Find Academic Supervisors

Tamara LorenzView profile

Associate Professor

• Human-Robot Interaction
• Human-Machine Interaction
• Human Movement Coordination
• +5 more

Tamara Lorenz is an Associate Professor at the University of Cincinnati, jointly appointed across three departments: Psychology, Mechanical and Materials Engineering, and Electrical Engineering and Computation Science. She holds a MSc (Dipl.-Ing.) in Mechanical Engineering from Technische Universität München (2008) and a Ph.D. in Systemic Neurosciences from Ludwig-Maximilians University (2015), with research focused on human interaction dynamics and their application to Human-Robot Interaction (HRI), rehabilitation engineering, and human factors. Research Interests: Her work explores human-robot collaboration, movement coordination, and embodied cognition. Key areas include HRI in manufacturing, rehabilitation exoskeletons, and immersive training modalities. She employs nonlinear dynamics and behavioral modeling to enhance human-machine interaction safety and efficiency. Recent Research Support: Notable grants include development of neurophysiologically integrated exoskeletons (2016–2018), Industry 4.0/5.0 AI projects (2023–2024), and studies comparing immersive vs. traditional training effectiveness (2023). She has led projects on trustworthy AI for autonomous systems and human-centered robotics. Courses Taught: Undergraduate and graduate courses on human factors, human-robot interactions, and perception-action cycles. Examples include 'Capstone Human Factors' and 'Humans, Machines, Robots and their Interactions.' Labs/Teams: Her research integrates interdisciplinary teams from engineering, psychology, and robotics, focusing on real-world applications such as industrial automation and healthcare robotics.

Paulo Cesar Tabares-VelascoView profile

Associate Professor

• Building Energy Simulation
• Thermal Storage
• Green Roofs
• +3 more

Dr. Paulo Cesar Tabares-Velasco is an Associate Professor in the Mechanical Engineering Department at the Colorado School of Mines. His research focuses on building energy simulation, thermal storage, green roofs, and smart grid integration. Previously, he worked at the National Renewable Energy Laboratory (NREL), developing tools like BEopt and advancing phase change material (PCM) modeling in EnergyPlus. He holds a PhD from Penn State University, an MSc from Colorado State University, and a BSc from the Monterrey Institute of Technology. His work has earned multiple awards, including the 2009 Crosby Field Award and the 2011 Green Roof Research Award. He leads the Advanced Multiscale Building Energy Research (AMBER) Group and contributes to the Sustainable Energy Undergraduate Program. Key research areas include cross-laminated timber (CLT) thermal performance, district energy systems, and energy storage optimization. Publications highlight innovations in PCM validation, CLT moisture monitoring, and building-grid interaction. He collaborates with national labs and industry partners to advance energy-efficient construction and grid resilience.

Peter Jan van LeeuwenView profile

Professor

• Data Assimilation
• Physical Oceanography
• Geophysical Fluid Dynamics
• +4 more

Peter Jan van Leeuwen is a Professor of Data Assimilation and Physical Oceanography at Colorado State University (since 2018) and a Professor of Data Assimilation at the University of Reading (since 2009). He holds leadership roles, including Director of the Data Assimilation Research Centre (DARC) and former Director of the National Centre for Earth Observation (NCEO). He earned his Ph.D. in Fluid Dynamics from Delft University of Technology (1992) and holds multiple advanced degrees in Theoretical Physics and Astrophysics from Dutch universities. His research focuses on advancing data assimilation techniques (including machine learning), causal discovery in non-intervenable systems, and their applications to geophysical fluids like oceans, atmosphere, and cryosphere. Key areas include ocean dynamics, air-sea interaction, sea-ice prediction, cloud microphysics, and hurricane modeling. He has been funded by NSF, NOAA, NASA, and European agencies like the ERC and NERC. Notable awards include the George T. Abell Outstanding Faculty Research Award (2025) and the ERC Advanced Investigator Award (2016). He has authored over 50 peer-reviewed articles and co-edited the open-access textbook Data Assimilation Fundamentals . Van Leeuwen leads the Center for Nonlinear Data Assimilation, Causal Discovery, and Prediction, emphasizing cutting-edge methods for high-dimensional systems and climate predictability.

