جست‌وجوی استاد و پژوهشگر

Matthew Lakinمشاهده پروفایل

دانشیار

• Molecular Computing
• DNA Nanotechnology
• Synthetic Biology
• +۷ مورد دیگر

Matthew Lakin is an Associate Professor with tenure in the Department of Computer Science at the University of New Mexico, with a courtesy appointment in the Department of Chemical & Biological Engineering. He is affiliated with the UNM Center for Biomedical Engineering and the School of Engineering, and collaborates extensively with the UNM Health Sciences Center and external institutions. Education: Ph.D., Computer Science, University of Cambridge, 2010 M.A. (Cantab), University of Cambridge, 2009 B.A. (Hons), Computer Science, University of Cambridge, 2005 Dr. Lakin's research focuses on molecular computing, DNA nanotechnology, synthetic biology, and formal verification of biomolecular circuits. He develops computational models and experimental systems for programmable biological devices, especially using heterochiral DNA to enhance stability in living cells. His work spans software tools for biodesign and experimental validation in mammalian systems, with applications in nanomedicine and biosensing. The recent publications highlight a strong trend in engineering robust, intelligent biomolecular systems. His work integrates machine learning concepts into chemical reaction networks, advances geometric modeling of DNA systems, and pioneers L-DNA-based circuits for intracellular applications. The research spans theoretical foundations, software tools, and wet-lab experimentation, emphasizing interdisciplinary innovation. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE), 2025 NSF CAREER Award, 2021 UNM School of Engineering Junior Faculty Research Excellence Award, 2021 Multiple student awards under his mentorship, including the Outstanding Graduate Student Award and DNA28 Best Student Presentation recognition Dr. Lakin has advised numerous graduate and undergraduate students, including Ph.D. graduates in Biomedical Engineering and Computer Science. He leads major funded projects such as the NSF CAREER grant on heterochiral molecular computing, an EPSCoR Research Fellowship, and a $3M NSF grant on heavy metal biosensing in collaboration with Native American communities. He is also PI on multiple NSF grants related to synthetic cells and nucleic acid technologies. He directs the Lakin Lab for Programmable Biology, which operates within the Department of Computer Science and collaborates with Chemical & Biological Engineering and the Center for Biomedical Engineering. The lab emphasizes both computational modeling and experimental molecular biology, and runs an NSF-funded biotechnology summer camp in partnership with ¡Explora! science museum to strengthen STEM education in New Mexico.

• Database Systems
• Data Management
• GPU Acceleration
• +۶ مورد دیگر

Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for her work in database systems and data management . Her research focuses on optimizing query processing for modern hardware, particularly GPUs and heterogeneous systems, and advancing cloud data analytics with serverless architectures like PixelDB . She has co-authored influential frameworks for adaptive query optimization , hardware-conscious database engines , and model-relational data management . Key research areas: GPU acceleration , HTAP , query approximation , spatial data processing , and cloud-native databases . Recent work emphasizes cross-task optimizations in distributed environments, efficient sampling , and context-aware joins integrating vector embeddings. In 2023, she contributed to adaptive recursive query optimization and speculative K-means clustering, while 2024 publications addressed proportional caching (HPCache) and model-relational systems . Her collaborations span institutions such as MIT, Microsoft, and ETH Zurich, with publications in top venues like SIGMOD , VLDB , and ICDE .

Naoki Yoshinagaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Multilingual and Multimodal NLP
• +۴ مورد دیگر

Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .

LEE Wee Sunمشاهده پروفایل

استاد

• Machine Learning
• Planning Under Uncertainty
• Approximate Inference
• +۳ مورد دیگر

LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.

