جستجوی استاد راهنمای دانشگاهی

Prof. Dr. Kai Cieliebak is a Professor of Mathematics at the University of Augsburg, where he holds the Chair of Analysis and Geometry within the Institute of Mathematics under the Faculty of Mathematics, Natural Sciences, and Materials Engineering. He has been at Augsburg University since 2012, following a professorship at Ludwig-Maximilians-Universität München from 2001-2012. His research group includes several researchers and postdocs working on symplectic geometry and related fields. Dr. Cieliebak earned his Diplom in mathematics summa cum laude from Ruhruniversität Bochum in 1992, with thesis on "Pseudo-holomorphe Kurven und periodische Orbits auf Cotangential Bündeln" under advisor H. Hofer. He completed his PhD in mathematics at ETH Zürich in 1996, with thesis "Symplectic boundaries: closed characteristics and action spectra," also advised by H. Hofer. His academic journey included positions at Harvard University, Stanford University, and research at IBM Zürich before his professorships in Munich and Augsburg. Prof. Cieliebak's research focuses on symplectic and contact geometry , with significant contributions to understanding symplectic manifolds, Lagrangian and Legendrian knots, Stein manifolds, and string topology. His work in Hamiltonian dynamics explores variational methods, periodic orbits, and celestial mechanics problems, particularly the restricted three-body problem. In global analysis , he investigates solution spaces of elliptic PDEs and symplectic field theory. His approach often bridges differential geometry, topology, and dynamical systems, with applications to mathematical physics. Over the past decade, Prof. Cieliebak's publications reveal a consistent focus on symplectic homology, Floer theory, and their applications to geometric problems. His work shows increasing integration of algebraic structures with geometric methods, particularly in cyclic homology and string topology. Recent research demonstrates strong collaboration with Urs Frauenfelder on celestial mechanics problems, applying symplectic techniques to the restricted three-body problem and related orbital dynamics. Prof. Cieliebak has secured significant research funding throughout his career, including multiple DFG grants under project codes CI 45/1 through CI 45/12, NSF grants, and participation in European Science Foundation networking programs. His most notable grants include "Foundations of Symplectic Field Theory" (2009-2015) and the current "Rabinowitz Floer Homology" project (since 2023), both in collaboration with U. Frauenfelder. He has mentored numerous researchers and maintains an active research group at Augsburg University, including postdocs and collaborators working on symplectic geometry problems. His team includes researchers such as Dr. Filip Broćić, Zhen Gao, Dr. Hanna Häußler, Emilia Konrad, Shuaipeng Liu, Dominik Meidert, Dr. Airi Takeuchi, Dr. Evgeny Volkov, Milan Zerbin, and PD Dr. Lei Zhao. Prof. Cieliebak has also organized numerous workshops on symplectic geometry, including the annual "Symplectic Field Theory" workshop series.

• Symplectic geometry
• Contact geometry
• Hamiltonian dynamics
• +۵ مورد دیگر

André Bardow is a Full Professor at the Department of Mechanical and Process Engineering, ETH Zürich. His research focuses on energy systems optimization, life cycle assessment, computer-aided molecular design, and CO2 capture/utilization. Professor (ETH Zürich, 2020–present) Head of Institute of Technical Thermodynamics (RWTH Aachen University, 2010–2020) Visiting Professor (University of California, Santa Barbara, 2015/16) Part-time Director (Forschungszentrum Jülich, 2017–2022) Associate Professor (TU Delft, 2007–2010) Research Interests: His work spans energy and process systems engineering, with emphasis on sustainable technologies. Key areas include: Computer-aided molecular and process design Machine learning for chemical engineering Carbon capture and utilization (CCU) Life cycle assessment (LCA) of industrial processes Thermo-economic modeling of energy systems Multiphase equilibrium analysis Publication Trends: Recent articles focus on integrating machine learning with process design, optimizing CO2 capture in steel production, and advancing electrochemical cooling technologies. Subfields include sustainable plastics, ORC working fluids, and solvent mixture design. Scientific Awards: Fellow of the Royal Chemical Society Recent Innovative Contribution Award (EFCE, 2019) PSE Model-Based Innovation Prize (2018) Covestro Science Award (first recipient) Arnold-Eucken-Award (VDI-GVC) Highly Cited Researcher (Clarivate, 2024) Advising and Grants: Professor Bardow mentors students in process optimization and leads projects like Systemic expansion of territorial CIRCULAR Ecosystems for end-of-life FOAM (Grant 101036854, EC).

