Find Academic Supervisors

• Music Performance
• Music History
• Musical Interpretation
• +3 more

Elena Dikova is a Professor at the National Academy of Music "Prof. Pancho Vladigerov" in Bulgaria, specializing in musicology and performance practice. Her research focuses on historical performance traditions, Bulgarian musical heritage, and interdisciplinary connections between music and drama. She has extensively studied the works of composers like Bartók, Beethoven, and Scriabin, with a particular emphasis on interpretation techniques and pedagogical approaches. Affiliations: National Academy of Music "Prof. Pancho Vladigerov" Research Areas: Music History, Ethnomusicology, Instrumental Pedagogy, and Cultural Heritage Preservation Her work explores the artistic legacies of Bulgarian pianists like Anton Dikov, analyzing their contributions to national and international musical traditions. She also investigates ancient music theories (e.g., Aristides Quintilianus) and modern pedagogical methods for instruments like the cello and trombone. Her studies bridge theoretical analysis with practical performance, emphasizing the role of psychoacoustic principles in artistic creation. Recent articles highlight themes such as theme-oriented concerts, Jewish song arrangements, and implied polyphony, demonstrating her interdisciplinary approach. While no awards are explicitly noted, her academic contributions reflect a deep engagement with both historical and contemporary musical practices. Elena Dikova has advised on numerous projects related to music education and performance but does not list formal advisees in the provided texts. She collaborates with academic and cultural institutions, including Bulgaria’s National Radio Folk Song Ensemble, to preserve and disseminate Bulgarian choral and instrumental traditions.

• Astrophysics
• Quantum Mechanics
• Cosmology
• +3 more

Dr. Tim Beadle is a Senior Lecturer in the Department of Physics and Astronomy at the University of Birmingham. He holds a PhD in Theoretical Physics from the University of Cambridge (2005) and a Master's degree in Physics from the University of Oxford (2002). His research focuses on the intersection of cosmology, quantum mechanics, and high-energy physics, with particular emphasis on dark matter, gravitational waves, and quantum gravity phenomena. He leads the Cosmology and Astrophysics Group at Birmingham and oversees a research portfolio valued at £2.5 million from the Science and Technology Facilities Council (STFC). Education: PhD Theoretical Physics (Cambridge, 2005), MPhys (Oxford, 2002) Dr. Beadle's research interests include astrophysical applications of quantum mechanics, cosmological structure formation, and observational tests of dark energy. He has pioneered studies on dark matter distribution in galaxy clusters and gravitational wave signals from primordial black holes. His work bridges theoretical modeling with cutting-edge observational data from facilities like the Square Kilometre Array and the Event Horizon Telescope. Key research trends in his publications include: 1) Exploring quantum effects in cosmological contexts, 2) Developing new methods for detecting dark matter via gravitational lensing, 3) Investigating gravitational wave signatures of early universe processes, and 4) Testing modified gravity theories against galaxy survey data. His recent work emphasizes multi-messenger astronomy and quantum foundations in expanding spacetimes. Royal Society University Research Fellowship (2020) Institute of Physics Early Career Physicist Award (2018) Marie Skłodowska-Curie Actions Fellowship (2015) Dr. Beadle currently supervises 10 PhD students and has mentored over 20 early-career researchers. He manages a £2.5M STFC grant exploring quantum gravity signatures in cosmological data and leads the Birmingham node of the UK National Cosmology Supercomputing Facility. His research group collaborates internationally with teams at CERN, the European Southern Observatory, and NASA's Jet Propulsion Laboratory. He directs the Cosmology and Astrophysics Group, which operates state-of-the-art computational facilities for cosmological simulations. Future work includes developing novel techniques for detecting cosmic neutrino backgrounds and refining theoretical models of dark energy dynamics using upcoming Euclid mission data.

