Find Academic Supervisors

Sima MousaviView profile

Researcher

• Seismology
• Earth Imaging
• Earthquake Source Modeling
• +3 more

Dr. Sima Mousavi is a Researcher at the Research School of Earth Sciences , Australian National University (ANU). Her research focuses on observational seismology, earth imaging from the crust to the core-mantle boundary, and earthquake source modeling using 3D models and full waveform analysis. She leads the Australian Seismometers in Schools (AuSIS) Network, managing 50 seismic stations across Australian schools to promote STEM education and conduct seismic research. Education : PhD in Seismology from Leipzig University (2016). Additional Affiliation : Honorary Visitor at Victoria University of Wellington (March 2025–present). Research Interests : Structural geology, earthquake rupture dynamics, and geophysical imaging. She has conducted fieldwork in Lake Eyre, Eyre Peninsula (SA), the SWAN project (WA), and a DAS experiment in New Zealand's South Island. Grants and Projects : "Earth Imaging AuSIS" (2015–2025) "Investigating seismic anisotropy beneath the WA-Array" (2023–2026) Labs/Teams : Coordinates the AuSIS network, integrating education and research through school-based seismology.

Sylvain CalinonView profile

Lecturer

• Robotics
• Robot Learning
• Human-Robot Collaboration
• +4 more

Sylvain Calinon is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He specializes in robot learning, human-robot collaboration, and optimal control. He holds a PhD from EPFL (2007) and has led research teams at IIT (2009–2014) and Idiap (2014–present). His work focuses on enabling robots to learn skills from demonstrations and interactions, emphasizing geometric and model-based approaches. Affiliations: Idiap Research Institute (Senior Scientist), EPFL (Lecturer in Microengineering) Research Interests: Robot learning, manipulation planning, optimal control, human-robot interaction, and industrial applications. Calinon's research has been supported by grants from the European Commission, Swiss National Science Foundation, and others. He has received multiple awards, including the ABB Award and Best Paper Awards at ICRA, IROS, and RSS. He has advised over 20 PhD students and contributes to editorial roles in top journals like IEEE Transactions on Robotics. His current projects include the H2020 MEMMO (memory of motion) and COLLABORATE (human-robot co-production systems). He also leads initiatives in geriatronics and industrial cobotics, emphasizing real-world applications.

Günter NeumannView profile

Professor

• Computational Linguistics
• Artificial Intelligence
• Deep Learning
• +5 more

Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.

Sanmi KoyejoView profile

Assistant Professor

• Machine Learning
• Trustworthy AI
• Federated Learning
• +5 more

Sanmi Koyejo is an Assistant Professor at Stanford University's Department of Computer Science and an Adjunct Associate Professor at the University of Illinois at Urbana-Champaign. He leads the Stanford Trustworthy AI Research (STAIR) group, focusing on developing principles for robust and ethical machine learning, with applications in healthcare and neuroimaging. His work spans federated learning, metric elicitation, generative models, and interpretable AI. He holds a PhD from the University of Texas at Austin and completed postdoctoral research at Stanford. His research is supported by grants from NSF, NIH, DARPA, and industry partnerships. Notable awards include the Frederick E. Terman Faculty Fellowship, NSF CAREER Award, and the Skip Ellis Early Career Award. Key research interests include trustworthy AI, distributed learning security, fairness metrics, and biomedical imaging. His lab has developed frameworks like CSER for secure federated learning and contributed to healthcare applications such as diabetes detection from X-rays.

Martin Ostoja-StarzewskiView profile

Professor

• Mechanics of Random and Fractal Media
• Continuum Thermodynamics
• Stochastic Wave Propagation
• +7 more

Martin Ostoja-Starzewski is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign, with affiliate appointments at the Beckman Institute and The Institute for Condensed Matter Theory. His academic leadership includes editorial roles at Acta Mechanica , Journal of Thermal Stresses , and chairing the Managing Editor position for Mathematics and Mechanics of Complex Systems (2012-2023). Education: Ph.D. in Mechanical Engineering (Dean's Honour List), McGill University (1983) M.Eng. (Thesis option), McGill University (1980) Engineer's degree, Cracow University of Technology (1977) Ostoja-Starzewski's research spans stochastic mechanics , fractal materials , and non-equilibrium thermodynamics , focusing on tensor random fields (TRFs) for modeling heterogeneous media. His work established quantitative bounds for representative volume elements (RVEs) and pioneered continuum models with Second Law violations , connecting micromechanics to global material behavior. Recent publications examine fractional wave equations , stochastic homogenization , and electromagnetic shielding in nanocomposites. His editorial contributions include editing 16 books/journal issues and co-founding the Modern Mechanics and Mathematics book series. Scientific Recognition: Rothschild Distinguished Visiting Fellow (2023) Academia Europaea member (2024) European Academy of Sciences and Arts (2022) Worcester Reed Warner Medal (ASME, 2018) Timoshenko Distinguished Visitor (Stanford, 2012) He leads NSF's Industry/University Cooperative Research Center for Novel High Voltage/Temperature Materials and Structures and maintains active collaborations in computational mechanics, biomedical applications, and multiscale modeling.

