جستجوی استاد راهنمای دانشگاهی

Dr. Camila Aquino serves as Assistant Professor in both the Department of Clinical Neurosciences and Department of Community Health Sciences at the University of Calgary's Cumming School of Medicine, and is a Full Member of the Hotchkiss Brain Institute. As Medical Director of the Deep Brain Stimulation Program, she specializes in Parkinson's disease, essential tremor, dystonia, and chorea management, including EMG-guided botulinum toxin injections. Her educational background includes: M.D. in Medicine from Escola Superior de Ciencias da Santa Casa (2005) M.S. in Neurosciences from Federal University of Sao Paulo (2012) Ph.D. in Neurosciences from Federal University of Sao Paulo (2019) M.S. in Health Research Methods, Clinical Epidemiology from McMaster University (2020) Dr. Aquino's research addresses unmet needs in movement disorders through innovative clinical trial designs for Parkinson's disease, focusing on precision-medicine approaches, early intervention, and outcome optimization. She actively evaluates neuromodulation therapies including Deep Brain Stimulation, Spinal Cord Stimulation, and MRgFUS, with emphasis on expanding indications and improving patient outcomes. As an International Parkinson and Movement Disorder Society member, she chairs the web-based learning committee and serves on evidence-based medicine and outcome measure committees. Analysis of her 15 most recent publications (2021-2025) reveals a cohesive research trajectory centered on Parkinson's disease management, with dominant themes in motor fluctuation treatments, deep brain stimulation methodology, and clinical trial innovation. Her work bridges fundamental clinical practice guidelines with advanced neuromodulation technologies, demonstrating consistent commitment to evidence-based solutions for movement disorders. Her scientific recognition includes: Parkinson Canada research grant Mohammed Al Zaibak Foundation scholarship CAPES scholarship from Brazilian Ministry of Education Dr. Aquino has secured significant research funding supporting clinical trials in Parkinson's disease neuroprotection and neuromodulation therapies. While specific student advisees aren't listed, her leadership of the DBS Program and active clinical trial portfolio indicate substantial mentorship responsibilities. Her grant portfolio emphasizes translational research with direct clinical impact. She directs the University of Calgary's Deep Brain Stimulation Program and contributes to the MRI-guided focused ultrasound initiative. Through her International Parkinson and Movement Disorder Society committee roles, she shapes global educational standards and evidence-based practices in movement disorders.

• Movement Disorders
• Parkinson's Disease
• Deep Brain Stimulation
• +۵ مورد دیگر

Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.

• Socially Assistive Robotics
• Human-Robot Interaction
• Assistive Technology
• +۷ مورد دیگر

Anamaria Crisan is an Assistant Professor at the University of Waterloo, affiliated with the Insight Lab. Her research focuses on interdisciplinary work at the intersection of Human-Computer Interaction (HCI), Data Visualization, and Applied AI/ML. She explores human-centered approaches to AI/ML systems, visualization design for decision-making, and healthcare data science applications. Dr. Crisan holds a PhD in Computer Science from the University of British Columbia (2019), an MSc in Bioinformatics (2010), and a BComp in Biomedical Computing from Queen’s University (2008). Her educational background bridges computer science, biology, and healthcare informatics. Her research interests include responsible AI/ML systems, interactive visualization for data-driven decisions, and leveraging visualization in healthcare to improve outcomes. She emphasizes transparency, trustworthiness, and human alignment in AI technologies. Her work spans diverse applications such as genomic epidemiology, dashboard design, and ethical AI evaluation. Notable contributions include studies on human-AI collaboration, visualization linters, and scalable dashboard census methodologies. She has published widely in top-tier venues like IEEE VIS and ACM CHI. Dr. Crisan’s lab (UW Insight Lab) focuses on human-centered approaches to automating data science and improving visualization practices in critical domains like healthcare and public health.

