جست‌وجوی استاد و پژوهشگر

Angelina Ananiمشاهده پروفایل

دانشیار

• Mining Systems Optimization
• Mine Planning and Production Scheduling
• Sustainable Mining Systems
• +۱۲ مورد دیگر

Dr. Angelina Anani is an Associate Professor in the Department of Mining & Geological Engineering at the University of Arizona, College of Engineering. She is also a member of the Graduate Faculty and actively contributes to research and teaching in mining systems optimization, mine planning, and sustainable mining practices. Education: PhD in Mining Engineering, Missouri University of Science and Technology, Rolla, Missouri, United States BS in Mining Engineering (Summa cum laude), Missouri University of Science and Technology, Rolla, Missouri, United States Research Interests: Dr. Anani's research spans a broad spectrum of mining engineering challenges, focusing on modeling and optimization of mining systems , mine planning and production scheduling , and sustainable mining system design . She investigates mine equipment reliability , tunneling and underground works , and energy and water efficiency . A significant portion of her recent work integrates machine learning and data-driven approaches into mine safety and planning, including 3D/4D/VR applications and digital twin systems . Her interdisciplinary approach also includes ethnographic research in mining communities and supply chain management in the mining sector. Publications Trends: Her recent publications reflect a strong shift toward intelligent systems in mining, with increasing focus on machine learning for safety, process mining for maintenance, and digital twin deployment. She combines traditional optimization techniques like discrete event simulation with modern AI to solve complex mining challenges, particularly in underground and transition mines. Scientific Awards: Freeport-McMoRan, Inc. Career Development Grant Society for Mining, Metallurgy and Exploration, Fall 2022 Faculty Core Advising and Grants: Dr. Anani supervises graduate research through MNE 900 (Research), MNE 910 (Thesis), and MNE 920 (Dissertation) courses. She has secured external funding such as the Freeport-McMoRan Career Development Grant, supporting her innovative work in mine optimization and safety. While current students are not listed, her active supervision load indicates ongoing mentorship of master’s and PhD candidates. Labs and Teams: She is actively involved with the San Xavier Underground Mine Laboratory, where she contributes to monitoring systems and digital twin development. Her collaborative work with researchers from Chile and Ghana highlights her international engagement. She is also affiliated with professional societies including the Society of Mining, Metallurgy and Exploration (SME), Society of Mining Professors, and Women in Mining (WIM), contributing to both technical and diversity initiatives in the field.

Barry Jordanمشاهده پروفایل

استاد

• Business Law
• Tax Law
• Law and Economics
• +۴ مورد دیگر

Barry Jordan is the Vice Dean for Faculty and Academic Affairs and the John B. Milliken Professor of Law and Taxation at the USC Gould School of Law. His research focuses on business law, tax law, and law and economics, with notable publications in Stanford Law Review , Virginia Law Review , and Journal of Political Economy . He teaches courses on corporate finance, tax policy, and law and economics, and has held prior positions at the University of San Diego School of Law and UC Berkeley School of Law. Education: Graduate of Cornell University and Stanford Law School (Managing Editor of the Stanford Law Review ). Awards include the Thorsnes Prize for Excellence in Teaching (twice), Herzog Endowed Scholar designation, and recognition as a “Favorite Faculty Member” by USD Law’s Public Interest Law Foundation. Research interests emphasize regulatory entrepreneurship, corporate governance, and taxation’s impact on innovation, particularly in the sharing economy. His work explores topics like empty voting in derivatives markets, anti-takeover defenses, and cross-jurisdictional tax policies. He frequently contributes to legal debates through amicus curiae briefs and policy analyses. Grants and advising: No specific grants listed, but his academic leadership roles suggest institutional support. No listed students, but extensive teaching and mentorship implied through awards like the Thorsnes Prize. Labs/teams: Led the Center for Corporate and Securities Law at USD and co-directed the graduate tax program. At USC, oversees faculty and academic affairs, implying leadership in institutional academic teams.