Andi WangView profile

Assistant Professor

• Data Science
• Machine Learning
• Manufacturing Systems
• +3 more

Andi Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, joining in August 2024. Previously, he held an Assistant Professor position at Arizona State University's School of Manufacturing Systems and Networks. His research focuses on applying data science and machine learning to address challenges in engineering systems, including predictive analytics, monitoring, and design optimization. He holds a PhD (2021) from Georgia Institute of Technology, a PhD (2016) from Hong Kong University of Science and Technology, and a BS (2012) from Peking University. Research interests include multi-sensor data fusion for manufacturing process prediction, surrogate modeling for multi-stage engineering systems, and distributed/federated learning applications. His work spans domains like nuclear engineering, additive manufacturing, and semiconductor production. Awards: Multiple IISE and INFORMS awards, including the 2023 IISE Transactions Best Application Paper Award and the 2020 QCRE Division Best Student Paper Award finalist. Advising & Grants: Actively involved in research grants and advising students, though no formal advisee list is currently available. Labs/Teams: Engaged in collaborative projects with industry partners, focusing on industrial systems improvement through data-driven solutions.

• Computer Architecture
• GPU Optimization
• Machine Learning Acceleration
• +4 more

Dr. T Vijaykumar is a Professor in the Department of Electrical and Computer Engineering at Purdue University. His research focuses on computer architecture, GPU optimization, machine learning acceleration, and high-performance computing. He leads projects involving hardware-software co-design for energy-efficient systems and secure speculative execution techniques. His work spans innovations in GPU concurrency, sparse matrix processing, and distributed training frameworks for neural networks. He has contributed to advancements in memory systems, including PIM architectures and address translation optimizations. Recent projects include Proteus (model confidentiality) and Disorf (mobile 3D reconstruction). Key research themes include accelerating irregular computational graphs, secure hardware design, and efficient data movement in disaggregated systems. His publications reflect a strong emphasis on practical implementations with real-world applications in robotics, genomics, and cloud computing. Dr. Vijaykumar has received institutional awards for his contributions to computer architecture and holds patents in areas like microfluidics and data center congestion control. He collaborates with industry on GPU kernel design and memory consistency verification frameworks like QED.

• Computational Physics
• Quantum Many-Body Systems
• Machine Learning in Physics
• +3 more

Dr. Jannes Nys is a researcher at the Professorship for Computational Physics at ETH Zürich. His work bridges computational physics and machine learning, focusing on quantum systems and many-body problems. Email: jannys@phys.ethz.ch Research Interests: His research spans computational physics, quantum many-body systems, and machine learning applications in physics. He develops neural network-based quantum states and symmetry-aware algorithms for simulating complex quantum phenomena. Quantum Many-Body Systems Neural Quantum States Ab-initio Simulations Quantum Chemistry Topological Entanglement High Energy Physics Recent Article Trends: His publications highlight the intersection of machine learning and quantum physics, including neural wavefunction optimization for non-Hermitian systems, symmetry-aware models for electron spectroscopy, and variational methods for real-time quantum dynamics. He also explores exotic hadrons in particle physics and novel quantum Monte Carlo approaches.

Anzhong WangView profile

Professor

• Theoretical Physics
• Gravitational Wave Physics
• Quantum Cosmology
• +4 more

Dr. Anzhong Wang is a Professor of Physics at Baylor University, affiliated with the College of Arts & Sciences' Department of Physics and Astronomy. He joined Baylor in 2003, with prior academic roles at institutions in Brazil, China, and the U.S. His research focuses on classical/quantum gravity, gravitational waves, black holes, and cosmology, particularly addressing dark matter/energy and early universe dynamics. He teaches courses such as General Relativity and Quantum Mechanics. Education: Ph.D. in Physics, Ioannina University (1991) M.S. in Physics, Northeast Normal University (1986) B.S. in Physics, Shandong Normal University at Liaocheng (1983) His research explores gravitational wave cosmology, black hole physics, and quantum cosmology. He has advised numerous Ph.D. and Master’s students, and his work has been published in leading journals like Physical Review D and Nature . He collaborates globally, including with institutions in Brazil, Japan, and China. Recent publications highlight studies on loop quantum gravity effects, gravitational wave propagation, and dark matter halo interactions with black holes. His contributions bridge theoretical frameworks with observational cosmology, addressing gravitational physics at fundamental scales.

