Find Academic Supervisors

Justin GottschlichView profile

Academic

• Machine Programming
• Artificial Intelligence
• Software Engineering
• +3 more

Justin Gottschlich is an Adjunct Lecturer in the Computer Science Department at Stanford University, where he teaches the graduate course Machine Programming (CS 329M) . He also serves as Founder, CEO, and Chief Scientist of Merly Inc., a startup focused on machine programming systems to improve software development efficiency and quality. Previously, he led the Machine Programming Research group at Intel Labs, pioneering advancements in automating software development through a fusion of machine learning, programming languages, and systems research. His academic roles include prior positions as Adjunct Professor at University of Colorado-Boulder and Adjunct Assistant Professor at University of Pennsylvania. He has advised numerous graduate students across institutions, contributing to their research in machine programming and related fields. Gottschlich has authored dozens of research papers and holds multiple patents, with his work highlighted by prominent outlets like the Wall Street Journal and Communications of the ACM. He actively contributes to academic committees, including serving as Steering Committee Chair for the ACM SIGPLAN Machine Programming Symposium (MAPS). His research interests span machine programming, autonomous software development, formal methods, and the integration of AI into software engineering practices. Education: PhD in Computer Science from University of Colorado-Boulder Keynote Engagements: LADSIOS (2021), MIT DSAIL (2021), Penn PRECISE (2019) Labs/Teams: Intel Labs Machine Programming Research Group, Merly's R&D Team Grants & Funding: Extensive industry and academic research funding through Intel Labs and Merly

• Optical Networking
• High-Speed Networks
• Data Center Technologies
• +6 more

Emmanouel (Manos) Varvarigos is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), where he leads the High Speed Communication Networks Laboratory. He holds a Diploma from NTUA (1988) and an M.S./Ph.D. from MIT (1990/1992). Prior roles include Professorships at UC Santa Barbara (1992-1998) and Delft University (1998-1999), and leadership at the University of Patras (1999-2015). He has coordinated over 30 EU research projects, including H2020 initiatives like ORCHESTRA and FLEXGRID. His research focuses on optical networking, cloud computing, smart energy grids, and high-speed network protocols. He has published over 450 papers and serves on numerous conference committees. Awards include the NSF Research Initiation Award. His lab employs 5 postdocs and 10+ PhD students, addressing challenges in networking, data centers, and grid computing. Education: Ph.D., Electrical Engineering and Computer Science, MIT, 1992 M.S., Electrical Engineering and Computer Science, MIT, 1990 Diploma in Electrical and Computer Engineering, NTUA, 1988 Research Interests: Optical networking, high-speed network protocols, data center architectures, cloud computing, smart energy grids, wireless networks, and distributed systems. Grants & Projects: Coordinated 7 EU projects (e.g., FLEXGRID, ORCHESTRA) and participated in 35+ others. National projects include roles as Scientific Director of the Greek School Network and CTI’s Network Technologies Division. Labs & Teams: Directs the High Speed Communication Networks Lab (HSCNL), collaborating with global institutions on optical networks, edge cloud, and smart grids. The lab’s work includes EU-funded projects on 5G, data centers, and renewable energy integration.

Juan AlonsoView profile

Professor

• Aerospace System Design
• Computational Fluid Dynamics
• Multi-Disciplinary Optimization
• +7 more

Prof. Juan Alonso is the Vance D. and Arlene C. Coffman Professor and James & Anna Marie Spilker Chair in the Department of Aeronautics & Astronautics at Stanford University. He directs the Aerospace Design Laboratory (ADL), focusing on high-fidelity computational methods for aerospace system design. His expertise spans transonic/supersonic/hypersonic aircraft, rotorcraft, and launch vehicles. Alumni include record-holding teams for human-powered watercraft and lightweight unmanned aerial vehicles. Education: PhD (1997) from Princeton University in Mechanical & Aerospace Engineering; M.A. (1993) Princeton; B.S. (1991) MIT Aeronautics/Astronautics. Research emphasizes multi-disciplinary optimization, numerical methods, and parallel computing applied to advanced aircraft design, sustainable aviation, and UAS systems. Notable contributions include computational design frameworks like SU2 and SUAVE, and initiatives in curriculum development for engineering education. Recent work focuses on: GPU-accelerated CFD solvers, multi-fidelity surrogate models (e.g., VortexNet), contrail simulation frameworks, and battery degradation modeling for electric aircraft. Active in urban air mobility and high-fidelity trajectory optimization for hypersonic systems. Labs/Teams: Aerospace Design Laboratory (ADL) leading open-source computational tools development. Involved in NASA-funded projects and industry partnerships for advanced propulsion systems.

