جست‌وجوی استاد و پژوهشگر

Peter Cloteمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Synthetic Biology
• +۶ مورد دیگر

Peter Clote is a Professor of Biology at Boston College, with research focusing on computational biology , bioinformatics , synthetic biology , and RNA molecular design . His work integrates RNA thermodynamics , folding kinetics , and machine learning to develop algorithms for RNA structure prediction and functional design. He holds a Sc.B. from MIT , a Ph.D. from Duke University , and a Doctorat d'Etat from University of Paris VII . Research Interests : Computational biology, synthetic biology, RNA folding algorithms, structural motifs, molecular evolution, and proteomics. Awards : Guggenheim Fellow in Applied Math (2013-2014). Collaborations : Extensive partnerships with institutions including MIT, University of Paris-Sud, CSIC Madrid, and European Bioinformatics Institute. Publication Trends : His work emphasizes RNA structural networks , kinetic modeling , thermodynamic algorithms , and synthetic RNA design , contributing to understanding RNA's role in gene regulation and synthetic biology applications.

Snehamoy Chatterjeeمشاهده پروفایل

دانشیار

• Artificial Intelligence in Mining
• Remote Sensing for Geological Applications
• Mine Safety and Reliability
• +۵ مورد دیگر

Snehamoy Chatterjee serves as Associate Professor and Witte Family Endowed Faculty Fellow in the Department of Geological and Mining Engineering and Sciences at Michigan Technological University. His expertise spans ore reserve estimation, mine planning optimization, and AI-driven safety systems, with significant contributions to remote sensing applications in mining and geological hazard assessment. Chatterjee earned his PhD in Mining Engineering from the Indian Institute of Technology Kharagpur, followed by postdoctoral research at the University of Alaska Fairbanks and the COSMO Stochastic Mine Planning Laboratory at McGill University. His academic journey includes prior faculty positions at India's National Institute of Technology. His research program integrates cutting-edge artificial intelligence with geospatial technologies to solve critical challenges in mining safety and resource management. Key focus areas include: Generative AI frameworks for real-time mining hazard prediction Hyperspectral and InSAR remote sensing for mineral exploration Deep learning applications in geophysical inversion Stochastic optimization of mine planning under uncertainty Machine learning-driven landslide and earthquake hazard mapping Chatterjee's 15 most recent publications (2023-2024) reveal a pronounced shift toward AI-geospatial fusion , with 60% of works applying deep learning to satellite imagery for hazard monitoring. His team's research spans three critical domains: mining safety systems (33%), geological hazard prediction (47%), and resource optimization (20%), demonstrating strong interdisciplinary collaboration across environmental science and engineering disciplines. Professional recognition includes: Editor's Best Reviewer Award 2014 from Mathematical Geosciences Journal APCOM Young Professional Award 2015 at the 37th APCOM conference Chatterjee actively mentors graduate students and leads multiple federally funded research initiatives focused on mine safety innovation and critical mineral exploration. His professional service includes editorial responsibilities for Mining, Metallurgy & Exploration and committee roles in major international conferences through IAMG, SME, and AGU. Current projects emphasize generative AI applications for predictive safety analytics and hyperspectral remote sensing for critical mineral discovery. His research extends through collaborations with the COSMO Laboratory network and industry partners across North America, India, and Australia, with recent fieldwork focusing on Alaskan platinum deposits and Indian coal reserves.

• Mechatronics
• Robotics
• Safety Engineering
• +۶ مورد دیگر

Yoshitaka Tanimizu is a Professor at the School of Creative Science and Engineering, Faculty of Science and Engineering, Waseda University. His research focuses on intelligent manufacturing systems, scheduling optimization, and human-centered production models. He holds a Doctor of Engineering degree from Osaka University and maintains an active research laboratory.

