جست‌وجوی استاد و پژوهشگر

• Computational chemistry
• QM/MM simulations
• Catalysis
• +۵ مورد دیگر

Nisanth N. Nair is Professor of Chemistry at the Indian Institute of Technology Kanpur (IITK), India, holding the position since 2018. He obtained his PhD from the Universität Hannover, Germany (2004) after completing his MSc in Chemistry at IIT Madras (2001). His research group pioneers advanced computational-chemistry methods to address grand-challenge problems in energy, healthcare and materials science. Education PhD (2004), Universität Hannover, Germany MSc (2001), Chemistry, Indian Institute of Technology Madras Research Interests Professor Nair’s work is organized around five tightly linked thrusts: Method development: massively parallel QM/MM algorithms, polarizable force-fields, metadynamics and hybrid functionals for large-scale catalytic systems. Energy catalysis: computational design of Rh/Al₂O₃ and Rh/TaON catalysts for efficient water-splitting and H₂ production. Healthcare: molecular mechanisms behind antibiotic resistance in NDM-1 and Class-C β-lactamase enzymes, guiding de-novo inhibitor discovery. Aerospace materials: multi-scale modelling of thermo-oxidative degradation of high-temperature polymers in collaboration with Boeing. Heterogeneous catalysis: olefin hydrogenation on Rh/Y-zeolite and single-atom catalysis phenomena. Publications Trend His recent articles (2011–2013) highlight an integrative approach combining rigorous electronic-structure calculations with micro-kinetic modelling to unravel complex catalytic cycles, antibiotic-resistance pathways and support-effects in single-atom catalysts. Honours & Awards P. K. Kelkar Young Faculty Research Fellow, IIT Kanpur (2012–2015) Young Associate, Indian Academy of Sciences, Bangalore (2012–2015) Young Scientist Medal, Indian National Science Academy, New Delhi (2013) Contact & Resources Office: SL 302, Department of Chemistry, IIT Kanpur, Kanpur 208016, India Phone: +91 512 259 6311 Email: nnair@iitk.ac.in Web: http://home.iitk.ac.in/~nnair

Bo Huمشاهده پروفایل

استاد

• Biostatistics
• Bioinformatics
• Neurosurgery
• +۷ مورد دیگر

Bo Hu is Professor of Biostatistics & Bioinformatics and Professor of Neurosurgery at Duke University, where he leads methodological and collaborative research at the intersection of biostatistics, bioinformatics, and clinical neurosciences. His dual appointments situate him within the Division of Biostatistics in the Department of Biostatistics & Bioinformatics and within the neurosurgical faculty. Education: Ph.D. in Biostatistics, University of Wisconsin–Madison, 2006 Research Interests: Professor Hu’s methodological work centers on advanced biostatistical and machine-learning techniques for high-dimensional biomedical data, including generative AI, synthetic data generation, and predictive analytics in medicine. Clinically, he collaborates on precision-medicine trials in oncology, neurodegeneration (Alzheimer’s disease), metabolic disease (type 2 diabetes and bariatric surgery), and treatment-resistant depression. His neuroimaging genetics portfolio explores structural brain endophenotypes in bipolar disorder and epilepsy using single-cell transcriptomic integration. Complementing his medical research, he maintains a vigorous program in remote-sensing informatics, developing deep-learning solutions for object detection, domain adaptation, and energy-infrastructure mapping from overhead imagery. Recent Grant Portfolio: Empagliflozin to Improve Right Ventricular Function in Pulmonary Arterial Hypertension – Cleveland Clinic Lerner College of Medicine (2025-2030) Gender and Asthma – Mayo Clinic Hospital-Arizona (2025-2027) Engaging Patients in Prenatal Genetic Testing Decisions – Cleveland Clinic Lerner College of Medicine (2025-2027) Laboratory & Collaborative Networks: Professor Hu leads interdisciplinary teams that bridge Duke’s Department of Biostatistics & Bioinformatics with clinical departments (Neurosurgery, Psychiatry, Medicine) and external partners such as Cleveland Clinic, Mayo Clinic, and multiple NIH consortia. These collaborations support large-scale clinical trials, multi-omics neuroimaging studies, and AI-driven remote-sensing analytics.

