Find Academic Supervisors

Scott D. CampbellView profile

Associate Professor

• Urban Planning Theory
• Sustainability
• Complex Systems Governance
• +3 more

Roles & Affiliations: Associate Professor of Urban Planning at the Taubman College of Architecture + Urban Planning, University of Michigan. Senior Fellow, Michigan Society of Fellows. Director of Doctoral Studies (2004–2012, 2017–2019). Affiliated with the Weiser Center for Europe and Eurasia. Education: Ph.D. in Urban and Regional Planning (University of California, Berkeley, 1990), M.C.P. in City and Regional Planning (University of California, Berkeley, 1985). Research Interests: Focuses on urban planning theory, sustainability, complex systems governance, and the intersection of equity and environmental policy. Explores historical and contemporary planning paradigms, particularly the 'Planner’s Triangle' and 'Planner’s Pentangle' models. Engages with wicked problems in urban governance and the application of complex systems modeling. Recent Work: Recent articles emphasize reimagining planning models for the 21st century, addressing sustainability through adaptive governance, and reconciling growth with social justice. His 2016 work on sustainable urban development critiques the tensions between economic growth and environmental equity. Awards & Grants: Recognized with the Most Valuable Planner Award (2000), Rackham Faculty Research Grant (1999–2000), and multiple grants from Rutgers University and the American Planning Association. Advising & Education: Advised over 20 doctoral students on topics ranging from climate resilience to global urbanization. Co-developed curricula for planning theory, methods, and sustainable development. Active in mentoring students through comprehensive exams and dissertation research. Labs & Teams: Collaborates with interdisciplinary teams in the Urban & Regional Research Collaborative (URRC) and contributes to initiatives like the Hines Student Design Competition. Engages with global urban challenges through projects in India, Uganda, and Southeast Asia.

Zsolt KiraView profile

Assistant Professor

• Robotics
• Machine Learning
• Artificial Intelligence
• +6 more

Zsolt Kira serves as an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with additional affiliations at the Georgia Tech Research Institute and as Associate Director of ML@GT. He leads the Robotics Perception and Learning (RIPL) Lab, driving research at the intersection of machine learning and robotics. Dr. Kira earned his Ph.D. in 2010 under Professor Ron Arkin, establishing foundational expertise in robotics and AI before transitioning to his current academic role after industry experience at SRI International Sarnoff. His research pioneers beyond supervised learning through unsupervised, semi-supervised, self-supervised, and continual/lifelong learning frameworks, while advancing distributed perception via multi-modal fusion and cross-robot information integration. This dual focus addresses core challenges in robotic autonomy and sensor processing. Analysis of his 15 most recent publications reveals dominant trends in embodied AI systems, robust foundation model adaptation, and multimodal learning architectures. Key themes include neural radiance field applications, reinforcement learning for locomotion, and novel benchmarks for memory evaluation in agents. While specific advisees and grants aren't documented in the source material, his RIPL Lab leadership implies active graduate mentorship and research funding acquisition. The lab's work directly enables next-generation robotic systems through algorithmic innovation in perception and learning. The RIPL Lab operates as a hub for developing machine learning techniques that solve difficult perception problems in robotics, with particular emphasis on unsupervised learning paradigms and distributed multi-robot systems that push the boundaries of autonomous operation.

Chenhao TanView profile

Associate Professor

• language and social dynamics
• human-centered machine learning
• multi-community engagement
• +3 more

Chenhao Tan serves as an Associate Professor in the Department of Computer Science and the UChicago Data Science Institute at the University of Chicago. His academic journey includes a PhD from Cornell University, followed by a postdoctoral position at the University of Washington, and previous affiliation with the University of Colorado Boulder. Dr. Tan's research focuses on language and social dynamics , human-centered machine learning , and multi-community engagement . He is broadly interested in computational social science, natural language processing, and artificial intelligence. His work explores the paradigm of 'machine-in-the-loop' systems where AI serves as an assistant rather than a replacement for human capabilities, particularly in complex tasks requiring human judgment. His publications demonstrate significant contributions across multiple domains including AI ethics, creative writing assistance, political language analysis, and healthcare applications. His research shows a clear trajectory toward developing AI systems that enhance human capabilities while addressing important ethical considerations in AI deployment. Sloan Research Fellowship Dr. Tan's research program examines how machine learning can be integrated into human workflows to augment human capabilities rather than replace them. His laboratory investigates applications across social media, healthcare, creative writing, and political discourse analysis, with current projects focusing on AI platform moderation, detection of subtle signals in professional communication, and computational analysis of social dynamics.

