String Algorithms Professors and Supervisors in Canada | ApplySharif

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استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱۶-۳۰ از ۴۱ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

David Becerraمشاهده پروفایل

مدرس ارشد

McGill UniversityCanada

David Becerra is a Senior Lecturer in the School of Computer Science at McGill University, where he joined in 2020. His expertise spans computational biology, algorithms, and programming competitions, with a focus on interdisciplinary applications in life sciences. Dr. Becerra earned his PhD in Computer Science with emphasis in Bioinformatics from McGill University in 2017 under Professor Jérôme Waldispühl. He completed a postdoctoral fellowship at the Terrence Donnelly Centre for Cellular & Biomolecular Research (2018-2020) supervised by Professor Philip Kim. His undergraduate and master's degrees are from Universidad Nacional de Colombia, where he worked with Professor Luis Niño. His research integrates machine learning, bio-inspired computing, and optimization to solve molecular biology problems at sequential and structural levels. Key areas include protein structure prediction, molecular docking, sequence alignment, and algorithm development for computational biology. He applies these techniques to model complex life science phenomena through computational simulation and data mining. Analysis of his 15 most recent publications reveals a strong trajectory in computational structural biology, with increasing use of deep learning (particularly graph neural networks) for protein design and folding. His work consistently bridges theoretical algorithms with practical biological applications, emphasizing multi-objective optimization approaches. Qualified for ICPC World Finals (2020, 2021) Dr. Becerra actively mentors students through undergraduate research projects (COMP396/400/401), course instruction (COMP204/251), and programming competition teams. He fosters an inclusive environment where students develop algorithmic confidence and interpersonal skills through collaborative problem-solving. He leads McGill's competitive programming initiative, which has created a thriving community preparing students for international competitions like the ICPC. This program emphasizes both technical excellence and team cohesion, resulting in consecutive world final qualifications.

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James Grierمشاهده پروفایل

استاد

Western UniversityCanada

James Grier , Professor of Music History at Western University's Don Wright Faculty of Music, is a leading scholar in medieval music and liturgy, textual criticism, and post-WWII popular music. Elected Fellow of the Royal Society of Canada (2016), his research spans Aquitanian chant, Adémar de Chabannes' works, and modern album production. Education : MusBac (Toronto, 1975), BA (Toronto, 1977), MA/PhD in Medieval Studies (Toronto, 1979/1985) Research Interests focus on medieval musical notation, stemmatics of liturgical sources, and the intersection of cultural trends with popular music. His Musical Notation in the West (2021) synthesizes historical and technical analyses. Recent Publications emphasize Aquitanian chant evolution and 20th-century concept albums, revealing patterns in musical literacy and countercultural storytelling. Scientific Awards : Two Margaret Wade Labarge Prizes (2014, 2019) Guggenheim Fellowship (2020-21) Killam Research Fellowship (2009-12) Fellow of the Royal Society of Canada (2016) Grants : Seven SSHRC grants, NEH Fellowship, and ACLS support. Formerly taught at Yale, Waterloo, Mount Allison, and Queen's Universities (cross-appointed to Classics).

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Himanshu Guptaمشاهده پروفایل

پژوهشگر ارشد

University of ReginaCanada

Himanshu Gupta is a PIMS Postdoctoral Fellow at the University of Regina's Department of Mathematics and Statistics under Dr. Shaun Fallat. He holds a Ph.D. from the University of Delaware (2023) and an M.S. from the Indian Institute of Science (2018). His research focuses on Algebraic Graph Theory, Combinatorics, and Spectral Graph Theory, with emphases on distance-regular graphs, eigenvalues, and combinatorial matrix theory. He has taught numerous undergraduate courses including Calculus, Linear Algebra, and Discrete Mathematics at both the University of Regina and University of Delaware. In 2022, he received the Baxter-Sloyer Graduate Teaching Award for excellence in teaching and course design. His service contributions include journal reviewing (e.g., Linear Algebra and its Applications ), organizing academic sessions (e.g., CMS Special Sessions), and co-chairing seminar series like the Hallenbeck Graduate Student Seminar. His work spans theoretical advancements and computational methods in graph theory, with applications to discrete mathematics and algebraic structures.

