جست‌وجوی استاد و پژوهشگر

Sriram Subramaniamمشاهده پروفایل

استاد

• cryo-electron microscopy
• structural biology
• drug-design
• +۴ مورد دیگر

Sriram Subramaniam is a Professor in the Department of Biochemistry and Molecular Biology at the University of British Columbia (UBC) and holds the Gobind Khorana Canada Excellence Research Chair in Precision Cancer Drug Design. His research leverages cryo-electron microscopy (cryo-EM) to advance structural biology and drug design, focusing on protein dynamics and therapeutic target identification. Education: PhD in Physical Chemistry (1987) from Stanford University; MSc in Chemistry (1981) from Indian Institute of Technology, Kanpur. Subramaniam's interdisciplinary work combines cryo-EM with computational tools and molecular biology to study protein structures at atomic resolution. His lab has pioneered cryo-EM applications in precision medicine, including mapping small molecule drugs on patient-specific cancer mutants. Recent publications (2024-2022) highlight his contributions to understanding SARS-CoV-2 immune evasion, structural mechanisms of ATPases, and AI integration in structural biology. His research spans viral entry mechanisms, CRISPR systems, and neurodegenerative disease pathways. Scientific Awards: Gobind Khorana Canada Excellence Research Chair NIH Director’s Award for Scientific Excellence Fellow of the Biophysical Society Breakthrough Prize nomination Based at the Djavad Mowafaghian Center for Brain Health, Subramaniam leads the Program in Cryo-EM Guided Drug Design, contributing to over 177 peer-reviewed publications with a career h-index of 58 and citations exceeding 12,340.

Angel Xuan Changمشاهده پروفایل

دانشیار

• Natural language processing
• 3D scene understanding
• Multimodal grounding of language
• +۵ مورد دیگر

Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.

Xilin Liuمشاهده پروفایل

استادیار

• Integrated Circuits
• Biomedical Engineering
• Brain-Machine Interfaces
• +۵ مورد دیگر

Xilin Liu is an Assistant Professor at the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (University of Toronto) and the Center for Advancing Neurotechnological Innovation to Application (CRANIA) . He obtained his PhD from the University of Pennsylvania and previously worked at Qualcomm Inc. in California. Expertise in integrated circuits and systems for brain-machine interfaces , neuromodulation , and edge AI Published in top venues including Nature Electronics , IEEE JSSC , and ISSCC Recipient of multiple best paper awards and IEEE Senior Member His research spans three main themes: High-speed data converters for wireless/wireline communication IC design for neural interfacing Accelerating machine learning via hardware Recent publications focus on closed-loop neuromodulation , ultra-wideband transceivers , and flexible biomedical sensors . These works integrate analog IC design , edge AI , and real-time neural interfacing across medical rehabilitation , parkinson's monitoring , and memory research . Awards include: IEEE Solid-State Circuits Society Predoctoral Achievement Award (2016) Best Paper Award at BioCAS (2015) ECE Department Teaching Award (2022) Multiple conference best paper finalists His lab collaborates with UHN , EMBS , and global institutions while maintaining strong commitments to equity, diversity, and inclusion (EDI) in research practices.

Lena Simineمشاهده پروفایل

دانشیار

• Theoretical Chemistry
• Chemical Physics
• Computational Materials Science
• +۳ مورد دیگر

Lena Simine is an Associate Professor in the Department of Chemistry at McGill University, affiliated with the Faculty of Science. She holds a B.Sc. (2009) and Ph.D. (2015) from the University of Toronto, followed by a postdoctoral fellowship at Rice University (2015–2019). Her laboratory is located in P&P 118A, focusing on developing computational approaches for modeling molecular phenomena in theoretical chemistry and chemical physics. Her research interests center on computational materials design, quantum dynamics, and the application of machine learning to chemistry. Specific areas include simulating amorphous materials, aptamer design, and quantum systems modeling. She teaches CHEM 365 (Statistical Thermodynamics) and CHEM 593 (Statistical Mechanics and Machine Learning for Chemistry). Her work explores interdisciplinary frontiers, such as path-integral simulations, GFlowNets for molecular design, and the physical principles underlying deep learning in materials science. Recent studies highlight innovations like DeltaGzip for binding affinity prediction and the MAP protocol for 3D disordered matter simulations. Her lab’s contributions span computational methods, material innovation, and quantum phenomena, with a focus on advancing both theoretical frameworks and practical applications in chemistry and materials science.