Jing Liمشاهده پروفایل

دانشیار

• Computer Architecture
• Compiler Design
• Distributed Systems
• +۷ مورد دیگر

Dr. Jing Li is an Associate Professor and Eduardo D. Glandt Faculty Fellow at the University of Pennsylvania , holding dual appointments in the Electrical and Systems Engineering and Computer and Information Science departments. As co-director of the CyberSavvy nationwide security research center and director of the Penn Computational Intelligence Lab (PennCIL) , she pioneers innovations in non-von Neumann computing paradigms. Her research spans post-CMOS technologies, in-memory computing, and hardware-software co-design for security and AI applications. PhD in Computer Engineering, Purdue University (2009) BSc in Electrical Engineering, Shanghai Jiaotong University (2004) Research Focus: Dr. Li's work addresses fundamental challenges in computer systems across the stack. Key areas include: In-Memory Computing: Liquid Silicon architecture combining RRAM with silicon CMOS through monolithic 3D integration Security Engineering: Transforming computer security from "Art" to formal "Engineering" discipline within CyberSavvy Virtualization: Cloud FPGA abstraction layers decoupling compilation from runtime resource management Graph Analytics: Degree-aware optimization techniques for massive-scale graph processing Deep Learning Systems: Roofline model extensions for FPGA-based CNN acceleration Scientific Impact: Awarded DARPA Young Faculty Award , NSF CAREER Award , and IBM CEO Milestone Award , her team has achieved world records in energy-efficient computing (ENIAD supercomputer). With 46 U.S. patents and over 80 publications, she leads ecosystem development for emerging computing architectures through initiatives like the open-source MEG simulation platform . Community Leadership: Dr. Li serves on program committees for flagship conferences ( ISCA , FPGA Symposium ), chairs the International Memory Workshop , and contributes to the MLsys conference's inaugural committee. She actively mentors through multiple PhD openings and industry collaborations.

Leslie Valiantمشاهده پروفایل

استاد

• Complexity Theory
• Machine Learning
• Parallel Computation
• +۴ مورد دیگر

Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in Harvard University's School of Engineering and Applied Sciences, where he has held a faculty position since 1982. A foundational figure in theoretical computer science, his work bridges artificial and natural computational phenomena across multiple disciplines. His academic background includes education at: King's College, Cambridge Imperial College, London Ph.D. in Computer Science from Warwick University (1974) Valiant's research spans computational complexity , machine learning theory , parallel systems , and computational neuroscience . He pioneered the PAC (Probably Approximately Correct) learning framework that established computational learning theory as a rigorous field. His holographic algorithms work revealed deep connections between computational complexity and statistical physics, while his neuroidal model and evolvability theory provide computational explanations for cognitive processes and biological evolution. Current investigations focus on cortical computation primitives and knowledge infusion architectures. His publication trends show increasing integration of neuroscience with computational theory since 2010, with dominant themes in holographic computation (2006-2018), cortical modeling (2012-2018), and evolvability (2009-2017). The work consistently applies computational complexity analysis to biological and cognitive systems. Major recognitions include: Nevanlinna Prize (1986) for mathematical aspects of computer science Knuth Award (1997) for foundational algorithms contributions EATCS Award (2008) for theoretical computer science impact Turing Award (2010) for computational learning theory and complexity Fellowship in the Royal Society and National Academy of Sciences Valiant's research has been supported by NSF and international grants enabling cross-disciplinary work in computational neuroscience and evolutionary algorithms. While specific advisees aren't documented in source materials, his theoretical frameworks have shaped generations of researchers in machine learning and complexity theory. His current research group explores neuroidal architectures for cognitive computation, investigating how cortical circuits achieve robust information processing through in-circuit testing methodologies. Ongoing projects aim to identify fundamental computational primitives in neural systems and develop biologically inspired AI frameworks.

