Find Academic Supervisors

Catherine De WolfView profile

Assistant Professor

• Circular Engineering
• Sustainable Construction
• Digital Innovation
• +3 more

Prof. Catherine De Wolf is an Assistant Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering and Deputy Head of the Institute of Construction and Infrastructure Management. She leads the Chair of Circular Engineering for Architecture (CEA), an interdisciplinary lab focused on automating building material reuse through digital innovation. Her work bridges academia, government, and industry, exemplified by partnerships with institutions like the Centre Pompidou and initiatives like Anku and the Digital Circular Economy (DiCE) Lab. She is also a faculty member at ETH Zurich's AI Center and collaborates with EMPA's Urban Energy Systems Lab. Her roles include Chair of the Design++ Advisory Board and PI in the National Centre of Competence in Research on Digital Fabrication (DFAB). Education: Catherine holds a PhD in Building Technology from MIT, with prior studies in Civil Engineering and Architecture at VUB and ULB. She has additional training in documentary filmmaking and postdoctoral research at the University of Cambridge and EPFL's Structural Xploration Lab, funded by Marie Sklodowska-Curie and Swiss Excellence scholarships. Research Interests: Her work centers on digital tools for circular construction, including blockchain-based material passports, AI-driven design, and automated deconstruction planning. Key areas include: Material reuse and lifecycle analysis Building Information Modeling (BIM) applications Decentralized data networks in construction Carbon reduction in structural systems Interdisciplinary collaboration across engineering, architecture, and computer science Awards: Marie Sklodowska-Curie Postdoctoral Fellowship (European Commission) Swiss Excellence Scholarship Advising & Grants: Catherine has overseen projects funded by EU initiatives and industry partnerships. Her research group actively collaborates with firms like Arup and Thornton Tomasetti through initiatives such as the Structural Engineers 2050 Commitment. She advises on policy frameworks like the EU's Level(s) sustainability standard. Labs & Teams: Her CEA lab operates with 20+ researchers and has pioneered tools like the '5D Digital Circular Workflow.' The lab's work is showcased in real-world projects such as the Centre Pompidou material reuse case study.

Anu LehtovuoriView profile

Lecturer

• Electronics Engineering
• Nanoengineering
• Antenna Design
• +3 more

University Lecturer Anu Lehtovuori is affiliated with the Department of Electronics and Nanoengineering at Aalto University, where she actively bridges teaching and research. Her roles encompass academic teaching, project leadership, grant writing (e.g., Academy of Finland applications), and collaboration with doctoral students. She specializes in cutting-edge topics such as antenna design for 5G/6G systems, reconfigurable MIMO architectures, and RF technology for mobile devices. Her research focuses on optimizing antenna performance in compact environments, mitigating interference, and enhancing wireless communication efficiency. Notable areas include wideband antenna systems, decoupling techniques for multi-element arrays, and adaptive antenna-amplifier integration. Lehtovuori emphasizes practical applications, addressing challenges like user interaction effects on mobile antenna performance and minimizing electromagnetic emissions. Research Trends: Dominant themes include 6G IoT antenna solutions, mmWave component integration, and reconfigurable systems leveraging mutual coupling. Grants: Actively pursuing funding through initiatives like the Academy of Finland. Her contributions span both theoretical advancements (e.g., bandwidth optimization algorithms) and industrial applications (e.g., antenna cluster techniques for full-screen smartphones). While no specific awards are documented, her work reflects a strong focus on impactful, industry-relevant innovations.

Christopher GilliamView profile

Assistant Professor

• Signal Processing
• Radar Systems
• Sampling Theory
• +5 more

Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +3 more

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.