Andrew Rau-Chaplinمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Algorithms
• Data Warehousing
• +۱۴ مورد دیگر

Andrew Rau-Chaplin is a Professor and Dean of the Faculty of Computer Science at Dalhousie University, where he leads the Risk Analytics Lab and contributes significantly to research in high performance computing, parallel algorithms, and risk analytics. He is affiliated with the Institute for Big Data Analytics and has a strong academic and administrative presence. Education: Postdoc - DIMCS (Princeton, Rutgers, Bell Labs) PhD - Carleton University (1993) MCS - Carleton University (1990) BCS - York University (1986) His research focuses on applying parallel and high performance computing to data-intensive domains such as data warehousing, OLAP, catastrophe modeling, and risk analytics. He emphasizes both algorithmic design and practical system implementation, with a strong grounding in experimental evaluation. His work spans theoretical studies and real-world applications in finance, bioinformatics, and geospatial systems. The 15 most recent publications reflect a consistent focus on parallel data processing, OLAP optimization, indexing techniques (e.g., Hilbert curves), and risk modeling. Key themes include scalable data cube computation, view selection, adaptive coding, and spatial analytics, demonstrating expertise in both algorithmic innovation and systems-level performance. He has served on numerous scientific committees and grant panels, including NSERC and Compute Canada, and has been a journal editor for JPDC and DMTCS. Dr. Rau-Chaplin has supervised a wide range of graduate students in areas including risk analytics, GPU computing, text analytics, and parallel algorithms. His lab has received funding for postdoctoral, graduate, and undergraduate research positions. He teaches courses such as Parallel Computing, Software Engineering, Data Structures, and Risk Analytics, and has developed software tools like LaHave, Clustal XP, and Digital Coliseum. His lab, the Risk Analytics Lab, focuses on integrating analytics, risk management, and HPC for challenges in catastrophe modeling and financial risk. The lab leverages technologies such as stochastic simulation, optimization, and spatial OLAP.

• Bioinformatics
• Genomics
• Gene Regulation
• +۴ مورد دیگر

Wyeth W. Wasserman is a Professor in the Department of Medical Genetics at the University of British Columbia's Faculty of Medicine, concurrently serving as Senior Scientist at the Centre for Molecular Medicine and Therapeutics and Investigator at the BC Children's Hospital Research Institute in Vancouver. His research pioneers computational methods for genome sequence analysis, specializing in cis-regulatory elements controlling gene transcription and their role in tissue development and rare pediatric disorders. The Wasserman Lab develops widely adopted bioinformatics software and databases, leveraging deep learning to decipher cellular diversity from identical DNA sequences, with direct applications in health informatics for improving genetic disease diagnosis. Actively engaged in graduate supervision across UBC's Bioinformatics, Genome Science and Technology, and Medical Genetics programs, Dr. Wasserman fosters interdisciplinary collaborations through the Wasserman Lab, which partners with diverse research teams at the computational-life sciences interface to advance genomic medicine.

Aurélie Labbeمشاهده پروفایل

استاد

• Data Science
• Statistical Learning
• Machine Learning
• +۷ مورد دیگر

Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.

Elizabeth A. Edwardsمشاهده پروفایل

استاد

• Bioremediation
• Environmental Biotechnology
• Anaerobic Microbial Processes
• +۳ مورد دیگر

Elizabeth A. Edwards is a University Professor at the University of Toronto in the Department of Chemical Engineering and Applied Chemistry , with cross-appointments to the Department of Cell & Systems Biology . As Director of BioZone and Canada Research Chair in Anaerobic Biotechnology, she leads groundbreaking research in microbial bioremediation of groundwater pollutants and industrial wastewater treatment through anaerobic digestion. Ph.D. in Environmental Engineering (Stanford, 1993) M.Eng. and B.Eng. in Chemical Engineering (McGill, 1985/1983) Licensed Professional Engineer (P.Eng.) Her research focuses on anaerobic microbial communities that degrade pollutants like chlorinated solvents and aromatic hydrocarbons. Using advanced molecular biology, genomics, proteomics , and biochemical pathway analysis , her lab investigates functional interactions in microbial consortia and translates discoveries to commercial applications such as the KB-1® bioproduct . Recent studies examine enzyme activity, lignocellulose degradation, and granulation dynamics in anaerobic reactors. Scientific Recognition: 2020 Killam Prize in Engineering 2016 Canada Research Chair (Tier 1) 2014 Royal Society of Canada Fellow 2011 Canadian Academy of Engineering Fellow 2011 AAAS Fellow 2008-2010 Killam Research Fellowship She supervises extensive graduate student projects spanning enzyme discovery, bioremediation optimization, and microbial consortium analysis. Her lab collaborates with GeoSyntec Consultants and SiREM Labs for field implementation of bioremediation strategies.