Craig S. Kaplanمشاهده پروفایل

استاد

• Computer Graphics
• Computational Geometry
• Mathematical Art
• +۴ مورد دیگر

Craig S. Kaplan is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science within the Faculty of Mathematics. His research bridges computer science and mathematical art, focusing on computational geometry, geometric pattern design, and algorithmic art. He holds degrees including a Ph.D. and M.Sc. from the University of Washington (2002, 1998) and a B.Math. from the University of Waterloo (1996). His work spans applications of mathematics in art and design, including Islamic geometric patterns, computer graphics, and computational geometry. Notable contributions include developing methods for generating Islamic geometric patterns, exploring aperiodic tilings (including the discovery of an aperiodic monotile in 2023), and creating tools for artistic visualization like RepulsionPak and FlowPak. He also engages with digital art forms such as generative algorithms, stereoscopic 3D rendering, and interactive applications for mindfulness and VR. His publications reflect a focus on geometric algorithms, pattern generation, and interdisciplinary art-science projects. Though no explicit awards are listed, his prolific output and contributions to mathematical art suggest significant recognition in his field. His work often emphasizes computational methods for artistic expression, combining rigorous mathematics with creative outcomes.

• Numerical Methods
• Computational Engineering
• Multiphysics Simulation Techniques
• +۲ مورد دیگر

Prof. Thomas Weiland is a Full Professor of Computational Electromagnetics at the Technische Universität Darmstadt since 1989. His research focuses on numerical methods, computational engineering, and multiphysics simulation techniques, particularly in accelerator physics and beam dynamics. He holds a Dr.-Ing. from TU Darmstadt and has held postdoctoral and research positions at CERN and TU Darmstadt. His work includes pioneering contributions to electromagnetic field simulations, including advanced finite element methods, discontinuous Galerkin techniques, and boundary element approaches. Education highlights include his Diplom in Electrical Engineering from TU Darmstadt (1975) and a Habilitation in Experimental Physics from the University of Hamburg (1984). His research spans computational electromagnetics, accelerator physics, and numerical methods for electromagnetic field problems. Notable areas of innovation include transparent boundary conditions, eigenmode calculations, and high-performance simulation frameworks for rotating systems and particle accelerators. His publications emphasize advancements in electromagnetic simulation tools, such as the MagPEEC method and Trefftz-discontinuous Galerkin approaches. Collaborative projects include modeling RF photoinjectors for light sources and electrohydrodynamic droplet dynamics. Technical contributions also extend to wake field analysis in particle accelerators and SAR distribution studies in bioelectromagnetics. Research interests further include multiphysics coupling (thermal-electromagnetic effects in surge arresters), stochastic modeling of electromagnetic systems, and field-circuit co-simulation techniques. His work addresses challenges in large-scale eigenvalue problems, adaptive mesh optimization, and high-precision numerical methods for complex geometries.

Martin Servinمشاهده پروفایل

دانشیار

• Digital Physics
• Computational Modelling
• Robotics
• +۳ مورد دیگر

Martin Servin is an Associate Professor at the Department of Physics, Umeå University, and leads the Digital Physics research group within the UMIT Research Lab. His work focuses on computational modeling and simulation of granular materials, robots, and vehicles, with applications in AI-based control and perception. He holds a doctoral degree from Umeå University (2003) and has pioneered research in real-time physics simulation, particularly in the context of autonomous machinery and off-road robotics. Research Interests : Digital physics, granular materials simulation, autonomous systems, reinforcement learning, and simulation-to-reality transfer. His group develops advanced simulation tools for industries like forestry, mining, and construction. Key Projects : Mistra Digital Forest (2019–2026) AILUR (Digital Twin for AI-controlled Lunar Robotics) XSCAVE (Explainable, Safe Control for Heavy Machinery) Publications emphasize simulation methodologies, AI integration, and real-world validation across robotics, vehicle dynamics, and granular mechanics. Notable contributions include work on wheel loader dynamics, deep reinforcement learning for control systems, and terrain modeling. Awards include the Spin-off award for industry-grade physics in Unreal Engine (2018) , recognizing his role in Algoryx Simulations, a spin-off company commercializing his research. Labs/Teams : UMIT Research Lab, Digital Physics Group, and collaborations with Algoryx Simulations.