Daniel J. Lizotteمشاهده پروفایل

دانشیار

• Machine Learning
• Biostatistics
• Health
• +۴ مورد دیگر

Dan Lizotte is an Associate Professor jointly appointed to the Department of Computer Science in the Faculty of Science and the Department of Epidemiology and Biostatistics in the Schulich School of Medicine & Dentistry at Western University. Additional affiliations include the Schulich Interfaculty Program in Public Health and a cross-appointment to the Department of Statistics and Actuarial Sciences. Based in Middlesex College, London, Ontario, his contact email is dlizotte@uwo.ca. His research centers on machine learning and biostatistics for health decision support, with emphasis on sequential decision-making in chronic disease management where evolving patient health status and preferences inform adaptive interventions. Core contributions involve adapting reinforcement learning frameworks to model dynamic health decisions in public health and primary care settings, addressing methodological challenges in personalized medicine and risk prediction. Analysis of his publication record reveals consistent focus on healthcare applications of machine learning, particularly in chronic disease risk modeling using electronic medical records, intersectionality frameworks in public health AI, and Bayesian methods for dose personalization. His work bridges reinforcement learning with clinical decision support systems, advancing dynamic treatment regimes and statistical methodologies for evolving patient data. No scientific awards were mentioned in the provided text. The text does not specify any advisees, grant funding, or educational background details. Lizotte leads a research laboratory focused on machine learning applications in health, as evidenced by the dedicated lab site referenced in his contact information. His team likely explores intersections of statistical methodology, AI ethics, and clinical implementation for personalized health interventions.

Amir-massoud Farahmandمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Machine Learning
• Computer Science
• +۴ مورد دیگر

Amir-massoud Farahmand is an Associate Professor at the Polytechnique Montréal (Department of Computer and Software Engineering) and a Status-Only Associate Professor at the University of Toronto (Department of Computer Science). He is also a Core Academic Member at Mila (Quebec AI Institute). His research focuses on computational and statistical mechanisms for designing efficient reinforcement learning (RL) agents and adaptive algorithms. Dr. Farahmand's research spans reinforcement learning, optimal transport, adversarial robustness, and model-based methods. He has extensively studied regularization in RL, distributional approaches, and algorithm design for stability and convergence. His textbook Lecture Notes on Reinforcement Learning (2021) emphasizes mathematical intuition over algorithmic collections. Recent publications highlight trends in high-update-ratio RL, distributional equivalence, and self-prediction for task understanding. He is actively involved in teaching, having previously instructed courses on machine learning, neural networks, and RL at the University of Toronto. Scientific Awards : Ontario Early Researcher Award (2024) for Accelerated Reinforcement Learning Algorithms Dr. Farahmand has mentored numerous students, including his first PhD graduate Yangchen Pan (now at Oxford) and MSc students like Allen Bao (AMD) and Farnam Mansouri (University of Waterloo). He is currently recruiting graduate students at Polytechnique Montréal and Mila for 2025 admissions.

• Statistics
• Optimization
• Machine Learning
• +۳ مورد دیگر

Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.

El Mahdi Chaytiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Meta-learning
• +۴ مورد دیگر

El Mahdi Chayti is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Department of Computer Science. He works under the Machine Learning and Optimization (MLO) lab, focusing on advanced optimization techniques and machine learning theory. Contact: el-mahdi.chayti@epfl.ch . Current Roles: Doctoral Assistant and PhD Student in Computer and Communication Sciences Research Interests: Machine Learning, Optimization Algorithms, Meta-learning, Second-order Optimization, Energy Forecasting His publications highlight expertise in stochastic and cubic Newton methods, hybrid deep learning models, and personalized collaborative learning. Recent works include Improving Stochastic Cubic Newton with Momentum (2025) and Hybridization of Deep Learning with Physical Knowledge for Energy Forecasting (2019) . Key trends in his research span optimization efficiency , meta-learning frameworks , and integration of domain knowledge into AI models. He emphasizes theoretical guarantees and practical scalability in algorithm design.

Gregory C. Grayمشاهده پروفایل

استاد

• Infectious Disease Epidemiology
• One Health Approach
• Occupational Disease Transmission
• +۴ مورد دیگر