Michael Bronsteinمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Pattern Recognition
• +۶ مورد دیگر

Michael Bronstein is a Professor at Università della Svizzera italiana (USI Lugano) in Switzerland and Imperial College London in the UK, where he holds the Chair in Machine Learning and Pattern Recognition. He serves as Head of Graph Learning Research at Twitter following the acquisition of his startup Fabula AI, and maintains a principal engineer position at Intel Perceptual Computing. His research focuses on the interplay between geometry, machine learning, and computer vision, with particular emphasis on non-Euclidean structured data. Professor Bronstein received his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford University, MIT, Harvard University (as a Radcliffe Fellow), and Tel Aviv University, and has been affiliated with multiple Institutes for Advanced Study including TUM-IAS where he was a Rudolf Diesel Industry Fellow (2017). He is a Fellow of IAPR, Senior Member of the IEEE, and a member of the Young Academy of Europe. His research program centers on theoretical and computational methods in spectral and metric geometry applied to computer vision, pattern recognition, and machine learning. He pioneered the field of geometric deep learning, developing novel neural network architectures that process non-Euclidean data structures like graphs and manifolds. His work spans from theoretical foundations to practical applications, with over 100 publications in top scientific journals and conferences, and has been featured in international media including CNN. Analysis of his recent publications reveals a strong trajectory in geometric deep learning with applications spanning computer vision, 3D shape analysis, social network analysis, and bioinformatics. His research consistently bridges theoretical innovation with real-world applications, developing novel neural architectures for processing complex data structures. The work demonstrates increasing interdisciplinary reach, connecting machine learning with fields from particle physics to molecular biology. Dalle Molle Prize (2018) Royal Society Wolfson Research Merit Award (2018) ERC Proof of Concept Grant (2018) Amazon AWS Machine Learning Research Award (2018) Fellow, International Association for Pattern Recognition (IAPR) Google Faculty Research Award (2017) Radcliffe fellowship, Harvard University (2017) Rudolf Diesel industrial fellowship, TU Munich (2017) ERC Consolidator Grant (2016) World Economic Forum Young Scientist (2014) Professor Bronstein has secured multiple ERC grants (Starting Grant 2012, Proof of Concept Grants 2016 and 2018, Consolidator Grant 2016) and has mentored numerous students who have contributed to over 30 granted patents. He has chaired more than a dozen conferences and workshops in his field and served as area chair at major computer vision conferences including ECCV 2016 and ICCV 2017. His research group at USI Lugano collaborates extensively with industry partners including Intel and Twitter. As a serial entrepreneur, Professor Bronstein co-founded Novafora (2005-2009) developing large-scale video analysis, Invision (2009-2012) which created low-cost 3D sensors and was acquired by Intel, and Fabula AI (2018-2019) focused on fake news detection which was acquired by Twitter. His work bridges theoretical research with commercial applications, with his technology contributing to Intel RealSense and Twitter's graph learning infrastructure.

Carlos Aguiarمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Social Interaction Design
• Assistive Technologies
• +۵ مورد دیگر

Dr. Carlos Aguiar serves as Assistant Professor in the Industrial Design Program at the University of Illinois Urbana-Champaign's School of Art and Design, with affiliate status in Informatics at the School of Information Sciences. He directs the Design, Technology, and Society Lab, where he investigates human-artifact interactions through critical design frameworks and cyber-physical systems development. His academic foundation includes a Ph.D. in Design & Human Behavior from Cornell University (2021) with minors in Science and Technology Studies and Information Science, an M.S. in Design Computing from the University of Washington (2017), and a Bachelor's in Architecture & Urbanism from Brazil's Universidade Estácio de Sá (2012). Aguiar's research integrates Science and Technology Studies, Design Philosophy, and Critical Theory to develop technologies that foster social change while examining material culture's societal implications. His dual-focus methodology simultaneously creates tangible artifacts that enhance human-agent-material relations and analyzes how emerging technologies shape societal structures through empirical investigation and critical reflection. This approach prioritizes inclusivity in future-making processes and examines emergent behaviors in technological appropriation. His publication trajectory (2016-2024) reveals consistent advancement in human-computer interaction, particularly in assistive technologies for rehabilitation and autism support, social interaction design in public spaces, and architectural robotics for community building. Key thematic developments include the evolution from single-user wearable devices (Erglove, GripAid) to community-scale cyber-physical systems (communIT, transFORM), with recent work expanding into eco-consciousness and restorative environments through socially interactive robotics. No scientific awards were documented in the provided source materials As lab director, Aguiar cultivates interdisciplinary collaboration between design, informatics, and engineering disciplines. His grant activities focus on developing cyber-physical artifacts that transform underused public spaces into community engagement hubs while addressing societal challenges through human-centered technological innovation. The Design, Technology, and Society Lab functions as an experimental platform where theoretical frameworks from STS and critical design directly inform the creation of responsive environments, objects, and spaces that actively shape social interactions and cultural practices.