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Troy Michael John Vasigaمشاهده پروفایل

مدرس

University of WaterlooCanada

Troy Michael John Vasiga is a faculty member at the David R. Cheriton School of Computer Science under the Faculty of Mathematics at the University of Waterloo , where he lectures undergraduate computer science courses and contributes to academic outreach. His research interests span Computer Science Education , Algorithms , Discrete Mathematics , and Theoretical Computer Science , with a focus on programming pedagogy, competitive programming, and combinatorial structures. Education : PhD in Computer Science (2008, University of Waterloo) with a thesis on error detection in number-theoretic algorithms. His publications highlight expertise in algorithm design , educational technology (e.g., CS Circles), and combinatorics (e.g., Thue-Morse sequence analysis). He has participated in events like IBM CASCON and ISSEP, presenting on computing education and algorithmic challenges. Previously serving as an undergraduate advisor, Vasiga now focuses on admissions and outreach, maintaining an active role in curriculum development and programming competition mentorship. Scientific Awards : No specific awards mentioned in the provided text. Labs & Teams : Involved with the Canadian Computing Competition and Waterloo’s computer science outreach initiatives, including organizing contest archives and mentoring students.

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Pierre Michaudمشاهده پروفایل

دانشیار

University of MontrealCanada

Pierre Michaud is an Associate Professor of Mixed Composition at the Faculty of Music of the University of Montreal, where he has been teaching since 2012. He serves as co-director of the Laboratory for Technology Integration in Music (LITEM) and is an active member of the Artistic Committee of the Société de musique contemporaine du Québec, the Canadian Music Centre, and the Canadian League of Composers. His professional activities are supported by major research agencies including the Quebec Research Funds (FQRSC), Canada Council for the Arts, and Quebec Arts Council. Michaud's research focuses on comprovisation, composer-performer collaboration, interdisciplinarity, interactive spaces, and new forms of musical representation. His work bridges traditional composition with electronic and digital technologies, exploring the relationship between acoustic instruments and electronic media. He has developed innovative approaches to mixed composition that incorporate algorithmic processes, interactive systems, and cross-cultural musical elements, particularly from Balinese gamelan traditions. His compositional output shows a clear trajectory toward increasingly sophisticated integration of technology with traditional musical forms, with recent works exploring non-linear narrative structures, hauntological concepts, and the relationship between Cuban popular music and electronic elements. The interdisciplinary nature of his work is reflected in collaborations across artistic disciplines and technological domains. Finalist, CBC Radio Young Composers Competition (2001) Opus Prize from Quebec Music Council for 'Le rêve de Grégoire' (2014) Michaud has supervised numerous graduate students through master's and doctoral programs, with thesis topics spanning mixed composition techniques, interactive music systems, cross-cultural musical integration, and theoretical approaches to contemporary composition. His research projects include the interdisciplinary Observatoire interdisciplinaire de création et de recherche en musique (OICRM) and initiatives focused on mixed composition pedagogy and technological sustainability in mixed music creation. He leads the Laboratory for Technology Integration in Music (LITEM), which serves as a hub for interdisciplinary research and creation at the intersection of music, technology, and other artistic disciplines.

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Neerja Mhaskarمشاهده پروفایل