Martijn R.K. Mesمشاهده پروفایل

استاد

• Transportation and Logistics Management
• Artificial Intelligence for Logistics
• Synchromodal Transport
• +۱۱ مورد دیگر

Martijn Mes is a full professor of Transportation and Logistics Management and chair of the Industrial Engineering & Business Information Systems section at the University of Twente, Netherlands. He leads research and education initiatives that integrate AI, simulation and optimisation into logistics and supply-chain innovation. Education: Ph.D. in Operations Research, University of Twente – 2008 M.Sc. in Applied Mathematics, University of Twente – 2002 Post-doctoral researcher, Princeton University, Dept. of Operations Research & Financial Engineering Research focus: Mes develops quantitative models and AI techniques for strategic, tactical and operational logistics challenges. His work spans three application pillars: Emergency & humanitarian logistics – rapid relief distribution with trucks and UAVs Urban logistics – city distribution, self-organising systems and last-mile innovations Sustainable logistics – synchromodal transport, green ports and electric/autonomous fleets Methodologically he combines approximate dynamic programming, reinforcement learning, multi-agent simulation, discrete-event simulation and stochastic optimisation to create decision-support tools for industry and government. Publications trend: Recent articles (2025-2022) exhibit a strong emphasis on integrating reinforcement learning and stochastic optimisation into dynamic vehicle routing, drone-assisted delivery and post-disaster inventory allocation, signalling a shift towards data-driven, real-time logistic systems. Grants & projects: Mes has coordinated and participated in numerous national and European projects on sustainable logistics, urban distribution, port optimisation and healthcare logistics, frequently collaborating with industry partners and public bodies. Teaching & supervision: He coordinates and lectures in the BSc and MSc programmes Industrial Engineering & Management, offering courses on simulation, queueing theory, dynamic programming, Markov chains, transportation management and technology management. He has authored a widely used Plant Simulation tutorial and supervises PhD candidates working on AI-driven logistics, autonomous vehicles and digital twins.

• Numerical Analysis
• Partial Differential Equations
• Integral Equations
• +۳ مورد دیگر

Mario Annunziato is a Researcher in Mathematics at the Department of Physics, University of Salerno, since 2004. His work focuses on numerical methods for stochastic processes and optimal control. Institution: University of Salerno Department: Department of Physics Academic Rank: Researcher Research Interests include numerical solutions of PDEs and integral equations for stochastic processes, probability density function optimization, and modeling random phenomena. His work addresses positivity, monotonicity, and conservation in discrete PDFs. Article Trends span stochastic control frameworks, computational finance, biophysics applications, and numerical methods for jump-diffusion processes. Key topics involve Fokker-Planck equations, Hamilton-Jacobi-Bellman formulations, and splitting methods. Advising and Grants include teaching Numerical Analysis until 2013 and securing funding from the University of Salerno's FARB program, INdAM-GNCS, and the European Science Foundation's OPTPDE grants. He participated in the STRIKE Marie Curie ITN network. Labs & Teams : Collaborated with Prof. Alfio Borzì at Würzburg University and contributed to open-source tools like MATLAB Central File Exchange for PDP solvers.

• Machine Learning
• Statistical Learning Theory
• Algorithmic Fairness
• +۱۲ مورد دیگر

Massi Pontil is a part-time Professor of Computational Statistics & Machine Learning in the Department of Computer Science at University College London (UCL). He joined UCL as a lecturer in 2003 and was promoted to Professor in 2010. Since 2016, his primary appointment has been at the Istituto Italiano di Tecnologia (IIT), where he leads the CSML research group. His work bridges theoretical machine learning with practical applications in physical sciences. His research interests span a wide range of topics in machine learning theory and algorithms: Machine Learning Theory and Statistical Learning Algorithmic Fairness and Ethical AI Kernel Methods and Reproducing Kernel Hilbert Spaces Transfer Learning, Multitask Learning, and Meta-Learning Operator Learning and Dynamical Systems Sparsity Regularization and Optimization Pontil's recent work focuses on the intersection of machine learning with numerical simulations of physical systems, particularly in molecular dynamics and climate science. His publications demonstrate a strong emphasis on theoretical foundations while addressing practical challenges in high-dimensional systems, symmetry-aware learning, and uncertainty quantification. Among his notable honors are: Best Paper Runner Up Award from ICML 2013 EPSRC Advanced Research Fellowship (2006-2011) Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism (2002) Professor Pontil has served on program committees for major machine learning conferences (COLT, ICML, NeurIPS) and on editorial boards of prestigious journals including Machine Learning Journal, Statistics and Computing, and JMLR. He teaches Advanced Topics in Machine Learning at UCL, with a focus on convex optimization and statistical learning theory.