Gregory C. Gray, MD, MPH, FIDSA is a Professor in Infectious Disease Epidemiology at the University of Texas Medical Branch (UTMB) in Galveston, Texas, with appointments across multiple departments including Internal Medicine (Infectious Diseases), Microbiology & Immunology, and Global Health & Emerging Diseases. Dr. Gray has conducted epidemiological studies of infectious diseases for over 25 years across five continents, with a particular focus on occupational disease transmission, especially respiratory viruses. His research has examined farmers, veterinarians, turkey workers, swine workers, poultry workers, horse workers, cattle workers, hunters, and military personnel. He pioneered the Millennium Cohort Study, a prospective cohort study of approximately 300,000 people projected to continue through 2068, and has led numerous large-scale vaccine trials. As a strong advocate for the One Health approach, Dr. Gray has established One Health centers in the USA, Romania, and China, and developed four graduate programs in One Health (PhD, MHS, and certificates). His recent research focuses on zoonotic spillover events, universal influenza vaccines, and coronavirus detection in agricultural settings, with numerous publications in 2024-2025 addressing H5N1 avian influenza, respiratory virus transmission, and environmental health impacts. Selected honors include: Department of Defense Legion of Merit Medal Secretary of Defense Medal for Outstanding Public Service Medal of Honor from Mongolia's Minister of Health The Peace Medal awarded by the President of Mongolia University of Texas Medical Branch Robert E. Shope, MD Professorship Dr. Gray has authored over 350 peer-reviewed manuscripts cited more than 21,000 times (H-index 76). His current research portfolio includes multiple active grants from the USDA, Fogarty International Center, and private industry focusing on zoonotic disease surveillance, influenza vaccines, and coronavirus detection in agricultural settings. He has mentored numerous students and established international collaborations across six continents.

Pragya Surمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Pragya Sur is an Assistant Professor of Statistics at Harvard University and currently on leave as a Visiting Professor at MIT’s Laboratory for Information and Decision Systems (LIDS). Her research focuses on high-dimensional statistics, machine learning, and artificial intelligence, particularly in overparametrized models, causal inference, and learning under distribution shifts. She has held postdoctoral positions at Harvard’s Center for Research on Computation and Society (hosted by Cynthia Dwork) and was a Simons Institute Long-Term Participant at UC Berkeley. She earned her Ph.D. in Statistics from Stanford University under Emmanuel Candès, and completed her B.Stat and M.Stat at the Indian Statistical Institute, Kolkata. Sur’s work has been supported by NSF awards, the Eric and Wendy Schmidt Fund, and the William F. Milton Fund. She was named an International Strategy Forum (ISF) Fellow (2023) and led the Institute of Mathematical Statistics (IMS) New Researchers Group (2022–2024). She serves as an Associate Editor for Statistical Science and Guest Co-Editor for a special issue on AI and statistics. Her research contributions span theoretical guarantees for machine learning, transfer learning, and debiasing techniques in high-dimensional inference. Awards: NSF CAREER Award, Theodore W. Anderson Dissertation Award, Ric Weiland Fellowship Education: Ph.D. in Statistics (Stanford, 2019); M.Stat (ISI Kolkata, 2014); B.Stat (ISI Kolkata, 2012) Professional Roles: Associate Editor, Statistical Science ; ISF Fellow (2023); former IMS New Researchers Group Lead Her current research emphasizes statistical theory for modern machine learning, including foundational work on overparametrized models and robust inference across heterogeneous environments.

Ingo Bojakمشاهده پروفایل

استاد

• Computational Neuroscience
• Neurodynamics
• Neural Population Models
• +۳ مورد دیگر

Ingo Bojak is a Professor at the School of Psychology and Clinical Language Sciences , University of Reading. His research focuses on computational neuroscience, neurodynamics, and neural population models. He explores topics such as biological mistake-making, EEG analysis, and the effects of anesthesia on brain activity. Bojak serves as an associate editor for Neurocomputing and related journals. His work bridges theoretical frameworks with experimental data, emphasizing cross-scale biological phenomena and neural network dynamics. Bojak’s research interests include understanding spontaneous neural oscillations, cortical activity modeling, and the integration of EEG/fMRI data. He has contributed to advancements in neural field theory and Bayesian uncertainty quantification. His studies on biological mistakes highlight adaptive mechanisms across biological systems. Related affiliations include collaborations with the School of Biological Sciences at the University of Reading. Recent publications emphasize theoretical biology, computational neuroscience, and interdisciplinary approaches to understanding neural systems. His work often addresses functional adaptation through error-driven mechanisms and explores the interplay between neural excitability and inhibition.