استادیار

McMaster UniversityCanada

Neerja Mhaskar is an Assistant Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. She is currently accepting graduate students and maintains an active research group. Her office is located in ITB 220, and she can be reached at pophlin@mcmaster.ca. Her educational background includes a PhD in Computer Science from McMaster University (2016), a Master's degree in Engineering Science from Louisiana State University, and a Bachelor's degree in Mechanical Engineering from Jawaharlal Nehru Technological University Hyderabad, India. Dr. Mhaskar's research focuses on the design and analysis of efficient algorithms and data structures, with particular emphasis on string processing for applications in bioinformatics, network security, and data compression. She develops algorithms for pattern matching, secure network design, and big data analysis. Her work bridges theoretical computer science with practical applications in privacy and security, as well as computational biology. Her recent publications demonstrate a strong trend in string algorithms, especially in covering problems and indeterminate strings, with growing applications in network security and bioinformatics. Over the past decade, she has contributed to foundational stringology and expanded into network segmentation and protein sequence analysis. Dr. Mhaskar has not been mentioned to have received any scientific awards in the provided text. Dr. Mhaskar has successfully supervised multiple graduate students. Her current advisees include PhD candidates Shafigh Ashrafi, Hamed Hasibi, Nivetha Raj Pappuraj, and Amin Kashef, as well as Master's students Samkith Kishore Kumar Jain and Shutong Wu. Among her graduated students are Tieyun Zhang, Meng Wang, Chenge Liu, Zehong Wang, Hossein Dehghani, and Holly Koponen, who have gone on to work at companies such as Siemens Canada, Robinhood, and Robotic Assistance Devices, or pursued further studies.

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Daniel Lemireمشاهده پروفایل

استاد

University of QuebecCanada

Daniel Lemire is a full professor of computer science at the Université du Québec (TELUQ), recognized as one of the top 2% most cited scientists globally according to Stanford University's 2024 rankings. He ranks among the 0.0006% most followed programmers on GitHub, with his work adopted by major technology companies including Google, Facebook, Intel, and Shopify. Education: Ph.D. in Engineering Mathematics (University of Montreal and Polytechnique Montréal), Master's in Mathematics (University of Toronto), Bachelor's in Mathematics with High Distinction (University of Toronto) Current Role: Editor of Software: Practice and Experience journal since 2020 Professional Recognition: Co-chair of NSERC's Computer Science Discovery Grants Committee (2020-2021) Professor Lemire's research focuses on software performance optimization and data indexing techniques. His work bridges theoretical computer science with practical applications, particularly in areas where performance bottlenecks exist in real-world systems. He specializes in leveraging hardware capabilities through vectorization (SIMD instructions) to dramatically improve processing speeds for fundamental operations that have remained inefficient for decades. His approach combines deep theoretical understanding with practical implementation, resulting in algorithms that are both mathematically sound and immediately applicable in production systems. Lemire's research portfolio demonstrates a consistent pattern of identifying critical performance bottlenecks in widely used software operations and developing innovative solutions that achieve order-of-magnitude improvements. His work spans multiple domains including JSON parsing, Unicode string processing, URL parsing, base64 encoding, and bitmap indexing. A common thread through his publications is the application of hardware-specific optimizations, particularly SIMD instructions, to accelerate operations that were previously considered near-optimal. His research has evolved from foundational algorithm development to influencing major software ecosystems, with his libraries becoming integral components of industry-standard tools. Among the 2% most cited scientists globally (Stanford University, 2024) Université du Québec's Prix d'excellence 2020 for research success Most read articles at Software: Practice and Experience (2024, 2025) Best voted talk at QCon San Francisco 2019 Editor of Software: Practice and Experience journal since 2020 Numerous citations in patents held by Microsoft, LinkedIn, Oracle, and Fujitsu Professor Lemire maintains an active research group that has graduated numerous PhD students, many of whom now hold key positions at leading technology companies. He offers automatic scholarships for all students making progress on M.Sc. theses and Ph.D. programs in his lab, with tuition waivers for international Ph.D. students. His laboratory is equipped with a diverse server farm featuring multiple processor architectures (Intel Xeon, Core i7, Xeon Phi, POWER9, ARMv8) specifically designed for software performance experiments. The lab also explores virtual reality applications in data science. Lemire actively recruits students who are passionate about high-performance programming and open-source development, with special programs for Canadian undergraduate and graduate students through NSERC funding mechanisms.