Jinjin GuView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Computer Vision
• +7 more

Jinjin Gu is a tenure-track Assistant Professor at Sofia University "St. Kliment Ohridski" 's INSAIT (Institute for Computer Science, Artificial Intelligence, and Technology), leading research on visual cognition and intelligence. Her work spans visual perception, processing, generation, and reasoning. Education: Ph.D. in Electrical and Computer Engineering (2024), University of Sydney B.Sc. in Computer Science and Engineering (2020), Chinese University of Hong Kong, Shenzhen Her research focuses on visual cognition , including agentic systems , diffusion models , GAN architectures , model interpretability , super-resolution , and multimodal vision-language systems . She has developed novel paradigms like HYPIR for diffusion-quality restoration at GAN speeds. Recent publications highlight advancements in image/video restoration , generative modeling , and visual reasoning . Her work addresses critical challenges in model generalization , causal interpretation , and real-world application robustness . Scientific Awards: Stanford University's World's Top 2% Scientists (2024) Yunfan Award at World Artificial Intelligence Conference (WAIC) (2023) She has advised students contributing to TPAMI, CVPR, and ICLR publications, and serves as Area Chair for ICLR 2026, NeurIPS 2025, and ICML 2025.

• Computer Architecture
• Interconnection Networks
• Approximate Computing
• +5 more

Natalie Enright Jerger is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She holds the Canada Research Chair in Computer Architecture and serves as Director of the Division of Engineering Science (2023-2028). Previously, she was the Percy Edward Hart Professor (2016-2019). She received her B.S. in Computer Engineering from Purdue University (2002), and M.S./Ph.D. in Electrical Engineering from University of Wisconsin-Madison (2004/2008). Her research focuses on: Multi/many-core architectures and on-chip networks Cache coherence protocols and memory hierarchy optimization Approximate computing and sustainable systems Intermittent computing for energy-harvesting devices Hardware acceleration for machine learning Her publications demonstrate strong emphasis on networks-on-chip (NoC) innovations, including routing algorithms, deadlock handling, power-efficient designs, and topology optimizations. Recent work expands into approximate computing, mobile architectures, and ML-driven hardware design. Major Awards: Fellow of Engineering Institute of Canada (2023) McLean Senior Fellow (2019) IEEE Micro Top Picks (2016) ACM/IEEE Microarchitecture Hall of Fame (2015) Sloan Research Fellowship (2015) Canada Research Chair (current) Distinguished Scientist, ACM Fellow, IEEE She leads the NEJ research group and collaborates with industry partners including Intel, AMD, Qualcomm, and IBM. Her work is funded by NSERC, CFI, and industrial grants. She co-chaired ASPLOS 2023 and HPCA 2014, and actively promotes diversity through WICARCH and ACM initiatives.

Monica DeHartView profile

Professor

• Cultural Anthropology
• Economic Development
• China-Latin America Relations
• +3 more

Monica DeHart is a Professor of Sociology and Anthropology and Director of the Global Development Studies program at the University of Puget Sound. Her work focuses on the cultural politics of economic development in Central America, particularly exploring how gender, class, race, and ethnic differences shape development projects and their outcomes. She has conducted ethnographic research in Costa Rica, Nicaragua, and Guatemala for over two decades, with recent emphasis on China-Central America relations. Her forthcoming book Transpacific Developments: The Politics of China and Chineseness in Central America examines the multifaceted roles of China, Taiwan, and local diaspora communities in the region. Education: B.A., University of California, Davis (1994) M.A., Stanford University (1997) Ph.D., Stanford University (2001) Research Interests: DeHart’s work critiques neoliberal development paradigms and explores how transnational actors and institutions redefine development. Her studies often highlight contradictions in policies that reframe ethnic identity as a tool for economic growth. Recent projects analyze China’s growing influence in Central America, including infrastructure investments, cultural diplomacy, and anti-corruption policies. She emphasizes the intersection of local and global dynamics in shaping development outcomes. Key Themes in Publications: Her articles and books address topics such as China’s transpacific political economy, the role of diaspora communities, and the historical legacies of anti-Chinese sentiment. She also examines how language institutes and infrastructure projects serve as geopolitical tools. Her work bridges anthropological theory with critical analyses of global development trends. Professional Activities: DeHart teaches courses on global development, anthropological theory, and cultural studies. She is currently on leave for Spring 2026. Her office is located in McIntyre 213P, and she can be reached at mdehart@pugetsound.edu.

