Early Algebra Learning Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱۶-۳۰ از ۳۵۰ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

James Saundersonمشاهده پروفایل

مدرس ارشد

Monash UniversityAustralia

Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.

پروفایل رسمی دانشگاه

Erica Litkeمشاهده پروفایل

دانشیار

University of DelawareUnited States

Erica Litke is an Associate Professor in the School of Education at the University of Delaware, with a dual role as Assistant Professor in the Department of Mathematical Sciences. Her research focuses on improving instructional quality in mathematics education, particularly in algebra and data/statistics, and addressing equity in mathematics policies. She has extensive experience as a high school teacher and has developed observation tools to measure teaching practices. **Education:** Ed.D. in Educational Policy, Leadership, and Instructional Practice, Harvard University (2015) Ed.M. in Administration, Planning, and Social Policy, Harvard University (2001) B.A. in Mathematics and English, Oberlin College (1997) **Research Interests:** Dr. Litke’s work emphasizes culturally responsive teaching, equitable instruction, teacher professional development, and the impact of district-level policies. She uses observation instruments to analyze instructional quality and has pioneered content-specific approaches for algebra education. **Grants & Awards:** She has led NSF-funded projects totaling over $5.9 million, focusing on equitable mathematics instruction and teacher coaching. Her awards include the Early Career Publication Award (AERA, 2021) and STaR Fellow recognition (2017–2018).

پروفایل رسمی دانشگاه

Chris Bowmanمشاهده پروفایل

استاد

University of YorkUnited Kingdom

Dr Chris Bowman is a Professor in the Department of Mathematics at the University of York, supported by an EPSRC Early Career Fellowship. His research focuses on the intersection of combinatorics, Lie theory, knot theory, and categorical representation theory, with a particular emphasis on symmetric groups, Cherednik algebras, and Kazhdan-Lusztig theory. He holds a PhD from the University of Cambridge and has held postdoctoral positions at Paris and London. Bowman is a member of EPSRC’s Early Career Forum and organizes the UK-European Representation Theory and Categorification Network (RepNet). His work explores theoretical frameworks guided by machine learning techniques and addresses conjectures like Foulkes’ conjecture. He currently supervises PhD student Benjamin Mills and leads projects funded by the EPSRC. Education: BSc at York, PhD at Cambridge (2012) Postdoctoral roles: Paris 7, City University London (Royal Commission Fellowship), University of Kent Research interests include diagrammatic algebra, Hecke categories, and representation theory of algebraic groups. His recent publications address plethysm coefficients, BGG resolutions, and modular decomposition numbers. Bowman has received prestigious awards, including the Royal Commission for the Exhibition of 1851 Fellowship. Awards: EPSRC Early Career Fellowship, Royal Commission Fellowship He collaborates widely, with projects on geometric and categorical Lie theory, and actively contributes to academic outreach through conferences and workshops.

پروفایل رسمی دانشگاه

NG Hui Khoonمشاهده پروفایل

دانشیار

National University of SingaporeSingapore

NG Hui Khoon is an Associate Professor at the National University of Singapore , affiliated with Yale-NUS College and the Centre for Quantum Technologies . She holds a PhD in Physics from the California Institute of Technology (Caltech), USA (2009). Research Interests: Her work focuses on theoretical aspects of quantum information and computation, particularly quantum error correction and fault tolerance , quantum noise modeling , and quantum tomography . She investigates how resource constraints limit quantum computing and develops adaptive methods for quantum state estimation using neural networks. Publication Trends: Her recent articles (2021–2013) emphasize quantum error correction frameworks, tomography techniques, and statistical methods for quantum systems. Key themes include fault tolerance under amplitude-damping noise, randomized benchmarking for time-correlated dephasing, and Bayesian approaches for prior-data conflict checking. Scientific Awards: Early Career Teaching Award (2019, Inaugural recipient) CQT Fellowship (2019 – current) Advising & Grants: No explicit advising or grant details are provided. She collaborates with institutions like the Centre for Quantum Technologies and Yale-NUS College. Labs & Teams: She is associated with the Centre for Quantum Technologies, a leading research center in quantum information science.

