Find Academic Supervisors

• Algorithms on strings
• Small-space algorithms
• Data structures
• +4 more

Tatiana Starikovskaya is an Assistant Professor in the Talgo team at the Computer Science Department of École normale supérieure (ENS Paris), France. Her work focuses on the design and analysis of algorithms for string processing, space-efficient computation, and complexity theory, with applications in bioinformatics and data security. Education : PhD in Mathematics (2013) and MSc in Mathematics (2009) from Lomonosov Moscow State University; MSc in Data Science (2009) from Moscow Institute of Physics and Technology and Yandex School of Data Analysis. Her research explores the intersection of algorithm theory and practical applications, particularly in streaming models , probabilistic text indexing , and clustering of sequence data . Key themes include handling wildcards , k-mismatch problems , and edit distance under space constraints. Current projects include PARSe (ANR-20-CE48-0001) , aiming to develop ultra-efficient algorithms for noisy string data, and AlgoriDAM (ANR-19-CE48-0016) , focusing on algorithmic theory for modern data models. She co-supervises PhD students Gabriel Bathie and T. El Ghazi, with collaborations spanning institutions like University of Wrocław and IRISA. She has served on program committees for major conferences (STACS, ESA, ICALP, CPM) and organized the CPM Summer School (2023) and New Horizons of Stringology (2024) workshop at Cirm. Teaching roles include courses at ENS Paris (Algorithmique, Programming for Non-CS Students) and Higher School of Economics (Moscow).

Işıl DilligView profile

Associate Professor

• Programming Languages
• Static Analysis
• Program Verification
• +4 more

Işıl Dillig is an Associate Professor in the Department of Computer Science at the University of Texas at Austin, leading the UToPiA research group. She focuses on programming languages, with emphasis on static analysis, verification, and synthesis to enhance software reliability and security. Education: BS, MS, PhD in Computer Science from Stanford University Her research addresses critical challenges in: Program analysis for security and correctness Automated program synthesis for complex tasks Verification of concurrent and database-driven systems Recent publications span neurosymbolic synthesis, database integration, and smart contract optimization. Trends include hybrid AI-formal methods approaches and domain-specific language design. Scientific recognition includes: Sloan Fellowship NSF CAREER award She actively contributes to academic leadership as committee member, program chair, and keynote speaker in top conferences like PLDI, OOPSLA, and POPL.

• Experimental Psychology
• Cognitive Science
• Psycholinguistics
• +5 more

Adam Parker is a Research Fellow at the University of Oxford . His work focuses on experimental psychology and cognitive science, particularly in eye movement studies during reading and language processing. He earned his PhD at Bournemouth University under the supervision of Tim Slattery and Julie Kirkby. Education: PhD in Psychology from Bournemouth University Parker's research explores reading comprehension , lexical ambiguity , word predictability , and laterality using eye-tracking experiments. His recent publications examine cross-linguistic processing in bilinguals and AI-assisted evaluation of open science practices . He has published extensively in journals like the Journal of Experimental Psychology and Laterality , often collaborating with researchers such as Julie Kirkby, Tim Slattery, and John Rodd. Parker's work also intersects with educational effectiveness and open science initiatives. Notable trends in his publications: Eye movement modeling in reading Lexical ambiguity resolution Language lateralization studies Open science methodologies Bilingual processing differences Perceptual biases in auditory/visual tasks

Rupak ChatterjeeView profile

Assistant Professor

• Quantum Information and Computation
• Quantum Machine Learning
• Quantum Optimization
• +4 more

Rupak Chatterjee is an Industry Assistant Professor in the Department of Applied Physics at New York University's Tandon School of Engineering. His research bridges quantum information/computation and mathematical physics, with a focus on quantum algorithms for machine learning, optimization, operator algebras, and quantum mechanical systems. Education: Postdoctoral Scholar, Physics (James Franck Institute, University of Chicago) Ph.D. & M.S., Physics (Stony Brook University) M.Math., Mathematics (University of Waterloo) B.Sc., Physics (University of Calgary) Chatterjee's research explores quantum systems for machine learning, quantum optimization protocols, and mathematical frameworks like C∗-algebras and supersymmetric quantum mechanics. His work integrates theoretical rigor with applications in quantum computing and complex physical systems. His recent publications (2019–2024) demonstrate a strong emphasis on quantum entanglement, chaos in optomechanical systems, adiabatic quantum optimization, and relativistic quantum mechanics. Several publications also apply quantum methods to finance and diffusion modeling. No awards, student advising, or grant information is documented in the provided text.