• Privacy-Preserving Algorithms
• Differential Privacy
• Federated Learning
• +6 more

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Libera Università di Bolzano (2018) and a Diploma in Media Informatics from the University of Munich (2014). His research focuses on developing algorithms and data structures for managing large and sensitive datasets, with a particular emphasis on privacy-preserving techniques like differential privacy and federated learning. He has held postdoctoral roles at the University of Warwick and King's College London before joining QMUL in 2023. Research Interests: Privacy-preserving algorithms, differential privacy, federated learning, data management systems, randomized algorithms, and efficient query processing. His work bridges theoretical foundations with practical applications, aiming to enable secure data sharing while preserving individual privacy. Teaching: Leads undergraduate modules in Database Systems and Operating Systems at QMUL. His teaching emphasizes foundational concepts in computer science through rigorous coursework and practical projects. Grants and Funding: Currently supervises a PhD studentship titled 'Privacy-Preserving Algorithms: Unlocking Data Sharing for Medical Sciences & Machine Learning', funded by QMUL and open to UK home students. The role involves exploring privacy-preserving frameworks for collaborative data analysis. Professional Contributions: Serves as a reviewer for top-tier conferences (NeurIPS, ICML, SIGMOD, ICDE) and journals (IEEE TKDE, Data & Knowledge Engineering). Actively involved in conference organization, including NeurIPS Area Chair (2024) and ICDT Proceedings Chair (2024). Labs and Collaborations: Affiliated with the Centre for Fundamental Computer Science at QMUL, fostering interdisciplinary research in theoretical and applied computing. Engages with industry partners on privacy-enhancing technologies and data management solutions.

Dharik MallapragadaView profile

Assistant Professor

• Energy System Decarbonization
• Low-Carbon Technologies
• Optimization Modeling
• +4 more

Dr. Dharik Mallapragada is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at NYU Tandon School of Engineering, affiliated with the Center for Advanced Technology in Telecommunications (CATT). His research focuses on mathematical modeling to analyze low-carbon technologies and their integration into energy systems, emphasizing decarbonization pathways for developed and developing economies. He holds a B.Tech from IIT Madras and a Ph.D. in Chemical Engineering from Purdue University. Key research areas include optimizing energy infrastructure planning, electricity market designs for decarbonization, and techno-economic assessments of hydrogen production and storage. Recent work explores repurposing coal plants into thermal storage, evaluating rate structures for renewables-dominant grids, and analyzing India’s energy transition challenges. Mallapragada’s work bridges computational techniques like optimization modeling and data analytics with real-world policy and geographical contexts. Publications highlight infrastructure synergies between power and gas systems, hydrogen production pathways, and electrification barriers in heavy industries. His research underscores the importance of flexible energy systems and equitable policy frameworks, particularly for emerging economies. The Sustainable Energy Transitions (SET) group he leads collaborates globally to address climate mitigation challenges through analytical tools and cross-sectoral solutions.

Umut DurView profile

Professor

• Market Design
• School Choice
• Matching Theory
• +3 more

Umut Dur is the Truist Distinguished Professor of Economics and Director of Graduate Programs at the Department of Economics, North Carolina State University (NC State). He holds a Ph.D. from the University of Texas at Austin (2013) and previously served as a Teaching Assistant at the University of Texas at Austin (2010-2012) and Boğaziçi University (2007-2008). His research focuses on Market Design , School Choice , and Matching Theory , with emphasis on equitable policy implementation in education and labor markets. Dr. Dur has been recognized with Summer Research Awards (2019, 2017) and the Faculty Research Award (2017). His work addresses challenges such as fairness in affirmative action policies, optimization of school assignment mechanisms, and labor market policy design. Notable contributions include analyses of magnet school lotteries, sequential school choice systems, and the impact of reserve policies on educational equity. Dur leads NC State's Economics Graduate Program and contributes to initiatives like the Business Analytics and AI Initiative (BAI) and Supply Chain Resource Cooperative (SCRC) . Key Research Themes: School choice mechanisms, labor policy design, matching algorithms, and policy fairness Awards: Multiple research awards from Poole College of Management Affiliations: Department of Economics, NC State; Graduate Faculty member

James RoscowView profile

Senior Lecturer

• Ferroelectric Materials
• Piezoelectric Energy Harvesting
• Energy Storage
• +3 more