Tien D. BuiView profile

Professor

• Digital Image Processing
• Handwritten Document Analysis
• Signal Processing
• +4 more

Tien D. Bui is a Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, Canada. He has been affiliated with Concordia for over 30 years, having joined the university in 1984 as a full professor. Previously, he held positions at McGill University and earned his education from Carleton University, the University of Ottawa, the University of Toronto, and York University. His roles include Chair of the Department of Computer Science (1985–1990) and Associate Vice-Rector Research (1992–1996). He currently serves on multiple research boards, including the Board of Governors at Concordia University and editorial roles for journals like the International Journal of Wavelets, Multiresolution and Information Processing. His research focuses on image processing, document analysis, machine learning, and pattern recognition. Notable contributions include work on wavelet-based image segmentation, handwritten document analysis, and applications of sparse representations in texture classification. He has supervised over 20 graduate students and authored/co-authored over 150 publications, including the book Computer Transformation of Digital Images and Patterns (1989). Bui has held visiting positions at the Istituto per le Applicazioni del Calcolo in Rome and the University of California, Berkeley. He has received significant research grants and served on numerous grant selection committees and federal/provincial panels. His work has practical applications in medical imaging, biometrics, and computational fluid dynamics. Current lab members include doctoral and master’s students focusing on advanced topics like control systems and algorithm design. Bui’s academic leadership extends to teaching courses such as Control Systems and Applications, Mathematics for Computer Science, and advanced design and analysis of algorithms.

Hristo ChipilskiView profile

Assistant Professor

• Data Assimilation
• Artificial Intelligence
• Numerical Weather Prediction
• +2 more

Hristo Chipilski is an Assistant Professor in the Department of Scientific Computing at Florida State University. His research focuses on data assimilation, artificial intelligence, numerical weather prediction, and atmospheric dynamics, combining theoretical, computational, and applied approaches. Education: 2021-2023 ASP Postdoctoral Fellow (NCAR), 2016-2021 PhD in Meteorology (University of Oklahoma), 2012-2016 MMet in Meteorology and Climate (University of Reading, UK) Research Interests: Data assimilation (optimal combination of models and observations) drives his work, particularly for atmospheric systems. He explores AI integration with traditional assimilation methods, targeting convective-scale weather prediction and real-time high-performance computing applications. His studies analyze bore-generating convection, cloud microphysics, and multiscale dynamics. Article Trends: Recent publications emphasize nonlinear ensemble filtering techniques, diffusion models, and generative AI for data assimilation in geophysical systems. Topics span surface quasi-geostrophic dynamics, partial observation handling, and real-time numerical weather prediction (NWP) frameworks optimized for high-performance computing.

Maya EndohView profile

Research Professor

• Polymer Physics
• Materials Science
• Biomedical Engineering
• +3 more

Maya Endoh is a Research Professor in the Department of Materials Science & Engineering at Stony Brook University, where she has held this position since 2015. Her research focuses on polymer structures and deformation mechanisms, utilizing advanced techniques like X-ray scattering, neutron scattering, and microscopy. She investigates molecular-scale structures of polymeric systems, including crystalline organization in nano-confined environments and adhesion dynamics of composites. Her work extends to biomedical applications, particularly microbial interactions with polymer surfaces. Dr. Endoh earned her Ph.D. in Polymer Physics (2005), M.E. in Polymer Chemistry (1997), and B.E. in Polymer Engineering (1995), all from Kyoto University, Japan. She held roles as Senior Research Scientist at Stony Brook University (2012–2014) and Beamline Scientist at NSLS, BNL (2012–2013). Her career includes postdoctoral research at the University of Tokyo and industry experience at Toyobo Co., Ltd. Her research interests span polymer physics, nanostructured materials, and biomedical applications. Notable awards include the 2024 NY State Chancellor's Award for Excellence in Adjunct Teaching and a JSPS Postdoctoral Fellowship. Her publications explore polymer dynamics, anti-biofouling surfaces, and composite material characterization. Recent work emphasizes protein-resistant polymer architectures and nano-scale dynamics in polymer-filler systems. Her contributions bridge fundamental polymer science with practical applications in materials engineering and biomedicine.

Yeongin KimView profile

Assistant Professor

• Flexible Electronics
• Semiconductor Materials
• Neuromorphic Engineering
• +2 more

Yeongin Kim is Assistant Professor in Electrical and Computer Engineering at University of Cincinnati, with secondary appointment in Materials Science and Engineering. His research develops novel electronic materials and devices for flexible/stretchable applications. Research focuses on remote epitaxy, van der Waals epitaxy, and solution-based processing to create soft electronic systems including neuromorphic devices, health monitors, and bio-inspired sensors. Prior to UC, he was Postdoctoral Associate at MIT and Research Assistant at Stanford University.

