جست‌وجوی استاد و پژوهشگر

Marlyse Baptistaمشاهده پروفایل

استاد

• Language Contact
• Cognitive Linguistics
• Pidgins and Creoles
• +۴ مورد دیگر

Marlyse Baptista is the President's Distinguished Professor of Linguistics at the University of Pennsylvania, Department of Linguistics. She is affiliated with the School of Arts & Sciences and MindCore initiative. Her research focuses on language contact, creolization processes, bilingualism, and experimental methods in creole studies. Baptista leads the Language Contact and Cognition Lab, previously known as the Cognition, Convergence and Language Emergence (CCLE) group at the University of Michigan. Education includes a PhD in Linguistics (Harvard, 1997), MA degrees from Harvard and the Université de Bordeaux III, and extensive training in multilingual education. Her work bridges generative syntax with experimental approaches, including artificial language learning to study language convergence. Key themes include the cognitive underpinnings of creole formation, bidirectional influences in Cape Verdean Creole, and the role of congruence in language acquisition. Research highlights include studies on Cape Verdean Creole's grammatical properties, genetic-linguistic admixture correlations, and pedagogical resources for creole language education. Recent projects explore experimental validation of creole genesis theories through multilingual acquisition studies. Scientific Awards: President's Distinguished Professorship (University of Pennsylvania) Labs/Teams: Language Contact and Cognition Lab, MindCore Grants/Projects: MULTI Project (creole language educational resources), NSF-funded studies on creole genesis mechanisms Baptista's work emphasizes interdisciplinary approaches, integrating syntax theory, psycholinguistics, and sociolinguistics to address foundational questions in contact linguistics and creolistics.

• Biomechanics
• Biomimetics
• Bio-inspired Structures
• +۱۴ مورد دیگر

Renate Sachse is a Researcher at the Chair of Structural Analysis, Technical University of Munich (TUM), where she has worked since May 2024. Previously, she held postdoctoral positions at Harvard University's Bertoldi Lab (2024) and TUM's Chair of Computational Mechanics (2021-2024), following academic staff roles at the University of Stuttgart (2015-2020). Her interdisciplinary work bridges civil engineering, biomechanics, and computational modeling. Her educational foundation includes a Master's in Civil Engineering from the University of Stuttgart (2014; thesis: 'Isogeometric contact analysis of thin-walled structures') and a Bachelor's from the same institution (2011; thesis: 'A Primary School Pavilion for Magagula in South Africa - Structural Analysis'). She also completed ERASMUS studies at ESTP Paris and internships at Foster + Partners and Werner Sobek AG. Dr. Sachse's research centers on biomechanics and biomimetics, with pioneering work on plant-inspired structures. She investigates snapping mechanisms in carnivorous plants (Venus flytrap, waterwheel plant) to develop bio-inspired adaptive systems, soft robotics, and metamaterials. Her expertise spans motion design for large-deformation structures, isogeometric analysis, and hygroscopic actuation in 4D-printed materials, emphasizing computational modeling of contact mechanics and structural stability. Analysis of her 15 most recent publications reveals a dominant focus on biomechanics (60% of articles), particularly plant movement mechanics translated into engineering solutions. Her work consistently integrates computational structural analysis with biological principles, showing increasing emphasis on motion design (25% of recent output) and additive manufacturing applications (15%). Key trends include translating snap-buckling phenomena into robotics and developing material design spaces for responsive structures. Her distinguished awards include the Bertha Benz Prize (2022), Klaus Tschira Boost Fund Fellowship (2022-2024), and University of Stuttgart Publication Award (2022). Additional recognition comprises GAMM Juniors Fellowship (2020-2022), AVK Innovation Award (2017), and Emil Mörsch Study Prize (2014). She has secured independent funding through the Klaus Tschira Boost Fund for high-risk interdisciplinary projects and participates in collaborative initiatives including CoDA, MistralWind, WINSENT, and FlexWing. While teaching advanced courses at TUM (Advanced Finite Element Methods, Theory of Plates), her mentorship focuses on computational mechanics and biomimetic design principles. Currently based at TUM's Chair of Structural Analysis under Prof. Bletzinger, she maintains active collaboration with Harvard University's Bertoldi Lab in developing next-generation adaptive structures.