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Lucian Ilieمشاهده پروفایل

استاد

Western UniversityCanada

Lucian Ilie is a Professor in the Department of Computer Science at Western University. He obtained his PhD in 1999 from the University of Turku, Finland, and has been active in academia since 2000. His research focuses on bioinformatics, machine learning, and string algorithms, particularly in the areas of protein interaction prediction, genome assembly, and algorithm design. Education: PhD in Applied Mathematics (1999), University of Turku, Finland. Research Interests: Machine Learning (Deep Learning, Explainable AI), Bioinformatics (Proteomics, Genomics), String Algorithms, and String Combinatorics. His work includes developing tools for sequence alignment, genome assembly, and protein interaction site prediction. Labs & Teams: Leads a research lab with a focus on computational biology and algorithm development. Collaborates extensively with interdisciplinary teams in genomics and proteomics.

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Thomas Wolfمشاهده پروفایل

استاد

Brock UniversityCanada

Thomas Wolf is a Professor of Mathematics at Brock University, affiliated with the Department of Mathematics and Statistics. His primary research focuses on differential equations, integrability, computer algebra systems, and applications to General Relativity and Combinatorial Game Theory. He has developed influential software tools like CRACK and ConLaw for solving overdetermined systems of equations and analyzing conservation laws. Research Interests: Integrable systems, symbolic computation, mathematical physics, and game theory. Notable Contributions: Algorithms for simplifying PDEs, contributions to the classification of integrable systems, and work on computer Go (Weiqi) strategies. Outreach: Founded the Brock Brain Benders Club and Caribou Math Contests, promoting mathematics education. His publications span theoretical advancements in differential equations and practical applications in physics and combinatorial games. Recent work includes exploring hyperdeterminants in discrete systems and supersymmetric hierarchies. Advising & Grants: While no formal advisees are listed, his collaborations include international researchers in mathematics and physics. His work has been supported by computational tools and interdisciplinary projects. Labs/Teams: Active in developing computational frameworks for mathematical research and organizing educational initiatives like the Caribou Contests.

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Matthew Johnsonمشاهده پروفایل

دانشیار

York UniversityCanada

Matthew Johnson is an Associate Professor in the Department of Physics and Astronomy at York University and an Associate Faculty member at the Perimeter Institute for Theoretical Physics. His research focuses on theoretical cosmology, including cosmic inflation, eternal inflation, dark energy, and confronting fundamental theories with observations of the Cosmic Microwave Background (CMB). He explores topics such as topological defects, string theory, and gravitation, with a particular interest in designing data analysis algorithms to interpret cosmological datasets. Johnson's affiliations include York University and the Perimeter Institute. His work spans computational and theoretical approaches to understanding the universe’s origins and evolution. He collaborates widely, contributing to projects like the Atacama Cosmology Telescope and the CMB-HD Collaboration. His research interests emphasize cosmological models, phase transitions in the early universe, and the implications of string theory’s extra dimensions for cosmology. He investigates the Multiverse concept and the dynamics of bubble universes in eternal inflation scenarios. Johnson actively supervises graduate students in the York University Physics and Astronomy program and collaborates with institutions globally. His funding sources include NSERC, the Foundational Questions Institute, and the New Frontiers in Astrophysics and Cosmology grant program.

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Rahul Kannanمشاهده پروفایل

استادیار

York UniversityCanada

Rahul Kannan is an Assistant Professor in the Department of Physics and Astronomy at York University, focusing on computational astrophysics. His work centers on modeling galaxy formation and evolution within a cosmological framework, particularly investigating feedback processes, plasma physics, and high-performance computing. His research interests include cosmological structure formation, interstellar medium dynamics, radiation hydrodynamics, and the role of anisotropic thermal conduction in intra-cluster media. He explores the origins of early stars and galaxies, the epoch of reionization, and develops algorithms for next-generation simulations. Rahul's projects, such as THESAN-ZOOM and THESAN, involve large-scale radiation-hydrodynamic simulations to study high-redshift galaxies, star formation efficiencies, and ionization feedback. His work bridges theoretical predictions with observational data from instruments like JWST, advancing understanding of galaxy evolution and reionization processes. He is eligible to supervise graduate students in the Physics and Astronomy program and contributes to the academic community through computational methods development and collaborative research initiatives.

