جست‌وجوی استاد و پژوهشگر

• Educational Technology
• Digital Games
• Computer Graphics and Simulations
• +۳ مورد دیگر

Veljko Aleksić is an Associate Professor at the Department of Information Technology, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. With over 15 years of academic experience, he specializes in Educational Technology, focusing on the intersection of digital games, computer graphics, and teaching methodologies. His work bridges theoretical educational frameworks with practical technological applications, making significant contributions to both national and international educational technology communities. Awarded the "Dr Milivoje Urošević" foundation award as the best graduate student, Aleksić earned his Bachelor's degree in Teacher of Technics and Informatics from the Technical Faculty in Čačak with an average grade of 9.59. He continued his academic journey with a Master's degree from the University of Kragujevac (average grade: 10.00) and completed his Doctoral studies at the University of Novi Sad, Faculty of Sciences, with a dissertation titled "Relation between digital gameplay and multiple intelligences profile of higher grade elementary school students." Professor Aleksić's research spans multiple interconnected domains within educational technology. His primary focus lies in investigating how digital games can enhance learning experiences, particularly examining the relationship between gameplay preferences and multiple intelligences profiles of students. This work has led to significant contributions in understanding how different learning styles interact with digital educational tools. His expertise extends to computer graphics and simulations, where he explores innovative approaches to visual learning and virtual environments. Additionally, he has made substantial contributions to teaching methodology, particularly in technics and informatics education, developing frameworks that integrate modern technologies into traditional educational settings. His work on neuroscience connections demonstrates his interdisciplinary approach to understanding educational processes. A review of Professor Aleksić's recent publications reveals a clear trajectory toward increasingly sophisticated applications of technology in education. His work has evolved from foundational studies on digital game-based learning to more complex investigations involving artificial intelligence, virtual reality, and cross-cultural educational comparisons. Recent publications demonstrate a growing emphasis on practical applications of educational technology, with several studies focusing on implementing these technologies in real-world educational settings across Serbia and internationally. His comparative studies between Serbian and Greek educational contexts highlight his expanding international research collaborations and the global relevance of his work. The thematic progression shows increasing sophistication in research methodology and broader educational impact. While specific major scientific awards aren't prominently listed in his profile, Professor Aleksić's extensive involvement as a reviewer for the National Accreditation and Quality Review Body in Higher Education and his participation in numerous international conference program committees represent significant professional recognition. His leadership roles in various educational projects funded by the Ministry of Education, Science and Technological Development of Serbia further demonstrate the value placed on his expertise by national educational authorities. His membership in the IEEE Computer Society Technical Committee on Learning Technology underscores his standing in the international educational technology community. Professor Aleksić has supervised numerous educational initiatives and projects, including "Osnaživanje kompetencija studenata - master profesora tehnike i informatike za preduzetničko obrazovanje i podršku profesionalnom razvoju učenika (MPTIPoPr)" and "Digitalna transformacija predmeta obrazovna tehnologija (DTPOT)" funded by the Ministry of Education. He has served as head of study programs including IAS Tehnika i informatika and MAS Predmetna nastava at the Faculty of Technical Sciences in Čačak. His extensive work with the Institute for Education Quality and Evaluation, and the Institute for the Advancement of Education demonstrates his significant contribution to national educational standards development. His involvement in international projects like ALLVET shows his commitment to European educational cooperation. As an active member of the IEEE Computer Society Technical Committee on Learning Technology and participant in numerous international research collaborations, Professor Aleksić contributes to cutting-edge educational technology development. His work with the Computer Science Laboratory at the Faculty of Technical Sciences in Čačak has fostered an environment for innovative research in educational technology, particularly in the areas of digital games development and computer graphics applications for learning. His leadership in organizing the Technics and Informatics in Education (TIE) conference series has created important platforms for scholarly exchange in the region.

