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Qianwen WangView profile

Assistant Professor

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +5 more

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

• Operations Management
• Supply Chain Management
• Sharing Economy
• +6 more

Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.

Alexandre Bouchard-CôtéView profile

Professor

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +4 more

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.

David B. LindellView profile

Assistant Professor

• Computational Imaging
• Computer Vision
• Non-Line-of-Sight Imaging
• +5 more

David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.

Mark CrowleyView profile

Associate Professor

• Reinforcement Learning
• Deep Learning
• Manifold Learning
• +12 more

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.

Khuzaima DaudjeeView profile

Professor

• Distributed Systems
• Database Management
• Cloud Computing
• +3 more

Khuzaima Daudjee is a Professor and David R. Cheriton Faculty Fellow in the Cheriton School of Computer Science at the University of Waterloo. His research focuses on systems-oriented problems at the intersection of systems and data management, particularly building large-scale systems, storage infrastructure in the cloud, and modern hardware applications. He leads projects in distributed database systems, elastic scaling, and resource optimization. His recent work includes Caerus (geo-replicated transactions), Tiresias (predictive storage), and MorphoSys (automatic physical design metamorphosis). Daudjee has chaired major conferences including ICDE 2026 and serves on editorial boards for VLDB, SIGMOD, and IEEE TKDE journals. His awards include ACM Distinguished Scientist and multiple best paper awards. Educational initiatives include developing distributed systems teaching materials and supervising graduate students across database and distributed systems domains. His industry collaborations involve cloud infrastructure optimization and scalable data processing frameworks.

Mark LewisView profile

Professor

• Mathematical Biology
• Spatial Ecology
• Animal Movement Modelling
• +7 more

Mark Lewis is the Kennedy Chair in Mathematical Biology at the University of Victoria, holding joint appointments in the Departments of Mathematics and Statistics and Biology. His research focuses on spatial ecology and mathematical modeling, addressing ecological challenges such as animal movement, invasive species, and disease dynamics. Lewis earned his D.Phil. in Mathematical Biology from the University of Oxford and has been elected a Fellow of the Royal Society UK. His work integrates mathematical analysis, field studies, and interdisciplinary approaches to solve ecological problems. Current projects include modeling polar bear populations, cyanobacteria dynamics, and the impact of climate change on wildlife. Lewis supervises students across both UVic and his former University of Alberta lab. Education: D.Phil. in Mathematics (Mathematical Biology), University of Oxford Awards: Royal Society Fellowship, CRM-Fields-PIMS Prize, and Okubo Prize Key Research Areas: Animal movement modeling, aquatic ecology, wildlife disease, and invasive species management Publications highlight his contributions to understanding disease spread, parasite dynamics, and ecological responses to environmental changes. Lewis collaborates widely, applying mathematical tools to real-world conservation and health challenges.

• Drug Delivery
• Nanomedicine
• Biopharmaceutics
• +7 more

Dr. Shyh-Dar Li is a Professor and Tong Louie Chair in Pharmaceutical Sciences at the University of British Columbia's Faculty of Pharmaceutical Sciences, where he also serves as Chair of Nanomedicine and Chemical Biology. With a BSc in pharmacy from National Taiwan University (1998) and PhD in pharmaceutical sciences from UNC Chapel Hill (2008), followed by postdoctoral training at UC San Diego's Moores Cancer Center (2009), Dr. Li has established himself as a leading researcher in advanced drug delivery systems. His research focuses on developing innovative nanomedicine platforms for targeted delivery of biological therapeutics including peptides, proteins, antibodies, and nucleic acids. Dr. Li's laboratory has pioneered several novel drug delivery approaches, particularly in lipid-based nanoparticles, phospholipid-free vesicles, and polymer systems for cancer immunotherapy, pain management, and protein delivery. His work bridges fundamental nanotechnology with translational applications for difficult-to-treat diseases. Analysis of his recent publications reveals a strong emphasis on tumor microenvironment modulation, endosomal escape mechanisms for nucleic acid delivery, and non-invasive delivery routes for protein therapeutics. His research demonstrates increasing sophistication in nanocarrier engineering, with recent work incorporating machine learning approaches to optimize nanoparticle design and expanding into immunomodulatory therapies that harness the body's immune system to fight cancer. Scientific Recognition: 2014 AFPC New Investigator Award 2013 AAPS New Investigator Award in Pharmaceutics and Pharmaceutical Technologies 2013 CIHR New Investigator Award 2013 CSPS Early Career Award 2012 Prostate Cancer Foundation Young Investigator Award Dr. Li's research program has been consistently supported by major Canadian funding agencies including CIHR, NSERC, and MITACS. He actively collaborates across disciplines and accepts graduate students into his research program, focusing on cutting-edge approaches to overcome biological barriers in drug delivery. His laboratory, the Laboratory of Targeted Drug Delivery and Nanomedicine, serves as a hub for innovation in pharmaceutical nanotechnology.

