carlo simulations, and bifurcation analysis to study diffusion processes, first passage times, and pattern formation in biological and physical systems. His studies bridge theoretical analysis and practical applications, such as optimizing T cell antigen recognition and modeling moth mating strategies. Grants & Advising: While no students are listed, his research is supported by grants in computational mathematics and biological modeling. His work often involves interdisciplinary collaborations with biologists and engineers. Labs/Teams: His research is conducted within the ACMS department, leveraging Notre Dame’s computational infrastructure.

Chao Tianمشاهده پروفایل

دانشیار

• Information Theory
• Distributed Data Storage
• Coding Theory
• +۶ مورد دیگر

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Sharmistha Guhaمشاهده پروفایل

استادیار

• Bayesian Methods
• High-Dimensional Regression
• Causal Inference
• +۴ مورد دیگر

Sharmistha Guha is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. Her research focuses on Bayesian methods for analyzing complex, high-dimensional data, with applications in neuroscience, network security, and social sciences. She develops techniques for supervised network data analysis, causal inference, and data privacy preservation. Education: Ph.D. in Statistical Science (Duke University, implied by dissertation recognition). Research Interests: Bayesian high-dimensional regression, object-oriented regression, causal inference in randomized trials, probabilistic record linkage, Bayesian nonparametric mixture models, and integration of heterogeneous data types like networks and functional data. Her work addresses challenges in neuroimaging (dMRI/fMRI) and big data analytics. Recent Contributions: Her articles span Bayesian methodologies for network analysis, differential privacy in treatment effect estimation, and applications in environmental health (e.g., PM2.5 exposure studies). Notable trends include leveraging Bayesian hierarchical models for complex data structures and advancing causal inference frameworks. Awards: 2022 Blackwell-Rosenbluth Award (ISBA) 2021 Savage Award Honorable Mention 2024 NSF-DMS Grant 2413721 Grants & Advising: Secured NSF funding for heterogeneous data integration research. Mentored students like Jose Rodriguez-Acosta (NSF GRFP Fellow) and Jacob Pagel (NIH-funded CoSIBS participant). Lab/Team: Leads a research group integrating statistical method development with domain collaborations in neuroscience and network security. Active in organizing workshops and serving on panels to foster academic engagement.

Rudy Guerraمشاهده پروفایل

استاد

• Biostatistics
• Bioinformatics
• Statistical Genetics
• +۴ مورد دیگر

Rudy Guerra is a Professor and Chair of the Department of Statistics at Rice University, where he has been since 2000. His research spans biomedical applications of statistics, including bioinformatics, statistical genetics, and medical imaging, alongside sociological research in education and Mexican migration. He holds academic leadership roles, including Director of the Data Science Minor and member of the BRIDGE and Doerr Institute steering committees. Guerra earned his Ph.D. in Statistics from UC Berkeley, M.A. in Mathematics from UC Berkeley, and B.S. in Applied Mathematics from UT San Antonio. Education: Ph.D., Statistics, UC Berkeley (1992) M.A., Mathematics, UC Berkeley (1987) B.S., Applied Mathematics, UT San Antonio (1984) Key Roles: Department Chair, Statistics (2019–present) Associate Chair, Statistics (2016–2019) Former Jones College Magister (Residential College Leader, 2005–2011) Research Interests: Dr. Guerra’s work integrates statistical methods with biomedical and social science challenges. His biomedical focus includes cancer genomics (e.g., osteosarcoma metastasis, biomarker discovery), medical imaging (e.g., CT ventilation analysis), and bioinformatics. In social sciences, he examines educational inequities and Mexican migration impacts on health. His recent projects include collaborations with Texas Medical Center institutions and sociologists at Rice. Articles Trends: His publications emphasize interdisciplinary applications, combining statistical rigor with domain-specific insights. Recent work spans oncology, public health, and computational biology, reflecting a commitment to bridging theory and practical medical/sociological challenges. Awards & Roles: Panel Member, Ford Foundation Fellowship (2016–present) Associate Editor, BMC Genetics (2014–present) Former Residential College Master of Jones College (2005–2011) Advising & Grants: Guerra advises students on statistical research and curricula. He has led initiatives like the Keck Center for Quantitative Biomedical Sciences Training and co-founded the Empowering Leadership Alliance (ELA) to support underrepresented minorities in STEM. His grants include funding for bioinformatics consortia and educational outreach programs. Labs & Teams: Active in the Gulf Coast Consortia for Bioinformatics and collaborates with multidisciplinary teams at MD Anderson, Baylor College of Medicine, and UT Health Science Center.