Christopher JohnsonView profile

Associate Professor

• Continuum Mechanics
• Geophysical Fluid Dynamics
• Granular Materials
• +3 more

Christopher Johnson is a Reader in Nonlinear Dynamics and Applied Mathematics at the University of Manchester, UK, affiliated with the Department of Mathematics and the Manchester Centre for Nonlinear Dynamics. He leads the postgraduate research program in mathematics and supervises PhD students in fluid and granular flows. His research focuses on continuum mechanics, particularly geophysical flows such as avalanches, debris flows, and density currents. He actively promotes interdisciplinary collaboration across fluid dynamics and granular material science. Education: PhD in Mathematics (2011), University of Manchester, supervised by Prof. J.M.N.T. Gray. Research Interests: Continuum mechanics modeling of granular and fluid flows. Geophysical hazards such as debris flows and snow avalanches. Particle segregation and levee formation in granular systems. Dynamic behavior of metamaterials under impact loads. Recent Work: His publications emphasize debris flow dynamics , granular heap stability , and fluid-particle interactions , with contributions to understanding vertical structure effects in granular-fluid systems and metamaterial wave propagation thresholds. His work bridges theoretical models with experimental data from the Manchester laboratories. Advising & Grants: Director of postgraduate research, overseeing ~90 students annually. Collaborates with experimental groups in Switzerland and the USA, and leads projects funded by UKRI and university grants. Active in developing novel multiscale models for energy storage systems and granular dynamics. Labs/Teams: Leads the Manchester Centre for Nonlinear Dynamics, collaborating with the School of Earth and Environmental Sciences and the Materials Science Institute.

Jessica LinView profile

Associate Professor

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +3 more

Jessica Lin is an Associate Professor in the Department of Mathematics and Statistics at McGill University, holding the Canada Research Chair in Partial Differential Equations and Probability (Tier 2). She specializes in stochastic homogenization, elliptic and parabolic equations, and reaction-diffusion systems. Prior to McGill, she was a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison, and earned her Ph.D. from the University of Chicago in 2014 with undergraduate studies at NYU. Her research focuses on partial differential equations and probability, particularly in nonlinear phenomena, stochastic processes, and their applications to mathematical physics and materials science. Key areas include front propagation in stochastic environments, nondivergence form equations, and phase transition models in periodic media. Dr. Lin has contributed to foundational work on quantitative homogenization estimates for elliptic and parabolic equations, asymmetric cooperative motion, and barycentric Brownian systems. Her recent articles explore generalized front propagation, anisotropic surface tensions, and KPZ universality in random growth models. She has been recognized with the Canada Research Chair (Tier 2), and her work spans over 15 peer-reviewed articles since 2013. Her research combines analytical techniques with probabilistic methods, addressing multiscale problems in applied mathematics.

Jim DaiView profile

Professor

• Applied Probability
• Operations Research
• Health Systems
• +3 more

Jim Dai is the Leon C. Welch Professor of Engineering at Cornell University's School of Operations Research and Information Engineering (ORIE). He previously held the Chandler Family Chair at Georgia Institute of Technology (1990–2012) and serves as a Special Term Professor at Tsinghua University and a Visiting Professor at National University of Singapore. His research focuses on applied probability models for service systems, healthcare operations, and transportation networks. He holds a B.S. and M.S. from Nanjing University and a Ph.D. in Mathematics from Stanford University. Research interests include stochastic models for resource allocation in processing networks, healthcare systems, and ridesharing networks. His work emphasizes fluid and diffusion models, Stein’s method, and reinforcement learning. Awards include the Erlang Prize (1998), ACM SIGMETRICS Achievement Award (2018), and the John von Neumann Theory Prize (2024). He is Editor-in-Chief of Mathematics of Operations Research and a Fellow of INFORMS and the Institute of Mathematical Statistics. Teaching spans Markov chains, stochastic processes (both undergraduate and graduate levels), and optimization. Service roles include editorial positions at journals like Stochastic Systems and advisory roles at Singapore University of Technology and Design.

Maria Paz GutierrezView profile

Professor

• Natural Materials Engineering
• 3D Printing in Architecture
• Multiscale Design
• +7 more

Maria Paz Gutierrez is a Professor of Architecture at the University of California, Berkeley, specializing in natural/living materials, 3D printing, and multiscale design. Her research bridges architecture, materials science, and environmental engineering to address sustainability, health, and equity in construction. PhD in Building Science/Materials Engineering, University of Cambridge Master of Architecture, University of Pennsylvania Architectural Diploma, Universidad Finis Terrae, Chile Gutierrez explores material innovation from nano to building scales, focusing on flood resilience, air detoxification via lichen composites, and democratizing 3D printing in remote regions. Her fieldwork spans the Americas, particularly the Northwestern Amazon, and she founded the BIOMS research group. Notable scientific awards include the US NSF Emerging Frontiers in Research and Innovation Award, Bakar Fellowship, Fulbright Fellowship, and shortlisting for the RIBA President's Medal. She holds two provisional patents on 3D printing and is authoring two books. ACSA Creative Achievement Award Evolo Skyscraper Award (Detox Towers) Buckminster Fuller Award (Semifinalist) Sarlo Distinguished Mentorship Award Blue Award at TU Vienna Her work with BIOMS emphasizes bioinspiration, material regeneration, and transdisciplinary approaches. She has contributed to major journals like Science , Nature , and Arq: Architectural Research Quarterly , often highlighting the intersection of culture and technology.