Vikramaditya G. YadavView profile

Associate Professor

• Biocatalysis
• Bioremediation
• Bioprocess engineering
• +15 more

Vikramaditya G. Yadav is an Associate Professor at the University of British Columbia (UBC) in the Department of Chemical and Biological Engineering, Faculty of Applied Science. He directs the Master of Engineering Leadership (MEL) Program in Sustainable Process Engineering and leads the BioFoundry research group. Education: B.A.Sc., University of Waterloo (2007) Ph.D., Massachusetts Institute of Technology (2013) Postdoctoral Associate, Harvard University (2014) His research spans sustainable chemical manufacturing, metabolic engineering, and biotechnology. Key areas include: Designing biosynthetic enzymes for biomass valorization Developing bioremediation strategies for industrial water quality Creating innovative drug delivery systems and tissue engineering solutions Advancing synthetic biology for pharmaceutical and bioenergy applications His recent work focuses on ocular drug delivery, cannabinoid biosynthesis in E. coli, lignin-based nanoparticles for cancer therapy, and computational analysis of plant secondary metabolites. Collaborations with start-ups, industry, and medical labs drive innovation in Canada's bioeconomy. Professional Leadership: Chair, Biotechnology Division of the Chemical Institute of Canada Associate Editor, The Canadian Journal of Chemical Engineering He is affiliated with UBC's BioProducts Institute and contributes to project-based learning pedagogy.

• Machine Learning
• Deep Learning
• Theoretical Foundations of AI
• +11 more

Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.

José Eduardo Villalobos GrailletView profile

Assistant Professor

• Spanish Film Studies
• Spanish Medieval Literature
• Latin American and Caribbean Studies
• +5 more

José Eduardo Villalobos Graillet is an Assistant Professor of Spanish at Idaho State University , specializing in Modern Peninsular Literature and Culture with a focus on visual culture (film and television) . He previously held a position as Spanish Instructor at Wilfrid Laurier University in the Department of Languages and Literatures. Research Areas : Spanish Film Studies, Spanish Medieval Literature, Latin American and Caribbean Studies, Teaching Spanish as a Foreign Language, Visual Culture, and Critical Pedagogy. His scholarly work examines film and television adaptations of 'La Celestina' , analyzing censorship dynamics across Francoist Spain, the Democratic Transition, and contemporary Spain. He also contributes to Spanish pedagogy , developing technological tools like Lang-8 for written expression and culturally immersive teaching units on topics such as winter festivals and Caribbean music. His publications span both critical academic analysis and methodological innovations for language instruction. Key Themes in Publications : Adaptation fidelity, political censorship, intercultural pedagogy, and the role of digital platforms in language learning.

• 19th-century Visual Culture
• Material Culture Studies
• Art History
• +5 more

Andrea Korda, PhD (University of California, Santa Barbara), is an Associate Professor of Fine Arts & Humanities and Associate Dean (Research) at the Augustana Campus of the University of Alberta. Her academic career bridges art history, material culture studies, and pedagogical innovation. Educations: BA in Fine Arts, Queen's University MA in Art History, Concordia University PhD in Art History, University of California, Santa Barbara Research Focus: Dr. Korda specializes in 19th-century visual and material culture, with a focus on education's visual dimensions, children's literature, and community-engaged projects like the award-winning Crafting Communities initiative. She co-curated the virtual exhibition Photographies (Bruce Peel Special Collections) and explores hands-on learning through historical crafts. Teaching: Courses include modern art history (AUART 220), photography theory (AUART 225), and special topics in visual culture (AUART 382). She emphasizes experiential learning and digital humanities integration, as seen in her work with the Getty Museum Challenge and OER projects. Collaborations: Leads interdisciplinary teams on projects like Crafting Communities , blending scholarship with public engagement. Her research bridges historical methodologies with contemporary pedagogical practices, fostering connections between academia and communities.