• Protein Foods
• Human Nutrition
• Cardiometabolic Health
• +۳ مورد دیگر

Sergio Burgos, PhD is an Associate Professor in the Department of Animal Science at McGill University's Faculty of Agricultural and Environmental Sciences, and a Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC). He leads an interdisciplinary research program integrating animal nutrition, human health, and environmental sustainability. Pan-American School of Agriculture, Zamorano, Honduras (Agrónomo) University of Florida (BSc Animal Science) University of California, Davis (MSc Animal Science) University of Guelph (PhD Animal Science, 2009) Research Focus examines protein foods' roles in: Human Nutrition & Cardiometabolic Health Dairy Production Systems & Rumen Microbiome Milk Metabolomics & Bioactive Compounds Climate Impact Reduction through Dietary Transitions Publication Trends demonstrate: 2024-2025: Prioritization of plant-animal protein substitutions for climate & health 2020-2023: Mechanistic studies on amino acid regulation of mammary cell function 2016-2020: Foundational work on insulin resistance and dairy nutrition Awards Richard and Edith Strauss Postdoctoral Fellowship (2010-12) Professional Affiliations Regroupement OptiLAIT (2015-present) American Society for Nutrition (2015-present) Canadian Diabetes Association (2012-present) Teaches core courses in animal nutrition, protein metabolism, and computational ration balancing.

Yangjun Chenمشاهده پروفایل

استاد

• Computer Science
• Database Systems
• Graph Databases
• +۶ مورد دیگر

Dr. Yangjun Chen is a Full Professor in the Department of Applied Computer Science at the University of Winnipeg, Canada. He holds a Ph.D. from the University of Kaiserslautern, Germany (1995). His research focuses on database systems, graph algorithms, computational complexity, and theoretical computer science. Key areas include Federated Databases, Deductive Databases, DNA Databases, and the P vs NP problem. Education: Ph.D. in Computer Science, University of Kaiserslautern, Germany (1995). Research interests span graph query processing, algorithm design, big data optimization, and NP-completeness. Recent work includes polynomial-time solutions for 2-MAXSAT and advancements in string matching algorithms for DNA databases. Publications highlight contributions to graph indexing, efficient reachability queries, and algorithmic efficiency in databases. His work often bridges theoretical foundations with practical database applications. Awards: Excellent Merit Award (2022-2023, 2021-2022) - University of Winnipeg Best Article, ACTA Scientific Computer Sciences (2022) Multiple Best Paper Awards at conferences like DBKDA 2016 and CyberC Summit Teaching includes Advanced Databases, Distributed Database Systems, and Algorithms courses at both undergraduate and graduate levels. Active in supervising research in database systems and theoretical computer science. Laboratory and team focus on database innovation, including projects on graph databases and efficient query processing techniques.

Guillaume Bourqueمشاهده پروفایل

استاد

• Bioinformatics
• Computational Genomics
• Comparative Genomics
• +۵ مورد دیگر

Guillaume Bourque is a Professor in the Department of Human Genetics at McGill University's Faculty of Medicine. He serves as an Investigator at the Victor Phillip Dahdaleh Institute of Genomic Medicine and as the Scientific Director of the Canadian Centre for Computational Genomics (C3G). His laboratory is based at 740 Dr Penfield Ave, Room 6103, Montréal, Québec, Canada, H3A 1A4, where he leads research in computational genomics and bioinformatics. Professor Bourque's research focuses on understanding mammalian genomes using comparative genomic and epigenomic analyses. His lab investigates the evolution of regulatory sequences , the role of transposable elements in gene regulation , and the impact of genome rearrangements in evolution and cancer . His team develops computational methods and resources for the functional annotation of genomes with special emphasis on sequencing-based assays including ChIP-seq, RNA-Seq, exome- and whole-genome sequencing, and single-cell analysis. The lab's work involves examining billions of DNA base pairs to interpret how variation impacts basic biology and disease. Recent publications (2024-2025) demonstrate Bourque's leadership in pangenome graph construction , transposable element analysis , epigenomic profiling , and cancer genomics . His work spans multiple disciplines from basic genome evolution to clinical applications in cancer and infectious disease. Notable projects include the development of tools like DeepPolisher for genome assembly polishing and contributions to understanding the genomic basis of long COVID. Bourque's laboratory is actively recruiting postdocs and graduate students with backgrounds in programming or statistics. The lab emphasizes quantitative approaches to biology, requiring applicants to have experience in quantitative biology as a plus. His collaborative work extends across multiple institutions and international consortia, reflecting the interdisciplinary nature of modern genomic research.