• Artificial Intelligence
• Optimization
• Scheduling
• +4 more

Marie-Louise Lackner is a Research Fellow (PostDoc Researcher) in the Department of Databases and Artificial Intelligence at Technische Universität Wien. She holds Diplom-Ingenieur and Dr. techn. degrees and actively contributes to the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (2017–2025). Her research focuses on Artificial Intelligence and Optimization , with emphasis on industrial scheduling, constraint programming, and algorithm design. Key domains include: Industrial oven scheduling and production leveling Metaheuristics (simulated annealing, large neighborhood search) Multi-objective optimization in manufacturing systems Combinatorial mathematics and discrete structures Her publications demonstrate a strong trajectory in applied optimization , with recent work concentrating on energy-efficient scheduling algorithms for electronic manufacturing, while earlier research explored permutation patterns and social choice theory. Methodologies consistently integrate theoretical computer science with industrial problem-solving. She supervises graduate researchers, including P. Malik's work on memetic algorithms for production leveling. Grant involvement includes the CD Laboratory project focused on AI-driven planning systems. Lackner is affiliated with the CD Laboratory for Artificial Intelligence and Optimization, conducting applied research in industrial scheduling systems within TU Wien's Databases and AI group.

Taolue ChenView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +6 more

Taolue Chen is a Senior Lecturer in the School of Computing and Mathematical Sciences at Birkbeck, University of London. He holds a PhD from Vrije Universiteit Amsterdam and MSc/BSc degrees from Nanjing University, China. His research focuses on neuro-symbolic software engineering, combining AI (Machine Learning/NLP) with formal methods in software engineering, program analysis, and verification. He has published extensively in top conferences like POPL, OOPSLA, and NeurIPS, and has received multiple awards including the ACM SIGSOFT Distinguished Paper Award (2024). Education: PhD: Centrum Wiskunde & Informatica (CWI) and Vrije Universiteit Amsterdam (2010s) MSc: Computer Science, Nanjing University (China) BSc: Computer Science, Nanjing University (China) Research Interests: Taolue's work bridges AI and traditional software engineering, with a focus on neuro-symbolic systems, code generation, adversarial robustness, and formal verification of cryptographic programs. He explores synergies between statistical (deep learning) and logical (constraint solving) methods to address challenges in software verification and cybersecurity. Articles Overview: His recent work emphasizes code generation improvements, adversarial defense mechanisms, and formal verification of secure cryptographic implementations. Key themes include enhancing model robustness, optimizing documentation in code generation, and applying large language models to symbolic reasoning tasks. Awards: Best Paper Award at SETTA’20 (2020) 1st Prize in CCF Software Prototype Competition (2022) QF Strings Competition Win (2023) ACM SIGSOFT Distinguished Paper Award (2024) Advising & Grants: Advises PhD student JEFERSON DORNELLES SCHNEIDER. His work is supported by grants focusing on neuro-symbolic systems, cybersecurity, and formal methods for software engineering. Labs/Teams: Leads research groups in neuro-symbolic software engineering and cryptographic verification at Birkbeck, collaborating with institutions like University of Oxford and University of Twente.

Juntao DuanView profile

Lecturer

• Machine Learning
• Statistics
• Data Science
• +3 more

Juntao Duan is a Lecturer at the University of California, Santa Barbara (UCSB). His research focuses on machine learning, statistical methodologies, and their applications in healthcare and finance. Key areas include developing advanced algorithms for text classification, optimizing portfolio strategies through covariance voting, and analyzing medical data related to hemodialysis patients and SARS-CoV-2 infections. Notably, he employs deep neural networks and Gaussian processes in his work, alongside exploring theoretical foundations like random projection invariance principles. Duan has contributed to both applied and theoretical domains, with publications ranging from biomedical informatics to computational finance. His educational background and specific institutional affiliations beyond UCSB are not detailed in the provided text. No scientific awards or grants are explicitly mentioned, and he currently has no listed advisees or students. His office is located in Old Gym 1203 at UCSB, reflecting his active role within the university's academic community.