Marina Vannucciمشاهده پروفایل

استاد

• Bayesian statistics
• Variable selection
• Graphical models
• +۵ مورد دیگر

Marina Vannucci is the Noah Harding Professor of Statistics at Rice University, with an adjunct appointment at the UT MD Anderson Cancer Center. She holds a Ph.D. and Laurea in Mathematics from the University of Florence, Italy. Her research focuses on Bayesian statistical methods for complex problems in genomics, neuroimaging, and engineering. She has supervised 31 Ph.D. students and 13 postdocs, published over 185 papers, and received prestigious awards including the Mitchell Prize, Zellner Medal, and Don Owen Award. She has served as Editor-in-Chief of Bayesian Analysis and co-Editor of the Journal of the American Statistical Association. Education: Ph.D. in Statistics (University of Florence, 1996), Laurea in Mathematics (University of Florence, 1992). Research Interests: Bayesian statistics, variable selection, graphical models, statistical computing, applications in genomics, neuroscience, and engineering. Awards: Includes Fellowships from ASA, IMS, AAAS, ISBA, and the 2020 Zellner Medal. Recent recognitions include the 2025 Don Owen Award for excellence in research and contributions to the statistical community. Grants/Advising: Over 30 Ph.D. students and 13 postdocs trained. Key roles include Department Chair (2014–2019) and President of the International Society for Bayesian Analysis (2018). Labs/Teams: Affiliated with Rice Neuroengineering, Ken Kennedy Institute, and the W.M. Keck Center for Interdisciplinary Bioscience Research.

Peter Chinمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +۸ مورد دیگر

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Chen Xuمشاهده پروفایل

دانشیار

• Sparse Modeling
• Statistical Learning
• Feature Selection
• +۵ مورد دیگر

Chen Xu is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds an M.A. from York University and a PhD from the University of British Columbia. His research focuses on sparse modeling, statistical learning, and big data processing, with an emphasis on both theoretical and computational advancements. Dr. Xu is affiliated with the Faculty of Science and contributes to editorial roles for journals such as the Journal of the American Statistical Association and Electronic Journal of Statistics. Education: M.A., York University PhD, University of British Columbia Research interests include feature selection, regularization methods, kernel methods, and high-dimensional regression. His work addresses computational challenges in big data, proposing efficient algorithms for tasks like singular value decomposition, clustering, and distributed feature screening. Recent publications highlight advancements in multiview PCA, low-tubal-rank tensor recovery, and model-free regression techniques. Publications span prestigious journals like the Journal of the American Statistical Association and IEEE Transactions series, focusing on statistical methodology, machine learning applications, and scalable computational solutions for complex data problems. Editorial Service: Associate Editor, Journal of American Statistical Association-T&M (2023–present) Associate Editor, Electronic Journal of Statistics (2023–present) Former Associate Editor, The Canadian Journal of Statistics (2019–2021) His research groups are Statistics and Biostatistics, and Data Science, Machine Learning, and Artificial Intelligence. He has no listed awards but maintains active editorial and academic collaborations in computational statistics and machine learning.

