جست‌وجوی استاد و پژوهشگر

David Meansمشاهده پروفایل

دانشیار مهمان

• Creative Writing
• American Fiction
• Short Story
• +۵ مورد دیگر

David Means is a Visiting Associate Professor of English at Vassar College, where he has taught since 2001. He holds a BA from The College of Wooster and an MFA in poetry from Columbia University. His academic work centers on creative writing, modern and contemporary fiction and poetry, and American culture. Education: BA, The College of Wooster MFA, Columbia University in the City of New York Means is a celebrated author of short fiction and novels, known for his lyrical prose, experimental narrative structures, and deep exploration of trauma, memory, and redemption. His work frequently appears in The New Yorker , Harper’s , The Paris Review , and other prestigious literary journals. His writing often returns to themes of violence, loss, and the quiet grace that emerges in their aftermath, drawing comparisons to Flannery O’Connor and Denis Johnson. His stories are marked by a nonlinear sense of time and a profound empathy for marginalized or broken characters. His recent publications include the story collection Instructions for a Funeral (2019) and Two Nurses, Smoking , with his novel Hystopia longlisted for the Man Booker Prize. His forthcoming non-fiction book, Pivot: Ruminations in the Shadow of Death , reflects on mortality and personal reflection. Selected Awards and Honors: Guggenheim Fellowship (2013) Three O. Henry Prizes Three Pushcart Prizes Los Angeles Book Prize for Fiction Finalist, National Book Critics Circle Award Finalist, Frank O’Connor International Short Story Prize Longlisted, Man Booker Prize Means has advised and influenced many emerging writers through his teaching and public readings. While specific students are not named in the sources, his role as a mentor is evident in his sustained presence in creative writing programs. He has no known research grants or lab affiliations, as his work is primarily literary and individual. He continues to publish and engage in public discourse on the nature of storytelling, including critiques of AI-generated fiction.

Gabrielle Lynchمشاهده پروفایل

استاد

• Comparative Politics
• African Politics
• Ethnic Identity Politics
• +۴ مورد دیگر

Professor Gabrielle Lynch is a leading scholar in Comparative Politics and African Studies at the University of Warwick. She holds the academic rank of Professor and serves as PAIS Impact Director, Comparative Politics cluster lead, and Warwick UCU Equalities Officer. Her research focuses on ethnic identities, elections, transitional justice, and social media's role in African politics. Lynch earned her BA, MPhil, and DPhil from the University of Oxford. She has conducted extensive fieldwork in Kenya, Ghana, and Uganda, supported by grants from the ESRC, Warwick Policy Support Fund, and others. Her awards include the ESRC Award for Outstanding International Impact (2019) and the Market Research Society’s President’s Medal. Lynch is editor of African Affairs and a member of the African Studies Association of the UK and British Institute in Eastern Africa. Lynch’s work bridges academia and public engagement, including columns in The Saturday Nation and The East African . She founded the African Judiciaries Research Network and co-authored influential books like Performances of Injustices and The Moral Economy of Elections in Africa . Her research emphasizes democratization challenges, electoral integrity, and the intersection of technology and politics in Africa.

Thomas R Overtonمشاهده پروفایل

استاد

• Dairy Cattle Nutrition
• Transition Cow Management
• Metabolic Health of Dairy Cows
• +۳ مورد دیگر