Brian Van KotenView profile

Assistant Professor

• Applied Probability
• Numerical Analysis
• Computer Simulation of Molecular Systems
• +4 more

Brian Van Koten is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on applied probability and numerical analysis, with applications to molecular systems simulation, variance reduction in statistical physics, and numerical methods for saddle point identification. He teaches courses such as Math 652 (Numerical Analysis II), emphasizing computational methods for partial differential equations and optimization in machine learning. Key research interests include computer simulations of biological and materials systems, rare event sampling techniques, and atomistic/continuum coupling methods. He has presented work at institutions like the Institute for Computational and Experimental Research in Mathematics (ICERM) and the Institute for Pure and Applied Mathematics (IPAM), focusing on topics like stratification for Markov chain Monte Carlo and the string method for minimum energy paths. Van Koten's work bridges theoretical numerical analysis with practical computational challenges, addressing issues in multiscale modeling and error analysis. His teaching philosophy integrates advanced numerical methods with real-world applications, supported by detailed course materials on topics like finite difference methods and optimization algorithms for large-scale data science problems.

• Computer Systems
• Functional Programming
• Hardware Description
• +3 more

John O'Donnell is an Honorary Lecturer at the University of Glasgow's School of Computing Science. His research spans functional programming, hardware description languages, parallel computing, and computer science education, with notable applications in music technology. Key research areas: Functional approaches to hardware design and simulation Parallel data structures and algorithms for specialized architectures Programming misconception identification and pedagogical tools Computational modeling of musical performance techniques Publications demonstrate consistent innovation in applying functional programming paradigms to diverse domains, from circuit design to music pedagogy. Recent work focuses on educational aspects of computer systems and programming.

Devin CrichtonView profile

Professor

• Cosmology
• Astrophysics
• Galaxy Clusters
• +5 more

Dr. Devin Crichton holds the Professorship for Physics at ETH Zürich, focusing on observational cosmology and radio astronomy. His research centers on large-scale structure studies, galaxy cluster physics, and cosmic microwave background (CMB) analysis through major experiments like the Atacama Cosmology Telescope (ACT) and the Hydrogen Intensity Real-Time Analysis eXperiment (HIRAX). Key projects include developing SKA simulation frameworks and investigating 21cm intensity mapping for cosmological insights. His work spans telescope instrumentation (e.g., HIRAX antenna design), CMB power spectrum analysis, and SZ-selected cluster catalogs. Contributions include advancing understanding of cosmic string wakes and quasar feedback mechanisms through multi-wavelength observations. Dr. Crichton collaborates on international initiatives such as the Simons Observatory and LiteBIRD, addressing questions about dark energy, neutrino physics, and early universe cosmology. His technical expertise includes data pipeline development for real-time analysis and precision measurement systems for radio telescope noise characterization.

• Real-time Systems
• Operating Systems
• Information Theory
• +3 more

Dr. Carlos A. Rincon C. is an Instructional Assistant Professor in the Computer Science Department at the University of Houston. His research focuses on real-time systems, operating systems, information theory applications to scheduling, and storage systems. He holds a PhD in Computer Science from the University of Houston and has industry experience in software development prior to his academic career. Current research projects include optimizing probabilistic compression algorithms based on symbol position, developing machine learning models for disk failure prediction in heterogeneous environments, and applying information theory principles to real-time multiprocessor scheduling. His work bridges theoretical computer science with practical system optimization challenges. Dr. Rincon teaches core computer science courses including Operating Systems (COSC 3360) and Introduction to Programming (COSC 1437), with learning objectives centered on foundational concepts in system design, process management, memory management, and object-oriented programming principles. As Director of the ConocoPhillips Computer Science Learning Center, he oversees academic support programs and resources for computer science students. His pedagogical approach emphasizes algorithmic thinking and practical problem-solving skills development.

Daniel StuttsView profile

Associate Professor

• Vibrations Mitigation
• Piezoelectric Actuator Design
• Embedded Systems Applications
• +5 more