Simon Mochrieمشاهده پروفایل

استاد

• Biophysics
• Condensed Matter Physics
• Single Molecule Biophysics
• +۷ مورد دیگر

Simon Mochrie is a Professor of Physics and Applied Physics at Yale University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. His research focuses on experimental biophysics and condensed matter physics, with emphasis on chromatin dynamics, nuclear mechanics, and super-resolution microscopy. He holds a Ph.D. from MIT (1985) and has pioneered techniques such as optical tweezers and STED microscopy to study biological systems like the ubiquitin-proteasome system in yeast. Current projects include single-molecule measurements on nucleosomes and developing novel imaging methods like LIVE-PAINT for live-cell super-resolution imaging. Educations: Ph.D., Physics, MIT (1985) Research interests center on understanding how chromatin organization influences nuclear mechanics, with studies on heterochromatin condensation, cohesin-driven loop extrusion, and chromatin-envelope interactions. His lab develops advanced microscopy techniques to visualize protein dynamics and subnuclear structures in real time. Recent work explores diffusive states of membrane proteins and the role of phase separation in heterochromatin mechanics. His articles demonstrate a focus on interdisciplinary approaches, combining biophysical experimentation with computational modeling to elucidate fundamental mechanisms in cell biology and soft matter physics. Notable themes include the interplay between chromatin structure and nuclear stiffness, loop extrusion dynamics, and quantitative analysis of intrachromosomal contacts. Teaching contributions include developing introductory physics courses tailored for life sciences students, emphasizing applications in biology and medicine. He actively participates in STEM education initiatives, including collaborative research networks for graduate students in physical biology. The Mochrie Lab also emphasizes instrumentation innovation, such as building fast-scanning STED microscopes and reversible peptide-based imaging systems.

Priyamvada Natarajanمشاهده پروفایل

استاد

• Cosmology
• Gravitational Lensing
• Black Hole Physics
• +۴ مورد دیگر

Priyamvada Natarajan is the Joseph S. and Sophia S. Fruton Professor of Astronomy and Professor of Physics at Yale University, serving as Chair of the Astronomy Department and Chair of the Women Faculty Forum. Her research focuses on cosmology, gravitational lensing, and black hole physics. She explores topics like dark matter distribution, supermassive black hole formation, and the interplay between galaxy evolution and AGN feedback. Natarajan has pioneered methods for mapping dark matter in galaxy clusters using gravitational lensing and has contributed to understanding cosmic structure formation through large-scale simulations. Education: Ph.D. in Astrophysics from Cambridge University (1998). Research interests include the nature of dark energy, galaxy formation, and the history and philosophy of science. She leads projects like DAVOS (Dwarf AGN variability studies) and QUOTAS, a platform for discovering supermassive black holes. Her recent work includes detecting overmassive black holes at high redshifts, analyzing gravitational-wave signals with NANOGrav, and utilizing JWST data to probe the cosmic dawn. Notable honors include the 2022 Liberty Science Center ‘Genius Award’. Natarajan’s collaborations span multi-messenger astronomy, leveraging gravitational lenses as cosmic telescopes. She advocates for gender equity in academia and has advised numerous observational and theoretical studies, though specific student names are not listed. Key projects involve the BUFFALO survey for cluster lens modeling and the ngEHT (next-generation Event Horizon Telescope) to study black hole environments. Her lab integrates cosmological simulations, observational data, and theoretical frameworks to address fundamental questions in astrophysics.

Soledad Villarمشاهده پروفایل

استادیار

• Optimization for Data Science
• Machine Learning
• Representation Learning
• +۵ مورد دیگر

Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics and a member of the Mathematical Institute for Data Science at Johns Hopkins University. She also contributes to the Data Science and AI Institute . Her research focuses on computational methods for extracting information from data, emphasizing optimization for data science, machine learning, equivariant representation learning, and graph neural networks. Dr. Villar holds a PhD in Mathematics from the University of Texas at Austin and has been a research fellow at New York University and the Simons Institute at UC Berkeley. Her work bridges theoretical foundations with practical applications in fields like scientific computing and political analysis. Awards include the National Science Foundation CAREER Award (2024). Her research has addressed topics such as gerrymandering detection, fluid dynamics modeling, and graph representation learning. She collaborates on interdisciplinary projects and organizes academic events like the One World MINDS Seminar and the Cibercoloquio Latinoamericano de Matemáticas . Her research interests span computational methods, equivariant machine learning frameworks, and graph neural networks, with applications in physics, engineering, and data-driven decision-making. She actively engages in advancing machine learning techniques for scientific and engineering challenges.