Meghan AllenView profile

Associate Professor

• Computer Science Education
• Teaching Methodologies
• Educational Technology
• +3 more

Meghan Allen is the Associate Head of Undergraduate Affairs and Associate Professor of Teaching in the Department of Computer Science at the University of British Columbia (UBC). She specializes in computer science education, with a focus on inclusive pedagogy, curriculum design, and teaching methodologies. Her work addresses challenges faced by English Language Learners (ELLs), non-majors, and diverse student populations in STEM education. Allen has taught numerous introductory computer science courses, including CPSC 107, 110, 121, and 210, emphasizing practical programming skills and systemic program design. Allen's research spans autograding innovations, student engagement strategies, and the efficacy of online learning tools. She has co-authored studies on technical writing in CS, universal design principles for accessibility, and the role of experiential learning in software engineering education. Her work is informed by partnerships with students and faculty in Scholarship of Teaching and Learning (SoTL). Awards: Incredible Instructor Awards; UBC CS Teaching Award (2007); multiple TA Teaching Awards. Leadership: Oversees undergraduate affairs in the UBC CS Department, including course coordination and academic policies. Publications: 15+ peer-reviewed articles on education innovation, including recent works on autograding systems and ELL support strategies. Her office is located in the ICCS Building room 243, and she actively contributes to departmental initiatives like the CPSC 110 Challenge Exam process. Allen advocates for inclusive curricula and has pioneered strength-based evaluation approaches to improve student outcomes.

H. Jane BaeView profile

Assistant Professor

• Turbulence Modeling
• Computational Fluid Dynamics
• Fluid Mechanics
• +3 more

H. Jane Bae is an Assistant Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. Her research focuses on turbulence modeling, particularly developing high-fidelity computational methods to simulate high-Reynolds-number flows for applications in aircraft design, wind farms, and atmospheric predictions. She integrates machine learning, information theory, and numerical techniques to enhance turbulence modeling efficiency. Education: B.S. in Aerospace Engineering from Caltech (2011), Ph.D. in Mechanical Engineering from Stanford University (2018). She joined Caltech in 2021. Research interests include near-wall turbulence dynamics, resolvent analysis, sparse identification of nonlinear dynamics, and reinforcement learning for wall models in LES. Her work addresses computational cost reduction and model accuracy in complex flow simulations. Awards: 2023 Outstanding Referee Award from Physical Review. Teaching includes courses on fluid mechanics (Ae/APh/CE/ME 101 abc) and turbulence (Ae 239 ab). Her lab combines turbulence theory, high-performance computing, and data-driven methods to study unsteady flows over complex surfaces. Notable contributions include machine learning-based wall models and resolvent analysis frameworks for non-stationary flows.

Negin AlemazkoorView profile

Assistant Professor

• Infrastructure Systems
• Artificial Intelligence
• Renewable Energy
• +7 more

Negin Alemazkoor is an Assistant Professor at the University of Virginia's School of Engineering and Applied Science, specializing in interdisciplinary research on infrastructure resilience. Her work focuses on developing AI-driven methodologies for analyzing interconnected systems like power grids, urban flood models, and transportation networks under uncertainty. Key areas include enhancing grid reliability through multi-fidelity modeling, hurricane evacuation equity analysis, and precision-compression techniques for large-scale data. She co-leads a NSF-funded initiative to democratize AI education in high schools. Her research integrates graph neural networks, physics-informed models, and machine learning to address challenges in energy systems, environmental monitoring, and disaster response. Notable projects include hurricane-induced power outage risk analysis under climate change and precision guarantees for smart-meter data analytics. She emphasizes computational efficiency and multi-fidelity approaches to balance accuracy with resource constraints. Recent contributions span AI applications in flood forecasting, renewable energy integration, and infrastructure cybersecurity. Her NSF grant aims to create inclusive AI curricula, reflecting her commitment to education and societal impact. She is affiliated with UVA Engineering’s research initiatives on resilient systems and data-driven decision-making.