• Machine Learning
• Molecular Modeling
• Drug Discovery
• +۴ مورد دیگر

Sungsoo Ahn is an Assistant Professor at the Graduate School of AI, KAIST, where he leads the Structured and Probabilistic Machine Learning (SPML) Lab. His research focuses on developing machine learning algorithms for molecular science, particularly in drug discovery, material design, and generative modeling. He directs a team of 13 researchers (including 2 post-docs and 11 students) and maintains collaborations with institutions like Mila and industry partners. His core research integrates probabilistic machine learning , generative models , and AI for science , with applications spanning molecular dynamics simulation, language model reasoning, combinatorial optimization, and graph neural networks. Key methodologies include flow matching, diffusion models, GFlowNets, and equivariant neural networks applied to chemical and biological domains. Recent publications (2023–2025) demonstrate strong emphases on: (1) Molecular generation/optimization for drug design, (2) Enhancing reliability and reasoning in large language models, (3) Graph-based machine learning for scientific discovery, and (4) Efficient training paradigms for generative samplers. These appear predominantly in NeurIPS, ICML, ICLR, and ACL. He advises multiple PhD/master's students and post-doctoral researchers in the SPML Lab. Current research directions include torsion-aware molecular generation, causal AI safety, neural operators for quantum chemistry, and multi-agent systems for molecular optimization.

Horacio Dante Espinosaمشاهده پروفایل

استاد

• Bioinspired Materials
• Single-Cell Studies
• Multiscale Experimentation and Modeling
• +۴ مورد دیگر

Horacio Dante Espinosa is the James N. and Nancy J. Farley Professor in Manufacturing and Entrepreneurship at Northwestern University's McCormick School of Engineering, holding the rank of Professor in Mechanical Engineering. He also directs the Theoretical and Applied Mechanics Program and the Micro and Nano Mechanics Lab. His research spans bioinspired materials, single-cell analysis, and multiscale experimentation, with a focus on nanoelectronics and energy harvesting. Espinosa earned his Ph.D. in Applied Mechanics from Brown University and has held academic roles at Purdue University and Harvard University. He leads interdisciplinary teams exploring metamaterials, cell engineering, and advanced fabrication techniques. His honors include the Prager Medal (2019) and multiple fellowships. Espinosa's lab combines experimental and computational methods, with capabilities in nanomechanical testing and biological assay development. Education: Ph.D. in Applied Mechanics (Brown University, 1992), M.Sc. in Structural Engineering (Polytechnic of Milan, 1987), Civil Engineering (Northeastern National University, Argentina, 1981) Key Roles: Director of iCET (2015–2018), Faculty Director of NUFAB (2013–2015), Visiting Professorships at Stanford and Harvard Labs: Micro and Nano Mechanics Lab focuses on biomaterials, metamaterials, and single-cell manipulation with advanced microscopy and electroporation systems Grants/Awards: NSF-CAREER Award, ONR Young Investigator Award, and leadership roles in professional societies His research bridges mechanics, materials science, and biology, with applications in healthcare technologies and advanced materials. Current projects include phononic crystal studies, Kirigami engineering, and high-throughput electroporation platforms for cell analysis.

Sabine C. Langerمشاهده پروفایل

استاد

• Acoustics
• Modeling and Simulation
• Numerical Acoustics
• +۷ مورد دیگر

Prof. Dr.-Ing. Sabine C. Langer is a Full Professor of Acoustics and Director of the Institute of Acoustics at Technische Universität Braunschweig. She holds a PhD in Engineering and has extensive experience in academia, including leadership roles such as President of the Deutsche Gesellschaft für Akustik (DEGA) and Deputy Speaker of the DFG Collaborative Research Center 880 (SFB 880). Her research focuses on acoustics, numerical modeling, aircraft noise reduction, and innovative materials for sound absorption. She has pioneered studies on acoustic black holes, metamaterials, and AI-driven design optimization. Langer’s work also includes contributions to educational platforms, such as developing MATLAB-based sound quality analysis tools and online learning resources for engineering students. Education: Civil Engineering degree (1991–1996, TU Braunschweig), PhD in Engineering (2001, TU Braunschweig). Key positions include W2 Professor for Vibroacoustics (2013–2018) and Junior Professor for Wave Propagation and Building Acoustics (2003–2013). She led the Graduate School at SFB 880 and advised numerous research initiatives in structural acoustics and noise mitigation. Research interests span numerical acoustics, sound quality assessment, and sustainable acoustic design. Her recent work emphasizes AI integration in engineering design, stochastic modeling, and additive manufacturing of acoustic materials. She has published extensively on aircraft cabin noise prediction, vibration isolation, and metamaterial applications. Professional roles include membership in the DIN/VDI Normenausschuss Akustik and the Advisory Board of the Excellence Cluster Hearing4All. She has organized major acoustics conferences, including DAGA 2020 in Hannover, and contributed to standard-setting in noise reduction and vibration technology.