Stavros Vougioukasمشاهده پروفایل

استاد

• Agricultural Robotics
• Mechanization of Specialty Crops
• Automation in Agriculture
• +۷ مورد دیگر

Stavros Vougioukas is a Professor and Vice Chair in the Department of Biological and Agricultural Engineering at the University of California, Davis, within the College of Engineering. He is actively involved in research and graduate mentorship, focusing on agricultural robotics and automation for specialty crops. His work integrates engineering solutions to improve efficiency and sustainability in farming systems. His research interests include agricultural robotics , automation of harvesting processes , sensors and control systems , precision agriculture , and wireless sensor networks for orchard environments . He develops technologies for robotic and robot-aided harvesting, particularly in strawberries and orchard crops, emphasizing optimal management of inputs and yield monitoring. The recent publications reflect a strong trend in robotics integration , real-time sensing , and data-driven decision-making in agriculture. His work spans mechanical design, signal processing, path planning, and structural durability, indicating a multidisciplinary approach to solving agricultural challenges through engineering innovation. Scientific Awards and Recognition: $1.6M grant (2021) to develop innovative fruit-picking machines CITRIS Seed Award (2023) for engineering solutions in agriculture Professor Vougioukas mentors graduate students and leads funded research projects focused on automation and robotics in agriculture. He has secured significant grants, including a $1.6M award for fruit-picking robotics, demonstrating strong research leadership. His collaborations span institutions and disciplines, particularly in agricultural machinery design and sensor network deployment. He leads research efforts in agricultural automation, particularly through projects involving robot-aided harvesting , orchard navigation systems , and wearable worker tracking devices . His lab contributes to the development of intelligent systems for sustainable farming, integrating mechanical, electronic, and computational components.

Scott T. Doyleمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Digital Pathology
• Bioinformatics
• +۷ مورد دیگر

Scott T. Doyle is an Associate Professor in the Department of Pathology and Anatomical Sciences at the Jacobs School of Medicine & Biomedical Sciences, University at Buffalo. His research integrates biomedical imaging, artificial intelligence, and computational pathology to develop quantitative tools for clinical diagnostics and anatomical modeling. Education: PhD in Biomedical Engineering, Rutgers, The State University of New Jersey (2011) BS in Biomedical Engineering, Rutgers, The State University of New Jersey (2006) Optical Microscopy & Imaging in the Biomedical Sciences, Marine Biological Laboratory (2014) hES Stem Cell Culture Training, WNYSTEM (2014) R Bioconductor Training, Roswell Park Cancer Institute (2016) Dr. Doyle’s research focuses on developing AI-driven algorithms for biomedical image analysis, particularly in digital pathology and 3D anatomical modeling. His work spans tumor segmentation, risk prediction in oral and thyroid cancers, and integration of virtual and physical anatomy in medical education. He applies machine learning, deep learning, and computational modeling to enhance diagnostic accuracy and patient outcomes. His recent publications reflect a strong trend in applying artificial intelligence to histopathology, with emphasis on active learning, 3D reconstruction, and multi-institutional data fusion. Key areas include oral cavity cancer recurrence prediction, thyroid cancer subtyping, and computational modeling of surgical margins and anatomical structures. Scientific Service and Recognition: Reviewer for NIH SPORE grants Peer reviewer for journals including Medical Image Analysis , BMC Bioinformatics , IEEE Transactions on Biomedical Engineering Program Committee and Session Chair, SPIE Medical Imaging: Digital Pathology (2016–present) Member, Graduate Program Steering Committee, Pathology & Anatomical Sciences Mentor, McNair Scholarship and CSTEP programs for underrepresented students Dr. Doyle has secured significant research funding as Principal Investigator on NIH and CTSI grants, including a $2M+ NIH grant for predicting oral cancer recurrence. He has also contributed to educational innovation through hybrid anatomy curriculum development and AI training for pathologists. He leads the 'Atoms to Anatomy' research initiative and is active in strategic planning at the Jacobs School. Laboratories and Collaborative Teams: Dr. Doyle collaborates with the Center for Computational Research (CCR) and is involved in the Structural Sciences Learning Center (SSLC). He has led projects with teams at Ibris, Inc., Veterans Affairs Hospital, and Mount Sinai School of Medicine.