Simon Godsillمشاهده پروفایل

استاد

• Statistical Signal Processing
• Digital Audio Restoration
• Bayesian Computational Methods
• +۵ مورد دیگر

Professor Simon Godsill MA PhD FIET FIEEE is a University Professor of Statistical Signal Processing in the Department of Engineering at the University of Cambridge. He heads a research team specializing in statistical signal processing, digital audio restoration, and Bayesian inference. His work addresses the processing and analysis of digital speech, audio, tracking systems, and financial datasets, with a focus on probabilistic modeling and computational methods. Research interests include statistical signal processing , degraded signal restoration , and Bayesian computational methods . Recent publications emphasize Gaussian processes, variational inference, and multi-object tracking for applications in audio enhancement and financial data analysis. He co-founded the audio remastering company CEDAR Audio Ltd in 1988. Scientific awards: Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Institute of Electrical and Electronics Engineers (FIEEE) Outside academia, he enjoys singing, cricket, piano/organ playing, and running. His team at Cambridge's Engineering department focuses on robust tracking algorithms and signal enhancement techniques.

• Multibody Dynamics
• Control of Nonlinear Systems
• Model Predictive Control
• +۳ مورد دیگر

Dr. Michael Mühlebach is a Lecturer at the Department of Information Technology and Electrical Engineering, ETH Zurich, and affiliated with the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He holds a Bachelor's (2010) and Master's (2013) from ETH Zurich, recognized with awards for academic excellence in Robotics, Systems, and Control. His research focuses on multibody dynamics, nonlinear system control, and model predictive control, with applications in robotics and aerospace systems. His work spans theoretical advancements in variational integrators and practical implementations in systems like the Cubli (a reaction wheel-based 3D inverted pendulum) and flying platforms for ducted fan actuation. Publications highlight contributions to model predictive control schemes with stability guarantees, nonlinear analysis, and distributed event-based state estimation. Key awards include the Outstanding D-MAVT Bachelor Award and Willi-Studer Prize. His research integrates control theory with real-world applications, emphasizing both foundational mathematics and engineering implementations. No grants or lab affiliations are explicitly listed in the provided text.

Muhammad Gulzariمشاهده پروفایل

استادیار

• Finite Element Modelling
• Structural Dynamics
• Applied Mechanics
• +۷ مورد دیگر

Dr. Muhammad Gulzari is an Assistant Professor at the School of Civil Engineering, University College Dublin (UCD). Previously, he held positions as Lecturer/Assistant Professor at the University of Galway (2023–2024), Adjunct Assistant Professor at Trinity College Dublin (2022–2023), and a Research Fellow at Trinity College Dublin (2021–2023). He earned his Ph.D. in Civil Structural Engineering from City University of Hong Kong (2021) and a B.Sc. in Civil Engineering from the University of Engineering and Technology Lahore (2017). Education: Bachelor of Engineering, Civil Engineering, University of Engineering and Technology Lahore Ph.D., Civil Structural Engineering, City University of Hong Kong Research Interests: His work focuses on structured materials and dynamics, including finite element modeling, phononic crystals, acoustic and mechanical metamaterials, vibration and noise control, and applications in structural health monitoring. He leads the Structured Materials and Dynamics Lab at UCD, exploring nonlinear and nonreciprocal metamaterials. Teaching and Awards: He has taught courses in Fluid Mechanics, Thermodynamics, and Combustion Engineering. Notable recognitions include the Seal of Excellence Award from the EU-Horizon MSCA Postdoctoral Fellowship (2021) and nominations for teaching excellence awards at Trinity College Dublin and University of Galway. Grants: TimberFlow: Enhancing Efficiency in Mass Timber Construction (Enterprise Ireland, 2025) Indoor Acoustic Quality via Acoustic Metamaterials (Enterprise Ireland, 2025) RUBBERPAVE: ELT Integration in Pavement Construction (Enterprise Ireland, 2024–2026) Deep Learning for Metamaterial Design (Irish Research Council, 2021–2023) Labs and Collaborations: His research group collaborates with industry partners like Amplitude Acoustics and G-frame Structures Ltd. Key projects include developing metamaterials for noise/vibration control and sustainable construction materials.