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Luiza Antonieمشاهده پروفایل

دانشیار

University of GuelphCanada

Luiza Antonie is an Associate Professor at the School of Computer Science, University of Guelph. She specializes in data mining methodologies applied to interdisciplinary research, particularly historical demography and economic analysis. Her work bridges computer science with fields like history and economics through projects such as longitudinal census data analysis and studies on gender wage gaps. Her research focuses on record linkage techniques, classification systems, and data integration challenges. Notable collaborations include analyzing 19th-century Canadian economic mobility patterns and creating automated coding systems for historical occupations. She also explores modern workforce trends, including future technical skills demands and accessibility in work-integrated learning. Antonie’s publications span machine learning applications, historical data analysis, and algorithm optimization. Her recent work emphasizes deep learning approaches for product matching and bias evaluation in data linkage processes. She maintains an active Google Scholar page and contributes to international data linkage research initiatives.

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Daochen Wangمشاهده پروفایل

استادیار

University of British ColumbiaCanada

Daochen Wang is an Assistant Professor of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on quantum computation, quantum algorithms, and quantum cryptography, with an emphasis on understanding quantum speed-ups and their applications. Education: Ph.D. in Computer Science from the University of Maryland (advisors: Andrew Childs and Carl Miller), BA and MMath in Mathematics from the University of Cambridge. Research interests include quantum algorithm design, post-quantum cryptography, and the theoretical foundations of quantum computing. His work spans quantum graph algorithms, lattice-based security, and variational quantum eigensolvers. Teaching includes undergraduate (CPSC 436Q) and graduate (CPSC 536W) courses on quantum computation. He advises PhD student Xingyu Zhou and undergrad Rain Zimin Yang. Key research contributions include quantum algorithms for reinforcement learning, cryptographic security analysis of post-quantum schemes (e.g., CRYSTALS-Dilithium), and foundational studies on quantum speedups via symmetry analysis.

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Alexander Maloneyمشاهده پروفایل

استاد

McGill UniversityCanada

Alexander Maloney is the James McGill Professor of Physics at McGill University, currently on leave for the 2024-25 academic year. His primary affiliation is with the Department of Physics within the Faculty of Science. He specializes in theoretical physics, with research interests spanning black hole thermodynamics, holography, quantum information theory, conformal field theory, and quantum cosmology. His work often explores the interplay between quantum gravity and strongly coupled systems through gauge/gravity duality frameworks. Maloney has taught advanced courses such as Physics 551 (Quantum Mechanics), Physics 673 (Quantum Field Theory 2), and Physics 260 (Modern Physics & Relativity). He holds office hours by appointment and maintains active participation in the McGill High Energy Physics Theory community. His research has been published in leading journals, with recent work addressing topics like wormhole dynamics, de Sitter quantum gravity, and holographic cosmology. Though no explicit grants or awards are listed in the provided materials, his extensive publication record and endowed professorship reflect significant contributions to theoretical physics. Maloney’s research integrates advanced mathematical techniques, including modular forms and conformal bootstrap methods, to probe foundational questions in quantum gravity and cosmology.

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Oscar Hernándezمشاهده پروفایل

استاد مدعو

McGill UniversityCanada

Oscar Hernández is an Adjunct Professor in the Department of Physics at McGill University and a Faculty Member at Marianopolis College. He holds affiliations with the Trottier Space Institute (TSI), Centre de Recherche en Astrophysique du Québec (CRAQ), and is a SALTISE Innovator. His research focuses on 21 cm cosmology , cosmic strings , neutrino physics , and machine learning applications in astrophysics . He develops algorithms like CosmicStringNN for detecting cosmic strings in CMB maps and contributes to software projects such as CMBstraightStrings. Teaching emphasizes active learning techniques, flipped classrooms, and visual pedagogy in physics and history of science courses. He engages Cégep and undergraduate students in theoretical physics research. His work spans collaborations with institutions like CRAQ and TSI, with publications in astrophysics and machine learning journals. Research highlights include studies on cosmic string signatures in 21 cm radiation, neutrino mass effects on cosmological structure, and neural network-based detection methodologies. His software contributions enable simulations and analyses critical to modern cosmological research.

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