Elizabeth Blantonمشاهده پروفایل

استاد

• High-energy astrophysics
• Optical and NIR observational astronomy
• Clusters of galaxies
• +۵ مورد دیگر

Elizabeth Blanton is a Professor of Astronomy at Boston University's College of Arts & Sciences, where she serves as Director of Undergraduate Studies. Her research primarily focuses on high-energy astrophysics and observational astronomy, with emphasis on galaxy clusters, radio galaxies, and AGN feedback mechanisms. She utilizes multi-wavelength approaches including X-ray, optical, infrared, and radio observations. Education: Ph.D., Columbia University M.Phil., Columbia University M.A., Columbia University A.B., Vassar College Professor Blanton's research centers on clusters of galaxies, particularly studying the X-ray emission from the intracluster medium using the Chandra X-ray Observatory. She investigates how central radio sources powered by supermassive black holes interact with and heat the surrounding gas, which has important implications for galaxy formation and evolution. Her work also explores using radio sources as tracers for high-redshift galaxy clusters for cosmological studies. Her publication record shows consistent research on galaxy cluster dynamics, particularly focusing on phenomena like gas sloshing, shock waves, and cavities created by AGN feedback. The research spans multiple wavelengths with heavy emphasis on X-ray observations from the Chandra Observatory, complemented by optical, infrared, and radio data. Her work has significantly contributed to understanding how energy from supermassive black holes affects the evolution of galaxy clusters. Notable Press Coverage: "Abell 2052: A Galaxy Cluster Gets Sloshed" featured in BU Research Magazine 2012, Chandra press release, NASA press release, and National Geographic image of the week "NGC 5813: An Intergalactic Weather Map" covered in Chandra and NASA press releases "Cosmic Battle Creates Milky-Way Sized Tunnel" featured in Naval Research Lab press release "NGC 1553: Black Holes in Distant Galaxy Points to Wild Youth" covered in Chandra press release Professor Blanton teaches a range of astronomy courses from introductory to graduate level, including Principles of Astronomy II, Stellar and Galactic Astrophysics, Introduction to Astrophysics, and Observational Techniques. Her Observational Techniques course provides hands-on experience with telescopes at Boston University and Lowell Observatory in Arizona. She leads research within the Interdisciplinary Cosmology Group at Boston University, which includes members from the Departments of Astronomy, Physics, and Data Sciences. Her work on galaxy clusters and AGN feedback continues to advance our understanding of the formation and evolution of large-scale structures in the universe.

Joel Lanningمشاهده پروفایل

دانشیار

• Engineering education
• Seismic design of civil structures
• Large-scale testing
• +۳ مورد دیگر

Joel Lanning is an Associate Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. Education: Ph.D. in Structural Engineering from UC San Diego (2014), M.S. in Structural Engineering from UC San Diego (2008), B.S. in Civil Engineering from The Ohio State University (2006) His research focuses on engineering education, seismic design of civil structures, large-scale testing methodologies, and alternative building materials. He integrates constructivist learning principles with active learning components like group problem-solving and interactive technology. Awarded the 2023 Distinguished Early-Career Faculty Award for Teaching, 2022 SSoE Early Career Innovation in Teaching Award, 2020 Dean’s Honoree for DTEI’s Celebration of Teaching, and the 2019 CEE Faculty of the Year Award, Joel has demonstrated excellence in pedagogical innovation and student engagement. He serves as Director of the MEng CEE Concentration and is a licensed Professional Engineer (Civil Engineering, California #80946). Joel actively mentors the UCI Steel Bridge Team, guiding hands-on structural design competitions.

Haoming Shenمشاهده پروفایل

استادیار

• Stochastic Optimization
• Integer Programming
• Data-Driven Decision Making
• +۴ مورد دیگر