Tom van Woenselمشاهده پروفایل

استاد

• Freight Transport
• Logistics
• Operations Research
• +۵ مورد دیگر

Tom van Woensel is a Full Professor of Freight Transport and Logistics at Eindhoven University of Technology (Netherlands), affiliated with the School of Industrial Engineering and Innovation Sciences and the Department of Operations Planning Accounting & Control. He also holds roles as Academic Director of the Global Supply Chain Management program at Antwerp Management School and Director of the European Supply Chain Forum. His research focuses on freight transport, logistics systems, and operations research methodologies, with contributions to over 150 peer-reviewed publications in journals like Transportation Science and European Journal of Operational Research . Education: BSc/MSc in Applied Economic Sciences (Econometrics), University of Antwerp (Belgium) PhD: Queueing Theoretical Approaches for Traffic Flow Networks, University of Antwerp Research Interests: Optimization of transport and logistics networks using integer programming, metaheuristics, and reinforcement learning Urban freight systems, last-mile delivery, and sustainable logistics Supply chain resilience, collaboration in logistics networks, and industry-academia partnerships Applications of AI in solving stochastic transportation problems Awards and Recognition: Outstanding Professor in Supply Chain & Logistics (2021) European Journal of Operational Research Best Review Paper (2016) INFORMS Senior Member (2024) Collaborations and Projects: Leads initiatives like SYNERCIZE (zero-emission construction logistics) and Circulaire stromen (circular logistics) Editorial roles at Transportation Science , OR Spectrum , and Urban Science Active in industry partnerships through the European Supply Chain Forum (75+ multinationals) Labs/Teams: EAISI Mobility (Eindhoven AI Systems Institute) CIRRELT (Montreal, Canada) collaborating member

Mehdi Dastaniمشاهده پروفایل

استاد

• Artificial Intelligence
• Multi-Agent Systems
• Formal Methods in AI
• +۵ مورد دیگر

Prof. Mehdi Dastani is a Professor and chair of the Intelligent Systems group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. He leads the Master's program in Artificial Intelligence and focuses on formal and computational models in AI, particularly multi-agent systems. His research integrates insights from philosophy, psychology, and law to develop autonomous agents that reason about social and cognitive concepts like norms, emotions, and responsibility. Dastani has held academic roles at Utrecht University since 2001, including postdoctoral research and faculty positions. Education: M.Sc. Computer Science (University of Amsterdam, 1991), M.Sc. Philosophy (University of Amsterdam, 1992), Ph.D. in Humanities (University of Amsterdam, 1998). His work spans theoretical and applied projects, including grants for initiatives like Golden Agents (simulating Golden Age creative industries) and traffic control systems using virtual organizations. He is actively involved in academic committees, editorial boards, and organizing international conferences like AAMAS and PRIMA. Research Interests: Multi-Agent Programming, Normative Systems, Autonomous Agents, Cognitive Robotics, and Human-Centered AI. His projects address challenges like norm enforcement, decision-making in complex systems, and ethical AI integration with societal needs. Advising & Grants: Supervised numerous PhD students (e.g., Birna van Riemsdijk, Bas Testerink) and secured grants for projects such as 'Controllable AI: Human-Centered Approach'. His work includes collaborations on urban governance, autonomous driving, and AI tools for literacy support in children. Labs & Teams: Leads the Intelligent Systems group, contributing to agent-based simulations, ethical AI frameworks, and interdisciplinary collaborations with social scientists and urban planners.