Karim SabraView profile

Professor

• Acoustics
• Structural Health Monitoring
• Biomechanics
• +7 more

Dr. Karim Sabra is a Professor at the George W. Woodruff School of Mechanical Engineering , Georgia Institute of Technology, specializing in Acoustics and Dynamics . He holds a Ph.D. from the University of Michigan (2003) and joined Georgia Tech in 2007 as an Assistant Professor. His research integrates theoretical and experimental approaches to study wave propagation in diverse fields including structural health monitoring, biomechanics, and ocean acoustics. Key areas of focus include passive imaging techniques using ambient noise and diffuse wave fields, with applications in non-invasive monitoring of mechanical systems and seismoacoustic environments. Education: Ph.D., University of Michigan, 2003 M.S., University of Michigan, 2000 M.Sc., École Nationale Supérieure de Techniques Avancées (France), 2000 Research Interests: Dr. Sabra’s work spans acoustics, structural health monitoring, biomechanical systems evaluation, underwater acoustics, and geophysics . Recent projects include developing passive elastography techniques for soft tissues using physiological vibrations and exploring ambient noise-based tomography for ocean environments. His interdisciplinary approach bridges multi-scale engineering challenges with multi-wave tools (acoustical, electrical, optical). Publications: His work focuses on advanced acoustic technologies, including underwater communication systems, passive acoustic identification tags, and ray-based tomography methods. Themes include seamount effects on sound propagation, machine learning for acoustic modeling, and environmental sensing using shipping noise. Awards: R. Bruce Lindsay Award (2011) Fellow of the Acoustical Society of America (2007) Institute of Acoustics A.B. Wood Medal (2009) Advising & Grants: Dr. Sabra mentors graduate students in acoustics and wave phenomena, emphasizing interdisciplinary collaboration. His research is supported by grants focused on underwater acoustics, environmental sensing, and biomedical applications. Labs/Teams: His research group develops novel sensors and algorithms for oceanographic and biomedical applications, collaborating with industry and academic partners.

Stefano MarkidisView profile

Professor

• High-Performance Computing
• Quantum Computing
• Computational Fluid Dynamics
• +5 more

Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.

• Data Science
• Machine Learning
• Harmonic Analysis
• +7 more

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Diego DonzisView profile

Professor

• Fluid Dynamics
• Turbulence
• Turbulent Mixing
• +3 more

Diego Donzis is a Professor in the Department of Aerospace Engineering at Texas A&M University, affiliated with the College of Engineering. He holds the Presidential Impact Fellow title. His work focuses on high-performance computing for fluid dynamics, particularly compressible turbulence, turbulent mixing, and shock-turbulence interactions. Donzis earned his Ph.D. and M.S. in Aerospace Engineering from the Georgia Institute of Technology. Research interests include large-scale simulations of turbulent flows, thermal boundary condition effects on turbulence, and the development of advanced numerical methods like Selected-Eddy Simulations (SES) for extreme-scale computing. His studies explore universality in turbulence scaling, energy spectra dynamics, and the interplay between compressibility and fluid mixing. Publications emphasize turbulence decay laws, shock-turbulence interactions, and the role of thermal non-equilibrium in turbulent flows. Notable contributions include advancing asynchronous algorithms for exascale CFD and analyzing density gradient statistics in compressible turbulence. Awards include the Presidential Impact Fellow distinction. Donzis collaborates on grants such as the Frontera Travel Grant for compressible turbulence research. His work bridges computational methods with fundamental fluid dynamics, addressing challenges in both numerical accuracy and physical modeling.

Diane GuignardView profile

Assistant Professor

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +6 more

Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.

• Robotics
• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• +9 more

Nabil Aouf is a Professor of Robotics and Autonomous Systems in the Department of Electrical and Electronic Engineering at City, University of London, a position he has held since January 2019. Previously, from 2006 to 2018, he was Professor of Autonomous Systems at Cranfield University’s Defence and Security campus, where he also served as Head of the System and Autonomy Group and Research Lead of the Centre of Electronic Warfare, Information and Cyber. He earned his PhD in Electrical Engineering from McGill University Faculty of Engineering between 1999 and 2002. His research focuses on Robotics, Autonomous Systems, UAV Navigation, Computer Vision, and Fault-Tolerant Control . Key areas include visual odometry, sensor fusion (vision/IMU, RGBD, thermal-visible), robust control for UAVs, fault diagnosis in inertial systems, 3D perception, and autonomous landing. His work integrates theoretical control methods with real-time implementation in aerospace and defense contexts. His recent publications reflect a strong emphasis on robust optimization, multispectral vision, and real-time autonomous navigation. Trends indicate a focus on enhancing autonomy under uncertainty—through illumination-invariant stereo matching, L∞ optimization, and robust feature matching—particularly for UAVs operating in challenging environments. Nabil Aouf has collaborated extensively with researchers such as M. Richardson, O. Araar, T. Mouats, and M. Boulekchour across numerous projects in UAV control, sensor fusion, and autonomy. While no scientific awards are listed in the provided text, his leadership roles and sustained publication record in high-impact journals and conferences underscore his academic contributions. He has supervised or collaborated with several advisees including S.H. Almutairi, L. Chermak, I. Vitanov, and D. Nam, contributing to both theoretical developments and practical implementations in autonomous systems. His work has applications in aerospace, defense, planetary exploration, and critical infrastructure inspection.

