Find Academic Supervisors

• Topology Optimization
• Additive Manufacturing
• Structural Engineering
• +3 more

Il Yong Kim is an Adjunct Associate Professor in the Department of Mathematics and Statistics at McMaster University . His research focuses on advanced structural optimization techniques, particularly topology optimization applied to additive manufacturing and aerospace engineering. Key areas include multi-material design, composite materials, and computational methods for lightweight structures. Research interests emphasize topology optimization , additive manufacturing , and multidisciplinary design , with applications in automotive, aerospace, and biomedical fields. Recent work addresses challenges in fiber-reinforced materials, build orientation optimization for 3D printing, and structural-acoustic performance. Publications from 2023–2025 highlight contributions to urban air mobility systems , lattice structure optimization , and machine learning-driven design . Over 160 scholarly outputs include conference papers (e.g., AIAA, SAE) and journal articles in Optimization and Engineering , Engineering Optimization , and Structural and Multidisciplinary Optimization . Collaborations span academia and industry, addressing real-world applications in aircraft components, automotive chassis, and biomedical devices. No explicit awards are listed, but his extensive publication record reflects significant contributions to computational mechanics and design optimization.

• Applied Mathematics
• Bayesian Inference
• Monte Carlo Methods
• +5 more

Simon Cotter is a Professor of Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, stochastic modeling, and computational methods, with applications in biological systems (e.g., tendon mechanics, placental development) and financial modeling. He contributes to the university’s Digital Futures research beacon and collaborates across disciplines including biomechanics, computational biology, and data science. Research Interests: Bayesian data assimilation and parameter estimation Monte Carlo methods (e.g., multi-index, adaptive importance sampling) Multiscale stochastic systems and reaction networks Inverse problems in material science and biological systems Recent work highlights include developing NuZZ (a numerical Zig-Zag algorithm for general models) and advancing hierarchical Bayesian methods for data selection. His projects address challenges in debt recovery forecasting, placental development modeling, and cell cycle heterogeneity analysis. He co-leads a multi-modal pregnancy research project aimed at understanding stillbirth mechanisms and is actively involved in initiatives promoting gender equity in academia.

• Machine Learning
• Computer Vision
• Robotics
• +5 more

Roberto Javier López Sastre is an Associate Professor of Signal Theory and Communications at the University of Alcalá. He holds a Ph.D. from the same institution (2010), focusing on 'Visual vocabularies for category-level object recognition' under Dr. Saturnino Maldonado Bascón's supervision. His research spans machine learning, computer vision, robotics, and astrophysics. Key areas include Bayesian neural networks for stellar dating, semantic navigation in robotics, and AI-driven medical assistive technologies. Affiliation: Department of Signal Theory and Communications, University of Alcalá Research Group: GRAM (Multisensorial Recognition and Analysis Group) His work interfaces computational methods with real-world applications, such as assistive robotics for neurodevelopmental disorder patients and deep learning models for astrophysical parameter estimation. Recent contributions include semantic segmentation-driven navigation systems and Bayesian approaches to black hole solutions. Publications emphasize interdisciplinary applications of AI, including live video processing, continual learning frameworks, and physics-informed neural networks. Ongoing research explores the intersection of robotics, healthcare, and astrophysics through collaborative projects. López Sastre's GRAM group focuses on multisensory data analysis for recognition and navigation challenges. His technical expertise spans signal processing, probabilistic modeling, and embedded AI systems.