• Data Science
• Human-Computer Interaction
• Information Visualization
• +4 more

Dr. Taoxin Peng is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. He is affiliated with the Centre for Algorithms, Visualisation and Evolving Systems where he conducts research in data science and human-computer interaction. His primary research focuses on: Data Science : Developing synthetic data generation tools and data quality frameworks Human-Computer Interaction : Creating innovative input methods like laser-based interaction systems Information Visualization : Designing techniques for online dataset exploration Artificial Intelligence : Foundational work in causal reasoning and knowledge representation Publication analysis shows consistent focus on data systems (65% of works) with recent expansion into HCI applications. Early career publications centered on AI fundamentals, transitioning to data quality research in the 2000s, and more recently to practical applications in synthetic data and interactive systems. Dr. Peng has supervised PhD candidate Andreas Steyven (2013-2018) on evolutionary swarm robotics research. He collaborates with researchers including Prof. Emma Hart and Prof. Ben Paechter through the Centre for Algorithms, Visualisation and Evolving Systems.

• Applied Mathematics
• Optimization
• Computational Mathematics
• +3 more

Yair Censor is a distinguished Professor in the Department of Mathematics at the University of Haifa. His work focuses on applied mathematics, particularly in optimization, projection methods, and computational algorithms with applications in medical physics and engineering. He has developed foundational techniques like the superiorization method, which balances feasibility-seeking and objective function reduction in iterative algorithms. Research Interests: Optimization theory, convex feasibility problems, superiorization, medical imaging algorithms, and parallel computing. Collaborations: Extensive work with Gabor T. Herman, Aviv Gibali, Reinhard Schulte, and others in computational methods for radiation therapy and tomography. His recent publications (2023–2025) emphasize feasibility-seeking algorithms, superiorization in floorplanning, and perturbation resilience. While no explicit awards or students are listed in the provided text, his contributions to iterative methods and medical physics remain influential.

Sam SalemView profile

Lecturer

• Electronic Music
• Computer Music
• Generative Models
• +5 more

Sam Salem is a PRiSM Lecturer in Composition at the Royal Northern College of Music (RNCM), specializing in audiovisual works that blend instrumental performance with electronics, video, and computational techniques. His interdisciplinary work spans electronic/computer music, visual art, computer science, and English literature, with a focus on exploring ambiguity and the Uncanny through machine learning and AI. He is a founding member and co-artistic director of Distractfold Ensemble, which received the Kranichstein Music Prize for Interpretation (first British ensemble to do so). His compositions have been performed globally and honored with awards such as the HearSay Prize (2015), Prix Palma Ars Acustica nomination (2015), and Concours Luc Ferrari (2012). Recent works include THIS IS FINE (2021), Midlands (2019), and The Lovers (2017), which was praised in the New York Times for its 'chamber-orchestra pandemonium with accomplished video work.' Current research focuses on machine-learning audio synthesis in collaboration with PRiSM’s Research Software Engineer, Christopher Melen.

• Musicology
• Russian Music
• Bulgarian Musical Traditions
• +3 more

Vlada Bespalova is a researcher affiliated with the National Music Academy, contributing to musicology and performance studies. Her work focuses on harmonic analysis in Russian piano music, Bulgarian folk traditions, and interdisciplinary studies in music education and acoustics. She has published extensively in journals such as MUSIC PERFORMANCE ART and MUSICAL AND RESEARCH ART . Her research interests include the compositional techniques of Modest Mussorgsky, Scriabin’s cosmic symbolism, and the pedagogical practices of Bulgarian and European musical instruments. She has also explored modern methodologies for citation analysis in musicology and the historical development of cello pedagogy in German schools. Bespalova’s articles analyze both classical and contemporary works, emphasizing the intersection of technical innovation and cultural context. Her work highlights the role of Bulgarian folk ensembles, psychoacoustic principles in audio production, and the preservation of Jewish songs through arrangements. She has collaborated on projects examining Aristides Quintilianus’ ancient treatises and modern theatrical-musical synergies. Though no formal awards are noted, her publications reflect a deep engagement with both historical and applied aspects of music research. She contributes to academic discourse through methodological studies, such as innovative training techniques for brass instruments and solfège pedagogy.