پروفایل رسمی دانشگاه

Alexander Artikisمشاهده پروفایل

دانشیار

Swiss Federal Institute of Technology in LausanneSwitzerland

Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications

پروفایل رسمی دانشگاه

Alexander D Maloneyمشاهده پروفایل

استاد

Syracuse UniversityUnited States

Alexander D Maloney serves as the Kathy and Stan Walters Endowed Professor of Quantum Science and Director of the Institute for Quantum and Information Science at Syracuse University's College of Arts and Sciences, Department of Physics. His leadership bridges theoretical physics and quantum information science through institutional and research initiatives. His academic foundation includes a Ph.D. in Physics from Harvard University (2003) with dissertation "Time-Dependent Backgrounds of String Theory," complemented by dual B.Sc. and M.Sc. degrees in Physics and Mathematics from Stanford University (1998). Maloney's research centers on quantum gravity and information theory, exploring black hole physics through string theory frameworks. His work connects quantum field theory, cosmology, and information paradox resolution, with particular focus on wormhole geometries, resurgence phenomena, and holographic dualities. This interdisciplinary approach examines how quantum information principles shape spacetime structure. Recent publications reveal evolving emphasis on Narain ensemble statistics, modular invariance applications, and quantum cosmology in reduced dimensions. His 2020-2025 output demonstrates methodological progression from semiclassical gravity to non-perturbative quantum gravity techniques, increasingly integrating machine learning concepts with theoretical frameworks. His scientific recognition includes: James McGill Professor at McGill University (2023) Sir William Macdonald Chair in Physics at McGill University (2020) Maloney secures major research funding including NSERC Discovery Grants (2015-2020, 2020-2025) and Simons Foundation's "It from Qubit" collaboration (2015-2022). He actively shapes academic discourse through search committee leadership, journal reviewing (Journal of High Energy Physics since 2007), and international conference organization at Aspen Center for Physics and Institute for Advanced Study. As Director of Syracuse's Institute for Quantum and Information Science, he cultivates collaborative research environments exploring quantum gravity applications to quantum computing and cosmological modeling.

پروفایل رسمی دانشگاه

Alexandra Fedorovaمشاهده پروفایل

استاد

University of British ColumbiaCanada

Dr. Alexandra Fedorova is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), with an Associate Member role in the Computer Science department. She leads the Systopia systems research group, focusing on system software design, memory/storage management, and accelerator-centric computing. Her work emphasizes performance optimization, energy efficiency, and hardware-software co-design. She holds a PhD from Harvard University (2006), where she researched operating system scheduling under Margo Seltzer. Prior to UBC, she was an Associate Professor at Simon Fraser University (2006–2015). Fedorova is a recipient of the Alfred P. Sloan Research Fellowship and the Anita Borg Early Career Award. She consults for MongoDB's storage engine team and collaborates with industry on storage and performance challenges. Her research spans tools like Non-sequitur for program trace visualization, studies on storage-class memory (e.g., Optane), and frameworks for GPU acceleration. Recent efforts include Sunstone (spatial accelerator scheduling) and ExtMem (application-aware memory management). Her work bridges low-level systems with high-performance computing needs. Key contributions include optimizing NUMA systems, improving storage engine performance, and exploring processing-in-memory architectures. Fedorova’s projects often involve open-source collaboration, reflected in her GitHub repositories such as vividperf and perf-logging , which support performance analysis tools.

پروفایل رسمی دانشگاه

Katherine E. Lewisمشاهده پروفایل

دانشیار

University of WashingtonUnited States

Katherine E. Lewis is an Associate Professor in the College of Education at the University of Washington, specializing in Special Education with a focus on high-incidence disabilities teacher education (SPED-TEP) within the Learning Sciences & Human Development area. She holds a Ph.D. and M.A. in Education from the University of California, Berkeley, an M.Sc. in Multimedia Systems from Trinity College, Dublin, and a B.A. in Psychology from the University of Notre Dame. Dr. Lewis's research fundamentally centers on designing accessible educational contexts, particularly for students with disabilities. Her primary research line focuses on students with mathematics learning disabilities (dyscalculia), where she rejects the deficit model in favor of a disability studies approach. She believes individuals are disabled not by neurological differences but by inaccessible spaces and contexts. Her work identifies how students with mathematics learning disabilities develop unconventional understandings of mathematical representations and compensatory strategies to gain access. Her second research line involves designing educational supports for foster and adoptive parents, including the resource website foster-edu.com. A third emerging area examines how American Sign Language can support Deaf students' understanding of mathematics concepts. Her publications reveal a consistent theme of bridging mathematics education and special education, challenging traditional deficit perspectives on learning disabilities. Her work emphasizes designing accessible instruction through detailed analysis of students' unconventional mathematical reasoning rather than attempting to remediate perceived cognitive deficits. She has developed innovative research methods, including clinical interviews and video analysis of one-on-one tutoring sessions, to document students' unique approaches to mathematics. Linking Research and Practice Outstanding Publication Award from the National Council of Teachers of Mathematics, 2018 National Academy of Education - Spencer Postdoctoral Fellow, 2015 Early Career Publication Award of the Special Interest Group on Research in Mathematics, 2015 Spencer Dissertation Fellowship, 2010 Dr. Lewis has created research-recaps to make her work accessible, including visual summaries and videos explaining her reconceptualization of mathematical learning disabilities as differences rather than deficits. She also maintains the research website klewismath.com and the resource site foster-edu.com for foster and adoptive parents. Her personal experience with dyslexia informs her conceptualization of learning disabilities as cognitive differences rather than irreconcilable deficits, driving her commitment to designing accessible educational contexts.