Haoming Shen is an Assistant Professor in the Department of Industrial Engineering at the University of Arkansas, College of Engineering. He received his Ph.D. in Industrial and Operations Engineering from the University of Michigan, Ann Arbor, along with master's degrees in Electrical and Computer Engineering and Mathematics from the same institution. His bachelor's degree is in Electrical Engineering from Xi'an Jiaotong University. Dr. Shen's research focuses on stochastic optimization and integer programming with applications in power grids and transportation systems. His work centers on data-driven decision-making under uncertainty, particularly using Wasserstein ambiguity sets for chance-constrained programming. His research has significant implications for optimizing critical infrastructure systems where uncertainty must be rigorously accounted for. His publications demonstrate a strong trajectory in optimization theory with applications to power systems. His work on Wasserstein ambiguity models for chance constraints has been published in top venues including Mathematical Programming and the IEEE Conference on Decision and Control. His 2022 paper on Wasserstein two-sided chance constraints with application to optimal power flow received an Honorable Mention in the INFORMS Optimization Society Best Student Paper Competition. Honorable Mention award in the 2022 INFORMS Optimization Society Best Student Paper Competition Rackham Professional Development DEI Certificate Dr. Shen actively engages in Diversity, Equity and Inclusion initiatives. While specific information about his advisees is not provided in the available materials, his research program appears to be actively developing with multiple recent publications in optimization theory and applications.

Diyi Yangمشاهده پروفایل

استاد مدعو

• Natural Language Processing
• Machine Learning
• Computational Social Science
• +۱ مورد دیگر

Diyi Yang serves as an Adjunct Professor within the School of Interactive Computing at the Georgia Institute of Technology, where they contribute to the Machine Learning (ML@GT) research initiative. This academic unit operates under Georgia Tech's College of Computing and focuses on human-centered computing, artificial intelligence, and interdisciplinary technological innovation. Dr. Yang's research spans critical domains including Natural Language Processing and Machine Learning, with significant contributions to Computational Social Science and Social Computing. Their work integrates algorithmic approaches to analyze linguistic patterns and social dynamics, developing systems that model human behavior through computational frameworks. This research bridges technical AI advancements with real-world societal applications, particularly in understanding online interactions and community structures. No scientific awards or honors were specified in the available information. The provided text contains no mention of prizes, fellowships, or medals received by Dr. Yang. While Dr. Yang holds an advisory capacity as an Adjunct Professor, specific details about graduate students supervised or research grants managed are absent from the source material. Their role appears focused on research collaboration rather than formal student mentorship based on the available data. Dr. Yang is actively affiliated with Georgia Tech's Machine Learning (ML@GT) research group, a cross-disciplinary consortium connecting over 100 faculty members across engineering, computing, and social sciences. This affiliation facilitates collaborative projects in algorithm development, ethical AI implementation, and large-scale data analysis for social phenomena.

• High-dimensional probability
• Randomized algorithms
• Numerical linear algebra
• +۴ مورد دیگر

Elizaveta Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. She is also associated with the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Education: Specialist degree from Moscow State University (2012), PhD in Mathematics from University of Michigan (2018 under Roman Vershynin) Prior Appointments: Postdoctoral Scholar at Lawrence Berkeley National Lab (2021), Assistant Adjunct Professor at UCLA Mathematics Department (2018-2021) Her research focuses on randomized numerical linear algebra , mathematics of data science , and high-dimensional probability . Key interests include developing algorithms for large-scale data with non-trivial structure, robust and interpretable learning, and stochastic optimization. Her recent work analyzes algorithmic convergence in structured settings, tensor-based data compression, and nonnegative matrix/tensor factorization under constraints. Recent publications span topics in randomized NLA , robust solvers , tensor methods , and nonnegative matrix factorization . Notable trends include improving convergence rates for iterative methods, handling adversarial noise in linear systems, and leveraging tensor structures for efficient data recovery. She supervises Ph.D. students including Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko. Her teaching at Princeton covers graduate probability theory (ORF526), convex optimization (ORF523), and network science (ORF387), with prior teaching roles at UCLA and University of Michigan.

Danijel Skočajمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

• Information Visualization
• Data Visualization
• Human-Computer Interaction
• +۴ مورد دیگر