Shaofan LiView profile

Professor

• Artificial Intelligence & Machine Learning in Engineering
• Computational Mechanics
• Finite Element Methods
• +15 more

Shaofan Li is a Professor in the Department of Civil and Environmental Engineering at the University of California, Berkeley. His research spans interdisciplinary applications of computational mechanics, artificial intelligence, and multiscale modeling in civil, environmental, and biomedical engineering contexts. Education: Ph.D., Mechanical Engineering, Northwestern University (1997) M.S., Aerospace Engineering, University of Florida (1993) M.S., Computational Mechanics, Huazhong University of Science and Technology (1989) B.S., Mechanical Engineering, East China University of Science and Technology (1982) Research Interests: Li's work focuses on advanced computational methods for sustainable infrastructure and complex physical systems. Key areas include: Physics-based AI/ML for 3D printing, inverse problems, and autonomous systems Phase-field and peridynamics modeling of fracture mechanics Nonlocal continuum mechanics for pathogen transmission and environmental fluid dynamics Nano/mesoscale characterization of cementitious materials Soft matter mechanics and cellular mechanotransduction Machine learning for crystal plasticity and dislocation dynamics Scientific Awards: International Association of Computational Mechanics Fellows Award (2017) NSF CAREER Award (2003) Laboratory: Berkeley Computational Nano & Soft Material Lab

Arkadiy SimonovView profile

Assistant Professor

• Materials Science
• Crystallography
• Condensed Matter Physics
• +3 more

Prof. Arkadiy Simonov is an Assistant Professor at the Department of Materials, ETH Zürich. His research focuses on structural disorder in materials, particularly in Prussian Blue Analogues (PBAs), disordered crystalline systems, and ferroelectric materials. He employs advanced techniques such as single-crystal diffuse scattering and computational modeling to study local ordering, phase transitions, and correlated atomic-scale phenomena. Key research areas include: Crystal growth and defect engineering in PBAs Multiscale disorder-property relationships in functional materials Magnetoelectric domain engineering Interaction space modeling for disordered systems His work bridges theoretical and experimental approaches, with applications in optoelectronics, energy materials, and nanophotonics. Simonov collaborates on structural analysis using X-ray and electron diffraction techniques, and his contributions advance understanding of materials with tunable properties through atomic-scale disorder. Contact: arkadiy.simonov@mat.ethz.ch

• Glycosaminoglycan Dynamics
• Enzyme Engineering
• Computational Biochemistry
• +3 more

Andrew Almond is a Reader (equivalent to Associate Professor) in Computational and Theoretical Chemistry at the University of Manchester's Faculty of Science and Engineering. He holds a BSc in Physics from Edinburgh and PhD in Biochemistry from Manchester. His research integrates computational and experimental approaches to study glycosaminoglycan dynamics, enzyme engineering, and molecular interactions in biological systems. His work focuses on developing multiscale models of sugar polymers, accelerating directed evolution in synthetic biology, and advancing NMR techniques for dynamic molecules. Current projects include heparanase inhibition strategies and molecular dynamics of extracellular matrices. Publications showcase methodological innovations in glycosaminoglycan characterization, enzyme design, and spectroscopic analysis, with applications spanning drug delivery, biotechnology, and molecular medicine.

Sophia HaussenerView profile

Associate Professor

• Renewable Energy
• Solar Energy Conversion
• Electrochemistry
• +4 more

Sophia Haussener is an Associate Professor at the Laboratory of Renewable Energy Science and Engineering (LRESE) within the School of Engineering at École polytechnique fédérale de Lausanne (EPFL). She contributes to research in Renewable Energy , Electrochemistry , and Materials Science , with a focus on solar energy conversion and CO2 reduction technologies. Her leadership extends to academic committees such as the Commission des prix de la recherche and Academic Strategy Committee . Her research group explores Multiphysics Modeling , CO2 Electrolysis , and Photoelectrochemical Systems , emphasizing scalability and industrial integration. Recent publications highlight advancements in Gas Diffusion Electrodes , Photostability , and Membrane Engineering , reflecting her interdisciplinary approach to renewable energy solutions. Scientific Awards Yellott Award (2024): ASME Solar Energy Division Cell Press’s 50 Scientists that Inspire (2024) Raymond Viskanta Award (2019): Elsevier & Journal of Quantitative Spectroscopy ABB Forschungspreis (2012) ETH Medal (2011) Dimitris N. Chorafas Prize (2011) PhD Students Agarwal Venu Gopal Delgado Díaz William Orlando Lorenzutti Francesca Mora-Monteros Jérémy Raphaël van Rooij Sarah and 21 others Article Trends Focus on CO2 Electrolysis , Photoelectrochemical Systems , and Multiphysics Modeling Key themes: Gas Diffusion , Membrane Technology , Photostability , and Industrial Integration