• Human-Computer Interaction
• Data Visualization
• Applied AI/ML
• +۳ مورد دیگر

Dr. Catherine Legault serves as an Assistant Professor in the Department of Neurology and Neurosurgery at McGill University and Co-director of the Stroke Programme at the McGill University Health Centre. She is a neurologist at The Neuro (Montreal Neurological Institute-Hospital), a bilingual academic healthcare institution and part of McGill's Neuroscience Mission. Her educational background includes: Medical degree from McGill University (2011) Internship in Internal Medicine at Montreal’s Jewish General Hospital Neurology Residency at The Neuro (2012-2015) with chief residency (2014-2015) Vascular Neurology fellowship at Stanford University NIH StrokeNet fellowship Dr. Legault's clinical research focuses on translational stroke care, emphasizing acute interventions, rehabilitation innovation, and systems optimization. Her work integrates wearable technology with traditional stroke recovery methods while addressing post-stroke cognitive and visual impairments. She actively develops patient-centered educational frameworks and investigates hemodynamic interventions like induced hypertension. As principal investigator for multiple stroke clinical trials at The Neuro's Clinical Research Unit, she bridges bench-to-bedside research. Her teaching spans emergency consultations, bedside clinical supervision, and outpatient stroke clinics for residents and medical students. Through multidisciplinary collaboration in the Stroke Programme, Dr. Legault advances integrated care models that connect acute treatment with long-term rehabilitation, focusing on measurable functional outcomes and healthcare system efficiency.

• Neurology
• Stroke Research
• acute stroke management
• +۹ مورد دیگر

Prof. Ramakrishna Gokaraju is a Professor and Graduate Chair in the Department of Electrical and Computer Engineering at the University of Saskatchewan. His academic journey includes roles as Assistant Professor (2003), Associate Professor (2009), and full Professor (2015). He holds a B.E. from NIT Trichy (1992), M.Sc. and Ph.D. from the University of Calgary (1996, 2000). His research focuses on power system protection, smart grids, and sustainable energy systems, including small modular reactors (SMRs) and renewable integration. He has advised 8 PhD and 25+ Master’s students, with over 80 publications in top journals/conferences. Dr. Gokaraju’s honors include the Izaak Walton Killam Memorial Scholarship (1998–2000) and the Professor of the Year Award (2008). He has held visiting roles at the University of Manitoba (2009–2010), IIT Kanpur (2018), and institutions in Australia and India. His work emphasizes high-speed digital relaying, PMU-based solutions, and transient stability protection. Current research includes wind generator modeling, SMR integration, and energy storage systems for remote communities. His technical contributions span fault location algorithms, grid resilience enhancement, and GPU-based optimization for transport systems. Ongoing projects explore hybrid energy systems combining SMRs with renewables. Lab affiliations include the Power Systems Research Group at the University of Saskatchewan, focusing on smart grid innovation and sustainable energy solutions.

• Power System Protection
• Smart Grid Applications
• Sustainable Energy Systems
• +۴ مورد دیگر

Sujaya Maiyya is an Assistant Professor at the Cheriton School of Computer Science, University of Waterloo. Prior to this, she completed a postdoc at Cornell University and earned her PhD from the University of California, Santa Barbara. Her research focuses on distributed systems, databases, and privacy/security, particularly in designing secure and efficient data management systems. She leads projects on oblivious databases, trusted execution environments (TEEs), and scalable privacy-preserving systems. Education: PhD in Computer Science, University of California, Santa Barbara (2018) MSc in Computer Science, University of California, Santa Barbara (2017) BE in Information Science, PESIT Bangalore (2014) Research Interests: Distributed systems, database privacy, oblivious datastores, genomics data security, and secure computation using TEEs. Her work emphasizes practical solutions for privacy-preserving storage and query processing, including tunable-privacy mechanisms and fault-tolerant ORAM systems. Awards and Grants: CFI/ORF Infrastructure Grant (2024-2029) NCC Research Awards (2024-2028) NSERC Discovery Grant (2023-2027) MIT EECS Rising Stars (2021) Teaching: Courses include CS348 (Introduction to Databases) and CS848 (Privacy Enhancing Data Systems). She emphasizes foundational concepts and system internals in database design and secure systems. Professional Service: Chair of Ontario Database Day (2024), PC member for SIGMOD, EDBT, VLDB, and ICDE. Frequent reviewer for journals like TKDE and DKE.

• Distributed Systems
• Database Systems
• Data Privacy
• +۳ مورد دیگر

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.