• Energy systems optimization
• Conceptual process design
• Life cycle assessment
• +۷ مورد دیگر

Sungkyung Kang is a Research Fellow at the University of Oxford 's Mathematical Institute and a Stipendiary Lecturer in Applied Mathematics at Keble College (Michaelmas 2023). As a Research Associate at Keble College (2023–2024), she investigates low-dimensional topology through Heegaard Floer theory and its connections to Seiberg-Witten gauge theory. Her work focuses on knot concordance, cobordism invariants, and cabling structures. Affiliation : Mathematical Institute, University of Oxford College Roles : Stipendiary Lecturer (Keble College, Michaelmas 2023), Research Associate (Keble College, 2023–2024) Research Interests : Knot theory, 3-manifold topology, and smooth concordance problems. Her research bridges Heegaard Floer homology with gauge-theoretic invariants to analyze cobordism structures and cabling phenomena in low dimensions. Core Methods : Heegaard Floer theory Key Areas : Smooth sliceness, Torsion in concordance groups

• Knot Theory
• 3-Manifolds
• Heegaard Floer Theory
• +۲ مورد دیگر

Raphaël Beuzart-Plessis is a CNRS Research Fellow affiliated with Aix-Marseille University and the Institute of Mathematics of Marseille (I2M) at Luminy Campus. He specializes in advanced areas of mathematics, including harmonic analysis, automorphic forms, representation theory, and number theory, with a focus on unitary groups and conjectures like Gan-Gross-Prasad. His work bridges algebraic geometry, differential geometry, and operator theory. Arithmetic, Geometry, Logic and Representations Group (AGLR) 2022-2027 ERC RELANTRA grant recipient Research interests span automorphic representations, L-functions, periods of automorphic forms, and harmonic analysis on real spherical spaces. He works extensively on the local and global Gan-Gross-Prasad conjectures, endoscopy, and supercuspidal representations. His recent publications analyze Plancher1el formulas, spherical characters, and congruences of automorphic forms. His 15 most recent publications (2014-2022) address topics such as the Gan-Gross-Prasad conjecture, Jacquet-Rallis's fundamental lemma, and the Asai Rankin-Selberg integrals. These works reflect his expertise in automorphic forms, representation theory, and number theory, often involving collaborations with leading mathematicians. 2016-2017 Peccot Prize for young mathematicians under 30 2022-2027 ERC RELANTRA grant for research in automorphic forms and representation theory Beuzart-Plessis has no listed students or laboratory teams but participates in the AGLR-RGR (Reduction Group Representations) team and has been an invited speaker at the 2022 International Congress of Mathematicians. His career includes guest lectures at Collège de France, including four sessions on Period factorizations and Plancherel formulas in 2017.

• Harmonic analysis
• Differential geometry
• Algebraic number theory
• +۵ مورد دیگر

Andrew Green serves as Professor and Metcalf Chair in Environmental Law at the University of Toronto Faculty of Law, where he teaches environmental law, climate change law, administrative law, natural resources law, judicial decision-making, and international trade. His scholarly contributions span multiple legal domains with particular emphasis on the intersection of environmental regulation and administrative governance. Green's research interests encompass Environmental Law , Administrative Law , Climate Change Policy , Judicial Decision-Making , and International Trade Law . His empirical approach to legal scholarship has produced significant insights into how judges make decisions, particularly in administrative contexts, and how trade rules interact with domestic environmental regulations. His work often bridges theoretical legal analysis with practical policy implications. His recent publications reveal a strong focus on Canadian climate policy implementation, judicial behavior in high courts, and the evolving standards of review in administrative law. The research demonstrates increasing interdisciplinary integration, incorporating machine learning approaches to analyze judicial decisions and examining the systemic challenges in environmental governance. His scholarship consistently addresses the tension between regulatory effectiveness and legal accountability. Metcalf Chair in Environmental Law Past co-President of the Society for Empirical Legal Studies Past President of the Canadian Law and Economics Association Former Associate Dean at the Faculty of Law Chair of the University of Toronto Academic Appeals Committee (2009-2018) Before joining the Faculty of Law, Green practiced environmental law in Toronto for six years, handling both litigation (including prosecutions, administrative appeals, and civil actions) and transactional work. His practical experience informs his scholarly work on regulatory design and implementation. His research has influenced policy discussions on climate change, securities regulation, and administrative law reform in Canada.