James Roscow is a Senior Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with the Centre for Integrated Materials, Processes & Structures (IMPS), IAAPS, and the Institute of Sustainability and Climate Change. His research focuses on developing ferroelectric composites for energy harvesting, sensing, and energy storage, with expertise in material fabrication, property tuning, and numerical modeling. He holds a PhD in Mechanical Engineering from the University of Bath and a BSc in Materials Science from the University of Manchester. Research interests include porous ferroelectric ceramics, piezoelectric and pyroelectric materials, and their applications in renewable energy and sensors. He has led or contributed to 12 projects funded by organizations like EPSRC and Innovate UK, exploring topics such as low-cost transducers, nanofluid cooling for solar panels, and phase transformations in ceramics. Key publications (2021–2025) address piezoelectric energy harvesting, porous material design, and advanced manufacturing techniques. His work aligns with UN SDGs, particularly sustainable energy and innovation. Roscow supervises PhD students in functional ceramics, energy storage, and sensor technologies. Notable collaborations include projects on hydraulic energy harvesters, SONAR transducers, and self-healing materials. He has contributed datasets on piezoelectric composites and energy storage systems, emphasizing reproducibility and applied research.

William PhanView profile

Assistant Professor

• Market Design
• Game Theory
• School Choice Mechanisms
• +2 more

Dr. William Phan is an Assistant Professor of Economics in the Department of Economics at North Carolina State University's Poole College of Management. He earned his PhD in Economics from the University of Rochester. His research focuses on theoretical and applied market design, particularly: School choice mechanisms and assignment algorithms Matching theory with applications to education and resource allocation Game theory and incentive design Fair division and representation in allocation systems His recent work examines crowding effects in school choice and representation in reserve systems.

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +3 more

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

• Health Economics
• Applied Microeconometrics
• Reproductive Health
• +4 more

Matilde P. Machado is an Associate Professor in the Department of Economics at Universidad Carlos III de Madrid and Director of the Master in Health Assessment and Market Access. She holds a PhD in Economics from Boston University. Her research focuses on Health Economics, Applied Microeconometrics, and the intersection of Education, Labor, and Gender from an Economic History perspective. She led a CAF-funded project on 'Intergenerational Mobility in Latin America' with Ricardo Mora and Karen Olivo. Teaching includes undergraduate courses in Health Economics (taught in both Spanish and English) and postgraduate courses in Industrial Economics (in English). Recent work explores historical parish records to analyze female labor force participation and maternal influences. Her research spans topics such as hospital quality measurement, moral hazard in health services, and healthcare efficiency.

Margarida CARVALHOView profile

Associate Professor

• Algorithmic Game Theory
• Operations Research
• Combinatorial Optimization
• +4 more

Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal. She holds the FRQ–IVADO Research Chair in Data Science for Combinatorial Game Theory and was awarded the 2018 EURO Doctoral Dissertation Award. Her research integrates algorithmic game theory and integer programming to address strategic decision-making challenges in healthcare, urban planning, and sustainability. Education: Bachelor's and Master's in Mathematics PhD in Computer Science (Universidade do Porto, 2016) Postdoctoral Fellow at Polytechnique Montréal (2017) Research Interests: Algorithmic Game Theory and Combinatorial Optimization Applications in Kidney Exchange Programs, EV Infrastructure, and Urban Decision-Making Development of fair and scalable optimization frameworks Awards & Grants: FRQ–IVADO Research Chair (2018–present) Teaching Excellence Award (2024) Lead on projects funded by CRSNG, MITACS, and Fonds de recherche du Québec Lab & Collaborations: Member of CIRRELT, CRM, Mila, and the AI Cybersecurity Lab (talents). Her team focuses on interdisciplinary solutions for complex socio-economic systems.

