Find Academic Supervisors

David DuvenaudView profile

Associate Professor

• Machine Learning
• Generative Models
• Bayesian Inference
• +5 more

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

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.

Freda ShiView profile

Assistant Professor

• computational linguistics
• natural language processing
• computational multilingualism
• +5 more

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.

Michael S. BrownView profile

Professor

• Computer Vision
• Image Processing
• Computer Graphics
• +4 more

Michael S. Brown is a Professor and Canada Research Chair at York University's Department of Electrical Engineering and Computer Science (EECS). He also serves as Senior Director at the Samsung AI Center in Toronto, Canada. His research focuses on computer vision, image processing, and computer graphics, with a deep specialization in camera imaging pipelines, color theory, and AI-driven ISP optimization. He has organized major conferences like ProCams, eHeritage, WACV, and ICCV, and holds editorial roles in journals like TPAMI and IJCV. He is renowned for his work on computational color constancy, ISP hardware algorithms, and AI-based image enhancement techniques. His recent ICCV 2023 tutorial detailed modern camera pipelines and AI applications in ISP components. His research is supported by grants from NSERC, Samsung, Adobe, Google, and Microsoft. Brown advises numerous graduate students and has mentored over 30 alumni now in academia and industry roles at Meta, Samsung, Microsoft, and startups. His lab focuses on camera systems, noise modeling, and cross-platform color management, with contributions to open-source datasets and software platforms for ISP experimentation.

Karen CheungView profile

Professor

• Biomedical microsystems
• Lab-on-a-chip systems
• Tissue engineering
• +9 more

Karen C. Cheung is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, with cross-appointments in the Faculty of Medicine and the School of Biomedical Engineering where she serves as Director of the Graduate Program. She holds her office in KAIS 3064 and can be reached at (604) 827-4114. Dr. Cheung received her BSc and PhD degrees in Bioengineering from the University of California, Berkeley in 1998 and 2002, respectively. From 2002-2005, she was a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne in Switzerland. She joined UBC in 2006 and has established herself as a leading researcher in biomedical microsystems. Her research spans multiple areas of biomedical engineering with particular focus on lab-on-a-chip systems for cell culture and characterization , inkjet printing for tissue engineering , and implantable neural interfaces . Her work integrates microfluidics, biosensors, and tissue engineering to create platforms that better mimic in vivo conditions for drug screening and disease modeling. Current projects include developing organ-on-a-chip models of the human airway for studying aerosol exposure effects, creating microscale tumor models for cancer research, and advancing silicon photonic biosensors for medical diagnostics. Dr. Cheung leads the Bio-Medical Micro Devices Laboratory at UBC, which houses multiple research teams working on cutting-edge biomedical technologies. Her lab has developed microfluidic platforms capable of precisely controlling oxygen levels around tumor spheroids to study cancer treatment responses under realistic physiological conditions. The lab also works on novel fabrication techniques for microelectrode arrays and tissue clearing protocols for 3D imaging of microtissues. As an educator, Dr. Cheung teaches several specialized courses including ELEC 361 (Molecules to Mechanisms), ELEC 464 (Nanotechnology and Nature), EECE 301 (Topics in Nanotechnology and Microsystems), ELEC 473 (Biological Micro-Electro-Mechanical Systems), and ELEC 521 (Biomedical Microdevices). She has supervised numerous graduate students through their MASc, PhD, and postdoctoral work, with many alumni now holding academic positions or working in the biomedical industry. Her research is supported through multiple funding sources and collaborations with industry partners. Dr. Cheung is affiliated with several research centers including the Airway Centre, Bionics Network, Centre for Blood Research, and the Institute for Computing, Information and Cognitive Systems (ICICS) at UBC.