Antonio Orvietoمشاهده پروفایل

استاد

• Deep Learning
• Optimization Theory
• Sequence Modeling
• +۷ مورد دیگر

Antonio Orvieto is a Professor and Principal Researcher at the Max Planck Institute for Intelligent Systems and ELLIS Institute Tübingen, where he leads the Deep Models and Optimization research group. He is also a lecturer at the University of Tübingen and faculty for the CLS, ELLIS, and IMPRS-IS PhD Programs. His research focuses on improving the efficiency of deep learning technologies through theoretical understanding of optimization dynamics and innovative neural network architectures. Dr. Orvieto earned his PhD from ETH Zürich under the supervision of Prof. Dr. Thomas Hofmann and Dr. Aurelien Lucchi. Prior to his PhD, he obtained his master's degree in Robotics, Systems, and Control from ETH. His educational background also includes undergraduate studies at Universita' degli studi di Padova in Italy. During his academic journey, he gained research experience at DeepMind (UK), Meta (US), MILA (CA), INRIA (FR), and HILTI (LI). Dr. Orvieto's research spans two main areas: understanding the intricacies of large-scale optimization dynamics and designing innovative architectures and powerful optimizers capable of handling complex data. His work particularly focuses on decoding patterns in sequential data, with applications in biology, neuroscience, natural language processing, and music generation. His theoretical approach to deep learning has led to significant contributions in understanding recurrent neural networks, transformers, and optimization methods. His recent publications reveal a strong focus on sequence modeling, optimization theory, and the theoretical foundations of deep learning, with particular emphasis on improving training efficiency and model performance. Schmidt Sciences AI2050 Early Career Fellow Dr. Orvieto actively mentors PhD students and leads a vibrant research group at the Max Planck Institute for Intelligent Systems. His Deep Models and Optimization group includes PhD students Destiny Okpekpe, Felix Sarnthein, Diganta Misra, Sajad Movahedi, and Wenjie Fan, among others. He is deeply involved in doctoral education as faculty for multiple PhD programs including CLS, ELLIS, and IMPRS-IS. His teaching includes the course "Nonconvex Optimization for Deep Learning" at the University of Tübingen. The Deep Models and Optimization research group investigates the interplay between optimizers and architectures in deep learning, with a focus on developing new networks for long-range reasoning. The group's mission is to design new optimizers and neural networks to accelerate technology and scientific discovery, with a strong theoretical foundation in optimization theory. They strongly believe that deep learning will revolutionize science and technology, and they aim to make powerful deep learning solutions accessible to scientists and engineers regardless of resource limitations.

Milan Strakaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Neural Networks
• Deep Learning
• +۱۲ مورد دیگر

Milan Straka is a professor at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University, Prague. His research focuses on machine learning, artificial neural networks, deep learning, and structured prediction with applications to natural language processing (NLP) tasks including POS tagging, dependency parsing, named entity recognition, and optical music recognition. University: Charles University School: Faculty of Mathematics and Physics Department: Institute of Formal and Applied Linguistics Straka has developed several NLP tools like UDPipe and NameTag, contributing to multilingual coreference resolution, Czech grammar error correction, and historical language processing pipelines. His recent work involves contextualized embeddings, semantic parsing, and large-scale dataset creation for Czech NLP tasks. He collaborates on European Language Grid initiatives and has participated in multiple shared tasks (CRAC, MRP, W-NUT). His publications span topics from algorithm design (functional data structures) to modern transformer-based language models (RoBERTa) with emphasis on Czech language resources.

Adam Sporkaمشاهده پروفایل

دانشیار

• Game Audio
• Interactive Music
• Adaptive Music Middleware
• +۳ مورد دیگر

Assoc. Prof. Adam Sporka, Ph.D., is an academic at Charles University's Faculty of Mathematics and Physics, Department of Software and Computer Science Education. He serves as a guest lecturer at universities across the Czech Republic, France, and the UK, while also teaching audio direction at University of California, Santa Cruz. His expertise spans game audio, focusing on interactive and adaptive music systems. Co-author of adaptive music middleware for Kingdom Come: Deliverance (Warhorse Studios) Published over 50 academic works in journals and conferences Developed music technologies like Gradrigo, Plexigan, and Adhaesio His collaborations include tech companies (Techland, devcom), cultural institutions (Prague Spring International Music Festival), and research partners (Czech Technical University, University of Edinburgh).