Stan SclaroffView profile

Professor

• Computer Vision
• Pattern Recognition
• Machine Learning
• +3 more

Stan Sclaroff is a Professor of Computer Science and Dean of the College of Arts & Sciences at Boston University. He holds affiliated faculty status in the Department of Electrical and Computer Engineering. His research focuses on computer vision, pattern recognition, and machine learning, with expertise in tracking, human motion analysis, and multimedia retrieval systems. He co-leads the Image and Video Computing research group and has contributed pioneering work like the ImageRover content-based image retrieval system. Education: PhD in Media Arts & Sciences from MIT (1995), SM from MIT (1991), and BS in Computer Science and English from Tufts University (1984). Research Interests: Human motion tracking, sign language analysis, deformable shape matching, and multimedia indexing. Notable contributions include early work on content-based image retrieval and foundational techniques in video analysis. Honors: IEEE & IAPR Fellowships, NSF CAREER Award (1996), ONR Young Investigator Award (1996), and BU's Mentor of the Year (2018). Over 40+ students advised, many now in academia and industry leadership roles. Key roles: Chair of BU Computer Science (2007–2013), Associate Dean for Mathematical & Computational Sciences (2015–2018), Interim Dean (2018–2019), and current Dean since 2019.

Ashish VenkatView profile

Associate Professor

• Computer Architecture
• Compilers
• Computer Security
• +12 more

Ashish Venkat is an Associate Professor in the Department of Computer Science at the University of Virginia, part of the School of Engineering and Applied Science. He holds a Ph.D. from UC San Diego and has established himself as a leading researcher in computer architecture, compilers, and computer security. Research Interests: His research focuses on cross-disciplinary hardware and software techniques to build secure, high-performance computing systems. He investigates robust exploit mitigations that maintain energy efficiency and programmability, with a particular emphasis on speculative execution, memory safety, hardware security, and privacy-preserving computing. He also explores the application of machine learning to detect security threats and model execution behavior. Publication Trends: His recent publications (2020–2025) demonstrate a strong focus on hardware-based security, particularly microarchitectural vulnerabilities (e.g., micro-op cache attacks), memory safety via microcode capabilities, and secure accelerators for bioinformatics. There is a consistent trend of publishing in top-tier venues like ISCA, MICRO, IEEE S&P, and USENIX Security, often featuring novel hardware/software co-design solutions. Scientific Awards: NSF CAREER Award (2023) NSF CRII Award (2018) IEEE Micro Top Pick (2019) IEEE Design & Test Top Pick (2020, 2021) HPCA Best Paper Runner-Up (2019) DATE Best Paper Nominee (2023) UVA Research Achievement Award (2023) ISCA Prolific Author of the Decade (2013–2022) Advising and Grants: He actively mentors graduate and undergraduate students, many of whom have pursued advanced degrees or joined leading tech companies. He has secured significant funding as PI or co-PI from NSF, DARPA, SRC, and Intel, including a $4.9M DARPA HERCULES grant and an NSF CAREER award. His projects focus on holistic security solutions, speculative optimization, and privacy-preserving machine learning frameworks. Labs and Teams: He leads a research group focused on secure and efficient computing systems, collaborating with researchers at institutions like UC San Diego, UC Riverside, and UC Irvine, as well as industry partners including Intel and IBM.

• Control Systems
• Petroleum Engineering
• Machine Learning
• +3 more

Alexey Pavlov is a Professor of Petroleum Cybernetics at the Department of Geosciences and Petroleum, Norwegian University of Science and Technology (NTNU). He holds an MSc in Applied Mathematics from St. Petersburg State University, a PhD in Mechanical Engineering from Eindhoven University of Technology, and has industrial R&D experience from Statoil and Ford Motor Co. Education: MSc (Applied Mathematics, St. Petersburg State University), PhD (Mechanical Engineering, Eindhoven University) His research focuses on control systems for petroleum engineering applications, including nonlinear control theory, iterative learning control, and data-driven optimization methods. Publications reveal a strong emphasis on real-time drilling optimization, well integrity monitoring, and synchronization in networked systems. Recent work trends include machine learning integration for oil well monitoring, moment matching in model reduction, and extremum seeking control for multi-agent systems. Collaborations span institutions like Ford Motor Co., Statoil, and Eindhoven University of Technology. Current affiliations include the Department of Geosciences and Petroleum at NTNU. No scientific awards or advisee information is explicitly mentioned in the provided texts.