Thomas R Overton serves as Department Chair and Professor of Dairy Nutrition and Management in the Department of Animal Science at Cornell University's College of Agriculture and Life Sciences (CALS). He also directs PRO-DAIRY, an extension program focused on enhancing dairy farm profitability and efficiency in New York through research-based strategies for transition cow management. Education: Doctorate, University of Illinois, 1998 Master of Science, University of Illinois, 1994 Bachelor of Science, Cornell University, 1991 Dr. Overton's research centers on the transition period (late pregnancy to early lactation) in dairy cows—the most critical phase for health and productivity. His work integrates nutritional physiology with practical farm applications, targeting hypocalcemia management, protein supplementation strategies, inflammation control, and biomarker validation for metabolic health. This research directly informs his extension efforts through PRO-DAIRY, which delivers actionable knowledge to dairy producers and industry professionals. Analysis of his 15 most recent publications (2022-2024) reveals consistent emphasis on transition cow nutrition, with growing focus on metabolic analytics, precision feeding, and real-world herd management. His work bridges controlled studies at Cornell's Ruminant Center with observational research on commercial New York dairy farms. Scientific Awards: 2024 Nutrition Professionals Applied Dairy Research Award (American Dairy Science Association) 2010 CALS Extension/Outreach Team Award (PRO-DAIRY) 2007 Foundation Scholar Award (American Dairy Science Association) 2007 CALS Extension/Outreach Team Award Dr. Overton advises undergraduate and graduate students in Animal Science while leading PRO-DAIRY's extension initiatives. His program secures industry partnerships and grants focused on improving dairy farm sustainability through nutritional science. The July 2025 'Farm of the Future' showcase at Cornell's Ruminant Center exemplifies his commitment to translating research into practical innovation. He operates within Cornell's PRO-DAIRY program and the Cornell University Ruminant Center—a living laboratory for dairy technology testing—collaborating closely with industry stakeholders to address emerging challenges in dairy production.

Hakan Kayalمشاهده پروفایل

استاد

• Nanosatellites
• Space Systems
• Extraterrestrial Missions
• +۳ مورد دیگر

Prof. Dr.-Ing. Hakan Kayal serves as University Professor for Aerospace Engineering at the University of Würzburg, holding the Chair of Computer Science VIII (Space Technology) and chairing the Interdisciplinary Research Center for Extraterrestrial Studies (IFEX). His leadership bridges computer science and space systems engineering within the university's Institute of Computer Science. Research focuses on three synergistic domains: nanosatellite development for extraterrestrial missions (including the SONATE-2 6U platform demonstrating AI-driven onboard processing), scientific investigation of Unidentified Anomalous Phenomena (UAP) through the university's collaboration with the Federal Aviation Office, and spacecraft autonomy systems enabling higher mission independence. Current projects include the NEAlight mission (extended to develop the Apophis Interceptor concept for the 2029 asteroid flyby), VaMEx3-MarsSymphony for Mars exploration, and JMU Space Observatory initiatives. Publication trends reveal strong emphasis on asteroid defense strategies (particularly for Apophis), CubeSat-based UAP detection methodologies, and real-time AI processing in constrained space environments. His team actively engages students through ADS-B tracking, Meteosat App development, and Moon Base 2030 projects, while recent recognition includes co-authoring a landmark UAP review in Progress in Aerospace Sciences with 33 international scientists.

Joseph Bentsmanمشاهده پروفایل

استاد

• Control of nonlinear systems
• Control of distributed parameter systems
• Nonlinear oscillations
• +۷ مورد دیگر

Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.

Veronika Eyringمشاهده پروفایل

استاد

• Climate science
• Climate modelling
• Machine learning for climate models
• +۷ مورد دیگر