• Acoustics
• Machine Learning
• Metamaterials
• +۳ مورد دیگر

Jonas Schmid is a researcher affiliated with the Technical University of Munich (TUM) , specifically within the Chair of Acoustics of Mobile Systems . His work bridges computational methods with acoustic engineering, leveraging advanced statistical and machine learning techniques. Email: jonas.m.schmid@tum.de Office: Room 5505.01.534, Boltzmannstr. 15, 85748 Garching bei München Research Focus: Schmid's research centers on: Bayesian inference in acoustic modeling Generative design of acoustic metamaterials Sound field reconstruction techniques Interactive educational simulations for acoustics Integration of machine learning in noise control Visualization of musical instrument acoustics Publication Trends: Recent works demonstrate expertise at the intersection of computational acoustics and machine learning. Key areas include metamaterial design (2021, 2022), Bayesian inference applications (2021-2023), and development of interactive acoustic simulations (2019-2021). Subfields span noise control, room acoustics, and educational technology.

Laura Bareißمشاهده پروفایل

پژوهشگر

• Music Education
• Educational Psychology
• Artificial Intelligence in Education
• +۳ مورد دیگر

Laura Bareiß serves as a Research Associate and doctoral student at Universität Stuttgart's Department of Teaching and Learning with Intelligent Systems (LLiS), focusing on implicit theories of musical giftedness and AI-integrated education. Her interdisciplinary work bridges music pedagogy, cognitive psychology, and educational technology. Her research spans Music Education , Educational Psychology , and AI in Learning Systems , examining how teachers perceive musically gifted students and how AI tools influence attention control in educational settings. Key projects include UFO and IKILeUS, exploring intelligent systems for personalized learning. Her publication trends reveal dual expertise: (1) analyzing implicit biases in music education through qualitative teacher studies, and (2) developing AI-driven attention management tools. Both strands emphasize practical applications for inclusive teaching methodologies. Laura actively teaches seminars on gifted education and empirical research methods for M.Ed. students at Universität Stuttgart, while pursuing dual graduate degrees in Music and Educational Psychology. As part of LLiS, she collaborates within interdisciplinary teams developing adaptive learning technologies, with membership in the Arbeitskreis für Musikpädagogische Forschung (AMPF) connecting her to broader music education research networks.

Florian Huberمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Visual Analytics
• +۴ مورد دیگر

Prof. Dr. Florian Huber serves as Professor of Data Science and Visual Analytics at the Department of Media, Faculty of Media, Hochschule Düsseldorf University of Applied Sciences (HSD), a position held since September 2021. He is additionally affiliated with the Center for Digitalization and Digitality (ZDD), where he bridges data science methodologies with real-world applications in scientific and cultural domains through machine learning and visualization techniques. His academic foundation includes: Diploma in Physics, University of Leipzig PhD in Biological Physics, University of Leipzig (2012), thesis: "Emergent structure formation of the actin cytoskeleton" Prior to HSD, he worked as a postdoctoral researcher at AMOLF (Amsterdam) and TU Delft, founded the sustainable sweets company KÄNDI, and served as Data Scientist at the Netherlands eScience Center (2018-2021). Huber's research focuses on developing data science algorithms for comparative analysis of complex datasets, combining machine learning with domain expertise to enable meaningful comparisons through latent space transformations. Key areas include mass spectrometry-based metabolomics (chemical identification/similarity prediction), natural language processing (e.g., lyrical evolution analysis), and audio data science, with emphasis on interpretable and reproducible computational approaches for biology, medicine, and cultural studies. Analysis of his 2023-2025 publications reveals dominant themes in mass spectrometry data science (tools like matchms, MS2Query, Mass2SMILES) and NLP applications in musicology. His work increasingly integrates deep learning for cross-domain similarity prediction while creating educational resources, reflecting a trend toward versatile open-source solutions for interdisciplinary data challenges. Huber actively contributes to open science through GitHub repositories and reproducible pipelines, suggesting collaborative funding models centered on software sustainability. His teaching includes the "Data Science" module (MMI 05.35) with practical components for engineering programs, offered in German or English. He leads initiatives within HSD's Center for Digitalization and Digitality, fostering collaborations between data scientists and domain experts. Current projects involve interdisciplinary teams tackling metabolomics, cultural analytics, and educational tool development using Python-based open-source ecosystems, with future work likely expanding into new application domains while advancing methodological rigor.