Zijian Diaoمشاهده پروفایل

دانشیار

• Quantum Computation
• Quantum Information Theory
• Natural Language Processing
• +۷ مورد دیگر

Zijian Diao is an Associate Professor in the Department of Mathematics at Ohio University's College of Arts and Sciences, where he contributes to both teaching and research. He is based at the Eastern Campus in Shannon Hall and teaches a wide range of undergraduate mathematics courses. Education: Ph.D. in Mathematics, Texas A&M University, 2001 M.S. in Computer Science, University of Illinois, 2003 B.S. in Automation, University of Science and Technology of China, 1996 His research spans interdisciplinary areas with a strong focus on quantum computation and information theory , where he has made contributions to Grover's algorithm, quantum counting, and quantum circuit design. He has also worked in natural language processing , particularly in multilingual speech-to-speech translation systems like MARS. Additional interests include control theory , partial differential equations , and mathematical modeling . His recent and ongoing publications reflect a blend of theoretical mathematics and applied computational science. The works trend toward quantum algorithms and foundational mathematical proofs, with earlier contributions in NLP and control systems. His research bridges pure mathematics with practical implementations in quantum computing and language technologies. Scientific Awards: Ohio University Regional Higher Education Outstanding Professor Dr. Diao advises undergraduate and graduate students in mathematics and related fields, though specific student names are not listed. He has been involved in research projects supported by academic grants, particularly in quantum computing and interdisciplinary technology development. His curriculum vitae indicates sustained scholarly activity across multiple domains. He is associated with research groups and collaborations in quantum computing, having co-authored works with G. Chen, C. Huang, and others. His work in NLP was part of larger team efforts involving researchers from industry and academia, suggesting active participation in collaborative technical teams.

Yuri Faenzaمشاهده پروفایل

دانشیار

• Discrete Optimization
• Operations Research
• Mathematical Programming
• +۴ مورد دیگر

Yuri Faenza is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia Engineering, with affiliations to the Data Science Institute (DSI) and Foundations of Data Science Center. His research bridges Discrete Optimization, Operations Research, and Computer Science, focusing on algorithmic theory and applications to Market Design (particularly School Choice) and Machine Learning. Key research areas include Knapsack , Matching , and Extended Formulations , with methodological work on polytope structures, greedy algorithms, and stochastic or semi-random models. His recent publications emphasize stable matching, optimization under uncertainty, and the intersection of discrete mathematics with data science. Scientific awards include the NSF CAREER award Meta Research Award . He has served on program committees for major conferences like ALGA, EC, APPROX, and IPCO, and is an Associate Editor for journals including Mathematical Programming and Discrete Optimization . Prior to Columbia, he held postdoctoral positions at the University of Brussels, EPFL, and University of Padua.

Yoram Alhassidمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Many-Body Quantum Systems
• Nuclear Many-Body Problem
• +۱۵ مورد دیگر

Yoram Alhassid is the Frederick Phineas Rose Professor of Physics in the Department of Physics at Yale University, where he leads a research group focused on theoretical nuclear and many-body physics. His work bridges nuclear physics, mesoscopic systems, and ultracold atomic gases, using advanced computational methods such as quantum Monte Carlo and the configuration-interaction shell model. His research interests include the nuclear many-body problem, femtoscience and nanoscience (nuclei, quantum dots, nanoparticles), and cold atomic Fermi gases. He has developed and applied the shell model Monte Carlo (SMMC) method to study statistical and collective properties of nuclei, such as level densities, deformation, and pairing correlations. He has extended these methods to study cold Fermi gases, particularly in the unitary regime, where he has investigated pseudogap phenomena, heat capacity, and pairing gaps. The recent publications highlight a strong focus on nuclear level densities, γ-ray strength functions, deformation effects, and quantum Monte Carlo methodologies. There is a clear trend toward microscopic, ab initio calculations of nuclear and many-body properties, with increasing attention to odd-mass and deformed nuclei, as well as the interplay between pairing and collective phenomena. Scientific Awards: Frederick Phineas Rose Professor of Physics Alhassid advises students and postdoctoral researchers, as evidenced by numerous co-authored publications. His group has developed advanced computational tools and codes (e.g., HF-SHELL) for finite-temperature mean-field and shell model calculations. The research is supported by high-performance computing and has implications for nuclear astrophysics, radioactive beam facilities, and quantum simulation with cold atoms. His lab focuses on theoretical and computational modeling of finite-size quantum many-body systems, with close collaborations across nuclear theory, condensed matter, and atomic physics. The group emphasizes method development, benchmarking against mean-field theories, and extracting model-independent signatures of physical phenomena such as deformation and pairing.