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +۵ مورد دیگر

Jeffrey Monaghan is an Associate Professor at the Institute for Criminology and Criminal Justice (ICCJ) at Carleton University. He holds a Ph.D. in Sociology from Queen’s University and serves on the editorial board of Criminological Encounters . His research focuses on the intersections of policing, surveillance, and systemic inequality, with a particular emphasis on settler colonialism, Indigenous movements, and the global expansion of security practices. Monaghan teaches courses on criminological theories, policing, and justice frameworks. His work examines how security and policing practices criminalize marginalized communities, especially Indigenous peoples and social movements resisting extractive development. Key projects include analyses of counterterrorism strategies, disability injustice in criminal systems, and the securitization of Canadian foreign aid. He has authored/co-authored books like Security Aid: Canada and the Development Regime of Security and Policing Indigenous Movements , and edited volumes such as Protests in the Information Age . Monaghan’s research integrates critical criminology, colonial studies, and sociolegal analysis. He is cross-appointed to Carleton’s Department of Sociology and Anthropology and the School of Canadian and Indigenous Studies, amplifying interdisciplinary engagement with security governance and social justice issues.

• Security Governance
• Policing Practices
• Surveillance Studies
• +۴ مورد دیگر

Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.

• Machine Learning
• Deep Learning
• AI Safety
• +۱۲ مورد دیگر

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.

• Computational Imaging
• Computer Vision
• Non-Line-of-Sight Imaging
• +۵ مورد دیگر

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.

• Spanish Film Studies
• Spanish Medieval Literature
• Latin American and Caribbean Studies
• +۵ مورد دیگر

Dr. Mi Jung Park is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. She is also a Canada CIFAR AI Chair at the Amii. Her research focuses on privacy-preserving machine learning, particularly differential privacy, synthetic data generation, and their applications in healthcare. She holds a PhD in Electrical and Computer Engineering from the University of Texas at Austin, supervised by Dr. Jonathan Pillow, and has held postdoctoral positions at the University of Amsterdam and University College London. Education : PhD, Electrical and Computer Engineering, University of Texas at Austin (2016) Master's, Electrical and Computer Engineering, University of Texas at Austin (2012) Bachelor's, Electrical and Computer Engineering, Hanyang University, Seoul, South Korea (2009) Research Interests : Her lab develops methods to balance privacy and accuracy in data analysis, emphasizing differential privacy's role in healthcare. Key areas include: Generating synthetic data with privacy guarantees Integrating fairness, interpretability, and causality into privacy-preserving models Bayesian techniques for model compression and uncertainty estimation Recent Work Trends : Her publications explore differential privacy in generative models (e.g., diffusion models, kernel methods) and neural network pruning. Recent work highlights privacy-preserving techniques for image classification, latent diffusion, and perceptual feature integration. Awards : Canada CIFAR AI Chair (2021). Advising & Grants : Supervises postdocs (e.g., Mingyu Kim), master's students (e.g., Amman Yusuf), and PhD candidates (e.g., Margarita Vinaroz). Her research is supported by the CIFAR AI Chair program and collaborations with institutions like the Max Planck Institute for Intelligent Systems. Labs & Teams : Leads the Privacy-Preserving Machine Learning Lab at UBC, advancing technologies to protect sensitive healthcare data while enabling clinical and research use.

• Privacy-Preserving Machine Learning
• Differential Privacy
• Machine Learning
• +۳ مورد دیگر