Olivia Di MatteoView profile

Assistant Professor

• Quantum Computing
• Quantum Software Engineering
• Quantum Compilation
• +5 more

Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.

Azadeh Ghari-NeiatView profile

Senior Lecturer

• Internet of Things (IoT)
• Mobile Computing
• Crowdsourcing
• +3 more

Dr. Azadeh Ghari-Neiat is a Senior Lecturer in Software Engineering at the University of Queensland's School of Electrical Engineering and Computer Science. She completed her PhD in Computer Science from RMIT University in 2018. Prior to joining UQ, she held academic positions at Deakin University as a Senior Lecturer and at the University of Sydney as a postdoctoral research fellow. Her research focuses on the intersection of Internet of Things (IoT), Mobile Computing, Crowdsourcing, and Cybersecurity. She develops innovative solutions for enhancing connectivity and security in modern computing environments through crowdsourced approaches. Key areas include service composition in sensor clouds, trust management frameworks, and optimization of drone-as-a-service systems. Her publications demonstrate consistent focus on IoT service ecosystems, with recent work exploring blockchain applications and machine learning techniques for dynamic systems. The research trends show evolution from fundamental service composition to AI-driven optimization in distributed environments. Dr. Ghari-Neiat leads projects involving energy service crowdsourcing and secure architectures for cyber-physical systems. Her work maintains strong emphasis on practical applications in delivery systems, UAV networks, and IoT marketplaces.

• Global Navigation Satellite Systems (GNSS)
• Avionics Systems
• Wireless Communication
• +7 more

René Jr Landry is a full Professor in the Department of Electrical Engineering at École de technologie supérieure (ETS), Université du Québec, specializing in Global Navigation Satellite Systems (GNSS), avionics, and wireless communication technologies. His academic journey includes a B.Ing. from Polytechnique Montréal, M.Sc. from University of Surrey (UK), and Ph.D. from SupAréo in Toulouse. He maintains active research leadership through two key laboratories: LASSENA (Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics) and LACIME (Communications and Microelectronic Integration Laboratory). His research spans critical aerospace navigation domains including GNSS signal processing, inertial navigation systems, software-defined radio for avionics, radio frequency interference mitigation, and indoor positioning technologies. Landry's work addresses real-world challenges in satellite navigation robustness, precision positioning in urban/denied environments, and next-generation avionic system security. His current projects focus on blockchain-enhanced IoT security, AI-driven GNSS disruption analysis, and adaptive RF front-ends for multi-band avionics applications. Analysis of his recent publications reveals strong emphasis on resilient positioning systems through multi-constellation integration (particularly Iridium-NEXT), blockchain applications for navigation security, and explainable AI techniques for GNSS signal quality assessment. His work increasingly bridges traditional navigation engineering with cutting-edge security and machine learning paradigms. 2014 Prix d'excellence du c.a. pour les services à la collectivité Landry has supervised over 100 graduate students across doctoral, master's, and research projects since 2005, with current supervision extending through Summer 2025. His research funding supports multiple industry partnerships focused on avionics certification, software-defined radio implementations, and next-generation navigation systems. The LASSENA laboratory under his leadership develops certified avionic products from open-source SDR platforms and advances multi-sensor fusion techniques for challenging navigation environments. His research infrastructure includes specialized facilities for GNSS signal simulation, avionics hardware testing, and multi-sensor integration. Current work emphasizes flight-tested validation of RF front-end technologies, blockchain-secured navigation data, and real-time interference mitigation systems for aviation applications.

Brad KnoxView profile

Associate Professor

• Reinforcement Learning
• Human-Computer Interaction
• Artificial Intelligence
• +4 more

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).

