جست‌وجوی استاد و پژوهشگر

Charles Murryمشاهده پروفایل

دانشیار

• Empirical Industrial Organization
• Antitrust Economics
• Market Power Analysis
• +۳ مورد دیگر

Charles Murry is an Associate Professor of Economics at the University of Michigan and a Research Associate at the National Bureau of Economic Research (NBER). He holds a Ph.D. from the University of Virginia (2014) and a B.S. in Economics from the University of Delaware (2006). His research focuses on empirical industrial organization and antitrust economics, with a particular emphasis on market power dynamics in industries like automobiles, airlines, and broadband. He has also contributed to econometric methodologies for analyzing market outcomes. Previously, Murry held positions at Boston College (2018–2024) and Pennsylvania State University (2014–2018). His work examines topics such as bundling strategies in public procurement, price discrimination in international markets, and regulatory impacts on market structure. He has published in top journals like the Quarterly Journal of Economics , Review of Economic Studies , and International Economic Review . Murry’s research bridges theoretical models and empirical evidence, addressing real-world policy issues such as competition enforcement and market design. He is actively involved in academic service, including contributions to antitrust-related amicus briefs and participation in policy panels.

Alexander Budzierمشاهده پروفایل

پژوهشگر ارشد

• Project Management
• IT-enabled Change
• Risk Management
• +۵ مورد دیگر

Alexander Budzier is a Research Fellow in the Department of Technology and Operations Management at Saïd Business School, University of Oxford. He specializes in managing projects across IT, infrastructure, energy, and mega-events, with a focus on risk mitigation and systems thinking. His roles include teaching on the MSc for Major Programme Management, MBA, and the Major Project Leadership Academy. Education: PhD from Saïd Business School (2014). Prior experience includes roles at T-Mobile International and McKinsey's Business Technology Office in Düsseldorf and Chicago, advising on IT and operations strategy. Research interests center on IT-enabled change, project setups, and risk analysis. Notable contributions include work on cost overruns in IT projects, the uniqueness trap in project management, and sustainable development via mega-events like the Olympics. Publications span journals like Harvard Business Review , iScience , and Event Management , with a focus on megaprojects, risk, and digital transformation. He has advised governments and corporations on infrastructure investments and project governance, emphasizing evidence-based decision-making.

Laura Bruckmanمشاهده پروفایل

دانشیار

• Materials Degradation Science
• Photovoltaic Materials
• Data Science Applications in Materials
• +۳ مورد دیگر

Laura Bruckman is a Climo Associate Professor in the Department of Materials Science and Engineering at Case Western Reserve University's Case School of Engineering. Her research focuses on predictive lifetime modeling for materials degradation, quantitative spectroscopic characterization of materials, and applying statistical analytics and data science to solve challenges in photovoltaic systems and long-lived engineering materials. Her work emphasizes understanding degradation mechanisms in photovoltaic materials (e.g., backsheets, encapsulants, and silicon cells) under environmental stressors, with applications in improving reliability and service life through advanced data-driven approaches. Dr. Bruckman has contributed to the development of machine learning methods for material characterization (e.g., ToF-SIMS analysis) and spatiotemporal models for predicting degradation patterns in field-deployed PV systems. Her research also extends to curriculum design for applied data science, emphasizing industry-relevant training in statistical modeling and interdisciplinary problem-solving. Her expertise bridges materials science, data science, and energy systems, with over 50 peer-reviewed publications and a patent in classification using multivariate optical computing. Key technical contributions include analyzing environmental impacts on solar module performance, quantifying crack propagation in polymers, and developing predictive frameworks for material aging. Her work has been supported by collaborations with industry partners and federal research initiatives.

• Management Science
• Operations Management
• Mathematical & Quantitative Methods
• +۸ مورد دیگر

Professor Lilian M. de Menezes is a Professor of Decision Sciences at Bayes Business School within the Faculty of Management at City St George's, University of London. Her expertise spans Management Science, Operations Management, Statistics, and Energy Markets. She holds a BSc and MSc from Pontifícia Universidade Católica in Brazil and a PhD from London Business School. Lilian's research focuses on forecasting methodologies, energy market integration, quality management, and flexible work arrangements. She has contributed to studies on healthcare performance measurement, workforce optimization, and sustainability in business excellence models. Her work bridges academic research with practical applications in industries like energy and healthcare. She has received notable awards, including the 2015 EEX Excellence Award for Melanie Houllier's thesis on European electricity markets and the 2014 Best Paper Award for analyzing job satisfaction and quality management links. Her affiliations include the European Operations Management Association and the Royal Statistical Society. Lilian's research also explores flexible working policies and their impact on employee performance and well-being. Her recent work addresses challenges in energy market forecasting and sustainable organizational practices. She maintains a visiting appointment at ESCP Europe’s Research Centre for Energy Management.

Refik Soyerمشاهده پروفایل

استاد

• Bayesian Statistics
• Reliability Modeling
• Decision Analysis
• +۴ مورد دیگر

Refik Soyer is a Professor of Statistics at The George Washington University. His research focuses on Bayesian statistics, reliability modeling, decision analysis, and time series analysis. He has made significant contributions to the application of Bayesian methods in reliability engineering, queueing systems, and adversarial risk analysis. Education: D. Sc. in Statistics (1985), George Washington University His recent publications highlight advancements in Bayesian reliability analysis, adversarial decision frameworks, and computational methods for time series and queueing systems. Areas of emphasis include dynamic INAR processes, accelerated life testing, and software failure modeling. Soyer's work bridges theoretical statistics with practical applications in call centers, healthcare fraud detection, and risk management.