Anne CondonView profile

Professor

• Computational Complexity
• Algorithms
• Molecular Programming
• +3 more

Dr. Anne Condon is a Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. Her research focuses on computational complexity, algorithms, and molecular programming, particularly in nucleic acid structure prediction and bioinformatics. She is an ACM Fellow and a Fellow of the Royal Society of Canada. Education: Bachelor's degree in Computer Science from University College Cork, Ireland (1982) Ph.D. in Computer Science from the University of Washington (1987) Research Interests: Dr. Condon's work bridges computer science and molecular biology, addressing challenges in algorithm design for nucleic acid systems, RNA secondary structure prediction, and energy parameter estimation. She explores computational models such as Chemical Reaction Networks (CRNs) and DNA strand displacement systems for molecular programming. Her research emphasizes practical applications in synthetic biology and biotechnology. Publications: Her recent work includes studies on chemical reaction networks for approximate majority consensus, efficient DNA kinetics modeling, and interpretable dimensionality reduction of single-cell transcriptome data. Key contributions span algorithm design, computational biology, and interdisciplinary collaborations. Awards & Affiliations: ACM Fellow Fellow of the Royal Society of Canada Member of the Institute for Computing, Information and Cognitive Systems (ICICS) and the Institute of Applied Mathematics at UBC Supervision & Grants: She actively supervises doctoral and master's students in areas like nucleic acid kinetics, bioinformatics, and molecular programming. Her grants and funding support interdisciplinary research at the intersection of computer science and molecular biology. Labs & Teams: Condon's affiliations with UBC's ICICS and Institute of Applied Mathematics facilitate collaborative research in computational methods for systems biology and molecular computing.

Pieter SchuermansView profile

Professor

• Composition
• Conducting
• Interdisciplinarity in the Arts
• +3 more

Pieter Schuermans is a Professor and Composition Coordinator at LUCA School of Arts campus Lemmens, KU Leuven. He holds a PhD from KU Leuven and is a versatile composer, conductor, and interdisciplinary artist. His roles include Special Guest Lecturer, Director of Artistic Practice, and Coordinator for Comics and Graphic Arts programs in Brussels and Ghent. He founded eaRis company, focusing on interdisciplinarity through performances like Looking Through Eardrums , touring Europe and beyond. As a musician, he has performed with leading ensembles including the Collegium Instrumentale Brugense and Ataneres Ensemble. He chairs Composers Archipelago Flanders (ComAV), promoting Flemish composers through initiatives like Belgian Music Days and the Fingerprints CD series. Education: Masters in Music (Composition, Double Bass, Flute, Symphonic Conducting) from LUCA, PhD in Music Theory (KU Leuven). Research focuses on composition, conducting, and interdisciplinary practices. Notable collaborations include work with Quatuor Danel, Brussels Philharmonic, and Radio Klara. His artistic output spans compositions, performances, and multimedia installations. Recent projects include Approach and Analysis of a Violin Technical Artistic Process (2023–2027) and exhibitions like Echoing Silence (2022). Awards and recognitions are noted but not explicitly listed. Leadership roles in youth orchestras and international conducting engagements highlight his broader impact on music education and performance.

Pablo GalazView profile

Lecturer

• Music Composition
• Sound Art
• Live Electronics
• +3 more

Dr. Pablo Galaz is a Lecturer in Composition and Analysis at the University of Southampton. His work bridges music composition, live electronics, and interdisciplinary collaboration. He actively participates in research projects such as 'Sound and mental health' and 'Machine Learning-Driven Gestural Control for Expressive Sound Synthesis', collaborating with researchers like Dr. Chaitanya Paruchuri and Dr. Richard Polfreman. His creative output includes multimedia installations and instrumental works with live electronics, reflecting interests in interactive performance and technological integration. Research Focus: Sound art, machine learning in music, and interdisciplinary collaboration Notable Projects: Human-like inclinations (AI-driven multimedia installation), Transnodal (cello/electronics) Publications: Focused on creative media artifacts blending acoustic and electronic elements His recent works (2023-2025) emphasize real-time interaction between performers and technology, while earlier pieces (e.g., Roren) explore structural composition in chamber music. No awards are listed but shows active grant-funded research activities.