• Parallel Computing
• Optimization Techniques
• High-Performance Computing (HPC)
• +5 more

Pascal BOUVRY is a full Professor of parallel computing and optimization techniques at the University of Luxembourg's Department of Computer Science , within the Faculty of Science, Technology and Medicine (FSTM) . He currently serves as the Dean of the FSTM and leads the Parallel Computing and Optimisation group . Additionally, he oversees the University's High Performance Computing (HPC) infrastructure. His research focuses on parallel computing, optimization algorithms, distributed systems, and their applications in bioinformatics and distributed environments. Prof. BOUVRY holds a Ph.D. in Computer Science from the University of Grenoble (France) and has extensive industry experience, including roles as CEO/CTO of tech firms and leadership in telecom and financial services sectors. He has pioneered initiatives like the Master in High-Performance Computing and Technopreneurship programs at the University of Luxembourg. His research interests span GPU programming paradigms , federated learning , distributed optimization , and trustworthy AI . Recent work includes advancements in HPC education frameworks, federated learning frameworks (e.g., FedPref), and quantum circuit optimization. He contributes to editorial boards of journals like IEEE Transactions on Sustainable Computing and IEEE Cloud Computing Magazine. Prof. BOUVRY has held international leadership roles, including as Honorary Chair of conferences and active participation in technical committees. His educational efforts emphasize bridging academic research with industrial applications, particularly in HPC and AI.

Tommaso GabrieliView profile

Associate Professor

• Urban Economics
• Real Estate Valuation
• Behavioral Economics
• +12 more

Dr. Tommaso Gabrieli is an Associate Professor in Real Estate at the Bartlett School of Planning, University College London (UCL), where he has been employed since September 2015. His academic career spans multiple institutions including the University of Reading, City University London, University of Warwick, and the Catholic University of Milan. His educational background includes: PhD in Economics from the University of Warwick (2009) MSc in Economics from the London School of Economics and Political Science (2003) Fellowship of the Higher Education Academy from the University of Reading (2013) As a theoretical economist trained in the ambrosian tradition of social welfare, Gabrieli's research focuses on the economic analysis of urban policy issues. His expertise encompasses economic modeling of real estate markets, financial viability of urban projects, multi-dimensional value measurements, and value-capture mechanisms. He has developed novel interdisciplinary methods bridging urban planning and design with economics, making him one of few economists actively collaborating with urban planning scholars in the UK. His work addresses Sustainable Development Goals including No Poverty, Good Health, Decent Work, Reduced Inequalities, Sustainable Cities, and Climate Action. His recent publications demonstrate a strong focus on urban design governance, value capture mechanisms, and the interface between economic theory and urban planning practice. The research spans theoretical explorations of post-growth planning and practical applications in land value recovery, particularly examining implications for housing affordability, wealth distribution, and community wellbeing in both urban and rural contexts. His work often integrates behavioral economics with spatial planning considerations. Professional recognition includes: Fellow of the Higher Education Academy Gabrieli has extensive experience supervising PhD and MSc dissertations across multiple institutions. His teaching portfolio includes Real Estate Appraisal and Valuation at UCL, where he leads relevant modules for both undergraduate and graduate programs. He has contributed to significant research projects including 'Street Appeal' commissioned by Transport for London and the Horizon 2020-funded 'UrbanMaestro' project worth 1 million Euros. His research impact has been formally recognized by Transport for London. Currently, he leads the 'Future Urban Growth Lab' project, funded by UCL Knowledge Exchange and Innovation Funding, in partnership with the Royal Town Planning Institute and Politecnico of Turin. This project aims to operationalize an urban growth model prototype for use by local authorities in planning future city development, bridging academic research with practical planning applications.

Charles PerinView profile

Assistant Professor

• Information Visualization
• Human-Computer Interaction
• Personal Visualization
• +5 more

Charles Perin is an Assistant Professor of Computer Science at the University of Victoria, leading the UViz research group. He holds a PhD from Université Paris-Sud (2014) and has held roles including Post-doc at the University of Calgary and Lecturer at City, University of London. His research focuses on information visualization, personal visualization, human-computer interaction, and sports visualization. Education: PhD in Computer Science (2014), Université Paris-Sud; Post-doc at University of Calgary (InnoVis lab); MS and earlier studies in Computer Science and HCI in France. Research interests include designing interactive visualization tools for personal data reflection, health data communication, and sports analytics. He emphasizes authoring tools for non-experts and physical/tangible visualization systems. Recent work explores embedded data physicalizations and mobile visualization design. His articles span topics like data storytelling, patient-generated health visualizations, and soccer data analysis, often appearing in top venues like IEEE VIS, CHI, and Eurovis. He has advised over 20 students across PhD, MSc, and undergraduate levels. Teaching includes courses on Information Visualization and HCI at UVic, City, and other institutions. He co-organized workshops on topics like Personal Visualization and Sports Data at IEEE VIS. His UViz lab collaborates internationally with institutions like Monash University and the National Archives (UK).

