Mariam Kharatyan is an Armenian-Norwegian Associate Professor in Classical Music and Music Education at the University of Agder. She holds dual Master's degrees in piano performance from the Komitas State Conservatory (Armenia) and the University of Agder. Her research explores Armenian folk music's influence on classical piano works by Komitas and Khachaturian, culminating in her doctoral project 'Armenian Fingerprints' and two acclaimed albums. Education: Master's in Western-European Music, University of Agder (Norway), 2012-present Master's in Piano Performance, Komitas State Conservatory (Armenia) Research Interests: Focus on multicultural interpretation, improvisation in classical performance, and artistic research methodologies. She bridges Armenian folk traditions with Western classical music, emphasizing cross-cultural pedagogy and score-based learning innovations. Explores themes like sostenuto pedal techniques, composer-performer-listener dynamics, and the role of improvisation in rigid classical frameworks. Performance Highlights: Soloist with Kristiansand Symphony Orchestra, Torun Symphony, and Sarajevo String Orchestra Albums: Khachaturian, Chamber Music (Simax Classics) and Komitas, Shoror (Grappa Musikkforlag) Featured in venues like Troldhaugen (Norway), National Gallery of Art (Washington DC), and Kilden Konserthus Grants & Projects: Lead researcher in EU-funded REACT project (Artistic Research-Based Learning) Erasmus+ Mobility Program activities in Poland and Armenia RHiMuCE research group member Labs/Initiatives: Develops interdisciplinary projects like Songs of the Soul and CreaTeME , blending live electronics with traditional Armenian music. Co-created the Artistic Research Based Learning Toolkit for European music institutions.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۶۷۶-۶۹۰ از ۴٬۳۹۳ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Dr. Tapabrata Chakraborty is a Principal Research Fellow at University College London (UCL) Cancer Institute and an Honorary Associate Professor in UCL's Department of Medical Physics and Biomedical Engineering. He serves as Lead Tutor for Information Engineering at the University of Oxford's Engineering Science Department and is a non-stipendiary Fellow of Linacre College, Oxford. As Theme Lead for the Alan Turing Institute's partnership with Roche, he drives advancements in transparent AI for precision healthcare. He is an invited expert on Responsible AI with the Global Partnership on AI (GPAI) and an Associate Editor for Springer Nature Computer Science . His research focuses on developing reliable AI systems for biomedicine, particularly leveraging multimodal data (imaging, clinicogenomics) in cancer research. He emphasizes explainable AI mechanisms, personalized uncertainty quantification, and ethical AI governance. His work has led to tools like 2dSpAn-Auto for spine analysis and frameworks like Pan-Ret for retinal disease detection. Education: PhD (details unspecified) Key Roles: Turing-Roche Partnership Lead, GPAI Advisor, HEA/IET Fellow His publications highlight breakthroughs in medical AI, including uncertainty quantification and multimodal data fusion. He advocates clinician-AI collaboration and policy-driven AI safety. Current projects include fair AI for skin lesion classification and drug discovery via synthetic data generation. Awards: HEA Fellowship, IET Fellowship Team Leadership: Oversees early-career researchers at Turing/UCL
Sylvain Arlot is a Professor at the Mathematics Department of Université Paris-Saclay, affiliated with the Probability and Statistics team at Laboratoire de Mathématiques d'Orsay. He leads the Celeste INRIA Saclay project-team and is a junior member of the Institut Universitaire de France (IUF) since 2020. His research focuses on statistical learning theory, non-parametric methods, model selection, and change-point detection. Arlot has contributed to foundational work on cross-validation, penalization techniques, and random forests. He co-organizes the Séminaire Palaisien and serves as an associate editor for the Annales de l'Institut Henri Poincaré B. Education: PhD in Mathematics from Université Paris-Sud (2007), HDR (Habilitation) from Université Paris Diderot (2014). Research Interests: Core areas include statistical learning theory, resampling methods (e.g., cross-validation and bootstrap), and applications in high-dimensional data analysis. His work bridges theoretical guarantees with practical algorithm design, emphasizing data-driven model selection and robust estimation techniques. Grants & Projects: Leads the PEPR IA Project Causali-t-AI (2023–2028) and was a member of the ANR Fast-Big project (2018–2023). He coordinates the math-AI program under Labex Mathématique Hadamard. Awards: Junior IUF membership (2020–2025). Labs/Teams: Heads the Celeste team at INRIA Saclay, collaborating on statistical machine learning and data science challenges.