Dr. Stuart Marshall serves as Associate Dean of Academic Development in the Faculty of Science and Engineering at Victoria University of Wellington - Te Herenga Waka, where he also holds the academic rank of Senior Lecturer. Previously, he served as Head of School for the School of Engineering and Computer Science from 2015-2021 after joining the institution as a Lecturer in 2003. His academic journey includes completing his BSc, MSc, and PhD in Computer Science at Victoria University of Wellington, with his doctoral research focusing on software reuse. Marshall's research interests center on information and data visualization, particularly exploring how visualization can function as collaborative tools in team environments rather than single-user applications. He also investigates explainable AI and mobile user interface design with emphasis on promoting healthy device usage patterns. His work bridges theoretical computer science with practical applications in education and environmental analysis. Analysis of his publication record reveals a consistent focus on visualization techniques, with recent work shifting toward immersive analytics and virtual reality applications for complex data analysis, particularly in ecosystem services. Earlier publications concentrated on mobile learning applications grounded in transactional distance theory, software visualization for large codebases, and graph layout algorithms. Marshall has supervised six PhD students and eight Masters students to completion, with current doctoral supervision spanning immersive environments for reading assistance, medical diagnosis interfaces, and cardiac procedure simulation. His grant portfolio includes multiple ACM ISS 2022 projects with industry partners including Sensor Holdings Limited, Niantic, Autodesk, and Northeastern University, along with an Australian Research Council grant for digital games preservation. Teaching responsibilities encompass foundational programming, safety-critical systems, human-computer interaction, and data visualization across undergraduate and graduate courses. He teaches subjects including SWEN326 (Safety-Critical Systems), CGRA151 (Introduction to Computer Graphics and Games), and DATA301 (Data Science in Practice), with teaching assignments scheduled through 2026.

• Environmental psychology
• Environmental neuroscience
• Behavioural plasticity
• +۳ مورد دیگر

Simone Kühn serves as Director of the Research Center for Environmental Neuroscience at the Max Planck Institute for Human Development in Berlin and holds the position of Heisenberg Professor at the University Medical Center Hamburg-Eppendorf since 2016. Previously, she led the Lise Meitner Group for Environmental Neuroscience at the Max Planck Institute (2019-2024) and currently directs the Psychiatric Environmental Neuroscience (PEN) working group, a collaboration between the MPIB and Charité-Universitätsmedizin Berlin since 2025. Her academic credentials include a Dipl.-psych from the University of Potsdam (2006), Dr. rer. nat. from the University of Leipzig (2009), and Habilitation in Psychology from Humboldt-Universität zu Berlin (2012). These qualifications established her expertise in the neural mechanisms underlying human-environment interactions. Dr. Kühn's pioneering research examines how natural versus built environments impact mental health, cognitive processes, and neural structures across the lifespan. Her work integrates environmental psychology, neuroscience, and clinical psychiatry to investigate how exposure to different environments shapes brain function and psychological well-being. She employs advanced methodologies including structural and functional MRI, virtual reality environments, and longitudinal study designs to uncover the neural pathways connecting physical environments to mental health outcomes. Her research has demonstrated that natural environments can reduce stress, enhance cognitive restoration, and positively influence brain structure, particularly in regions associated with emotion regulation and memory. Analysis of her extensive publication record reveals a consistent trajectory toward increasingly sophisticated investigations of environmental influences on the brain. Her recent work has expanded into virtual reality applications for mental health treatment, the neural mechanisms of architectural design preferences, and the impact of air pollution on brain health. She has pioneered experimental approaches to study environmental effects through controlled exposure studies, including the development of virtual nature environments that can be precisely manipulated to isolate specific environmental features. Her scientific contributions have been recognized through prestigious appointments: Election to the German National Academy of Sciences Leopoldina Membership in the DFG-Network WAS (Wirkungsforschung in Architektur und Städtebau) Service on the Psychology Review Board of the German Research Foundation (DFG) Fellowship at the German Institute for Economic Research (DIW) Early recognition through admittance to Studienstiftung des deutschen Volkes (2005) Dr. Kühn has mentored numerous doctoral and master's students whose theses explore diverse aspects of environmental neuroscience, from the impact of natural environments on stress physiology to the neural correlates of architectural preferences. Her research has been supported by substantial funding that has enabled large-scale investigations of environmental influences on brain health across different age groups and populations, including vulnerable individuals with mental health conditions. As leader of the Center for Environmental Neuroscience, she directs a multidisciplinary team of researchers who employ cutting-edge methodologies to investigate how environmental factors shape brain development, function, and mental health. The center's work has significant implications for evidence-based urban planning, therapeutic interventions using nature exposure, and understanding the neural basis of human-environment interactions in an increasingly urbanized world.