Michelle GainesView profile

Associate Professor

• Materials Science
• Polymer Chemistry
• Nanotechnology
• +4 more

Michelle Gaines, Ph.D., serves as Associate Professor in the Department of Chemistry and Biochemistry at Spelman College since 2017. Her academic foundation includes a B.S. in Chemical Engineering and Biomolecular Engineering from Michigan State University (2003) and a Ph.D. in Materials Science and Engineering from North Carolina State University (2008), where her dissertation explored nanoparticle-block copolymer interfacial chemistry. She maintains an active research program centered on soft materials and interfacial science while teaching core chemistry courses. Her educational background includes: Ph.D. in Materials Science and Engineering, North Carolina State University (2008) B.S. in Chemical Engineering and Biomolecular Engineering, Michigan State University (2003) Dr. Gaines' research focuses on interfacial properties of soft materials , with dual applications in advanced materials engineering and biological systems. Her work integrates Polymer Synthesis, Materials Science, Cell Biology, and Spectroscopy to investigate energy dissipation in thermo-responsive microgels for lithium-ion battery separators and self-actuating biosensors. A significant emerging direction examines material properties of hair through her planned “Hair Academy” initiative, aiming to develop quantitative frameworks for curly hair phenotypes while advancing Spelman College’s legacy in culturally inclusive science. Analysis of her publication record reveals a strategic evolution from foundational work on nanoparticle-block copolymer interactions (2006-2010) toward cell-material interface studies (2016-2018), with recent publications (2023-2025) demonstrating a pronounced shift into cosmetic science and quantitative hair phenotyping. This trajectory reflects her commitment to bridging fundamental materials chemistry with socially relevant applications, particularly in underrepresented communities. No scientific awards or honors were documented in the provided information. Dr. Gaines teaches General Chemistry (CHE 111/112), Physical Chemistry Lab (CHE 346L), and Inorganic Chemistry (CHE 421/421L), mentoring students through her research lab. Her “Hair Academy” initiative represents a significant educational venture to explore how phenotypic hair differences produce distinct material properties, fostering interdisciplinary collaboration between students and faculty. The Gaines Lab operates within Spelman’s Albro-Falconer-Manley Science Center, employing Atomic Force Microscopy and polymer synthesis techniques to study microgel particles, block copolymers, and cell-hydrogel interactions. Current projects focus on developing synthetic 3D culture microenvironments to control cell behavior through precise manipulation of extracellular matrix properties.

• Operations Research
• Machine Learning
• Healthcare
• +6 more

Dr. Esra Büyüktahtakιn Toy is a Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech, directing the Systems Optimization and Machine Learning Lab (SysOptiMaL). Her research focuses on integrating machine learning (ML), optimization, and artificial intelligence (AI) to address high-stakes decision-making in healthcare, environmental sustainability, and biosecurity. She has secured over $3 million in external funding from NSF, USDA, DOD, and others, and has authored 45 peer-reviewed publications, including 23 in A/A-ranked journals. Education: Ph.D. in Industrial and Systems Engineering, University of Florida (2009) M.S. in Management Science, Lehigh University (2007) M.S. in Industrial Engineering, Bilkent University (2005) Her research interests span multi-stage stochastic programming, reinforcement learning, and epidemic modeling. Recent work addresses resource allocation for disease outbreaks (e.g., Ebola, HIV), invasive species control (emerald ash borer), and AI-driven logistics optimization. She has graduated 8 Ph.D. students now in academic/industry roles and currently advises 8 more. Awards: 2016 NSF CAREER Award Five INFORMS Best Publication Awards Dr. Toy has pioneered the integration of epidemic modeling with logistics systems and contributed scenario dominance algorithms for stochastic optimization. She serves as Associate Editor for the INFORMS Journal on Computing and previously led the INFORMS Junior Faculty Interest Group (JFIG). Lab & Collaborations: The SysOptiMaL Lab develops scalable algorithms combining optimization theory and deep learning. Current projects include AI-driven multi-stage optimization frameworks for healthcare and environmental systems, supported by grants from NSF, USDA, and U.S. Forest Service.

Vladimir PalitsinView profile

Research Fellow

• Development of analytical instrumentation
• Mass spectrometry and imaging mass spectroscopy
• Ion beam applications for material analysis
• +3 more