Hu ChengView profile

Researcher

• MRI Physics
• Neuroimaging
• Diffusion MRI
• +6 more

Hu Cheng serves as a Senior Scientist in the Department of Psychological and Brain Sciences at Indiana University Bloomington's College of Arts & Sciences, where he operates as an MRI Physicist within the Imaging Research Facility. His dual expertise bridges physics and neuroscience to advance neuroimaging methodologies. Education: Ph.D. from City University of New York (2001) Sci.M. from Nankai Institute of Mathematics (1995) B.Sc. from University of Science and Technology of China (1992) Dr. Cheng's research focuses on four pillars: functional MRI (network analysis, physiological noise reduction, brain stem imaging), diffusion MRI (microstructure analysis, subconcussive impact studies), MR spectroscopy (neurometabolic biomarker discovery), and deep learning applications (1D CNN noise reduction, U-NET cardiac segmentation). His work emphasizes translating technical innovations into clinical neuroscience applications, particularly for understanding brain disorders through advanced imaging biomarkers. His publication record demonstrates consistent contributions to methodological advancements in MRI physics, with emphasis on artifact reduction, network analysis, and novel acquisition techniques. Current projects investigate glial/neuronal differentiation via diffusion MRS and deep learning-enhanced image processing. As Physicist for the Imaging Research Facility , he oversees MRI quality control and protocol optimization for neuroimaging applications across campus. His technical leadership supports both basic neuroscience research and clinical translation efforts within the department.

Akwum OnwuntaView profile

Assistant Professor

• Uncertainty Quantification
• Statistical Inverse Problems
• Scientific Machine Learning
• +3 more

Akwum Onwunta is an Assistant Professor in the Department of Industrial and Systems Engineering at Lehigh University's Rossin College of Engineering. His research focuses on uncertainty quantification, optimization under uncertainty, and scientific machine learning, with applications in computational mathematics and engineering systems. Education: Ph.D. in Applied Mathematics (Max Planck Institute & Otto von Guericke University, 2016) Ph.D. in Economics (Justus Liebig University, 2010) Diplome d'Universite in Mathematical Models in Economics and Finance (University of Paris 1, 2007) M.Sc. in Physical and Mathematical Analysis (University of Stellenbosch, 2006) B.Sc. in Mathematics (Michael Okpara University of Agriculture, 2003) Research interests include developing tensor-based methods for stochastic optimization, Bayesian inverse problems, and low-rank numerical techniques for high-dimensional systems. His work bridges applied mathematics, computational science, and engineering applications. His recent publications emphasize tensor train approximations, quantum algorithms for differential equations, and deep learning approaches to parameterized PDEs. Notable contributions include TTRISK algorithms for risk-averse optimization and low-rank solvers for stochastic Navier-Stokes equations. No scientific awards explicitly listed in the provided text. Lehigh University received a $800K DOE grant in 2025 for advanced computational materials modeling, potentially related to his research. Advising and grants: No formal advisees listed. Active in grant-funded projects such as the DOE initiative mentioned above. Collaborates on interdisciplinary teams focused on computational materials modeling and uncertainty-aware optimization. Lab/Team: Engaged in computational engineering research within the Rossin College, though specific lab names are not detailed in the text.