Veronika Eyring serves as Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. She holds dual appointments at these leading institutions, directing cutting-edge research at the intersection of climate science and artificial intelligence. Education: 2008: Habilitation in Environmental Physics at the University of Bremen 1999: PhD in Physics from the University of Bremen 1994: Diploma in Physics from the University of Erlangen Professor Eyring's research program focuses on improving climate models and projections through innovative integration of machine learning techniques and spaceborne Earth observations. Her work spans process-oriented modeling, development of observationally-based performance metrics, and understanding systematic biases in climate models. She has pioneered approaches to weighting model projections based on their performance using machine learning, significantly advancing the field of climate model evaluation. Her research has critical applications across multiple sectors including aeronautics, space research, transportation, and energy systems. Analysis of her recent publications reveals a clear trajectory toward deeper integration of machine learning with traditional climate modeling approaches. Her work has increasingly focused on developing community tools like the Earth System Model Evaluation Tool (ESMValTool) and leading major international initiatives such as the USMILE project (Understanding and Modelling the Earth System with Machine Learning). The publications span climate science, machine learning, Earth system modeling, and remote sensing, with specific emphasis on climate model evaluation, parameterization techniques, and improved climate projections. Scientific Awards: AGU Ambassador Award (2024) TUM Distinguished Affiliated Professor (2024) Gottfried Wilhelm Leibniz Prize (2021) ERC Synergy Grant (2019) Thomson Reuters Highly Cited Researcher (2016-2021) Top female researchers award, Helmholtz-Society (2015) Professor Eyring actively supervises a large research group comprising PhD students working on ML-based sea ice parameterizations, causal model evaluation for air-sea interactions, and machine learning-based detection of droughts in climate projections. She leads the prestigious ERC Synergy Grant USMILE and secured significant funding through the DFG Gottfried Wilhelm Leibniz Prize. Her research group at DLR includes multiple postdocs, research scientists, and software engineers working collaboratively on climate informatics projects. Professor Eyring leads the Earth System Model Evaluation and Analysis Department at DLR, which encompasses research groups focused on CMIP model evaluation, ESMValTool development, and machine learning applications in climate science. She founded and supervises the 'Climate Informatics' Group at the DLR Institute for Data Science in Jena. Her department maintains strong international collaborations, particularly with the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, where she serves as an Affiliate Scientist.

John P. O'Dohertyمشاهده پروفایل

استاد

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +۳ مورد دیگر

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

Kathryn Roederمشاهده پروفایل

استاد

• Statistical Genetics
• Genomics
• Biostatistics
• +۵ مورد دیگر

Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.

Ronald Parrمشاهده پروفایل

استاد

• Reinforcement Learning
• Markov Decision Processes
• Artificial Intelligence
• +۳ مورد دیگر

Ronald Parr is a Professor of Computer Science in the Department of Computer Science at Duke University's Pratt School of Engineering. He has been at Duke since 2000, progressing from Assistant Professor to Associate Professor with tenure, and ultimately to Full Professor. From 2014 to 2017, he served as Department Chair and delivered graduation speeches in 2015, 2016, and 2017. Dr. Parr received his Ph.D. in Computer Science from the University of California, Berkeley in 1998, with a dissertation titled "Hierarchical Control and Learning in Markov Decision Processes" under advisor Stuart Russell. He earned his A.B. in Philosophy, cum laude, from Princeton University in 1990. His primary research focuses on methods for solving large stochastic planning problems using Markov Decision Processes and approximate dynamic programming techniques. His work spans reinforcement learning, value function approximation, game theory, sensing, and robotics. Dr. Parr's research has been consistently funded by major agencies including NSF, DARPA, and ARO, with recent projects focusing on feature encoding for reinforcement learning, neurosymbolic hierarchical reinforcement learning, and reasoning in large, structured, uncertain domains. His publication record demonstrates consistent contributions to top venues including NeurIPS, ICML, and AAAI, with work that bridges theoretical foundations and practical applications. Dr. Parr has received numerous honors including being elected as an AAAI Fellow in 2023, receiving an AAAI Outstanding Paper Honorable Mention in 2013, winning the IJCAI-JAIR Best Paper Award in 2007, receiving an NSF CAREER award in 2006, and being named an Alfred P. Sloan Fellow in 2003. He has advised ten graduate students to completion across various research areas within AI and robotics. His research has been supported by over $1.5 million in direct funding to his lab, with additional collaborative funding from multiple NSF, DARPA, and ARO grants. Dr. Parr has served extensively on program committees for major AI conferences including ICML, NeurIPS, AAAI, and UAI, and has held leadership roles such as Program Co-Chair and General Chair for UAI. Dr. Parr maintains an active research group focused on reinforcement learning and sequential decision making, with ongoing projects in neurosymbolic AI, hierarchical reinforcement learning, and interpretable machine learning models, continuing to bridge theoretical foundations with practical applications in robotics and AI systems.

• Automatic parameter estimation in electric machines
• Multiphase drives
• Sensorless control
• +۷ مورد دیگر

Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.