Dr. Vladimir Palitsin is a Research Fellow at the University of Surrey's Ion Beam Centre (IBC), specializing in ion beam technologies and analytical instrumentation. He holds a PhD in Physics from the University of Warwick (2006) and an MSc in Physical Electronics from Tashkent Technical University (1985). His research focuses on developing novel analytical techniques such as ambient pressure MeV SIMS, proton beam writing (PBW) for microfabrication, and correlative imaging methods for biomedical and archaeological applications. Education: MSc in Physical Electronics, Tashkent Technical University (1985) PhD in Physics, University of Warwick (2006) Research Interests: Development of analytical instrumentation and systems engineering Mass spectrometry imaging and elemental analysis Ion beam applications in material science and microfabrication Proton beam writing for 3D microstructures (e.g., lab-on-a-chip, neural biosensors) His work bridges fundamental physics with applied research, including collaborations on archaeological provenance studies (e.g., lapis lazuli analysis via μ-PIXE and μ-IBIL) and biomedical imaging (e.g., correlative lipidomics and elemental mapping in tissues). Recent advancements include optimizing proton beam damage reduction for multimodal tissue imaging and developing ambient pressure MeV SIMS for organic sample analysis. Dr. Palitsin’s contributions to microfabrication techniques, such as edge-following algorithms for PBW, enhance precision in microfluidic device fabrication. His team also explores forensic and security applications via paper spray mass spectrometry for rapid drug and explosive detection. Labs/Teams: Leading projects at the Surrey Ion Beam Centre (IBC) Collaborations with medical, archaeological, and forensic groups

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +3 more

George Dimitri Konidaris is a Professor at Brown University in Providence, RI, USA, specializing in artificial intelligence and robotics. His research focuses on developing advanced algorithms for reinforcement learning, robotic planning, and hierarchical skill acquisition. He leads investigations into model-based reinforcement learning, temporal abstraction, and human-robot interaction frameworks. Konidaris's research bridges fundamental machine learning concepts with applied robotics. Key interests include: Hierarchical reinforcement learning for complex decision-making Robotic skill transfer and generalization Language-guided agent learning Continuous control in high-dimensional spaces Uncertainty-aware planning systems His publications demonstrate consistent focus on reinforcement learning theory applied to robotic systems, with recent emphasis on language integration and hierarchical abstraction. Work frequently appears in premier venues including ICML, NeurIPS, ICRA, and IROS.

Norbert JungView profile

Professor

• Cybersecurity
• Embedded Systems
• Biometrics
• +4 more

Prof. Dr. Norbert Jung is a Professor of Secure Embedded Systems at the Bonn-Rhein-Sieg University of Applied Sciences (Department of Computer Science), where he also served as Founding Director of the Institute for Security Research (2011–2024). He is a core member of the NRW Promotionskolleg for Applied Research and leads the Biometrics Evaluation Center (BEZ) at the university’s Sankt Augustin campus. His expertise spans cybersecurity, embedded systems, and optoelectronic sensor technology, with a focus on biometric security, real-time signal processing, and safety-critical machine systems. Prof. Jung holds a Diplom-Ingenieur in Communications Engineering from RWTH Aachen University, where he also earned his Dr.-Ing. in optoelectronics and signal processing. His career includes R&D roles at Philips Electronics in medical imaging systems. Since 1999, he has led numerous national and industrial projects, including Biolab II (biometric system evaluation), ROSE3D (robust 3D camera systems), and SMOOTH (safety switches for industrial machinery). His work addresses challenges in anti-spoofing biometrics, industrial robot safety, and sensor-based material classification. Key research themes include: Biometric system resilience against presentation attacks Safety-critical embedded systems for manufacturing Near-infrared (NIR) and multispectral imaging for skin detection Optoelectronic sensor innovation for industrial and medical applications He has spearheaded projects such as the BEZ center, which evaluates biometric systems for government and industry. His lab infrastructure includes optoelectronics and laser labs (H008/H010) and collaborates with the German Federal Office for Information Security (BSI). Prof. Jung is also the laser safety officer for his department.