• Phonology
• Computational Linguistics
• Austronesian Languages
• +۷ مورد دیگر

Kie Zuraw is a Professor in the Department of Linguistics at the University of California, Los Angeles. Her research focuses on computational and quantitative approaches to phonology, with a particular emphasis on constraint-based modeling, code-switching, loanword phonology, and language change in Austronesian languages like Tagalog, Palauan, Malagasy, and Korean. Research Interests Probabilistic grammars and Harmonic Grammar Phonology of code-mixing and bilingualism Exceptional linguistic patterns and learnability Computational modeling of speech communities Phonotactic constraints and their interaction Prosodic structure in Austronesian languages Her recent research output includes work on English-Spanish code-switching (2025), Maximum Entropy constraint grammars (2024), and speech planning models (2021). She has also contributed to pedagogical innovations through skills-based grading in phonology courses (2019). Teaching and Software Currently teaching Phonology I and Phonology of Global Pop Music at UCLA, she developed the maxent.ot R package for constraint modeling and maintains the educational tool Pheatures for phonological feature learning.

Anthony Yezziمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Shape Optimization
• +۴ مورد دیگر

Anthony Yezzi is the Julian T. Hightower Chair and Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology (College of Engineering). He directs the Lab of Computational Computer Vision (TSRB 427) and joined Georgia Tech in 1999. His research focuses on variational methods in computer vision and medical imaging, with applications in segmentation, 3D reconstruction, and AI-driven diagnostics. Education: Ph.D. in Electrical Engineering, University of Minnesota (minors in Mathematics and Music) B.S. in Electrical Engineering, University of Minnesota Postdoctoral Research Associate, Laboratory for Information and Decision Systems, MIT Research Focus: Dr. Yezzi develops geometric PDEs and active contour models for problems in medical imaging (cardiac MRI/CT, ultrasound) and computer vision. Key areas include: Shape optimization and surface evolution theory AI for medical image segmentation and reconstruction Radar-based scene analysis and event-based vision Robust neural network training via PDE frameworks Publication Trends: His recent work (2022-2025) emphasizes deep learning integration with variational methods , particularly for medical applications (pathology, radiology, dentistry) and 3D vision. Themes include stabilization of neural representations, PDE-based optimization, and AI-driven clinical tools. Professional Activities: Consults on industrial applications in medical imaging and visual inspection. Teaches DSP and develops courses in computer vision/medical image processing.

Tom Hallمشاهده پروفایل

مدرس پاره‌وقت

• Electronic Music
• Sound Design
• Audio-Visual Art
• +۳ مورد دیگر

Tom Hall is a Part-Time Lecturer at the University of Southern California's Thornton School of Music, specializing in Music Technology and Composition. He is an Australian electronic audiovisual artist based in Los Angeles, CA, with a career spanning two decades in the music industry across multiple continents. Educational Background: Australian National University (Media Arts major), Kyoto Seika University (sound studies). Hall's artistic practice explores time interpolation, peripheral environments, phenomenology, and non-linear narratives through computer-programmed sound synthesis, reactive visuals, and physical installations. He collaborates with companies like Cycling '74, Ableton, Moog, and Roli, contributing to R&D for music hardware/software including the Arturia Minibrute2s and Max for Live educational series. Recent activities include the 2025 album Trip Computer and performances at Gray Area (San Francisco). He co-founded the Space Song Foundation (2019) for interstellar art projects with NASA/JPL scientists. Key Affiliations: Thornton School of Music (USC) Space Song Foundation (founding member) Cycling '74 (developer) USC Gray Area performance venue collaborations

• Digital Humanities
• Digital Cultural Heritage
• Cultural Analytics
• +۷ مورد دیگر