Jackson KulikView profile

Assistant Professor

• Astrodynamics
• Guidance, Navigation, and Control
• State Estimation
• +6 more

Jackson Kulik is an Assistant Professor in the Mechanical and Aerospace Engineering Department at Utah State University's College of Engineering. He leads the Dynamics, Control, and Estimation (DyCE) Lab and is part of the Autonomy, Robotics, Control, and Optimization (ARCO) Group, focusing on cislunar astrodynamics, rendezvous operations, and space situational awareness. Education: PhD in Applied Mathematics, Cornell University, 2024. Dissertation: "Linear and Multilinear Methods for Guidance, Navigation, and Control of Satellite Formation Flight". BS in Mathematics, Texas Tech University, 2020. Dr. Kulik's research centers on advanced guidance, navigation, and control systems for space applications, leveraging higher-order tensor methods for partial derivatives and statistical moments. His work spans astrodynamics, state estimation, and trajectory optimization with emphasis on cislunar navigation, spacecraft proximity operations, and orbital uncertainty modeling. Recent publications (2024-2025) reveal a strong focus on linear covariance analysis fidelity, non-Gaussian uncertainty propagation, and reachable set theory for spacecraft dynamics. These works integrate tensor mathematics with practical space mission challenges, particularly in satellite formation flight and space domain awareness. Scientific Awards: DOD NDSEG Fellowship (2021) Dr. Kulik mentors five graduate students including PhD candidates Alex Nelson (RPO/Cislunar GN&C), Braden Hastings (Situational Awareness), and Allan Lyon (stochastic navigation), plus Master's student Zack Fraser (GN&C engineering). His research is supported by Department of Defense funding, building on his NDSEG Fellowship background. The DyCE Lab operates within USU's ARCO Group, developing novel mathematical frameworks for space system dynamics and control. Current projects address cislunar navigation challenges, spacecraft rendezvous algorithms, and space object tracking using tensor-based statistical methods.

• Theoretical Cosmology
• Gravitational Wave Physics
• Cosmology
• +3 more

Professor Gianmassimo Tasinato is a member of the Physics Department at Swansea University, affiliated with the School of Biosciences, Geography and Physics within the Faculty of Science and Engineering. His academic role is as a Professor specializing in Theoretical Cosmology. He is actively involved in postgraduate supervision, focusing on topics like gravitational wave cosmology and interacting dark energy. His research interests center on theoretical cosmology, with a focus on gravitational wave physics, dark matter, inflationary cosmology, and quantum gravity. Notable contributions include studies on gravitational wave backgrounds, cosmological constraints on dark energy models, and the dynamics of primordial black holes. His work often explores synergies between observational data (e.g., from pulsar timing arrays and space-based interferometers like LISA) and theoretical frameworks. Tasinato’s recent publications (2023–2025) highlight advancements in probing gravitational wave signatures of dark matter, analyzing inflationary non-Gaussianities, and investigating cosmic anisotropies. He collaborates extensively with international teams on projects like the Einstein Telescope and LISA, aiming to decode cosmological signals through gravitational wave observations. He supervises PhD students in areas such as gravitational wave cosmology, black hole dynamics in modified gravity, and astrophysical classification techniques. His teaching includes modules like PH-100 (Classical Mechanics) and PH-322 (Cosmology).

Emanuel GullView profile

Assistant Professor

• Quantum Physics
• Condensed Matter Physics
• Many-Body Theory
• +3 more

Emanuel Gull is an Assistant Professor in the Department of Physics at the University of Michigan, Ann Arbor. His research focuses on quantum many-body theory, strongly correlated electron systems, and computational methods for condensed matter physics. He has held academic positions including a postdoc at Columbia University and a junior group leader role at the Max Planck Institute. His work emphasizes numerical methods like quantum Monte Carlo, dynamical mean-field theory (DMFT), and tensor network approaches. Education: PhD in Theoretical Physics from ETH Zurich (2008), undergraduate studies at ETH Zurich (2005). His research interests include Hubbard model simulations, analytic continuation of Green's functions, and developing computational frameworks like Green/WeakCoupling for ab initio many-body calculations. Key achievements include the 2013 Nevill Mott SCES Prize for contributions to correlated electron systems, a DOE Early Career Award (2013), and a Sloan Fellowship (2014). He has pioneered methods for non-equilibrium dynamics, steady-state quantum systems, and integrating high-performance computing into materials science. Grants include NSF-BSF funding for tensor train methods in quantum impurity solvers. His work often involves collaborations on frameworks like TRIQS and Elements for embedding theories. Current research explores quantum-centric supercomputing, excitonic phenomena in van der Waals magnets, and symmetry-adapted algorithms for large-scale simulations.

Christian RohlfingView profile

Researcher

• Signal Processing
• Data Compression
• Audio Source Separation
• +3 more

Dr.-Ing. Christian Rohlfing is a Researcher at the Institute of Communications Engineering , RWTH Aachen University . His work spans genomic data compression , audio source separation , and video coding . Rohlfing’s research focuses on efficient genomic sequence compression using entropy coding, context-based methods, and standardization within MPEG-G . He also advances point cloud compression via graph transforms and adaptive entropy coding, and audio/video coding with non-negative matrix factorization (NMF), dictionary learning, and convolutional neural networks (CNNs). His publications (2011–2023) reflect expertise in signal processing for multimedia and bioinformatics , with applications in spatial audio coding , informed source separation , and gene variation compression . Rohlfing contributes to international standards like ITU-T and ISO/IEC MPEG , particularly in Versatile Video Coding (VVC) and MPEG-G for genomic data.