Nevzat Bircan Bugdayci is an Assistant Professor of Mechanical Engineering at Michigan State University (MSU), appointed since Spring 2024. Previously, he was a Postdoctoral Research Associate at the University of Michigan's Smart and Sustainable Automation Lab (2023). He holds a Ph.D. in Mechanical Engineering from ETH Zurich (2022), an M.Sc. from Koç University (2013), and a B.Sc. from METU (2011). His research focuses on physics-based and data-driven approaches in advanced manufacturing, including additive manufacturing optimization (e.g., LPBF), milling process stability, and in-situ process monitoring using machine tool signals. Education: Ph.D., Mechanical Engineering, ETH Zurich, Switzerland (2022) M.Sc., Mechanical Engineering, Koç University, Turkey (2013) B.Sc., Mechanical Engineering, METU, Turkey (2011) Research Interests: Physics-based optimization of laser scan strategies in additive manufacturing Data-driven stability predictions in milling Non-invasive process monitoring using controller signals Residual stress reduction in LPBF processes Adaptive manufacturing systems for material inhomogeneity Teaching: Instructor: ME 391 Mechanical Engineering Analysis at MSU (Spring/Fall 2024) Prior Teaching Assistant roles at ETH Zurich and Koç University Labs/Teams: Former member of the Smart and Sustainable Automation Lab at the University of Michigan. Collaborates with industry on LPBF and machining process innovations. Future Work: Expanding SmartScan to industrial-scale additive manufacturing applications and developing real-time adaptive milling systems for heterogeneous materials.
Mark Goldman is a Professor in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis. His research focuses on computational neuroscience, neuroengineering, and the study of neural circuits underlying memory, learning, and sensorimotor control. He is affiliated with the Center for Neuroscience and the Ophthalmology and Vision Science program. His work integrates mathematical modeling with experimental data to understand neural mechanisms in systems such as the cerebellum, oculomotor systems, and reinforcement learning circuits. Key research themes include synaptic plasticity rules, dynamics of neural circuits during memory consolidation, and the interplay between circuit architecture and adaptive behavior. Recent studies explore closed-loop systems, oscillatory mechanisms in short-term memory, and context-dependent neural control. His computational frameworks bridge theoretical neuroscience with experimental observations in both vertebrate and microbial systems. Goldman has contributed to education through innovative course-based research programs combining microbial biology with mathematical modeling. His publications reflect a deep engagement with interdisciplinary methods, including systems theory, machine learning, and biophysical modeling.
Anish Sevekariمشاهده پروفایل
پژوهشگر
- Machine Learning
- Algorithms
- Optimization
- +۳ مورد دیگر
Anish Sevekari is a Postdoctoral Associate at the University of Pittsburgh. His research focuses on machine learning, algorithms, optimization, and theoretical computer science. He investigates topics such as neural network training dynamics, generative models, algorithmic analysis beyond worst-case scenarios, and efficient inference techniques. His work bridges theoretical foundations with practical applications in areas like probabilistic modeling and combinatorial optimization. Key research interests include normalizing flows, ensemble methods, score-based learning, stochastic optimization, and combinatorial algorithms. His recent publications explore acceleration of NCE convergence, progressive ensemble distillation, and provable benefits of score matching. He has published extensively in top-tier venues, with a focus on theoretical guarantees and practical efficiency. His research trends emphasize bridging gaps between machine learning and traditional algorithmic analysis, particularly in probabilistic frameworks and high-dimensional data problems. No scientific awards or grants are explicitly mentioned in the provided information.
Paria Shiraniمشاهده پروفایل
استادیار
- Cybersecurity
- IoT Security
- Vulnerability Research
- +۴ مورد دیگر
Paria Shirani is an Assistant Professor and Tier 2 Canada Research Chair in Cybersecurity at the School of Electrical Engineering and Computer Science (EECS), University of Ottawa. She holds a PhD in Information Systems Engineering from Concordia University (FRQNT Doctoral Scholarship recipient) and completed an NSERC Postdoctoral Fellowship at Carnegie Mellon University (CMU). Her research focuses on cybersecurity, including IoT security, vulnerability detection, malware analysis, threat intelligence, and AI/ML applications. She leads funded projects across undergraduate, master’s, PhD, and postdoctoral levels, emphasizing equity, diversity, and inclusion (EDI). Research Highlights: Develops AI-driven solutions for IoT security and vulnerability detection. Pioneers binary code fingerprinting and firmware analysis techniques. Advances threat intelligence through machine learning and anomaly detection. Key awards include the NSERC Postdoctoral Fellowship, FRQNT Doctoral Scholarship, and the Tier 2 Canada Research Chair. Collaborations involve institutions like Concordia University, Carnegie Mellon University, and IBM’s Cyber Range. She actively serves on editorial boards (e.g., ACM Computing Surveys) and organizes conferences (e.g., SecureComm, PST).