• Environmental Law
• Administrative Law
• Climate Change Law
• +۴ مورد دیگر

Prof. Dr. Raphael Sznitman serves as Director of the ARTORG Center for Biomedical Engineering Research and Head of the Artificial Intelligence in Medical Imaging group at the University of Bern, Switzerland, holding a Full Professor position in AI for Medical Imaging since 2015. Education: PhD in Computer Science, Johns Hopkins University (2011) MSc in Computer Science, Johns Hopkins University (2009) BSc in Cognitive Systems, University of British Columbia (2007) Research Interests: Sznitman's work centers on computational vision , probabilistic methods , and statistical learning applied to medical imaging challenges. His group develops AI algorithms for ophthalmic diagnostics, surgical robotics, and medical image analysis, with emphasis on OCT, surgical phase recognition, and domain adaptation techniques. Key application areas include retinal disease detection and cataract surgery automation. Publication Trends: His 2021-2025 publications reveal concentrated efforts in deep learning for medical imaging , particularly in ophthalmology (OCT analysis) and surgical video understanding. Emerging themes include LLM applications for clinical monitoring, unsupervised out-of-distribution detection for surgical safety, and physics-informed AI for multimodal medical data fusion. Research Leadership: As ARTORG Center Director, Sznitman oversees interdisciplinary research bridging computer science and clinical medicine. His group collaborates extensively with Bern University Hospital clinicians on translational projects, securing funding for AI-driven diagnostic tools and surgical assistance systems. Current initiatives focus on real-time intraoperative guidance and spaceflight ophthalmology applications. Laboratory: The Artificial Intelligence in Medical Imaging group operates within ARTORG's dedicated facilities, maintaining partnerships with surgical robotics labs and ophthalmology departments for clinical validation of AI systems. Their work integrates multimodal data streams including OCT, VR perimetry, and surgical video feeds.

• Computer Vision
• Probabilistic Methods
• Statistical Learning
• +۲ مورد دیگر

Dr. Michael Kleeberger is a Researcher at the Chair of Materials Handling, Material Flow, Logistics (FML) at the Technical University of Munich, based at Boltzmannstr. 15 in Garching. He collaborates closely with Prof. Johannes Fottner and maintains an active research profile in crane dynamics and mechanical systems simulation. His research specializes in Materials Handling and Logistics with emphasis on Crane Dynamics, Flexible Multibody Systems, and Control Systems. He develops advanced models for hydraulic actuated cranes, focusing on dynamic behavior during hoisting, slewing, and trajectory operations using port-Hamiltonian formulations and geometrically exact beam theory. His work bridges theoretical mechanics with industrial applications in heavy machinery. Analysis of his 15 most recent publications reveals consistent focus on numerical methods for flexible crane structures, with growing emphasis on optimal control strategies (2020-2025). Key trends include port-Hamiltonian system applications, lunar crane feasibility studies, and vibration mitigation techniques for lattice boom and knuckle boom configurations across diverse operational scenarios. As part of FML, Dr. Kleeberger contributes to TUM's leadership in logistics engineering through industry-collaborative projects and fundamental research in material flow systems, maintaining the chair's reputation for excellence in mechanical dynamics and practical engineering solutions.