Keith A. BrownView profile

Associate Professor

• Hierarchical Soft Matter
• Autonomous Experimentation
• Scanning Probe Techniques
• +4 more

Keith A. Brown is an Associate Professor in Mechanical Engineering at Boston University's College of Engineering with additional appointments in Materials Science & Engineering and Physics. He serves as Associate Chair for Graduate Programs in ME and leads the interdisciplinary KABLab research group. Education: PhD, Harvard University Dr. Brown's research centers on hierarchical soft matter systems including polymers and smart fluids. His group develops innovative approaches to accelerate materials research through nanocombinatorics , autonomous experimentation , and scanning probe lithography . Key focus areas include connecting nanoparticle properties to bulk smart fluid behavior, designing 3D-printed structures with programmed mechanics, and creating self-driving laboratories for materials discovery. His recent publications (2024-2025) demonstrate a strong emphasis on autonomous experimentation systems integrating machine learning with physical research. This work spans energy-absorbing foam design, nanoscale fluid manipulation, and physics-informed modeling for mechanical systems, establishing new paradigms in accelerated materials development. Scientific Awards: The Early Career Research Excellence Award, College of Engineering, 2021 Professor of the Year, Mechanical Engineering, 2020 Frontiers of Materials Award, The Minerals Metals and Materials Society (TMS), 2020 Dean’s Catalyst Award (2018) Dean’s Catalyst Award (2020) Moorman-Simon Interdisciplinary Career Development Professor, 2016 Dr. Brown teaches undergraduate courses including Fluid Mechanics (ME 303), Introduction to Materials (ME 306), and Nanomanufacturing (ME/MS 576). His research is supported by: Federal Grants : AFOSR MURI, NSF Nanomanufacturing, ACS Petroleum Research Fund Foundations : Gordon and Betty Moore Foundation Industry : Google Faculty Research Award University : BU Dean's Catalyst Award, Nanotechnology Innovation Center The KABLab employs interdisciplinary teams to develop novel instrumentation for hierarchical soft matter research, with particular expertise in autonomous experimentation platforms that combine scanning probe techniques with machine learning for accelerated materials discovery.

Tom ConteView profile

Professor

• Computer Architecture
• Compiler Optimization
• Manycore Architectures
• +4 more

Tom Conte is an academic leader with a joint appointment in the School of Electrical & Computer Engineering and School of Computer Science at Georgia Institute of Technology. As the founding director of the Center for Research into Novel Computing Hierarchies (CRNCH), he specializes in computer architecture and compiler optimization. His work focuses on manycore architectures, energy-efficient microprocessor design, and embedded system architectures. Prior to Georgia Tech, he directed the Center for Embedded Systems Research at North Carolina State University. He holds IEEE Fellow status and served as 2015 President of the IEEE Computer Society, co-leading the IEEE Rebooting Computing Initiative since 2011. Dr. Conte earned his bachelor’s degree in Electrical Engineering from the University of Delaware (1986), followed by M.S. and Ph.D. degrees in Electrical Engineering from the University of Illinois at Urbana-Champaign (1988 and 1992). His research has been recognized with prestigious awards including the IEEE Computer Society’s Golden Core Member award and the National Science Foundation’s CAREER Award (1996). His research interests span quantum computing, 3D chip architectures, energy-efficient processing, and post-Moore computing innovations. He has pioneered initiatives like the Superstrider architecture and CREEPY energy-efficient processing frameworks. Recent work includes advancements in quantum programming languages (e.g., Qwerty) and hybrid quantum-classical systems. Awards: IEEE Fellow, Young Alumni Achievement Award, CAREER Award Leadership: IEEE Computer Society President (2015), CRNCH Director Key Projects: Rebooting Computing Initiative, Superstrider Architecture His lab’s contributions include novel compiler optimizations for manycore systems, smart NIC offloading techniques, and thermodynamically inspired computing models. Conte’s work bridges academic research with industry needs through interdisciplinary collaborations and standardization efforts.