پروفایل رسمی دانشگاه

Rameshwar Pratapمشاهده پروفایل

دانشیار

Indian Institute of Technology Hyderabad (IITH)India

Rameshwar Pratap is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). Previously, he served as an Assistant Professor at the School of Computing and Electrical Engineering, IIT Mandi for three years. Education: Ph.D. in Theoretical Computer Science, Chennai Mathematical Institute Research Interests: His research lies at the intersection of theory and practice, focusing on extremely simple yet practical approximation algorithms with provable guarantees . Key themes include: Sketching and dimensionality reduction algorithms for tensors and similarity measures Improving speed, scalability, and accuracy of existing sketching methods Applications in machine learning: node embedding in large-scale networks, itemset mining, model compression He extensively employs techniques from matrix and tensor algebra, sampling, random projection, and randomized hashing . Publications Overview: Across 2021–2025 his work has appeared in top venues such as IEEE Globecom, Theoretical Computer Science, Acta Informatica, Information Processing Letters, Algorithmica, UAI, ICALP, Machine Learning, TKDE, and ACML. Recurring themes are randomized sketching, locality-sensitive hashing, compressed matrix multiplication, variance reduction, and subspace approximation , demonstrating both theoretical depth and practical impact. Awards & Honors: Early Career Research Grant (PM-ECRG) 2025, Anusandhan National Research Foundation (ANRF) Best Paper Award, COCOON 2020 Students & Funding: First Ph.D. student: Bhisham Dev Verma (co-advised with Prof. Manoj Thakur) graduated June 2025 MS by Research student: Punit Pankaj Dubey graduated October 2022 Currently hiring 1 Junior Research Fellow for the PM-ECRG project “Improving Similarity Search in Practice” Labs & Teams: Works within the Algorithms & Theory group at IIT Hyderabad, collaborating with national and international researchers. Erdös number is 3.

پروفایل رسمی دانشگاه

Elizaveta Rebrovaمشاهده پروفایل

استادیار

Princeton UniversityUnited States

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.

پروفایل رسمی دانشگاه

Judit Gervainمشاهده پروفایل

استاد

University of QuebecCanada

Judit Gervain is a Full Professor at the Department of Developmental and Social Psychology at the University of Padua, Italy, and a CNRS Senior Research Scientist (Directeur de Recherche) at the Integartive Neuroscience and Cognition Center (CNRS & Université Paris Descartes), Paris, France. Her research focuses on early speech perception, language acquisition in monolingual and bilingual infants, and the neural mechanisms underlying language learning. She pioneered studies on newborn speech perception using near-infrared spectroscopy (NIRS), revealing prenatal influences on perceptual abilities and the emergence of grammatical structures in preverbal infants. Education: PhD in Cognitive Neuroscience (2002, SISSA, Trieste), postdoctoral research at the University of British Columbia (2007–2009), and CNRS researcher since 2009. She has authored over 100 peer-reviewed articles in journals like Science Advances , Nature Communications , and Developmental Science . Research Interests: Infant language processing, bilingualism, neuroimaging techniques (fNIRS), and comparing infant learning trajectories to artificial intelligence systems. Her work bridges developmental psychology, neuroscience, and computational linguistics, emphasizing the role of innate biases and environmental input in language acquisition. Her recent studies explore how infants’ learning mechanisms differ from Large Language Models (LLMs), focusing on input requirements, critical periods, and the role of multimodal integration. She also investigates the impact of prenatal auditory experience on neonatal speech perception and the neural foundations of linguistic structure detection. Labs/Teams: Affiliated with the CNRS’s Integartive Neuroscience and Cognition Center and the University of Padua’s developmental psychology group. Serves as associate editor for Developmental Science and Neurophotonics .