John Baillieulمشاهده پروفایل

استاد

• Robotics
• Nonlinear control theory
• Mechanical systems control
• +۶ مورد دیگر

John Baillieul is Distinguished Professor at Boston University with joint appointments in Mechanical Engineering, Systems Engineering and Electrical & Computer Engineering. He directs experimental laboratories for real-time control of lightweight robotic systems and applies nonlinear control theory to complex multi-body, networked, and bio-inspired systems. Education: Ph.D., Harvard University Research Interests: Baillieul’s work spans robotics, nonlinear control, and networked systems. Early contributions resolved motion-planning for kinematically redundant manipulators; current themes include neuromimetic learning, vision-based navigation, and resilience of infrastructure networks such as power grids. His group couples rigorous geometric control with real-time hardware to create lightweight, high-performance robots and to uncover fundamental information limits in feedback systems. Recent Publication Trends (2021-2025): Over the past five years his output has concentrated on three synergistic directions: (i) neuromimetic and Koopman-based data-driven methods for estimating and controlling nonlinear systems, (ii) vision-based guidance and sparse optical-flow primitives for agile autonomous flight, and (iii) network-theoretic decomposition and information-rate studies for resilient operation of power grids and collective dynamics. Honors & Awards: IEEE Fellow 2025 Roger W. Brockett Control Systems Award Former Editor-in-Chief, IEEE Transactions on Automatic Control Affiliations & Service: He is a member of Boston University’s Center for Information and Systems Engineering (CISE), has served as Editor-in-Chief of IEEE Transactions on Automatic Control, and remains active in editorial and organizational roles across the IEEE control systems community.

Paul D. Asimowمشاهده پروفایل

استاد

• Geochemistry
• Geology
• Chemical Petrology
• +۴ مورد دیگر

Paul D. Asimow is the Eleanor and John R. McMillan Professor of Geology and Geochemistry at the California Institute of Technology (Caltech), part of the Division of Geological and Planetary Sciences. He holds a B.A. from Harvard University (1991), an M.S. (1993), and a Ph.D. (1997) from Caltech. His career progression includes roles as Assistant Professor (1999–2005), Associate Professor (2005–2010), and Professor (2010–present), with the McMillan Professorship since 2016. Education: A.B. in Geology, Harvard University, 1991 M.S. in Geology, Caltech, 1993 Ph.D. in Geology, Caltech, 1997 Research Interests: Focuses on computational, experimental, and observational approaches to igneous petrology and mineral physics. Key areas include adiabatic mantle melting, water's role in mantle dynamics, high-pressure mineral physics, and processes at mid-ocean ridges. His research utilizes advanced facilities like the Lindhurst Laboratory of Experimental Geophysics and the alphaMELTS software package for thermodynamic modeling. Articles Overview: Recent work spans planetary crust formation, Martian petrogenesis, and high-pressure mineral behavior. Themes include experimental techniques, computational modeling, and cosmochemical studies of meteorites. Awards and Honors: James B. Macelwane Medal (AGU) Frank Wigglesworth Clarke Medal (Geochemical Society) Richard P. Feynman Prize for Teaching Excellence (Caltech) Fellow of the American Geophysical Union Fellow of the Mineralogical Society of America Grants and Labs: Received NSF funding for developing an interactive phase equilibria curriculum. Leads the Lindhurst Laboratory, focusing on shock-wave experiments and high-pressure mineral physics. Collaborates on software tools like alphaMELTS and MAGMASOURCE. Labs and Teams: Active in the Caltech Shock Wave Laboratory, advancing experimental methods for planetary material studies. Engages in interdisciplinary projects on Mars geology and terrestrial planet formation.