• Materials Handling
• Logistics
• Crane Dynamics
• +۳ مورد دیگر

Harish Ravichandar is an Assistant Professor at the School of Interactive Computing , Georgia Institute of Technology, and a core faculty member of the Institute for Robotics and Intelligent Machines (IRIM) . He leads the Structured Techniques for Algorithmic Robotics (STAR) Lab , focusing on structured computational frameworks and learning algorithms with inductive biases to enhance robot efficiency, reliability, and self-sufficiency in human-robot collaboration and complex applications like dexterous manipulation and multi-agent coordination. His research bridges robot learning , human-robot interaction , and multi-agent systems , emphasizing stable, frugal, and safe skill acquisition from human demonstrations. Key themes include intention inference , trajectory optimization , and heterogeneous team coordination , often leveraging Koopman operators , hypernetworks , and graph-based methods . Scientific recognition includes the NSF CAREER Award , IEEE MRS Best Paper Award , and Georgia Tech’s College of Computing Outstanding Post-Doctoral Research Award . His work also received the ASME DSCC Best Student Paper Award and P&W Institute Graduate Fellowship . Harish’s educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Connecticut (2018) , an M.S. from the University of Florida (2014) , and a B.E. in Instrumentation and Control Engineering from Anna University (2012) . He previously held postdoctoral and research scientist roles at Georgia Tech before his current position.

• Robotics
• Machine Learning
• Human-Robot Interaction
• +۲ مورد دیگر

Dr. Min Chi is a Professor in the Department of Computer Science at North Carolina State University, where she joined in 2013 as a Chancellor's Faculty Excellence Program cluster hire in the Digital Transformation of Education. Her academic journey includes a Ph.D. and M.S. in Intelligent Systems from the University of Pittsburgh and a B.E. in Information Science and Technology from Xi'an Jiaotong University, China. She completed postdoctoral fellowships at Carnegie Mellon University's Machine Learning Department and Stanford University's Human Sciences and Technologies Advanced Research Institute. Dr. Chi's research focuses on the development and empirical evaluation of cutting-edge Artificial Intelligence, Deep Learning, and Reinforcement Learning frameworks tailored for addressing human-centric challenges. Her work spans multiple domains including advanced learning technologies, AI and intelligent agents, data sciences and analytics, and human-computer interaction. She has made significant contributions to intelligent tutoring systems, healthcare applications, nuclear power systems, and humanitarian efforts such as food distribution and disaster relief. Her publication record demonstrates a strong focus on applying AI techniques to real-world educational challenges, with recent work examining metacognitive knowledge transfer, reinforcement learning for pedagogical policy induction, and deep learning approaches for proactive help in educational settings. Her research also extends to healthcare applications, food distribution systems, and other socially impactful domains. 10 Best Paper, Best Student Paper, and Outstanding Paper Awards Prestigious Alcoa Foundation Engineering Research Achievement Award NSF CAREER Award Dr. Chi leads multiple significant research projects funded by the National Science Foundation, National Institutes of Health, and the Department of Energy, with a total funding exceeding $7 million. Her work bridges theoretical advances in AI with practical applications that address critical societal challenges in education, healthcare, and humanitarian operations.

• Artificial Intelligence
• Deep Learning
• Reinforcement Learning
• +۵ مورد دیگر

Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .

• Control Theory
• Machine Learning
• Nonlinear Estimation
• +۶ مورد دیگر

Miroslav Pajic serves as a Professor in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. He also holds joint appointments as Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science and Associate Professor of Computer Science. As Director of Master's Studies, he oversees the graduate program in Electrical and Computer Engineering and teaches numerous courses spanning embedded systems, cyber-physical systems design, and robotics. Education: Ph.D. in Electrical and Computer Engineering from University of Pennsylvania (2012) Miroslav Pajic's research focuses on the design and analysis of cyber-physical systems (CPS) with varying levels of autonomy and human interaction. His work spans the intersection of embedded systems, artificial intelligence, machine learning, control theory, formal methods, and robotics. He specializes in developing high-assurance autonomous systems with applications in robotics, automotive systems, and medical devices, with particular emphasis on CPS security and resilient autonomy. His research addresses fundamental challenges in creating systems that can operate reliably in uncertain environments while maintaining security against potential cyber attacks. Analysis of Pajic's recent publications reveals a strong interdisciplinary research program bridging theoretical foundations with practical applications. His work spans secure sensor fusion for distributed autonomy, medical applications of CPS (particularly deep brain stimulation for neurological disorders), and innovative sensing technologies for autonomous vehicles. A significant portion of his research addresses security challenges in cyber-physical systems, including stealthy GPS attacks on UAVs and methods for attack-resilient state estimation. His publications increasingly integrate machine learning techniques with traditional control theory to create more adaptive and robust autonomous systems. Pajic actively mentors graduate students and leads research groups focused on cyber-physical systems security and high-assurance autonomy. His research is supported by multiple grants, including the NSF AI Institute for Edge Computing (Athena), which he co-leads. He has received funding from various sources to support his work on secure and resilient cyber-physical systems, medical device security, and autonomous vehicle technologies. Pajic collaborates extensively with medical researchers on applications of cyber-physical systems in healthcare, particularly in deep brain stimulation for neurological disorders. His work bridges the gap between theoretical control systems and practical implementations in safety-critical domains, with a growing emphasis on translating research into real-world applications that improve system security and reliability.