Wei-Yin Loh is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His primary research focuses on classification and regression trees, with applications in machine learning, healthcare, and engineering. He has contributed extensively to the development of algorithms and methodologies in tree-based modeling, including the GUIDE software package. Dr. Loh's work spans theoretical advancements and practical implementations, addressing challenges such as missing data imputation, subgroup identification in precision medicine, and variable importance assessment. His research integrates statistical rigor with real-world problems in public health, federal agency performance analysis, and environmental modeling. Key contributions include studies on tobacco use prediction, cardiovascular health modeling, and pandemic-related mortality analysis. He has also explored applications in engineering, such as pavement thickness analysis and project delivery system evaluation. His publications emphasize interpretable models with strong predictive power, balancing complexity and practical utility. Awards and grants are not explicitly listed in the provided text, though his prolific publication record underscores his academic impact. Advising activities and student mentorship are not detailed here, but his research collaborations likely involve training the next generation of statisticians and data scientists.
David Bolandمشاهده پروفایل
مدرس ارشد
Dr. David Boland is a Senior Lecturer at the School of Electrical and Computer Engineering, University of Sydney. He holds an MEng and PhD from Imperial College London. His research focuses on energy-efficient hardware acceleration, particularly using FPGAs and application-specific integrated circuits (ASICs), to optimize computational efficiency in domains like machine learning and optical communications. He has contributed to projects involving custom hardware accelerators, federated learning for edge computing, and real-time signal processing. Education: MEng, Imperial College London, 2007 PhD, Imperial College London, 2012 Research Interests: Dr. Boland’s work emphasizes reducing computational overhead through customized hardware solutions. He explores techniques for minimizing unnecessary computations while maintaining accuracy, leveraging FPGA-based designs for parallelism and energy efficiency. Key areas include: Hardware acceleration for machine learning FPGA optimization for neural networks Energy-efficient algorithms for edge computing Online arithmetic and latency-accuracy trade-offs Grants & Collaborations: 2022: On-Board Federated Learning in Orbital Edge Computing (NSW Department of Industry) 2017: Fast Automated Anomaly Detection in Communication Networks (Defence Science & Technology Group) Affiliations: Member of the Net Zero Institute, collaborating on sustainable computing solutions.
Shahadat Uddinمشاهده پروفایل
مدرس ارشد
Dr. Shahadat Uddin is a Senior Lecturer at the University of Sydney, holding dual positions in the School of Electrical and Computer Engineering and the School of Project Management within the Faculty of Engineering. He earned a PhD in Data Science (2011) from the University of Sydney, a Master's in Information Systems from Central Queensland University, and a Bachelor's in Computer Science from Bangladesh University of Engineering and Technology. His research focuses on AI, machine learning, and complex networks applied to health informatics and project analytics. Notably, he is recognized in the Stanford-Elsevier list of top 2% global scientists in AI and image processing since 2021. Education: PhD in Data Science, University of Sydney (2011) Masters in Information Systems, Central Queensland University Bachelor in Computer Science and Engineering, Bangladesh University of Engineering and Technology Research Interests: Dr. Uddin's work emphasizes ethical AI, equitable decision-making algorithms, and applications in healthcare and project management. His contributions include predictive models for adverse drug reactions, stakeholder network analysis in projects, and fairness assessment in deployed ML systems. Awards: University of Sydney Strategic Research Fund Award (2024) World's top 2% Scientists in AI & Image Processing (2021–Present) Dean’s Research Award (2014), Research Excellence Award (2010) Advising & Grants: He supervises projects like Tommy Zhou's work on adverse drug reaction prediction. His grants include the 2024 University of Sydney Strategic Research Fund. He also leads projects on predictive analytics and stakeholder engagement in projects. Labs/Teams: Active collaborations with the Sydney Southeast Asia Centre and Charles Perkins Centre on interdisciplinary research.
Song Shuمشاهده پروفایل
استادیار
Dr. Song Shu is an Assistant Professor in the Department of Geography and Planning at Appalachian State University, specializing in remote sensing applications for cryospheric and hydrospheric studies. She holds a Ph.D. from the University of Cincinnati (2013–2019), an M.S. and B.S. from East China Normal University (2010–2013 and 2006–2010). Her research focuses on satellite altimetry, lake hydrology, and climate change impacts using advanced remote sensing techniques. She teaches courses such as GHY 3812: Geographic Information Systems, GHY 3310: Environmental Remote Sensing, and GHY 4810/5810: Digital Image Processing. Her work emphasizes improving satellite altimetry for Earth surface dynamics, retrieving hydrosphere/cryosphere parameters (e.g., snow depth, lake levels), and analyzing climate change effects on lakes. Key contributions include studies on Arctic snow accumulation, Tibetan Plateau vegetation dynamics, and urban heat island mitigation. Dr. Shu collaborates on interdisciplinary projects, including ICESat-2 ice shelf analysis and water quality modeling in inland lakes. Publications span high-impact journals like Remote Sensing of Environment (IF 9.09), IEEE Transactions on Geoscience and Remote Sensing (IF 5.86), and Nature Communications Earth & Environment. Her research integrates multi-source remote sensing data and geospatial methods to address global environmental challenges.