Emmanuel VAZQUEZView profile

Professor

• Bayesian Statistics
• Computer Experiments
• Uncertainty Quantification
• +3 more

Emmanuel Vazquez is a Professor and researcher at CentraleSupélec, specializing in Bayesian design and analysis of computer experiments. He coordinates Data Science projects and leads research in Gaussian processes, uncertainty quantification, and optimization. He holds a PhD (2005) and HDR (2015) from Université Paris-Saclay, and has taught Bayesian statistics at CentraleSupélec. His work focuses on sequential design strategies, multi-fidelity simulations, and risk assessment in engineering and microbiology. He collaborates with institutions like the GdR MascotNum and PGMO, advancing methodologies for efficient simulation-based decision-making. His research includes applications in TBM excavation modeling, renewable energy integration, and food safety through quantitative microbial risk analysis. Education: École Normale Supérieure de Cachan (1997–2001), DEA/M2 in Mathematics (2001), PhD in Applied Mathematics (2005), Habilitation (2015). Research interests span Bayesian optimization, Gaussian process metamodeling, and stepwise uncertainty reduction (SUR) strategies. He develops algorithms for efficient sampling in expensive-to-evaluate systems, with applications to engineering, environmental modeling, and public health. His recent articles emphasize quantile set inversion, risk assessment in food safety, and scalable Gaussian process methods. Collaborations include projects on railway infrastructure digital twins (MINERVE) and multi-year renewable energy planning. He has contributed to open-source tools like the STK toolbox for kriging.

Alexander TereninView profile

Research Professor

• Machine Learning
• Artificial Intelligence
• Decision Theory
• +7 more

Alexander Terenin is an Assistant Research Professor at Cornell University , specializing in machine learning and artificial intelligence. His work focuses on decision-making under uncertainty, Bayesian optimization, and Gaussian processes, particularly in non-Euclidean spaces. He has contributed to geometric learning, scalable Gaussian process methods, and applications in robotics, plasma science, and legal AI. His research integrates theoretical foundations with practical algorithms, emphasizing principles like the Gittins Index for optimal decision-making. Notable projects include the GeometricKernels software package for manifold learning and the Cambridge Law Corpus for legal AI. His work bridges statistics, geometry, and computer science to address challenges in autonomous systems, energy optimization, and data-driven decision-making. Recent Talks and Contributions: An Adversarial Analysis of Thompson Sampling (INFORMS Applied Probability Society 2025) Cost-aware Bayesian Optimization (NeurIPS 2024) Stochastic Poisson Surface Reconstruction (ICML 2025) Key Research Themes: Bayesian Optimization for multi-objective problems (e.g., plasma-driven energy systems) Geometric Gaussian Processes for robotics and 3D modeling Statistical guarantees for Gaussian processes on manifolds Grants and Collaborations: His work involves interdisciplinary projects with institutions like Carnegie Mellon University, ETH Zürich, and the University of Cambridge, reflecting a global network in AI and statistical learning.

• Haptic Perception
• Multisensory Integration
• Material Properties
• +4 more

Dr. Anna Metzger is a Lecturer in Psychology at Bournemouth University since 2021. She holds a BSc in Biology from Hanover, MSc in Cognitive Science from Osnabrück, and a Dr. rer. nat. in Psychology from Justus-Liebig University Giessen. Her research focuses on haptic and visual perception, particularly object shape/material property encoding through active touch and multisensory integration. She uses motion/eye-tracking, computational modeling, and virtual/augmented reality. Research Interests include: Exploration strategies in tactile perception Unsupervised learning of material properties Neural correlates of haptic perception Computational modeling of tactile saliency Her work has been recognized with a Best Paper Award (2018) from the Eurohaptics Society. Current grants explore haptic texture synthesis and 3D shape perception. She teaches courses on cognitive neuroscience, experimental design, and statistics at both undergraduate and postgraduate levels. Grants : Synthesising Haptic Textures through Unsupervised Learning (British Academy, 2024-) Perception of objects’ 3D shape (Royal Society, 2024-) She collaborates internationally and serves as Associate Editor for IEEE Transactions on Haptics and Eurohaptics Conference Proceedings .