Daniel Stutts is an Associate Professor at Missouri University of Science and Technology in the Department of Mechanical and Aerospace Engineering. He directs the Electromechanical Systems and Parameter Estimation Lab and chairs the Information Technology and Computing Committee. His career spans over three decades in academia and research. Education: Ph.D. (1990), M.S. (1987), and B.S. (1983) in Mechanical Engineering from Purdue University and Louisiana State University Dr. Stutts specializes in Dynamics , Vibrations , Piezoelectric Actuator Design , and Parameter Estimation . His research bridges Mechatronics , Heat Transfer , and Structural Health Monitoring applications in aerospace and mechanical systems. Recent publications focus on Lunar Regolith Processing , Vibration Analysis in drilling systems, and Thermal Conductivity Estimation . His work integrates Ultrasonic Vibrations for dust mitigation, Molten Salt Electrolysis for lunar aluminum production, and Piezoelectric Transducers for mechanical and aerospace applications. Dr. Stutts leads the Electromechanical Systems and Parameter Estimation Lab , advancing technologies in Dynamic Parameter Estimation and Vibration Mitigation for industrial and aerospace systems.

Justin AntosView profile

Adjunct Professor

• Music Education
• Pre-service Teacher Training
• Programming Trends in Instrumental Music Education
• +3 more

Dr. Justin Antos serves as Adjunct Band Director at Saint Xavier University and Director of Bands/Orchestras at Dwight D. Eisenhower High School in Blue Island, Illinois. He transformed the high school's music program from 40 students to over 300, leading award-winning bands, orchestras, and ensembles. Since 2014, he has also been an adjunct professor at Trinity Christian College. His expertise spans music education, pre-service teacher training, and culturally-responsive teaching. Antos holds multiple certifications including National Board Teaching Certification in Instrumental Music Education. He has authored articles in academic journals and presented at national conferences, earning prestigious awards like the Grammy Music Educator Award (Top 10 Finalist, 2021). Education: Ph.D. in Music Education, Auburn University Ed.S. in Music Education, Auburn University M.M. in School Improvement Leadership, Olivet Nazarene University M.M. in Instrumental Conducting, Central Michigan University M.M. in Music Education, Boston University B.M. in Percussion Performance & Music Education, Saint Xavier University Research Interests: Impact of music competitions on student attitudes Culturally-responsive teaching practices Instrumental music program development Pre-service teacher training methodologies Key Honors: Grammy Music Educator Award Finalist (2021) Midwest Music Festival Hall of Fame (2020) National Board Certified Teacher (Music) Professional Contributions: Mentors educators and designs professional development programs Freelance percussionist and recording studio operator Community volunteer with leadership roles in school equity initiatives

Qin ZhangView profile

Professor

• Algorithms for Big Data
• Theoretical Foundations of Machine Learning
• Quantum Information and Computing
• +4 more

Roles and Affiliations: Professor of Computer Science at Indiana University Bloomington, Adjunct Professor in Mathematics. Previously at IBM Almaden Research Center and Aarhus University's Center for Massive Data Algorithmics. PhD from HKUST in Computer Science and Engineering. Education: PhD in Computer Science, HKUST, advised by Mordecai Golin and Ke Yi Research Interests: Focuses on algorithms for big data, including communication-efficient distributed computation, streaming/sketching algorithms, and quantum data management. Explores theoretical foundations of machine learning, particularly distributed learning. Leads projects funded by NSF grants on parallel reinforcement learning, distributed graph algorithms, and noisy data processing. Publications and Awards: Over 100 papers in top venues like FOCS, SIGMOD, and NeurIPS. Won the Best Paper Award at SPAA 2017 for distributed clustering work. Invited to special journal issues for top conferences. Teaching and Grants: Teaches advanced courses on sublinear algorithms and algorithm design. PI on NSF grants totaling over $2M, including collaborative projects on distributed computing and epidemiological modeling. Advises PhD students in areas like quantum algorithms and reinforcement learning. Service: Served on program committees for SIGMOD, NeurIPS, ICML, and PODS. Organizes theory seminars and Midwest Theory Day workshops.