• Cyber-Physical Systems
• Embedded Systems
• Artificial Intelligence
• +۶ مورد دیگر

Valerie Viet Triem Tong is a Research Professor at the Paris Institute of Electrical and Electronic Engineering, School of Electrical and Electronic Engineering. She has established herself as a leading researcher in cybersecurity with a particular focus on information flow control systems, Android security, and malware analysis. Her work spans both theoretical foundations and practical security tools development. Her research interests center on Information Flow Control , where she has developed frameworks for monitoring and enforcing security policies at both operating system and application levels. She has made significant contributions to Android Security , creating tools for detecting malicious behavior in mobile applications and addressing privacy concerns in smartphone environments. Her work in Malware Analysis includes developing advanced techniques for tracking and visualizing malware behavior, with emphasis on evasive Windows malware and Android malware in the wild. She also investigates Peer-to-Peer Network Security , focusing on Sybil attack resistance and distributed identity management. Analysis of her publication record reveals a consistent trajectory from foundational work in information flow theory to increasingly applied security research. Her recent work shows a strong emphasis on practical security tools (DaViz, GUI-Mimic, BAGUETTE), security evaluation methodologies (Digital twin, CERBERE), and addressing contemporary challenges in malware analysis (debiasing datasets, handling obfuscated applications). A notable trend is her integration of visualization techniques with security analysis to make complex security data accessible to both experts and machine learning systems. As an advisor, she has mentored numerous researchers who have become first authors on significant publications, including Radoniaina Andriatsimandefitra, Tomás Concepcion Miranda, and Cedric Herzog. Her research has been supported by multiple grants focused on cybersecurity infrastructure, though specific grant details are not provided in the available information. Dr. Tong is actively involved with the CIDre security research group in Rennes, contributing to collaborative projects that bridge theoretical security models with practical implementation challenges. Her work on information flow monitoring has evolved from basic research to applied systems that address real-world security concerns across multiple platforms.

• Information Flow Control
• Android Security
• Malware Analysis and Detection
• +۵ مورد دیگر

Rahul Sarpeshkar is a Professor of Engineering, Microbiology & Immunology, Physics, and Molecular & Systems Biology at Dartmouth College, holding the Thomas E. Kurtz Professorship and chairing the Neukom Computational Science Cluster. His research bridges analog circuits with quantum physics, synthetic biology, and ultra-low-power systems. BS in Electrical Engineering and Physics from MIT (1995) PhD in Computation and Neural Systems from Caltech (1998) His research focuses on analog synthetic biology , quantum circuit design , and bio-inspired supercomputing , emphasizing noise, thermodynamics, and energy efficiency. He develops cytomorphic chips to model biochemical networks and quantum-inspired circuits for spectrum analysis. Recent work integrates quantum and classical computation for biological simulations, drug cocktail formulation , and ATP energy measurement in living cells. Patents highlight innovations in quantum emulation and medical devices. Scientific awards include: Fellow, National Academy of Inventors (2018) IEEE Fellow (2018) NSF CAREER Award ONR Young Investigator Award Packard Fellow Award Junior Bose Teaching Award, MIT He leads a wet lab for synthetic microbial circuit implementation and a dry lab for quantum and nanoelectronics, mentoring a multidisciplinary team of physicists, bioengineers, and computer scientists.