Anna Maria Neubert is a Researcher at Bielefeld University's Faculty of History, Philosophy and Theology , Department of History. Her work bridges Digital Humanities and Project Management , focusing on Digital Cultural Heritage , Cultural Analytics , and interdisciplinary collaboration. She serves as Managing Director of the Center for Uncertainty Studies (CeUS) and the Bielefeld Center for Data Science (BiCDaS). PhD (2025) in History/Digital History MA in Cultural and Media Management (2017) MA in Digital Humanities (2013) BA in Musicology and Digital Humanities (2013) Her research explores how third-party funding shapes Digital Humanities, with a focus on collaborative research rhythms , digital editions , and data science integration . Trends in her publications highlight project lifecycle dynamics , interdisciplinary coordination , and digital infrastructure development . She has contributed to music encoding standards and historical evidence practices through software development and analytical articles. Neubert's leadership roles in institutions like BiCDaS and CeUS underscore her administrative impact. Her work emphasizes responsible project management and addressing disciplinary differences in academic collaboration.

Geir Draugsvollمشاهده پروفایل

استاد

• Classical Accordion Performance
• Chamber Music
• Accordion Repertoire Development
• +۵ مورد دیگر

Geir Draugsvoll is a Norwegian accordionist and Professor at the Danish Academy of Music and Dramatic Arts (DKDM) , where he has been employed since 1995. He specializes in classical accordion performance and serves as a subject coordinator for accordion education. Draugsvoll has pioneered the accordion’s role in classical music through international collaborations, premieres of contemporary works, and editorial contributions to accordion notation. Born: 1967 Education: Danish Academy of Music and Dramatic Arts Appointments: Professor at DKDM since 2012; Visiting Professor at Tianjin Conservatory, Graz Hochschule, and others Research and Artistic Practice focuses on: Expanding classical accordion repertoire through commissions and world premieres Development of notation standards and digital tools like Accordeon Font software Distance learning pedagogy for accordion Cultural exchange via international festivals like Nordaccordion Projects and Publications include the Classical Accordion Edition series (1995–2012), the Handbook in Accordion Notation (2001), and software innovations for accordion notation. His 2025 Fachwerk recording with the London Symphony Orchestra marks a milestone in accordion-symphony integration. International Engagement spans performances at major venues like Concertgebouw, Barbican Hall, and Mariinsky Theatre, alongside masterclasses at institutions such as CNSM Paris and Sibelius Academy. He has served as jury member in prestigious competitions including Klingenthal (Germany) and Castelfidardo (Italy).

Jessica Grimmerمشاهده پروفایل

مدرس

• Digital Humanities
• Musicology
• Ethnomusicology
• +۵ مورد دیگر

Jessica Grimmer is a Lecturer at the University of Maryland's College of Information and an Affiliate Faculty member in the School of Music. Her work bridges information science, digital humanities, and musicology, focusing on digital collection sustainability and Holocaust-related musical studies. She co-leads the Music Encoding Initiative as part of the Special Collections in Performing Arts and UMD Libraries . Ph.D. in Historical Musicology, University of Michigan Master of Library and Information Science (MLIS), University of Maryland Master of Music, University of Cincinnati Bachelor of Music, University of Cincinnati Her research explores digital preservation of musical heritage through MEI (Music Encoding Initiative) standards and pedagogical applications of encoded music. A secondary focus examines musical responses to the Holocaust across Europe and North America, including works by Arnold Schoenberg and Darius Milhaud. Key projects include the Sustaining Digital Community Collections initiative funded by the Andrew W. Mellon Foundation, where she developed frameworks for community-driven digital sustainability. She also serves as Associate Director for the Center for Archival Futures (CAFe). Awards include: Best Paper Award (2022), Music Encoding Conference Best Short Paper Award (2021), Association for Information Science and Technology As a cultural archivist and educator, Grimmer has: Led digital humanities workshops at Tufts University and the University of Maryland Collaborated with institutions like Howard University and Vanderbilt University Contributed to anti-racism initiatives in music libraries Her technical work intersects with software projects like Verovio and MEI GitHub Repository , while maintaining active roles in professional organizations including the Music Encoding Initiative and Music Library Association .