Mona Merlingمشاهده پروفایل

دانشیار

• Algebraic K-Theory
• Equivariant Stable Homotopy Theory
• Manifold Theory
• +۳ مورد دیگر

Mona Merling is an Associate Professor in the Department of Mathematics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. She holds a Ph.D. from the University of Chicago (2014) under Peter May. Prior to Penn, she was a J.J. Sylvester Assistant Professor at Johns Hopkins University. Her research focuses on algebraic K-theory, its applications to number theory and manifold theory, and equivariant stable homotopy theory. She has held research positions at institutions like the Mathematical Sciences Research Institute (MSRI) and the Max Planck Institute for Mathematics. Merling teaches advanced courses in algebraic topology, homotopy theory, and calculus, including innovative programs like the Penn Directed Reading Program and collaborations with the Prison Teaching Initiative to provide education in incarcerated settings. She organizes conferences and workshops, such as the Algebraic Topology Bridge Summer Workshop, fostering accessibility in topology education. Her research explores cutting-edge topics like scissors congruence K-theory, equivariant infinite loop space theory, and parametrized cobordism categories. Recent work includes collaborations on derived scissors congruence and multiplicative equivariant K-theory, with applications to geometric and algebraic structures. Her grants include funding for social justice initiatives in education.

Eugene Demlerمشاهده پروفایل

استاد

• Theoretical Condensed Matter Physics
• Atomic and Molecular Physics
• Quantum Optics
• +۱ مورد دیگر

Eugene Demler is a Full Professor at the Department of Physics, ETH Zurich. Previously, he held academic positions at Harvard University from 1998 to 2021, including Assistant Professor (2001-2004), Associate Professor (unspecified dates), and Full Professor (2005-2021). His work bridges theoretical condensed matter physics, atomic and molecular physics, quantum optics, and quantum simulations. Education: MSc in Physics, Moscow Institute of Physics and Technology (1993) Diploma work, Lebedev Physics Institute (1992-1993) PhD in Theoretical Physics, Stanford University (1998), supervised by S.C. Zhang Demler's research focuses on strongly correlated quantum systems, spintronics, quantum sensing, and photo-induced phase transitions. His recent publications explore topics such as quantum polarons, Josephson plasmons, magnon dynamics, and terahertz spectroscopy in superconductors. He has pioneered hybrid quantum-classical methods for electron-phonon systems and cavity-mediated quantum materials. His Google Scholar articles (2023-2025) span theoretical and experimental domains, with keywords including Quantum Physics , Condensed Matter Physics , and Quantum Optics . Subfields include Quantum Control , Superconductivity , Spin Waves , Quantum Sensing , Non-Equilibrium Dynamics , and Quantum Simulation . Scientific Awards: Hamburg Prize for Theoretical Physics (2021) Simons Investigator (2021) Moore Distinguished Scholar (2020) Hanna Visiting Scholar (2019) Highly Cited Researcher (2017-2020) Senior Fellow at ETH Zurich's Institute for Theoretical Studies (2015) Simons Fellowship (2015) Distinguished Scholar at Max Planck Institute of Quantum Optics (2015) Thomson Reuters Highly Cited Researcher (2014) Siemens Research Award (2006) Johannes Gutenberg Lecture Award (2006) NSF Career Award (2002) Sloan Fellowship (2002) Demler teaches courses such as Statistical Physics and Strongly Correlated Systems in Atomic and Condensed Matter Physics . His work integrates theoretical modeling with experimental collaborations, particularly in quantum optics and condensed matter systems.

Govind P. Agrawalمشاهده پروفایل

استاد

• Nonlinear Optics
• Quantum Electronics
• Laser Physics
• +۷ مورد دیگر

Govind P. Agrawal is a Professor of Optics and Physics at the University of Rochester. He holds concurrent positions as a Fellow of the IEEE and the Optical Society of America (OSA). His research focuses on theoretical optics, nonlinear optics, laser physics, and semiconductor lasers, with notable contributions to fiber optics and optical communications. Education: Agrawal earned his MS (1971) and PhD (1974) from the Indian Institute of Technology, New Delhi. He has held prior positions at École Polytechnique (Paris), City University of New York, and AT&T Bell Laboratories before joining the University of Rochester in 1989. Research Interests: Agrawal's work explores quantum electronics, soliton dynamics, Raman scattering, and temporal reflection phenomena. He has authored seminal books such as Nonlinear Fiber Optics and Semiconductor Lasers , and published over 300 journal articles. His recent studies emphasize spatiotemporal dispersion, multimode fiber optics, and integrated photonics systems. Editorial Roles: He served as Topical Editor for Journal of the Optical Society of America B (1993–1998) and currently contributes to editorial boards for optics journals and book series. Awards: Honored as a Fellow of the IEEE and OSA for his transformative contributions to optics and photonics. Key Contributions: Pioneered theories on graded-index fiber amplifiers, temporal reflection in nonlinear media, and soliton-based technologies. His research bridges fundamental optics with practical applications in telecommunications and photonics systems.