• Invertebrate Zoology
• Evolutionary Ecology
• Molecular Ecology
• +۳ مورد دیگر

Timothy Rawlings is an Associate Professor in the Department of Biology at Cape Breton University. He holds a BSc (Honours) and MSc in Zoology from the University of British Columbia and a PhD from the University of Alberta. His research focuses on the evolutionary ecology and molecular ecology of freshwater and marine invertebrates, particularly examining factors influencing their evolution in response to environmental and anthropogenic pressures. Dr. Rawlings' expertise spans invertebrate zoology, marine ecology, and molecular analysis. His work often integrates morphological and genetic approaches, as seen in studies of vermetid snails, hydroids, and invasive molluscs. He has conducted research at the Bamfield Marine Sciences Centre and contributed to biodiversity assessments in Cape Breton and Pacific coral reefs. His publications explore topics such as phylogenetic relationships, species delimitation, and ecological adaptations of marine organisms. Recent work includes investigations into novel shell morphologies, invasive species dynamics, and the evolutionary strategies of sessile invertebrates. Dr. Rawlings emphasizes the importance of understanding human impacts on marine ecosystems, including the introduction of non-native species and climate change effects on coastal communities.

Svetlana Yanushkevichمشاهده پروفایل

استاد

• Biometric Technologies
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

Emilia Limمشاهده پروفایل

استادیار

• Bioinformatics
• Cancer
• Aging
• +۵ مورد دیگر

Dr. Emilia Lim is an Assistant Professor in the Department of Biochemistry and Molecular Biology at the University of British Columbia's Faculty of Medicine , with an investigator role at the Edwin S.H. Leong Centre for Healthy Aging . She specializes in computational analysis of environmental exposures, particularly pollution, in age-related diseases like cancer. Education: BSc from University of Alberta (2009) PhD from University of British Columbia (2016) Postdoctoral training at Francis Crick Research Institute, University College London (2023) Her research integrates multi-omic approaches (genomics, transcriptomics, epigenomics) to investigate how environmental pollutants drive disease states in samples like cancer patients. Recent work focuses on lung cancer evolution , immune evasion , and epigenetic aging under pollution exposure, with applications in precision oncology and healthy aging initiatives across Canada. Publications highlight her expertise in cancer genomics , environmental toxicology , and computational biology , with key contributions to understanding air pollution-induced carcinogenesis (2023) and subclonal tumor dynamics (2020, 2023). Her lab develops tools like SPRINTER for single-cell cancer evolution analysis and explores haplotype-specific copy number in tumor heterogeneity ( Refphase , 2023).

• Physical Organic Chemistry
• Electrochemistry
• Biosensors
• +۳ مورد دیگر

M'hamed Chahma serves as Full Professor in the Department of Chemistry and Biochemistry at Laurentian University's School of Natural Sciences, Sudbury, Canada. Appointed Associate Professor in 2006 and promoted to Full Professor in 2013, he maintains an active research laboratory in F-522 of the Science Building with ongoing projects through 2025. His educational background includes: Bachelor in Chemistry from University Cadi Ayyad/Ibn-Zohr (Agadir, Morocco) MSc (DEA) in Electrochemistry from Denis Diderot University (Paris 7, France)/ESPCI PhD in Electrochemistry from Denis Diderot University (Paris 7, France)/ESPCI Dr. Chahma's research bridges physical organic chemistry and surface science, specializing in chiral conducting surfaces for biomolecule detection. His group develops electrode interfaces using amino acid-functionalized oligo/polythiophenes via electropolymerization, creating stable chiral electrodes capable of enantioselective recognition through hydrogen bonding interactions. Current investigations focus on radical-polymer hybrids combining nitroxide/verdazyl radicals with conducting polymers for energy storage applications where radical incorporation modulates electronic and optical properties. Analysis of his 2010-2025 publications reveals consistent innovation in chiral electroanalysis and radical-functionalized materials, with significant contributions to electrochemical biosensors (particularly for amino acids and oligonucleotides) and fundamental studies of electron transfer in modified interfaces. Key journals include New Journal of Chemistry, Journal of Organic Chemistry, and Chemical Reviews. His scientific recognition includes: Undergraduate scholarship Graduate scholarship Dr. Chahma teaches undergraduate and graduate courses while mentoring student researchers, and previously served four years on NSERC-RTI application evaluation committees. His laboratory employs cyclic voltammetry, ATR-FTIR, and electrochemical synthesis to characterize chiral electrode interfaces and radical-polymer systems, with ongoing work focused on optimizing capacitive response for biomolecular recognition and exploring verdazyl radical applications in energy storage devices.