• Longitudinal Data Analysis
• Time-to-Event Analysis
• Survival Analysis
• +۵ مورد دیگر

Alessandra Meddis serves as an Assistant Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences. Her academic work centers on developing and applying advanced statistical methodologies for longitudinal and time-to-event data analysis, with significant contributions to public health research in Denmark and internationally. Her institutional affiliation is clearly established through university contact details and departmental listings. Her primary research interests include correlated survival data analysis, competing risk modeling, informative cluster size methodology, causal inference techniques for observational studies, and environmental epidemiology applications. Dr. Meddis has developed specialized expertise in handling complex survival data structures while maintaining focus on real-world public health problems, particularly in HIV comorbidity patterns, environmental exposure effects, and pandemic-related mortality analyses. Her methodological innovations directly address challenges in clustered and censored data common across medical research domains. Analysis of Dr. Meddis's recent publication record reveals a consistent trajectory of high-impact interdisciplinary research spanning clinical medicine, epidemiology, and statistical methodology. Her work appears in leading journals across biostatistics, infectious diseases, and public health, demonstrating strong collaborative networks with clinical researchers and epidemiologists. Key thematic areas include HIV treatment outcomes, environmental health exposures, and critical care applications during the pandemic, with recent methodological papers advancing survival analysis techniques for complex data structures. Scientific Awards: No scientific awards were specified in the available institutional profile. Advising and Grants: The institutional profile does not provide details regarding graduate student supervision or specific research grant funding. Her collaborative publications suggest involvement in multi-investigator projects including the COCOMO HIV cohort study and pandemic-related research initiatives. Labs and Teams: Dr. Meddis is affiliated with the Section of Biostatistics within the Department of Public Health, though specific laboratory facilities or dedicated research teams are not described in the source material. Her extensive co-authorship patterns indicate active participation in multiple research consortia across medical specialties.

Joanna Millsteinمشاهده پروفایل

پژوهشگر ارشد

• Fracture mechanics
• Ice dynamics
• Remote sensing
• +۵ مورد دیگر

Joanna Millstein is a Post-doctoral Fellow in Geophysics at the Colorado School of Mines. She earned her Ph.D. in Geophysics from the Massachusetts Institute of Technology in 2023 as part of the MIT-WHOI Joint Program in Oceanography and Engineering, where her dissertation focused on The Flow and Fracture of Antarctic Ice Shelves . She also holds an A.B. in Earth Sciences from Dartmouth College (2017). Her research centers on the deformation and fracture of glacier ice, working at the intersection of fracture mechanics, remote sensing (particularly SAR and InSAR processing), statistical mechanics, and stochastic models. Millstein uses observational data from satellites and field measurements to derive mechanical and statistical models for glacier ice processes, with particular focus on reconciling observations of ice fracture and iceberg calving with theoretical models. Her work aims to resolve the nonlinear physics of glacier ice to better understand future global climate change impacts. Millstein's publication record shows a strong focus on Antarctic ice dynamics, with recent work applying extreme value theory to analyze 47 years of iceberg calving events. Her research demonstrates consistent attention to both theoretical modeling and practical applications for understanding climate change impacts on polar regions. She has developed computational tools including CryoCloud, reflecting her commitment to open science and cloud-based infrastructure for cryosphere research. Her scientific contributions span ice shelf mechanics, fracture prediction, rheology, and statistical modeling of glacial processes. Millstein maintains an active research presence with publications extending to 2025, demonstrating ongoing contributions to the field of glaciology and climate science. She is affiliated with the glaciology research center at Colorado School of Mines and maintains an active GitHub presence with climate-related code repositories. Her work bridges theoretical geophysics with practical climate change impact assessment, particularly regarding ice sheet stability and sea level rise projections.

Jillianne Codeمشاهده پروفایل

دانشیار

• Learner agency
• Educational technologies
• Technology-enhanced learning
• +۹ مورد دیگر

Dr. Jillianne Code is an Associate Professor at the University of British Columbia's Faculty of Education, Department of Curriculum & Pedagogy, directing the ALIVE Research Lab. Her work focuses on learner agency, educational technologies, and social media's impact on student success. Specializes in immersive virtual environments for assessment Researches self-regulated learning and formative feedback Investigates health education through digital interventions Her recent publications examine pandemic-era educational transitions, digital literacy in teacher training, and cognitive tools in virtual reality. She has received Outstanding Paper Awards for her work on immersive assessment. Dr. Code leads research projects on learning analytics, student engagement in digital spaces, and technology education sustainability. Her work bridges educational theory with practical interventions through digital badges, mobile health applications, and 3D learning environments.