Katja Beesdo-Baumمشاهده پروفایل

استاد

• Clinical Psychology
• Developmental Psychopathology
• Mental Disorders Epidemiology
• +۳ مورد دیگر

Prof. Dr. Katja Beesdo-Baum is a Full Professor of Behavioral Epidemiology at the Institute of Clinical Psychology and Psychotherapy, Faculty of Science, Technical University of Dresden. Her research focuses on the epidemiology and clinical aspects of mental disorders, integrating neurobiological, developmental, and environmental factors through large-scale observational and experimental studies. She holds editorial roles at journals like Child Psychiatry & Human Development and contributes to global initiatives such as the WHO’s ICD-11 clinical practice network. Her work spans anxiety disorders, depression, and prevention strategies in youth, with a strong emphasis on translational research and healthcare system integration. Education includes a Diploma in Psychology (2000) and a Dr. rer. nat. (2006), both from TU Dresden, followed by Habilitation in 2010. Professional experience includes postdoctoral training at the National Institute of Mental Health (USA) and leadership roles in clinical and research groups. Awards include the ECNP Fellowship and Dr.-Walter-Seipp Dissertation Award. Her research explores mechanisms linking stress, neuroendocrine systems, and mental disorders, with recent studies focusing on brain structure alterations in anxiety disorders (ENIGMA collaborations) and the efficacy of prevention programs. She investigates digital health tools, stigma reduction, and sociodemographic barriers to mental healthcare access in adolescents and young adults. Key Research Themes: Behavioral epidemiology, developmental psychopathology, neuroimaging, prevention science. Recent Trends: Machine learning in brain-based classification of anxiety disorders, longitudinal studies on stress biomarkers (e.g., cortisol, androgens), and implementation science for pediatric mental health services. Awards highlight her contributions to clinical guidelines (DSM-5 Task Force) and global mental health policy. Her grants and collaborations span European and international funding bodies, emphasizing interdisciplinary approaches to mental health challenges. Labs/Teams: Active in the ENIGMA Anxiety Working Group, BeMIND study (behavioral-mind health cohort), and Dresden-based clinical research networks.

Jonathan Blazekمشاهده پروفایل

استادیار

• Cosmology
• Astrophysics
• Galaxy Clustering
• +۵ مورد دیگر

Jonathan Blazek is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in observational and theoretical cosmology. His research focuses on large-scale astronomical surveys to understand cosmic structure and dark energy, particularly through galaxy clustering and weak gravitational lensing. He is a key member of the Dark Energy Survey and Vera C. Rubin Observatory collaborations, leading efforts to combine multi-wavelength datasets for cosmological insights. Blazek earned his Ph.D. from UC Berkeley and completed postdoctoral fellowships at EPFL (Switzerland) and Ohio State University. Education: Ph.D. in Physics, University of California, Berkeley Postdoctoral Fellowships: EPFL (Switzerland), Ohio State University Research Interests: His work centers on cosmological modeling using galaxy surveys, particularly refining analytic and numerical methods to connect observations with theoretical frameworks. Key areas include: Weak gravitational lensing and galaxy clustering Combined-probe cosmology (integrating datasets across wavelengths) Dark matter and dark energy dynamics Large-scale structure formation Publications & Grants: Blazek has authored over 50 peer-reviewed articles, including foundational work on intrinsic alignment modeling and cosmic shear analysis. He leads the NSF CAREER grant project exploring dark sector physics with galaxy surveys. His recent publications address baryonic feedback effects, CMB lensing cross-correlations, and next-generation survey methodologies. Labs & Collaborations: He contributes to the Northeastern Cosmology Group and the Dark Energy Science Collaboration, advancing projects like the Legacy Survey of Space and Time (LSST) at Vera Rubin Observatory.

Rachel Beanمشاهده پروفایل

استاد

• Cosmology
• Dark Energy
• Gravitational Physics
• +۲ مورد دیگر

Rachel Bean is Jacob Gould Schurman Professor of Astronomy at Cornell University and Senior Associate Dean for Math and Science. Her cosmology research focuses on dark energy properties, gravitational physics, and the early universe using cosmic microwave background and galaxy survey data. As co-recipient of the Gruber and Breakthrough prizes, she contributed to precision cosmology through the WMAP mission. Research develops methods to extract cosmological information from large astrophysical datasets, including cross-correlation techniques between CMB experiments (Atacama Cosmology Telescope, Simons Observatory) and galaxy surveys (DESI, Rubin LSST). Current projects investigate modified gravity constraints using cluster abundances and novel statistical approaches to kSZ velocity reconstruction. Publication themes include precision cosmology, gravity tests, and multi-messenger astrophysics. Recent work advances machine learning applications for cosmological inference, while earlier research established foundations in semiconductor device physics. Articles consistently demonstrate innovative approaches to cosmological parameter estimation and physical theory testing. Awards: Gruber Prize (2012), Breakthrough Prize (2018), Presidential Early Career Award, and Cottrell Scholar Award. Leadership includes former chair of LSST Dark Energy Science Collaboration and service on the Astronomy and Astrophysics Advisory Committee.