Martin AchmusView profile

Professor

• Geotechnical Engineering
• Offshore Engineering
• Soil Mechanics
• +3 more

Prof. Dr.-Ing. Martin Achmus is a Professor at Leibniz University Hannover, affiliated with the Faculty of Civil Engineering and Geodetic Science and the Institute for Geotechnical Engineering. He holds multiple leadership roles, including Designated Dean of Civil Engineering and Executive Director of the Institute of Geotechnical Engineering. His research focuses on geotechnical engineering challenges in offshore wind energy, cyclic soil-pile interactions, and foundation design for renewable energy infrastructure. Positions: Designated Dean, Executive Director (Institute of Geotechnical Engineering), Board Member (Testzentrum Tragstrukturen), Exchange Coordinator (Civil Engineering/Computer-Assisted Engineering). Key Projects: Collaborative Research Center (SFB) 'Offshore-Megastructures', DFWind, ProBucket, Ho-Pile, and SEALENCE, focusing on offshore foundations, cyclic loading, and soil-structure interactions. Research Interests: Load-bearing behavior of offshore foundations, soil-pipeline interaction, cyclic loading effects on piles, and geotechnical design methodologies. His work bridges experimental testing (e.g., model tests, particle image velocimetry) and numerical simulations to address challenges in renewable energy infrastructure and geotechnical systems. Publications: Over 150 peer-reviewed articles, emphasizing cyclic soil responses, monopile foundations, suction bucket behavior, and district heating pipeline interactions. Recent work explores novel p-y curve models, capacity degradation under cyclic loading, and scaling approaches for sand behavior. Grants & Collaborations: Funded by DFG, BMWi, and EU initiatives. Collaborates with industry partners on offshore wind turbine foundations and geotechnical innovations. Labs/Teams: Leads research teams within the Institute of Geotechnical Engineering, focusing on experimental and computational geotechnics, with active participation in national and international research networks.

Felix MotzoiView profile

Associate Professor

• Quantum Technologies
• Optimal Control Theory
• Machine Learning
• +6 more

Prof. Felix Motzoi is an Associate Professor at the University of Cologne and Division Leader & Head of the 'Automatic Optimization, Control and Design' group at the Peter Grünberg Institute (PGI-8) in Jülich. His research focuses on advancing quantum technologies, including superconducting and semiconducting architectures, trapped cold atoms/ions, Rydberg qubits, and long-range entanglement. He leads theoretical efforts in quantum control theory, machine learning applications, hardware co-design, and error mitigation strategies. Key research areas include developing optimal control methodologies (e.g., DRAG, STA), numerical optimization, and dynamics modeling for quantum systems. His work bridges theoretical frameworks with experimental implementations, emphasizing practical solutions for scalable quantum computing. Recent publications highlight innovations in quantum gate design, error suppression via pulse shaping, and hybrid optimization techniques combining machine learning with physics-driven approaches. His team collaborates across disciplines to address challenges in qubit coherence, entanglement stabilization, and robust quantum processing.

Jacob FishView profile

Professor

• Multiscale Computational Science
• Computational Mechanics
• Homogenization Methods
• +5 more

Jacob Fish is the Robert A.W. and Christine S. Carleton Professor and Chair of the Department of Civil Engineering and Engineering Mechanics at Columbia University. He directs the Multiscale Science and Engineering Center and leads Columbia's Computational Science and Engineering initiative (iCSE), coordinating 65+ faculty. With 35 years of pioneering research, he specializes in multiscale computational methods bridging aerospace, automotive, and healthcare industries. His research integrates multiscale computational science with applications in: Homogenization and reduced-order methods for complex materials Stochastic modeling of heterogeneous systems Coupled thermo-chemo-electro-mechanical processes Data-physics driven frameworks for industrial processes Recent work emphasizes AI-enhanced modeling for composites, porous media, and environmental systems. His 15 most recent publications (2023-2025) demonstrate strong trends toward: Data-physics integration in manufacturing (e.g., resin transfer molding) Multiscale environmental applications (canopy flows, CO2 mineralization) Advanced numerical methods (discontinuous Galerkin, solver-free homogenization) Digital twin development for composite lifecycle management Scientific Awards & Honors: 2018 JSCES Grand Prize 2010 IACM Computational Mechanics Award 2005 USACM Computational Structural Mechanics Award 2003 Rensselaer Research Award Fellowships: AAM, USACM, IACM Two Best Paper awards He founded the commercial Multiscale Designer software suite (250+ global clients) and secured major grants including an NSF-DFG collaboration on thermoplastic interfaces. His textbooks are used in 200+ universities worldwide. Leads the Multiscale Science and Engineering Center focusing on industrial-scale computational challenges and mentors researchers through Columbia's iCSE initiative. Former President of USACM and current IACM Vice-President for the Americas.