Marios Koutsiasمشاهده پروفایل

مدرس ارشد

• Company Law
• Corporate Governance
• Comparative Company Law
• +۲ مورد دیگر

Marios Koutsias is a Senior Lecturer at Essex Law School, University of Essex, specializing in EU Commercial Law, Company Law, and Data Protection. He holds an LLB from Aristotle University of Thessaloniki, an LLM in EU Law from the University of Essex, an MA in Politics and International Relations from King's College London, and a PhD in Law from the University of Essex. Prior to his academic career, he practiced as a lawyer on Greek Law and European Union Law in Thessaloniki and Brussels, and remains a member of the Thessaloniki Bar Association, Greece. His educational background includes: LLB from Aristotle University of Thessaloniki, Greece LLM in EU Law from University of Essex MA in Politics and International Relations from King's College, London PhD in Law from University of Essex Dr. Koutsias' research focuses on Company Law, Corporate Governance, Comparative Company Law, European Union Law with a special emphasis on the Internal Market, and Privacy and Data Protection. His work critically examines shareholder primacy versus stakeholder theory, the influence of national cultural identities on corporate governance frameworks, and the evolving landscape of data protection regulations in the digital economy. He has made significant contributions to understanding how Brexit might reshape European corporate governance paradigms and the tensions between commercial interests and privacy rights in global trade. His publication record reveals a strong interdisciplinary approach, connecting theoretical legal concepts with practical commercial applications. The research trends show increasing focus on post-Brexit regulatory alignment, stakeholder governance models, and data protection challenges in international commerce. His most recent 2025 article argues that Brexit should not sever UK-EU privacy regulatory connections to protect both trade interests and human rights. Dr. Koutsias supervises PhD students in Law with expertise in corporate governance frameworks across different jurisdictions. His current supervisees include Tioluwani Comfort Tioluwani and Iman Abdulrahman M Alfaraj, while he has successfully guided seven PhD students to completion since 2016, including Christina Gkampitsaki (awarded January 2024) and Tioluwani Comfort Tioluwani (awarded December 2023). His teaching responsibilities encompass Company Law (LW225), Law of the European Union (LW302), Final Year Research Project (LW304), Introduction to the Law of the European Union (LW430), Industry Placement (LW811), and Data Protection Law in the Digital Age (LW656). He has secured research funding including the 2019 Mondaq KTP 2 grant from Innovate UK. Dr. Koutsias has co-authored three significant books: 'Company Law in the New Europe' and 'The Nature of Corporate Governance: The Significance of National Cultural Identity' (both Edward Elgar Publishers), and 'Company Law' by Palgrave with Professor Janet Dine, which is considered one of the leading textbooks in the field. He also serves as a Specialist Contributor to Gore Brown on EU Company Law publications, demonstrating his recognized expertise in this specialized area.

Gladis Kersaintمشاهده پروفایل

استاد

• Mathematics Education
• Teacher Education
• Educational Technology
• +۲ مورد دیگر

Dr. Gladis Kersaint is Vice Provost for Strategic Initiatives and Professor of Mathematics Education at the University of Connecticut's Neag School of Education. Previously serving as Dean from 2016 to 2021, she led initiatives that maintained the school's top-20 ranking, raised over $10 million for scholarships, and enhanced diversity. Her academic background includes a Ph.D. in Mathematics Education from Illinois State University, an M.S. in Education from the University of Miami, and a B.S. in Mathematics from the same institution. Her research focuses on mathematics education, teacher preparation, educational technology, rural women's development, and diversity initiatives. She has secured approximately $30 million in grants from NSF, DOE, and Florida DOE, supporting collaborative STEM projects involving school districts and university faculty. Dr. Kersaint has published extensively on topics including multilingual mathematics education, technology integration, and teacher retention. Her scholarship demonstrates consistent focus on improving educational outcomes through innovative pedagogical approaches and systemic reforms. She serves on national boards including the National Council of Teachers of Mathematics and the American Association of Colleges for Teacher Education. Prior to academia, she taught high school mathematics in Miami-Dade County Public Schools.