David Calvertمشاهده پروفایل

دانشیار

• Artificial Neural Networks
• Classification
• Modeling
• +۳ مورد دیگر

David Calvert is an Associate Professor in the School of Computer Science at the University of Guelph, where he has been since 1998. His research centers on artificial neural networks for classification and modeling, with applications in agricultural data, public health, and music analysis. He holds a PhD from the University of Waterloo and previously worked as a programmer/researcher at INM Inc., E.S. Aquatic Co., and Mortice Kem Systems Inc. His research spans: Artificial Neural Networks for sequence modeling and simulation Development of software tools for atonal music analysis using pitch class sets GPU-based parallel computing for pattern recognition Calvert's work integrates biological data from agricultural and public health domains with computational methodologies, emphasizing practical implementations in interdisciplinary contexts. His awards include: Natural Science and Engineering Research Council of Canada grants (1999, 2005) SHARCNET Site Leader Grant (2007) He actively mentors graduate students, undergraduates, and postdoctoral fellows through SHARCNET-supported high-performance computing projects, with current openings available for new researchers.

JooYoung Seoمشاهده پروفایل

استادیار

• Accessible computing
• Data science accessibility
• Human-computer interaction
• +۱۰ مورد دیگر

JooYoung Seo is an Assistant Professor in the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign. He maintains multiple faculty affiliate positions with the Siebel School of Computing and Data Science, Illinois Informatics Institute, National Center for Supercomputing Applications (NCSA), and Beckman Institute for Advanced Science and Technology. Additionally, he is an RStudio double-certified data science instructor and an internationally certified accessibility professional through the International Association of Accessibility Professionals (IAAP). Dr. Seo's educational background includes: Ph.D. in Learning, Design, and Technology from Pennsylvania State University (2021) M.Ed. in Learning, Design, and Technology from Pennsylvania State University (2016) Double B.A. in Education and English Literature from Sungkyunkwan University, Seoul, South Korea (2014) As an emerging learning scientist and information scientist, Dr. Seo's research focuses on making computational literacy more accessible to people with disabilities through multimodal data representation. His work spans accessible computing, universal design, inclusive data science, and equitable healthcare technologies. He has developed open-source data science packages for accessibility and has worked on projects involving web accessibility, human-centered design, inclusive makerspaces, tangible block-based programming, and accessible data science tools. His research bridges learning sciences, information science, and human-computer interaction with a strong emphasis on creating inclusive technologies that serve diverse populations. Analysis of Dr. Seo's recent publications reveals a consistent interdisciplinary focus on accessibility across multiple domains. His work spans human-computer interaction, data science, education, and healthcare technologies, with particular emphasis on creating accessible solutions for people with visual impairments. Key themes include multimodal data representation (sonification, tactualization, verbalization), accessible programming environments, inclusive educational tools, and healthcare technologies designed with universal principles. His research demonstrates a strong integration of learning sciences, computer science, and accessibility studies to develop practical solutions addressing real-world challenges faced by people with disabilities. Dr. Seo has received numerous honors and awards: Early Career Award from the Institute of Museum and Library Services (IMLS), 2023-2026 Teach Access Faculty Award, 2023 Teacher Ranked as Excellent at the University of Illinois (multiple semesters) 2021-2022 Emerging Scholar from the International Society of the Learning Sciences RStudio-Certified Shiny and Tidyverse Instructor Qualcomm Fellowship (2020) Doctoral Dissertation Fellowship from Penn State (2020) Dr. Seo's research is supported by significant funding from national institutes, industrial partners, and academic societies including the Institute of Museum and Library Services (IMLS), National Science Foundation (NSF), National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), Posit Software (formerly RStudio), Teach Access, and the International Society of the Learning Sciences. He currently teaches independent study courses and mentors students through various research projects. His current research projects include developing an AudioTactile Data System for Blind or Low Vision individuals in STEM fields, the MAIDR project for Multimodal Access and Interactive Data Representation, and initiatives to promote Computational Thinking Skills for Blind and Visually Impaired Teens through Accessible Library Makerspaces. Dr. Seo leads the XAbility Lab at the University of Illinois, which focuses on developing accessible computing solutions and conducting research at the intersection of human-computer interaction, learning sciences, and accessibility. The lab brings together interdisciplinary researchers from information sciences, computer science, and education to tackle complex challenges in making technology accessible to all users regardless of ability. Current projects in the lab focus on multimodal data representation, accessible data science tools, and inclusive educational technologies that serve diverse populations.