Min Hu is an Assistant Professor in the Department of Economics, Philosophy, and Political Science at the University of British Columbia, Okanagan Campus. He holds an adjunct position at Dalhousie University's School of Health Administration. His primary research focuses on Health Economics, Applied Microeconomics, and Labour Economics, with an emphasis on vulnerable populations in Canada, Indigenous health, income inequalities, and cancer economics. He teaches courses in Microeconomics, Health Economics, and Statistics. Education: PhD in Economics (2019), Dalhousie University; Postdoctoral Fellowship in Health Economics (2019–2020), Dalhousie University. Previous degrees include an M.A. (2014–2016) and B.A. in Economics from Dalhousie University. Research interests include the socio-economic determinants of health disparities, particularly among Indigenous peoples and immigrants, and the economic impacts of cancer-related policies. He employs applied econometric methods to analyze labor market outcomes and social policy effectiveness. His recent articles explore sustainable biomass utilization, energy policies, and environmental engineering innovations. Key themes include biomass supply chain optimization, CO₂ reduction strategies, and waste-to-energy technologies. Scientific Awards: Recipient of SSHRC New Frontiers Grant (2023–2026), CIHR Team Grant (2024–2026), and multiple UBC startup grants. His work is supported by SSHRC, CRDCN, and Indigenous Services Canada. He advises students in Economics and Global Studies (IGS) programs. Grants and funding span health economics, Indigenous well-being, and sustainable energy systems. Collaborations include interdisciplinary teams in health administration and environmental policy. Labs/Teams: Engaged with UBC's Okanagan Sustainability Initiatives and Dalhousie's Health Economics Research Group. Affiliated with professional organizations like the American Economic Association and Canadian Health Economics Study Group.

• Health Economics
• Applied Microeconomics
• Labour Economics
• +۷ مورد دیگر

Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute, where she holds a Canada CIFAR AI Chair. She joined the University of Waterloo in July 2024 after completing her Ph.D. at the Toyota Technological Institute at Chicago. Educational Background: Ph.D. in Computer Science, Toyota Technological Institute at Chicago (2024), advised by Professors Karen Livescu and Kevin Gimpel Bachelor's degree in Intelligence Science and Technology (Computer Science Track) with a minor in Sociology, Peking University (2018) Dr. Shi's research focuses on computational linguistics and natural language processing, particularly on deeper understandings of natural language and the human language processing mechanism. She is especially interested in learning language through grounding, computational multilingualism, and related machine learning aspects. Her work aims to inform the design of more efficient, effective, safe, and trustworthy NLP systems. She leads the CompLING Lab at the University of Waterloo, which investigates how language models process spatial relationships and acquire linguistic structures through grounded experiences. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on spatial reasoning in vision-language models, multilingual chain-of-thought capabilities, and grounded language acquisition. She has published in top-tier conferences including ACL, EMNLP, ICLR, and NAACL, with several papers receiving notable recognition including Best Paper Nominee status at multiple venues. Her research bridges theoretical linguistics with practical NLP applications, demonstrating how linguistic insights can improve AI systems. Scientific Recognition: Canada CIFAR AI Chair (2024) Google Ph.D. Fellowship Thesis of Distinction for her doctoral work Multiple Best Paper Nominee awards at major NLP conferences Dr. Shi teaches CS 784: Computational Linguistics and CS 486/686: Introduction to Artificial Intelligence at the University of Waterloo. She actively contributes to the NLP research community through conference participation, program committee service, and collaborative projects. Her research has significant implications for creating more robust, human-like language understanding systems and advancing the field of grounded language learning in artificial intelligence.

• computational linguistics
• natural language processing
• computational multilingualism
• +۵ مورد دیگر

Anwar Hithnawi is an Assistant Professor of Computer Science at the University of Toronto, where he leads the Privacy Preserving Systems Lab (PPS Lab). His research focuses on data privacy, applied cryptography, and secure systems, with emphasis on privacy-preserving machine learning, federated learning, and encrypted data processing. He holds a Ph.D. in Computer Science from ETH Zurich and was a postdoctoral researcher at UC Berkeley. Previously, he served as an Ambizione Fellow and research group leader at ETH Zurich. Research Interests: Data Privacy & Security Applied Cryptography (Homomorphic Encryption, Zero-Knowledge Proofs) Privacy-Preserving Systems (Federated Learning, Secure Analytics) IoT Security & Privacy Secure Collaborative Learning Awards: Google Research Award SNF Ambizione Grant ETH Medal for Outstanding Master Thesis (student Lukas Burkhalter) Microsoft Research Ph.D. Award (student Lukas Burkhalter) Lab Activities: The PPS Lab develops systems for privacy-preserving computation, secure collaborative learning, and encrypted data stream processing. Notable projects include Zeph, HECO, and Cohere. Recent achievements include acceptance of DPolicy at IEEE S&P 2025 and RoFL at Oakland 2023.

• Data Privacy
• Applied Cryptography
• Privacy-Preserving Systems
• +۵ مورد دیگر