پروفایل رسمی دانشگاه

Jordan Cotlerمشاهده پروفایل

استادیار

Harvard UniversityUnited States

Jordan Cotler is an Assistant Professor of Physics at Harvard University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. He holds a BS in physics and mathematics from MIT (2015) and a PhD in physics from Stanford University (2020). Before joining Harvard's faculty, he served as a Junior Fellow at the Harvard Society of Fellows from 2020 to 2024. His research focuses on the intersection of quantum information, computation, and spacetime physics. Key interests include quantum algorithms for analyzing many-body and quantum gravitational systems, information-theoretic frameworks for chaotic dynamics, and non-perturbative methods in quantum cosmology and field theory. Cotler's work has advanced quantum algorithm design for experimental platforms and contributed to understanding black hole microstructure and cosmological spacetimes. He has been recognized with prestigious early-career awards, including his Harvard Society of Fellows Junior Fellowship. His publications span foundational topics such as quantum gravity, holography, computational complexity, and quantum chaos, reflecting a multidisciplinary approach to theoretical physics.

پروفایل رسمی دانشگاه

Youssef Marzoukمشاهده پروفایل

استاد

Massachusetts Institute of TechnologyUnited States

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

پروفایل رسمی دانشگاه

Giulia Guidiمشاهده پروفایل

استادیار

Cornell UniversityUnited States

Giulia Guidi is an Assistant Professor of Computer Science at Cornell University, affiliated with the Cornell Ann S. Bowers College of Computing and Information Science. She leads the Cornell High-Performance Computing (HPC) Group and is an Affiliate Faculty at Lawrence Berkeley National Laboratory’s Performance and Algorithms Research Group. Her research focuses on high-performance computing for computational sciences, sparse linear algebra, and scalable software infrastructure for parallel systems. She holds a PhD in Computer Science from UC Berkeley (2022) and has been recognized with awards including the 2024 SIAG/Supercomputing Early Career Prize and the 2023 ISSNAF Young Investigator Award. Her work addresses challenges in genomics, population genetics, and scalable computational methods through collaborations like the NSF-funded 'ACED' project with April Wei’s Lab. Guidi mentors a diverse group of PhD, MEng, and undergraduate students, emphasizing parallel programming and HPC applications. Her lab’s research spans GPU-accelerated algorithms, sparse matrix computations, and bioinformatics tools like the Popcorn and BELLA aligners. She is also a Graduate Field Faculty in Computational Biology and Applied Mathematics at Cornell.

پروفایل رسمی دانشگاه

Darrell Earnestمشاهده پروفایل

دانشیار

University of Massachusetts AmherstUnited States

Darrell Earnest is an Associate Professor in the College of Education at the University of Massachusetts Amherst, specializing in the Department of Teacher Education & Curriculum Studies (TECS). He focuses on STEM education, particularly how students conceptualize time and tools like analog clocks, hourglasses, and digital representations. His work emphasizes equity in mathematics education and the interplay between representations, tools, and learning. Education: B.A., Washington University, 1998 M.A., Tufts University, 2002 M.A., University of California, Berkeley, 2007 Ph.D., University of California, Berkeley, 2012 Research Interests: Time as a mathematical and interdisciplinary concept Curriculum design and teacher education Equity in STEM education Cognitive development in elementary mathematics Key Projects: Clock Work : Investigates children’s understanding of time through analog/digital clocks and non-standard tools like hourglasses. Time Literacy Project : Explores time management practices among undergraduate students, focusing on neurodivergent learners and academic success. Lesson Planimation : Examines how preservice teachers adapt curriculum materials into student-centered lessons, using animations to visualize instructional design. Advising & Grants: Supervises graduate students (e.g., Alicia Gonzales, Christine McGrail) and collaborates with institutions like Disability Services. His work has been funded through projects on time literacy and curriculum translation. Labs/Teams: Collaborates with Dr. Julie Amador (University of Idaho) on curriculum design and teacher education initiatives.

پروفایل رسمی دانشگاه