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +4 more

Michail G. Lagoudakis is a Professor at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete (TUC), Greece. He leads the Intelligent Systems Laboratory (InteLLigence), focusing on machine learning, robotics, and decision-making under uncertainty. His academic journey includes a Ph.D. from Duke University (2003) and prior roles at Georgia Tech and AT&T Labs. Education: Ph.D. in Computer Science, Duke University, 2003 M.Sc. in Computer Science, University of Louisiana, 1998 Diploma in Computer Engineering and Informatics, University of Patras, 1995 Research Interests: His work spans reinforcement learning, multi-agent systems, robotics (team coordination, probabilistic methods), and complex systems. Notable projects include the EURECA-PRO university network, RoboCup Kouretes team, and algorithmic advancements like LSPI. Teaching: Courses include Artificial Intelligence, Machine Learning, and Robotics. His teaching philosophy emphasizes hands-on learning with tools like ROS and NAO robots. Projects & Collaborations: Current projects include aerial-ground robot collaboration, dialogue management systems, and solar energy optimization. Earlier work includes auction-based coordination and DPLL solver enhancements (RLSAT). Lab & Teams: The Intelligent Systems Lab develops autonomous systems, robotic platforms, and learning algorithms. The Kouretes team competes in RoboCup leagues, showcasing adaptive multi-agent strategies.

• Artificial Intelligence
• Machine Learning
• Multi-agent Systems
• +6 more

Leandro Soriano Marcolino is a Lecturer (Assistant Professor) in Data Engineering at Lancaster University's Computing and Communications Department. He holds a PhD from the University of Southern California (USC), advised by Milind Tambe, and completed his master's in Japan under a Monbukagakusho Scholarship and his undergraduate studies at Universidade Federal de Minas Gerais in Brazil, where he graduated with top honors. His research focuses on multi-agent systems, machine learning, and robotics, emphasizing teamwork, online learning, and real-world applications in domains like swarm robotics, computer games, and architectural design. Education: PhD in Computer Science, University of Southern California (USC), 2016 Master's Degree in Japan (Monbukagakusho Scholarship) Undergraduate Degree in Computer Science, Universidade Federal de Minas Gerais (Brazil), 2009 Research Interests: Multi-agent teamwork and coordination Online learning and planning Robotics (swarm robotics, real-time strategy games) Applications in computer vision, social networks, and bioinformatics Highlights: Recipient of the Best Dissertation and Best Research Assistant Awards at USC (2016, 2015) Best Paper Nomination at AAMAS 2011 and Best Undergraduate Paper in Brazil (2009) Active in PhD supervision, mentoring students in AI, multi-agent systems, and robotics Co-founder of the COLAB research group at Lancaster University Labs/Teams: Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRAS), LIRA - Fundamentals, SCC (Data Science).

Daniel BüscherView profile

Researcher

• Autonomous Robot Navigation
• Deep Learning
• Computer Vision
• +1 more

Daniel Büscher is a post-doctoral researcher at the Robot Learning Lab of the University of Freiburg. His work focuses on autonomous robot navigation , deep learning , and probabilistic state estimation . He has contributed to robotics research through projects involving mobile manipulation and audio-visual navigation . Research Interests : Autonomous navigation, deep learning, computer vision, probabilistic estimation Teaching : Lecturer and tutor for Introduction to Mobile Robotics and Robot Mapping (2017–2023) Publication Trends : Recent work emphasizes robot design optimization , language-grounded scene graphs , and uncertainty-aware perception . His research connects reinforcement learning with mobile manipulation and cross-domain navigation challenges.

Chen WangView profile

Assistant Professor

• Random Matrix Theory
• Time Series Analysis
• High-dimensional Data Analysis

Chen Wang is an Assistant Professor in the Department of Statistics and Actuarial Science at The University of Hong Kong. He holds a PhD in Statistics from the National University of Singapore (NUS). His research focuses on Random Matrix Theory, Time Series Analysis, and High-dimensional Data Analysis. His work explores theoretical foundations and applications in econometrics, multivariate statistics, and high-dimensional inference. Key contributions include studies on spurious factor analysis, spectral distribution of time series, and cointegration analysis in large VARs. His teaching includes courses such as STAT2602 (Probability and Statistics II) and STAT3600 (Linear Statistical Analysis). Recent publications highlight advancements in AI-driven methodologies for single-cell biology, molecular modeling, and biomedical applications. Notable trends include integrating AI agents for experimental design, spatial biology analysis, and deep learning for medical imaging. Chen's work bridges statistical theory with practical applications, emphasizing high-dimensional data challenges in diverse scientific domains.