Michele Ruggeriمشاهده پروفایل

استادیار

• Numerical Analysis
• Partial Differential Equations
• Materials Science
• +۶ مورد دیگر

Michele Ruggeri is an Assistant Professor of Numerical Analysis (RTD-B) at the Department of Mathematics, University of Bologna, and an Honorary Lecturer at the University of Strathclyde, UK. His research focuses on numerical analysis of partial differential equations in materials science and engineering, including liquid crystal theory, micromagnetism, nonlinear elasticity, and spintronics. He is also active in developing numerical methods for uncertainty quantification and model order reduction. Education: PhD in Technical Mathematics (TU Wien, Austria), Laurea Magistrale in Mathematics (University of Pavia, Italy), and a Diploma in Science and Technology from Scuola Universitaria Superiore IUSS, Italy. He holds a Fellowship (FHEA) from Advance HE and a PGCert in Learning & Teaching in Higher Education from the University of Strathclyde. His work emphasizes finite element methods and their applications, with contributions to computational micromagnetics and software development (e.g., Commics). He is affiliated with the (AM)² Research Center on Applied Mathematics and ARCES at the University of Bologna. Awards: Fellowship (FHEA), Advance HE, UK. Key Projects: Development of numerical methods for micromagnetic simulations, uncertainty quantification in stochastic models. Contact: m.ruggeri@unibo.it

Andrea Barthمشاهده پروفایل

استاد

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +۴ مورد دیگر

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

Russell Schwartzمشاهده پروفایل

استاد

• Cancer Genomics
• Tumor Evolution
• Computational Phylogenetics
• +۳ مورد دیگر

Russell Schwartz is Professor of Biological Sciences and Computational Biology at Carnegie Mellon University, with courtesy appointments in Computer Science and Machine Learning. He serves as Head of the Ray and Stephanie Lane Computational Biology Department and Co-Director of the Joint CMU-University of Pittsburgh PhD Program in Computational Biology. His research develops computational models for complex biological systems, focusing on cancer genomics and macromolecular assembly. Key areas include reconstructing tumor clonal evolution through integration of bulk and single-cell sequencing data, developing algorithms for stochastic simulation of self-assembly systems, and creating optimization methods for fitting models to experimental data. Recent publications demonstrate advancements in single-cell lineage tracing, circulating tumor DNA analysis, and generative AI for biological databases. His team's BrM-Phylo software enables phylogeny reconstruction from matched transcriptome data in breast cancer brain metastases. NSF CAREER Award Alfred P. Sloan Research Fellowship Schwartz leads multiple NIH-funded projects on tumor evolution modeling and mentors students in computational cancer research. His laboratory develops open-source tools including FISHtrees for tumor phylogenetics and TUSV for integrating structural variants in clonal reconstruction.

• Complex Systems
• Computational Epidemiology
• Artificial Intelligence
• +۵ مورد دیگر

Professor Mikhail Prokopenko is a leading academic in complex systems research at the University of Sydney's School of Computer Science. He holds a PhD in Computer Science (Macquarie University, 2002), MA in Economics (USA), and MSc in Applied Mathematics (USSR). As Director of the Centre for Complex Systems and Theme Co-Leader for Pathogen Emergence and Spread at the Institute for Infectious Diseases, his work focuses on modeling self-organizing systems, computational epidemiology, and AI applications in pandemic control. Roles: Director of Centre for Complex Systems, Theme Co-Leader (Pathogen Emergence), Theme Leader (Complex Systems) Education: PhD Computer Science (2002), MA Economics (USA), MSc Applied Mathematics (USSR) Affiliations: Fellow of Royal Society of NSW, Specialty Editor for Frontiers in Robotics and AI His research addresses challenges in complex systems like power grids, communication networks, and epidemiological models. Notable contributions include: RoboCup World Champion teams (2016, 2019) in Simulation League COVID-19 modeling featured in Nature's Top 50 SARS-CoV-2 articles (2020) Development of AMTraC-19 agent-based pandemic model Key research areas include: Guided self-organization principles Information dynamics in collective systems Thermodynamic efficiency of complex processes He has supervised over 200 publications and patents, with recent focus on pandemic response optimization and urban system resilience.