Markus Bachmayrمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +۴ مورد دیگر

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.

Gerhard Müllerمشاهده پروفایل

استاد

• Structural Dynamics
• Vibroacoustics
• Dynamic Soil-Structure Interaction
• +۵ مورد دیگر

Prof. Dr.-Ing. Gerhard Müller is a Full Professor at the Chair of Structural Mechanics within the TUM School of Engineering and Design at Technical University of Munich (TUM). Since 2004, he has held this distinguished position, and since 2014, he has served as Executive Vice President for Academic and Student Affairs at TUM. His research focuses on structural dynamics and vibroacoustics, with specific expertise in dynamic soil-structure interaction, sound radiation analysis, and seismic risk assessment. Professorship: Structural Mechanics University: Technical University of Munich School: TUM School of Engineering and Design Department: Chair of Structural Mechanics in Civil Engineering Prof. Müller's research spans multiple domains, including: Structural Dynamics : Examining building and vehicle vibrations, seismic soil-structure interaction, and advanced model order reduction techniques Vibroacoustics : Investigating sound radiation from vibrating structures and developing acoustic metamaterials for noise control Computational Methods : Pioneering hybrid deterministic-statistical approaches, Wave Based Methods (WBM) for saturated elastodynamic structures, and parametric model order reduction His recent publications demonstrate expertise in: Wave propagation analysis in poroelastic media Bayesian parameter updating for structural models Acoustic metamaterials for vibration control Advanced numerical methods for seismic risk assessment Hybrid ITM-FEM approaches for soil-structure interaction Energy flow analysis in timber structures Awarded the Spindler Prize in 1984 , Prof. Müller also holds significant academic leadership roles: President of European Association for Structural Dynamics (EASD) Chairman of Bavarian-French University Center (BayFrance) Active member of ASIIN accreditation agency and Bavarian Chamber of Engineers Previously served as Dean of Civil Engineering and Surveying at TUM (2010-2014) He leads the Structural Dynamic Lab (formerly Vibroacoustics Lab) and has developed interactive web apps for engineering education. His work bridges theoretical advancements with practical applications in construction acoustics, transportation noise control, and geothermal energy infrastructure analysis.

Carl Henrik Ekمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Bayesian Non-parametric Methods
• +۶ مورد دیگر

Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.

Ebrahim Sarabiمشاهده پروفایل

دانشیار

• Variational Analysis
• Optimization
• Parametric Optimization
• +۲ مورد دیگر

Ebrahim Sarabi is an Associate Professor in the Department of Mathematics at Miami University, located in Oxford, Ohio. He holds a Ph.D. in Applied Mathematics from Wayne State University. His primary research interests focus on variational analysis, optimization, and parametric optimization, with an emphasis on numerical algorithms and applications in continuous optimization. Dr. Sarabi's research explores advanced topics such as tilt-stable minimizers, epi-differentiability, and the role of subgradients in polyhedral functions. His work often intersects with second-order variational analysis and composite optimization problems. His contributions span theoretical developments and practical algorithmic advancements in optimization. His publications from 2020–2025 highlight trends in augmented Lagrangian methods, smoothness of subgradient mappings, and primal-dual convergence analysis. Notable themes include the study of partly smooth functions, spectral functions, and generalized Newton algorithms. No scientific awards were explicitly mentioned in the provided texts. He advises no listed students or research teams, though his work involves collaborations in parametric optimization and variational systems. His office is located in Bachelor Hall, and he can be reached via email at sarabim@miamioh.edu .