Thilo Fehmelمشاهده پروفایل

استاد

• Social Policy
• Social Administration
• Social Work
• +۱۲ مورد دیگر

Thilo Fehmel is a Professor of Social Administration and Social Policy at the Leipzig University of Applied Sciences and serves as Dean of the Faculty of Architecture and Social Sciences . His academic career spans institutions like the University of Leipzig (habilitation in sociology) and Cooperative State University Gera. Research Focus: Social policy, welfare state theory, European sociology, labor sociology, and sociological theory (conflict, autonomy, deviance). Projects: Led research on Citizen Participation in the Digital Society (2019-2021, funded by Saxony) and Social Policy by Collective Agreement (2015-2019, funded by Hans Böckler Foundation). Publications: Over 25 monographs and edited volumes, including Tarifsozialpolitik (2021) and Sicherungsbewahrung (2013). Academic Leadership: Contributed to journals like Traverse and Soziologie , and edited special issues on welfare state transformation. His work bridges theoretical sociological analysis with practical policy implications, emphasizing the role of collective bargaining and institutional frameworks in shaping social security systems across Europe.

Yiannis Giannakopoulosمشاهده پروفایل

مدرس ارشد

• Algorithms
• Algorithmic Game Theory
• Computational Complexity
• +۶ مورد دیگر

Yiannis Giannakopoulos is a Senior Lecturer (equivalent to Associate Professor) in Algorithms and Complexity at the University of Glasgow and a Turing Fellow. He previously held faculty positions at FAU Erlangen-Nürnberg and TU Munich, and was a postdoctoral researcher at TU Munich and the University of Liverpool. PhD (DPhil) in Computer Science, University of Oxford, advised by Elias Koutsoupias MSc in Logic, Algorithms and Computation (MPLA), University of Athens BSc in Mathematics, University of Athens His research centers on algorithms, complexity, and optimization, with a strong emphasis on algorithmic game theory. Key topics include auction design, mechanism design, equilibrium computation, congestion games, and computational economics. His work bridges theoretical computer science and economic theory, focusing on the algorithmic aspects of strategic behavior. The recent publications highlight a consistent focus on equilibrium computation in auctions and congestion games, optimal mechanism design, and complexity analysis. Trends include smoothed complexity, approximation algorithms for equilibria, robust revenue maximization, and structural analysis of inefficiency in games. His work frequently appears in top venues such as EC, SAGT, ICALP, SICOMP, and MOR. Scientific Awards: Best Paper Award at ICALP 2015 for 'Selling Two Goods Optimally' He is actively supervising PhD students, including Johannes Hahn, and has mentored several others such as Charalampos Kokkalis and Alexander Grosz. He is involved in academic service, including organizing the 1st UK Workshop for Junior Researchers in Economics and Computation (2025) and serving on the program committee of SAGT 2025. PhD positions are currently available with funding for UK students. He is affiliated with research groups in Algorithms & Complexity and has collaborated extensively with leading researchers in the field, including Aris Filos-Ratsikas, Elias Koutsoupias, and Diogo Poças. His work is supported by institutions such as the Simons Foundation.

• artificial intelligence
• image analysis
• deep learning
• +۸ مورد دیگر

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.