• Databases
• Data Mining
• Machine Learning
• +3 more

Ming-Syan Chen is a distinguished academic holding dual roles as a Distinguished Research Fellow and Director of the Research Center for Information Technology Innovation (CITI) at Academia Sinica, Taiwan, and a Distinguished Professor jointly appointed across multiple departments at National Taiwan University (NTU), including Electrical Engineering (EE), Computer Science and Information Engineering (CSIE), and the Graduate Institute of Communication Engineering (GICE). His career spans academia and industry, with prior roles as a research staff member at IBM Watson Research Center and leadership positions in Taiwan's technology sector. Education: He earned a B.S. in Electrical Engineering from National Taiwan University, followed by M.S. and Ph.D. degrees in Computer, Information, and Control Engineering from the University of Michigan, Ann Arbor. Research Interests: Chen's work focuses on databases, data mining, machine learning, multimedia networking, and cloud computing. He has authored over 350 papers and holds numerous patents, contributing to foundational advancements in query processing, data management, and networked systems. Award Highlights: Recipient of ACM and IEEE Fellowships, National Chair Professorship (lifetime honor), Teco Award, Pan Wen Yuan Distinguished Research Award, and IBM's Outstanding Innovation Award. His contributions span research, teaching, and technology commercialization. Leadership & Service: Former Dean of NTU's College of Electrical Engineering and Computer Science, CEO of Taiwan's Networked Communication Program, and Editor-in-Chief of the International Journal of Electrical Engineering. He has chaired international conferences and served on editorial boards of journals like IEEE TKDE and VLDB. Labs & Teams: Leads the Network Database Laboratory and collaborates on national initiatives in information and communication technologies. His research groups focus on data science, distributed systems, and social network analysis.

Amr YoussefView profile

Professor

• Applied Cryptography
• Network Security
• Cyber-Physical Systems Security
• +4 more

Amr Youssef is a Professor at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on applied cryptography, network security, cyber-physical systems security, blockchain, and privacy. He has contributed extensively to securing smart grids, IoT devices, and web applications through cryptographic protocols and machine learning-driven solutions. His work addresses critical challenges in cybersecurity, including e-voting systems, fault-tolerant differential protection, and privacy-preserving communication protocols. He explores vulnerabilities in modern systems such as SSO permissions, JavaScript exploits, and stalkerware tools, while developing frameworks like MEGR-APT for APT detection and TEE-Receipt for non-repudiation. Youssef’s research integrates interdisciplinary approaches, combining cryptography with AI (e.g., vision transformers for power quality analysis) and leveraging trusted execution environments (TEE) for secure computing. His projects often involve collaboration with industrial standards (e.g., IEC 61850), emphasizing practical implementations in real-world systems.

Hugo de LasaView profile

Professor

• Catalysis
• Photocatalysis
• Chemical Reactor Engineering
• +5 more

Hugo de Lasa is a Full Professor at the Department of Chemical and Biochemical Engineering, Faculty of Engineering, University of Western Ontario. He holds a Bachelor in Chemical Engineering (1968) from Universidad Nacional del Sur, Argentina, and a Doctoral degree (1971) from Université de Nancy, France. Research Focus: Catalysis, Photocatalysis, Chemical Reactor Engineering, Fluidization, Biomass Gasification Awards: Research Excellence Prize (1998), Fellow of the Chemical Institute of Canada (2000), Medal of Research and Development (2000), Doctor Honoris Causa (2004, 2018) His work spans chemical reactor design , photocatalytic hydrogen production , and fluidized bed technologies . Recent publications highlight machine learning applications in chemical equilibrium modeling and CO2 capture using microalgae. He founded the Chemical Reactor Engineering Centre (CREC) and Recat Technologies Inc. , a university spin-off commercializing reactor innovations. Awards include the Vanguard Award (2019) and Commemorative Issue in Catalysts Journal (2020). His research has generated 389 peer-reviewed publications , 14 patents , and over 10,000 citations .