Eva Cantoniمشاهده پروفایل

استاد

• Robust statistics
• Variable and model selection
• Nonparametric regression
• +۶ مورد دیگر

Eva Cantoni is a Full Professor at the Research Center for Statistics within the Geneva School of Economics and Management , University of Geneva. Her expertise spans robust statistical methodology, model selection, and applications in ecology and medicine. Ph.D. from University of Geneva Accredited European Statistician (FENStatS) Research Interests : She specializes in Robust statistics for real-world data Variable/model selection in high-dimensional settings Nonparametric and semi-parametric regression Zero-inflated and overdispersed count models Longitudinal and spatiotemporal data analysis Her work addresses ecological challenges (fish stock assessment), medical applications (hospital congestion modeling), and housing market analysis. Recent Trends in Publications : Recent articles focus on Confidence intervals for robust mixed models Editorial leadership in robust statistics Applications to fisheries science and public health Flexible modeling frameworks for complex data Comparative studies of statistical measures Extremes modeling in healthcare Leadership & Grants : She has served as: Vice-Dean for Teaching (2020-2023) Director of Master's in Statistics (2012-2019) Director of Applied Statistics Certificate (2015-2019) President, Swiss Federal Statistics Committee (2024-2027) Specialty Chief Editor, Frontiers in Applied Mathematics (2024) Grants include projects on Robust solutions for modern data (2023-2025) Sustainable fisheries modeling (2018-2021) Advancements in state-space models (2014-2017) Software Contributions : Developed R packages for robust statistical methods: confintROB (bootstrap confidence intervals) RobSSM (robust state-space models) R2_LMM (explained variation measures)

Daniele Venturiمشاهده پروفایل

استاد

• Stochastic modeling
• Uncertainty quantification
• Numerical analysis
• +۷ مورد دیگر

Daniele Venturi is a Professor of Applied Mathematics at the University of California, Santa Cruz, where he has been faculty since 2015, rising from Assistant Professor to full Professor by 2021. Previously, he was a Research Assistant Professor at Brown University from 2010-2015. His academic journey began at the University of Bologna, where he earned both his combined B.S./Sc.M. in Mechanical Engineering (2002) and Ph.D. in Applied Physics with a focus on thermo-fluid dynamics (2006). University of Bologna: B.S./Sc.M. Mechanical Engineering (2002), Ph.D. Applied Physics (2006) Brown University: Research Assistant Professor (2010-2015) UC Santa Cruz: Assistant to Associate to Full Professor (2015-present) Professor Venturi's research spans multiple cutting-edge areas in computational mathematics. His primary interests include stochastic modeling and uncertainty quantification, numerical tensor methods for high-dimensional PDEs, data-driven modeling approaches, approximation of functional-differential equations, and theoretical/computational fluid dynamics. His work bridges theoretical mathematical frameworks with practical computational implementations, particularly focusing on overcoming the curse of dimensionality in complex systems. His recent research has been heavily focused on hierarchical tensor methods for solving high-dimensional partial differential equations. The analysis of his publication record reveals a strong emphasis on developing computational frameworks that address high-dimensional challenges in uncertainty quantification and model reduction. His work frequently intersects machine learning techniques with traditional numerical methods, particularly in developing physics-informed neural networks and multifidelity modeling approaches. A consistent theme across his publications is the development of mathematical frameworks that maintain computational tractability while preserving physical fidelity in complex systems. Professor Venturi has secured substantial research funding from major agencies including the Air Force Office of Scientific Research (AFOSR), Department of Energy (DoE), National Science Foundation (NSF), Army Research Office (ARO), and Defense Advanced Research Projects Agency (DARPA). His most significant current grant is a 2024-2029 AFOSR MURI award totaling $7.5M as co-PI for 'Tensor Network for simulating kinetic systems.' 2024-2029: AFOSR MURI, $7.5M (co-PI) 2023-2027: DoE, $3.8M (co-PI) 2023-2026: AFOSR, $2.5M (co-PI) 2020-2025: NSF TRIPODS, $2.3M (co-PI) At UC Santa Cruz, Venturi teaches a range of courses including Fundamentals of Uncertainty Quantification, Applied Dynamical Systems, Nonlinear Dynamical Systems, and Numerical Methods for Differential Equations. His teaching spans both undergraduate and graduate levels, reflecting his expertise across theoretical and computational mathematics. His lecture notes for these courses are publicly available and demonstrate his commitment to pedagogical excellence in complex mathematical subjects.

• Machine Learning
• Bayesian Inference
• Probabilistic Machine Learning
• +۱۷ مورد دیگر

Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.