• Analog synthetic biology
• Quantum circuit design
• Feedback control systems
• +۶ مورد دیگر

Dr. Catrin Lundström is an Assistant Professor of Ethnicity and Migration at the Department of Migration, Ethnicity and Society (REMESO) within the Department of Culture and Society (IKOS) at Linköping University's Faculty of Philosophy. Her research focuses on the intersection of migration, whiteness, and gender, examining how Swedish women experience transnational migration and return migration processes. Dr. Lundström received her PhD in Sociology from Uppsala University in 2007 and became an Associate Professor (Docent) in Sociology there in 2011. Her academic journey includes teaching and research positions at multiple Swedish institutions including Uppsala University, Stockholm University, Mälardalen University, Södertörn University, Umeå University, and Linköping University. She has also been a visiting researcher at the University of Arizona and the University of California. Dr. Lundström's research centers on whiteness as both a place-bound and transnational construction. She challenges conventional migration studies by focusing on privileged Swedish women migrants, developing the concept of 'white capital' to understand how whiteness functions as transferable cultural capital across national borders. Her work examines how racial privilege, gender, and class intersect in migration experiences, particularly among women who migrate for lifestyle, companionship, or professional reasons. Her recent publications demonstrate an expanding focus on the relationship between race, whiteness, and educational systems in Sweden, as well as critical examinations of contemporary political debates through the lens of critical race theory. Lundström's work increasingly addresses how colorblind discourse operates in Swedish society while simultaneously exploring the politics of national identity in a globalized world. Dr. Lundström has led significant research projects including: 'White Migrations: Gender, Whiteness and Privilege in Transnational Migration' (2014) 'Tre faser av hegemonisk vithet' (Three Phases of Hegemonic Whiteness) with Tobias Hübinette 'Att återintegrera svenskheten' (Reintegrating Swedishness) on returning Swedish migrant women, funded by the Swedish Research Council Her research has been supported by organizations including the Sweden-America Foundation and the Swedish Research Council. Dr. Lundström is actively involved in several academic networks: IMISCOE Standing Committee on Gender, Migration and Transnational Lives White Spaces Network at Leeds University Lifestyle Migration Hub, an international network of researchers studying lifestyle migration She regularly lectures within and outside academia, both in Sweden and internationally, and has received significant media attention for her research on school segregation, colorblind language, and gender equality in migration contexts.

• Migration Studies
• Whiteness Studies
• Gender Studies
• +۶ مورد دیگر

C. Daniel Meliza is an Associate Professor in the Department of Psychology at the University of Virginia. His research focuses on the neural mechanisms of auditory learning and perception, primarily using zebra finches as a model system to understand how brains process complex vocal communication. Dr. Meliza's research investigates how neural circuits enable auditory learning and perception in songbirds. His lab studies experience-dependent plasticity, examining how early acoustic environments shape auditory processing. They also investigate how birds form internal models of vocal signals and use them to reconstruct degraded communication in noisy environments. This work has implications for understanding speech perception and communication disorders in humans. His recent publications reveal a strong focus on intrinsic plasticity mechanisms in the auditory cortex, computational modeling of neural systems, and how experience shapes neural coding of vocalizations. The work spans from cellular mechanisms to systems-level processing, with increasing integration of computational approaches to understand neural dynamics. Dr. Meliza has received significant recognition for his research: NIH R01 grant from NIDCD to examine mechanisms of intrinsic plasticity in early auditory learning (2021) NSF CAREER Award to study neural mechanisms of auditory restoration (2020) UVA Presidential Fellowship for Collaborative Neuroscience (2022) Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (2023) UVA Double Hoo Award (2023) Dr. Meliza has successfully mentored multiple PhD students including Yao Lu, Samantha Moseley, Christof Fehrman, and Margot Bjoring. His lab is well-funded through competitive grants from NIH and NSF, supporting research into the fundamental neural mechanisms underlying auditory learning and perception. The lab employs a multidisciplinary approach combining behavioral experiments, electrophysiology, computational modeling, and molecular techniques. The Meliza Lab at the University of Virginia operates at the intersection of neuroscience, psychology, and computational modeling. The team uses zebra finches to investigate how the brain processes complex vocal communication, with particular focus on how experience shapes neural circuits during development. Current research directions include examining how complex acoustic environments influence auditory perception and neural coding, and how neural circuits implement rapid gain control mechanisms.

• Systems Neuroscience
• Behavioral Neuroscience
• Auditory Neuroscience
• +۳ مورد دیگر