• Addiction Medicine
• Psychiatry
• Neuroscience
• +۷ مورد دیگر

Keith Humphreys is a Professor at Stanford University , teaching courses in psychiatry and behavioral sciences. His research focuses on addiction medicine, opioid crisis interventions, and public health policy. He employs interdisciplinary methodologies, including neuroscience and machine learning, to analyze substance use disorders and treatment outcomes. Teaching: PSYC 290 (Teaching in Psychiatry), PSYC 370 (Medical Scholars Research), and others His recent work explores opioid epidemic monitoring via social media, neuromodulation therapies for alcohol use disorder, and policy frameworks for psychedelics and cannabis. Publications span clinical trials, health informatics, and global epidemiology. Key trends in his 15 most recent articles (2023–2025) include: Neuroscience-driven interventions (e.g., theta burst stimulation for addiction) Digital epidemiology (social media analysis for real-time public health insights) Health policy evaluation (opioid regulation, cannabis legalization, overdose mortality) Machine learning applications in predicting treatment outcomes

• AI Safety
• Reliability of AI Systems
• Testing of Digital Circuits
• +۱ مورد دیگر

Annachiara Ruospo is a Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She holds a Fixed-Term Researcher position under Italian Law 240/10, art.24-A, and is actively involved in teaching and research projects. Research Interests : AI Safety, Reliability of AI Systems, Testing of Digital Circuits, Statistical Reliability Investigations. Her recent research focuses on hardware reliability for AI systems , including fault injection methodologies, quantized neural networks, and side-channel vulnerabilities. She collaborates on EU-funded projects like REACT and commercial contracts such as TC4xx NVM Test Strategies . Scientific Awards : None explicitly mentioned. Supervised Students : Antonio Porsia (PhD candidate, Reliability and Security of AI-Based Systems) and Vittorio Turco (PhD candidate, Reliability Evaluation and Hardening of AI Accelerators). She contributes to AI hardware security through patents like A Shannon-Hartley-Based Approach to Measure the Criticality of Synaptic Weights and actively participates in conferences such as the IEEE Latin American Test Symposium and IEEE VLSI Test Symposium .

Eric F. Lockمشاهده پروفایل

دانشیار

• Statistical genomics
• High-dimensional data analysis
• Machine learning
• +۷ مورد دیگر

Eric F. Lock is an Associate Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota's School of Public Health. He is also a Member of the Masonic Cancer Center (MCC) and has been at the University of Minnesota since 2014, after completing his PhD in Statistics from the University of North Carolina in 2012 and a postdoctoral fellowship in Statistical Genomics at Duke University in 2014. Lock's research focuses on developing methods for the analysis of multi-faceted high-dimensional data, particularly in "omics" fields such as genomics, metabolomics, and proteomics. His work emphasizes the integrated analysis of data from multiple sources (e.g., gene expression, metabolomics, imaging) or measured in multiple dimensions (e.g., multiple tissue types or body regions). He also specializes in exploratory factorization and clustering methods, and Bayesian nonparametric inference. His recent publications demonstrate significant contributions to tensor data imputation (BAMITA), matrix decomposition (EV-BIDIFAC), and methods for handling complex genomic data. His work bridges statistical theory with practical applications in molecular biology, addressing challenges in data integration across multiple biological measurement platforms. Delta Omega, Honorary Society in Public Health (2019) As an active researcher and educator, Lock serves on dissertation committees, including for Mykhaylo M. Malakhov who recently defended his PhD at the University of Minnesota School of Public Health. He is involved in organizing and participating in major conferences such as STATGEN 2025, demonstrating his leadership in the biostatistics community.