Joey PaquetView profile

Associate Professor

• Design and implementation of programming languages
• Context-driven computing
• Demand-driven computing

Dr. Joey Paquet is a Tenured Associate Professor and Department Chair in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He holds a PhD and specializes in research areas including Design and Implementation of Programming Languages, Context-Driven Computing, and Demand-Driven Computing. His work focuses on advancing programming paradigms and their applications in cybersecurity, distributed systems, and autonomic computing. Dr. Paquet is actively involved in thesis supervision across Computer Science and Software Engineering programs at both master's and doctoral levels. Research interests are centered around programming language design, particularly in demand-driven and context-aware systems. His contributions include frameworks like GIPSY and OpenISS, enabling scalable data processing and forensic computing. Recent work explores autonomic intent-driven networking, real-time gesture recognition, and IoT forensics. These efforts highlight his expertise in both theoretical constructs and practical implementations. His advising role supports students in MCompSc, MASc, and PhD programs. While specific grants are not detailed, his projects often involve interdisciplinary collaboration. Dr. Paquet is affiliated with platforms like LinkedIn and ResearchGate, reflecting an active academic presence. Key technical contributions include pioneering work on forensic computing backends, intent expression languages, and multimodal interaction systems. His research addresses challenges in cybersecurity, distributed systems, and service composition, with a focus on resilience and scalability.

Zhenman FangView profile

Associate Professor

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +8 more

Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

Xiaoxiao LiView profile

Assistant Professor

• Federated Learning
• Medical Imaging Analysis
• AI Fairness and Trustworthiness
• +7 more

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.

Euijin ChooView profile

Assistant Professor

• Data-driven Cybersecurity
• Big Data Analytics
• Graph Mining
• +6 more

Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.

• Internet of Things (IoT)
• Wireless Sensor Networks (WSN)
• Active Learning Classrooms
• +5 more

Maher Elshakankiri is an Assistant Professor, Teaching Stream at the University of Toronto's Faculty of Information. He holds a Ph.D. in Computer Engineering from Ain Shams University (Egypt), followed by a postdoctoral fellowship at the University of Regina. His research focuses on IoT, Wireless Sensor Networks (WSN), and pedagogical integration of technology and gaming. He has supervised over 100 student projects and authored a book on WSNs. Education: Ph.D. in Computer Engineering, Ain Shams University, Egypt M.Eng., B.Eng. in Engineering, Ain Shams University, Egypt Postdoctoral Fellowship, University of Regina, Canada Research Interests: IoT in healthcare, agriculture, and sports Active learning classrooms and technology in education Wireless communication protocols (V2V, UAV, RFID) Leadership & Grants: Director, Bachelor of Information (BI) Program (2024–present) Coordinator, Information Systems and Design (ISD) Concentration (2023) SSHRC Grant: 'Gaming in teaching towards a more inclusive class' (2022–2023) Professional Activities: Member, SCC IoT & Digital Twins Standards Committee Reviewer for journals including Wireless Networks , Telematics and Informatics , and Computational Intelligence Technical Program Committee member at multiple conferences Teaching: Courses include INF1340 (Programming for Data Science), INF1005/1006 (IoT Workshops), and INF452 (Information Design Coding).

• High-speed data converters
• Wireless transceivers
• Digital phase-locked loops
• +7 more

Shiyu Su is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on high-speed data converters, wireless transceivers, digital phase-locked loops (PLL), and AI-assisted analog/mixed-signal design automation. He holds a Ph.D. from the University of Southern California (2019) and teaches courses such as ECE 340 (Electronic Circuits 2) and ECE 432 (Radio Frequency Integrated Devices and Circuits). Education: B.S. from Beijing University of Post and Telecommunication (China) and Queen Mary, University of London (UK), 2011; M.S. and Ph.D. from USC, 2013 and 2019, all in electrical engineering. Research Interests: High-speed ADCs/DACs RF/mm-wave transceivers Time-approximation filters (TAF) Analog/mixed-signal design automation Memristor-based computing Biomedical interfaces Key Awards: IEEE SSCS Predoctoral Achievement Award (2017–2018) Best Student Paper Award at IEEE RFIC (2022) Ming Hsieh Institute Scholar (2019–2020) Lab Focus: The Shiyu Su Lab develops integrated circuits for communications, sensing, and computing, with a focus on AI-driven methodologies and digital-analog co-design. Collaborations include work with Prof. Wei Wu (USC) on memristor-based systems.