Gregory ValiantView profile

Associate Professor

• Algorithms
• Machine Learning
• Statistics
• +3 more

Gregory Valiant is an Associate Professor of Computer Science at Stanford University , specializing in Algorithms, Machine Learning, Statistics, and Information Theory. He holds a PhD from UC Berkeley and a BA in Mathematics from Harvard University. His research focuses on designing efficient algorithms for inferring information from limited data, addressing challenges in computation, memory, communication, and data quality. He advises multiple PhD students and collaborates with institutions like Microsoft Research New England. Educations: PhD, Computer Science, UC Berkeley (2012) BA, Mathematics, Harvard University (2006) Research Interests: Gregory’s work centers on the interplay between algorithms and statistical inference, particularly in high-dimensional settings. His lab explores topics like distribution learning, sample amplification, and adversarial robustness. Recent projects include developing algorithms for trace reconstruction, matrix completion, and transformer-based in-context learning. His methodologies often combine theoretical rigor with practical applications in machine learning and data science. Key Article Trends: His publications span theoretical foundations (e.g., sample amplification, convex optimization complexity) and applied machine learning (e.g., transformer capabilities, adversarial testing). Notable work includes contributions to distribution testing, statistical estimation under constraints, and algorithmic lower bounds. Advising: Current advisees include Annie Marsden, Steven Cao, and Chirag Pabbaraju. Former students have pursued roles at institutions like Harvard, Berkeley, and companies like Google, Waymo, and Facebook.

Christopher HampsonView profile

Senior Lecturer

• Modal and Temporal Logics
• Algorithms and Computational Complexity
• Theoretical Computer Science
• +4 more

Dr. Christopher Hampson is a Senior Lecturer in Computer Science (Education) at King’s College London's Department of Informatics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He specializes in theoretical computer science, modal logics, and algorithmic complexity. He holds an MSc in Mathematical Logic from the University of Manchester and a PhD from King’s College London on modal logic topics. His research focuses on Modal and Temporal Logics Computational Complexity Formal Verification Counter Machine Theory Computer Science Education Recent work includes studies on non-repudiation in healthcare AI systems (2024), termination analysis of faulty counter machines (2021), and dialogue systems for handling misunderstandings (2022). He collaborates with the Computing Education Research Centre (CERC) to advance pedagogical tools and curricula in computer science. Awards: none explicitly listed. Supervision: no named advisees found. Active in research groups focused on formal methods and education innovation.

Steffen WinterView profile

Lecturer

• Fractal Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +1 more

Steffen Winter is a Lecturer (PD Dr.) at the Institute of Stochastics, Karlsruhe Institute of Technology (KIT). His research specializes in Fractal Geometry, Geometric Measure Theory, Stochastic Geometry, and Dynamical Systems. He leads the DFG-funded project Scaling of curvature measures and the modified Weyl-Berry conjecture and serves as Principal Investigator for project 12 ( Morphometric Roughness of Nanostructured Surfaces ) within the DFG Priority Programme 2265 (Random Geometric Systems). Winter's work explores the mathematical foundations of fractals, stochastic processes, and geometric measurements. Key themes include Minkowski content, curvature measures, self-similar sets, percolation models, and applications to materials science and geoscience. Recent publications emphasize fractal dimensionality, surface roughness quantification, and stochastic convergence in complex systems. He teaches advanced courses including Stochastic Geometry , Fractal Geometry , and Markov Chains , and mentors students through seminars and proseminars. No awards or research grants besides DFG projects are documented.

• Formal Methods
• Program Analysis
• Automata Theory
• +5 more

Ondřej Lengál is an Associate Professor at the Department of Intelligent Systems, Faculty of Information Technology, Brno University of Technology. His academic career spans over a decade with continuous research funding and publication output, progressing from doctoral work to leading multiple significant research projects. His research interests include: Formal Methods and Verification Automata Theory and Applications Program Analysis and Static Verification Theoretical Foundations of Computer Science Model Checking Techniques Software Engineering with Formal Guarantees Dr. Lengál's publication record demonstrates a consistent research trajectory in automata-based verification techniques. His recent work (2021-2023) has focused on Büchi automata complementation, with significant contributions to rank-based algorithms and tighter complexity bounds. He has also made substantial contributions to forest automata for shape analysis and heap verification, as well as symbolic techniques for program analysis. His research bridges theoretical computer science with practical applications in software verification. Dr. Lengál leads and participates in multiple research projects funded by GACR (Czech Science Foundation), EU Horizon programs, and internal university grants. His current projects include QUAK: Quantum Program Analysis using Automata Toolkit (2025-2027), Verification and Analysis for Safety and Security of Applications in Life (2024-2027), and Reliable, Secure, and Intelligent Computer Systems (2023-2026). As an educator, Dr. Lengál teaches several advanced courses including Formal Program Analysis, Advanced Mathematics, Operating Systems, and Theoretical Computer Science, demonstrating his ability to connect theoretical foundations with practical applications.