Arash Asadpourمشاهده پروفایل

دانشیار

• Data Science/Analytics
• Operations Management
• Operations Research
• +۷ مورد دیگر

Arash Asadpour is an Associate Professor at the Narendra Paul Loomba Department of Management, Zicklin School of Business, Baruch College, City University of New York (CUNY). His research focuses on operations research, optimization, and marketplace dynamics, particularly in gig economy platforms and online retailing. PhD in Management Science and Engineering, Stanford University 2010 B.Sc. in Computer Engineering, Sharif University of Technology, Tehran, Iran 2005 His work spans data science , dynamic pricing , stochastic optimization , and matching markets , with applications to ridesharing, online advertising, and resource allocation. Recent publications emphasize platform economics, algorithmic game theory, and submodular function optimization. Arash has received prestigious awards including the Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA) in 2010, top honors in Iran’s national graduate entrance exam (2004), and silver medals in national informatics olympiads (1999-2000).

Pinar Heggernesمشاهده پروفایل

استاد

• Algorithms
• Graph Theory
• Combinatorics
• +۳ مورد دیگر

Pinar Heggernes is a Professor at the Department of Informatics at the University of Bergen , specializing in algorithms and graph theory . Her research focuses on solving NP-hard graph problems through polynomial-time and fixed parameter tractable algorithms , particularly on chordal , split , and outerplanar graphs . She served as the elected deputy rector of the University of Bergen (2021–2025) with responsibility for education and digital knowledge, and previously as head of the Department of Informatics until 2021. Her career spans leadership in externally funded projects, editorial roles in international journals, and active participation in national research policy, including co-founding the Norwegian Artificial Intelligence Research Consortium (NORA). Education: PhD in Informatics from the University of Bergen. Research Interests: Algorithms for graph classes, computational complexity, parameterized algorithms, and combinatorial optimization. Collaborations: Extensive partnerships with academia, businesses, and public administration, including advisory roles in the Bergen Chamber of Commerce and the Norwegian Cognitive Center . Grants: Project leader for four Research Council of Norway FRIPRO grants (CLASSIS, SCOPE, MIST, and earlier projects) and participant in EU, Trond Mohn Foundation, and National Security Authority grants. Students: Supervised Paloma T. Lima (PhD, 2019) and co-supervised numerous advisees through collaborative projects. Labs/Teams: Coordinated interdisciplinary ICT research and education initiatives at UiB, with international collaborations in the USA, France, and Turkey.

Cormac Lucasمشاهده پروفایل

مدرس ارشد

• Mathematical Optimisation
• Stochastic Optimisation
• Asset and Liability Management (ALM)
• +۵ مورد دیگر

Dr. Cormac Lucas is a Senior Lecturer in the Department of Mathematics at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. His work bridges mathematical optimization with practical applications in finance and operations management. Lucas specializes in Mathematical Optimisation Stochastic Optimisation Asset and Liability Management (ALM) Risk Analytics Portfolio Optimization Supply Chain Planning Under Uncertainty His research combines theoretical advancements with industrial projects, such as US Coast Guard Cutter Scheduling, Insight Investment's ALM, and Unilever's Natural Oil Buying Policy. Recent publications (2013–2024) highlight his focus on Portfolio Rebalancing with Transaction Costs Scenario Generation for Stochastic Programming Heuristic Algorithms for Cardinality Constraints Queuing Systems with Standby Servers Robust Supply Chain Planning Financial Derivative Modeling These works utilize methods like Variable Neighbourhood Search, Differential Evolution, and Lagrangian Relaxation. Email: cormac.lucas@brunel.ac.uk

• Non-Euclidean Geometry
• Convex Geometry
• Minkowski Geometry
• +۱ مورد دیگر

Ákos G. Horváth is a Professor at the Department of Geometry , Budapest University of Technology and Economics. His academic career focuses on geometric theories and their applications, with a particular emphasis on Minkowski geometries and discrete geometry. Room: H222 Phone: +36-1-463-2645 Email: ghorvath@math.bme.hu Personal Website: https://math.bme.hu/~ghorvath/ His research areas include Non-Euclidean Geometry, Convex Geometry, and Minkowski Geometry. He investigates topics such as generalized Minkowski spaces, Dirichlet-Voronoi cells, and bisectors in normed spaces, contributing to advancements in discrete and computational geometry. Key trends in his publications span over two decades, with significant work on: Unimodular lattices (1996) Bisectors in Minkowski spaces (2000) Semi-indefinite inner products (2010)