Prof. Hille Perl is a passionate Professor of Viola da Gamba at the University of the Arts Bremen, specializing in early music performance and pedagogy. Her career spans decades of international concert tours with ensembles and as a soloist, notably collaborating with lutenist Lee Santana. She emphasizes teaching emotional expression and non-competitive collaboration among students. Her research focuses on bridging historical and contemporary musical practices through improvisation and interpretation. Notable achievements include three Echo Klassik Awards for her recordings of Baroque and early Classical repertoire. Her work explores 17th-century improvisational techniques, cross-cultural musical fusion, and the revival of neglected Viola da Gamba works. She has released acclaimed albums such as Doulce Memoire (2000), which won the Echo Prize, and TINTO (2003) with her trio Los Otros. Prof. Perl also organizes annual workshops like the International WinterAcademy of Early Music in Bremen, fostering global exchange among early music practitioners. Her teaching philosophy integrates historical performance practices with modern pedagogical approaches, encouraging students to embrace both technical mastery and artistic creativity.
Prof. Dr. Silke Lehmann is a Professor of Music Pedagogy at the Institute of Music (IfM) at Osnabrück University of Applied Sciences. She holds a PhD from the Hamburg University of Music and Theatre (2007) on 'Movement and Language as Pathways to Musical Rhythm'. Her career includes extensive work in music education, including roles as a freelance music educator, member of the German Music Teachers' Association, and coordinator of music education projects in Hamburg and South Korea. Research focuses on rhythm pedagogy, music therapy with elderly populations, and early childhood music education. Notable projects include collaborations with the Wohnstift am Westerberg (dementia care through music) and refugee integration via music programs. Publications span rhythmic development, ensemble teaching, and music education methodologies. Teaching responsibilities include music pedagogy courses and supervision of applied music projects. Her work bridges academic research with practical applications in community and clinical settings.
Lizz Ulteeمشاهده پروفایل
پژوهشگر
- Glaciology
- Climate Science
- Glacier Dynamics
- +۵ مورد دیگر
Lizz Ultee is a glaciologist and Researcher at NASA Goddard Space Flight Center, with a faculty affiliation at Middlebury College. She holds a B.Sc. in Mathematical Physics from Queen’s University (Canada) and a PhD in Climate Science from the University of Michigan. Prior to her current role, she was a postdoctoral researcher at MIT and Georgia Tech. Her research focuses on glacier and ice sheet dynamics, societal impacts of climate change, and the development of numerical models to predict future ice sheet behavior. Current affiliations: NASA Goddard and Morgan State University (2024–present) Middlebury College roles: Taught courses like Natural Hazards, Climate Dynamics, and Glaciology Research interests include calving processes, drought buffering in mountain regions (e.g., the Andes), and statistical methods for large-ensemble simulations. She collaborates on interdisciplinary projects linking glaciology to societal outcomes. Publications include work on irreversible glacier changes, inter-model differences in runoff projections, and the Greenland Ice Sheet Large Ensemble (GrISLENS) project.
- Choral Music
- Music Education
- Conducting
- +۳ مورد دیگر
Trent R. Brown is a Professor of Music at Florida Gulf Coast University (FGCU), serving as Program Coordinator for Music Performance and directing choral ensembles. He teaches courses in choral literature, conducting, and choral methods while supervising student teachers. His international collaborations include conducting in Uruguay, China, and across Europe through the ACDA ICEP Fellowship (2017). Dr. Brown leads Choral Artistry , an adult professional ensemble, and has conducted major works like the SWFL premiere of Considering Matthew Shepard (2022). He also conducts with Gulfshore Opera and the Southwest Florida Symphony Orchestra. He holds degrees from the University of Arizona (DMA), University of Wisconsin-Milwaukee (MM), and University of Wisconsin-Eau Claire (BME). Beyond academia, he is an avid long-distance runner, completing over ten marathons, including the Boston Marathon (2022). Awards: ACDA ICEP Fellowship (2017) Conducted Florida All-State Reading Choir (2017) Professional Affiliations: ACDA, Chorus America, College Music Society, FMEA, Voice Care Network.