Paul StapletonView profile

Professor

• New Musical Instrument Design
• Music Performance
• Sound Design
• +4 more

Paul Stapleton is a Professor of Music at the Sonic Arts Research Centre (SARC) within Queen’s University Belfast’s School of Arts, English and Languages. His work focuses on new musical instrument design, sound sculpture creation, and critical improvisation studies. He collaborates internationally across disciplines, including art, medicine, and technology. Stapleton holds a Visiting Scholar position at Stanford University’s Center for Computer Research in Music and Acoustics (CCRMA), and regularly presents at conferences such as NIME. He leads AHRC-funded projects like Humanising Algorithmic Listening and co-directed the Translating Improvisation research group. His creative work includes the acclaimed immersive audio-theatre Reassembled, Slightly Askew , exploring brain injury recovery. Education: Practise-Led PhD in The Development of Dialogic Music (2001–2004) at the University of Central Lancashire. Key projects include the Bonsai Sound Sculpture (BoSS) and collaborations with artists like Simon Rose and Ens Ekt trio. Awards include the NIME 2017 Best Poster Award for Virtual-Acoustic Instrument Design . Research interests span instrument design, sound art, and interdisciplinary improvisation. His work bridges technology and human experience, with recent focus on algorithmic listening and embodied aesthetics. Grants include AHRC, EU, and Wellcome Trust funding. Advising PhD students and mentoring emerging artists through practice-based research. Labs/Teams: SARC, collaborations with biomedical teams at Belfast’s Royal Victoria Hospital, and international networks like NIME and SIEMPRE. His albums, such as FAUNA (with Rose), blend experimental soundscapes with improvisational rigor.

• Computational Linguistics
• Natural Language Processing (NLP)
• Language Model Interpretability
• +4 more

Dr. Dan Jurafsky is a Professor at the Department of Linguistics within Stanford University's School of Humanities and Sciences. His work spans computational linguistics, natural language processing, and AI ethics, with a particular focus on language model behavior, speech recognition, and ethical implications of anthropomorphism in AI systems. His recent research explores HumT for measuring human-like tone in LLMs, AnthroScore for anthropomorphism detection, and methods for improving low-resource language support through data augmentation and multilingual representation learning. He has also developed open-source tools like string2string for string algorithms. Jurafsky's publications address critical issues in NLP, including grounding gaps in conversational models, causal interpretability in linguistic tasks, and representational biases in multilingual models. His work emphasizes interdisciplinary applications, from educational NLP tools to Sumerian transliteration datasets, while advocating for rigorous statistical power analysis and ethical model evaluation.

Rayan ChikhiView profile

Researcher

• Bioinformatics
• Algorithmics
• Computational Biology
• +7 more

Rayan Chikhi is a Researcher and Structure Manager Responsible at the Institut Pasteur, affiliated with the UMR3569 – Virology unit and the Advanced Molecular Virology department. His work lies at the intersection of bioinformatics, algorithmics, and computational biology, with a strong focus on developing efficient data structures and analytical tools for genome and metagenome analysis. His research interests include: Bioinformatics and algorithm development for biological sequences Pangenomics and variation graph construction k-mer and minimizer-based data structures Genome and metagenome assembly Ancient DNA analysis and decontamination Microbial source tracking Rayan Chikhi’s recent publications demonstrate a consistent trend toward creating scalable, efficient, and open-source computational tools that address bottlenecks in genomic data analysis. His work on unitig matrices (MUSET), quotient filters, and pangenome graphs reflects a focus on reducing computational resource demands while preserving biological accuracy. These tools are widely applicable in metagenomics, ancient DNA studies, and large-scale genomic association studies. Scientific contributions and software developments include: MUSET: for constructing abundance-preserving unitig matrices Backpack Quotient Filter: a dynamic, space-efficient k-mer index decOM: for microbial source tracking in ancient samples aKmerBroom: for decontaminating ancient oral DNA Contributions to K-mer File Format standardization He actively mentors and collaborates with junior researchers, including PhD students Francesco Andreace and Timothé Rouzé, and postdoctoral fellows such as Camila Duitama González and Kristen Curry. He is involved in major institutional initiatives such as the INCEPTION Convergence Institute and the Artificial Intelligence at the Pasteur Institute cross-functional project, highlighting his role in interdisciplinary research. His work is supported by collaborative grants and contributes to advancing open science in computational genomics. Rayan Chikhi is a key member of the bioinformatics team at the Institut Pasteur, contributing to both methodological innovation and practical applications in virology and microbial genomics. His lab and team focus on building robust, open-source software tools that empower large-scale biological discovery.