Lourdes Pena-CastilloView profile

Professor

• Bioinformatics
• Genomics
• Machine Learning
• +5 more

Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.

Rong ZhengView profile

Professor

• Mobile computing
• Wireless networking
• Cyber physical systems
• +4 more

Rong Zheng is a Professor in the Department of Computing and Software and a member of the School of Biomedical Engineering at McMaster University, Canada. She holds a Tier-1 Canada Research Chair in Mobile Computing and serves as Acting Chair of the Computing and Software department from July to December 2025. She is also an Associate Member of the Electrical & Computer Engineering department. Education: Ph.D. in Computer Science, University of Illinois, Urbana-Champaign, USA Master of Engineering (thesis) in Electrical Engineering, Tsinghua University, Beijing, China Bachelor of Engineering in Electrical Engineering, Tsinghua University, Beijing, China Dr. Zheng's research lies at the intersection of mobile computing, wireless networking, and machine learning, with a strong focus on applications for aging populations. She directs the NSERC Smart Mobility for the Aging Population CREATE program. Her work encompasses sensor development, wireless network design, and mobile data analytics to address real-world challenges in healthcare, mobility, and data center monitoring. She has developed innovative solutions like the MacQuest campus navigation app and has captured first prize in indoor localization competitions. Her recent publications demonstrate a clear trajectory toward applying wireless sensing technologies (particularly acoustic, Wi-Fi, and mmWave) to health monitoring and mobility assessment for older adults. There's a strong emphasis on developing efficient edge computing solutions that can process data in real-time on resource-constrained devices, as exemplified by her TeamNet framework for collaborative inference on the edge. Her work bridges theoretical advances with practical applications that have social impact. Scientific Awards: Tier-1 Canada Research Chair in Mobile Computing US National Science Foundation CAREER Award (2006) Joseph Ip Distinguished Engineering Fellow (2015-2018) Dr. Zheng leads the Wireless System Research Group (WiSeR) at McMaster University, which has secured significant funding including a $1.65M NSERC CREATE grant for smart mobility research for older adults. Her research has been supported by multiple funding agencies including NSERC, NSF, UH GEAR, and DURIP. She actively mentors graduate students and has developed specialized courses including CAS 772 (Mobile Data Analytics) and CAS 781 (Mobility in the Aging Population). The WiSeR group conducts impactful research on communication, networking, and data analytics issues in Cyber Physical Systems, with applications spanning healthcare, smart infrastructure, and data center monitoring. Their work on data center infrastructure monitoring networks has been featured in EurekAlert and Data Center Dynamics, and they've made significant contributions to indoor localization technology.

Linyi LiView profile

Assistant Professor

• Deep learning
• Trustworthy machine learning
• Computer security
• +4 more

Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.

Sharan VaswaniView profile

Assistant Professor

• Reinforcement Learning
• Optimization
• Machine Learning
• +3 more

Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their interplay with machine learning generalization. He holds a PhD from the University of British Columbia (2019) and postdoctoral experiences at the University of Alberta and Mila. His academic journey includes MSc (UBC, 2015) and BTech (BITS Pilani, 2012) degrees. Education: PhD (UBC, 2019), MSc (UBC, 2015), BTech (BITS Pilani, 2012) Postdoctoral Work: University of Alberta (2020-2021), Mila (2019-2020) Teaching includes courses on Probability and Computing (CMPT 210), Optimization for Machine Learning (CMPT 409/981), and Theoretical Foundations of Reinforcement Learning (CMPT 419/983). His research group focuses on developing scalable optimization algorithms with theoretical guarantees. He advises multiple PhD and MSc students, contributing to areas like constrained MDPs, adaptive learning rates, and reinforcement learning theory. Research Highlights: Contributions to bandit algorithms, stochastic gradient methods, and reinforcement learning theory. Notable work includes global convergence analysis of policy gradients and variance-reduced optimization frameworks.