• Topology
• Algebra
• Mathematical Physics
• +۳ مورد دیگر

Florian Naef is an Assistant Professor in the School of Mathematics at Trinity College Dublin. His research spans topological and algebraic structures with applications to mathematical physics, including string topology, Poisson geometry, and homotopy theory. Publications emphasize formality theorems, torsion invariants, and quantization methods. Recurring themes include loop spaces, deformation quantization, and connections between differential geometry and algebraic topology.

Somayeh Allahyariمشاهده پروفایل

استادیار

• Operations Research
• Optimization
• Logistics
• +۶ مورد دیگر

Dr. Somayeh Allahyari is an Assistant Professor in Operations and Supply Chain Management at the Birmingham Business School, University of Birmingham. She holds a PhD in Industrial Engineering (2021) and has academic qualifications including an MSc (2013), BSc (2011), and a Diploma in Mathematics and Physics (2006). Her research focuses on Operations Research methodologies applied to Logistics, Network Design, and Supply Chain Management. Key interests include Optimization, Heuristics, Decision Support Systems, and Business Analytics. She has led industry projects such as the Drone Medical Logistics project for the Solent Future Transport Zone Programme, exploring multi-modal logistics solutions. Teaching responsibilities include modules on Supply Chain Management (UG), Operations Management (MSc), and Business Analytics (MSc Singapore). She actively supervises PhD candidates in areas like Logistics, Blockchain Technology, and Digital Transformation. Her publications appear in top journals like Transportation Research Part E and European Journal of Operational Research , with conference contributions at INFORMS Transportation Science. She serves as a peer reviewer for major journals and conferences in her field.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +۷ مورد دیگر

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

Scott Armstrongمشاهده پروفایل

استاد

• Partial Differential Equations
• Calculus of Variations
• Probability Theory
• +۳ مورد دیگر

Scott Armstrong is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on partial differential equations, calculus of variations, and probability theory, with a specialization in stochastic homogenization of PDEs in random media and related statistical mechanical systems. He holds a Ph.D. from UC Berkeley (2009) and a B.S. from Texas A&M University (2002). Education: Ph.D. in Mathematics, University of California, Berkeley, USA (2009) B.S. in Mathematics, Texas A&M University, USA (2002) Research Interests: Scott's work addresses fundamental questions in homogenization theory, including quantitative estimates for elliptic and parabolic equations in random media, renormalization group methods, and applications to statistical mechanics. His contributions bridge analysis, probability, and mathematical physics, with a focus on rigorous mathematical frameworks for understanding macroscopic behavior from microscopic models. Publications: His recent work includes studies on anomalous diffusion, renormalization group techniques, and quantitative homogenization in high-contrast media. Over 50 peer-reviewed articles highlight his expertise in stochastic PDEs, elliptic regularity, and variational methods. Awards: No specific awards listed in the provided text. Advising & Grants: No student advisees or grant details explicitly mentioned in the text. Labs/Teams: No dedicated labs or collaborative teams explicitly noted, though his research likely involves interdisciplinary collaborations within the Courant Institute.

Ali Hirsaمشاهده پروفایل

استاد

• Algorithmic Trading
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Ali Hirsa is a Professor of Professional Practice at Columbia Engineering within the Department of Industrial Engineering and Operations Research. He has also served as an Adjunct Professor at Columbia University since 2000 and is the Managing Partner at Sauma Capital, LLC, a New York-based hedge fund. PhD in Applied Mathematics from University of Maryland College Park (advised by Professors Howard C. Elman and Dilip B. Madan) Research Interests : Ali’s research spans algorithmic trading , machine learning , data mining , computational finance , quantitative finance , and optimization . He focuses on developing learning algorithms for signal extraction from financial data, with applications in trading strategies and financial modeling. Scientific Awards and Affiliations : He was a Fellow at NYU’s Courant Institute Mathematics of Finance Program (2004–2014) and holds affiliations with Columbia’s Data, Media and Society , Financial and Business Analytics , and Foundations of Data Science centers. Publications and Patents : Ali authored Computational Methods in Finance (2012) and co-authored the third edition of An Introduction to Mathematics of Financial Derivatives . He co-invented US Patent 8,799,146 for fair post-trade allocation algorithms. He also serves as editor of the Journal of Investment Strategies . Leadership and Governance : He has been Vice Chair of the Board of Visitors at the University of Maryland’s College of Computer, Mathematical, and Natural Sciences since 2016 and previously served on the Board of Visitors at the A. James Clark School of Engineering (2008–2018) and as a trustee of the University of Maryland College Park Foundation (2011–2016).

Tianyi Linمشاهده پروفایل

استادیار

• Optimization
• Game Theory
• Machine Learning
• +۳ مورد دیگر

Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.

• Operations Research
• Management Science
• Dynamic Pricing
• +۴ مورد دیگر

Arash Asadpour Rahimabadi is an Associate Professor at the N. P. Loomba Department of Management within the Zicklin School of Business at Baruch College, CUNY . His research bridges Operations Research and Management Science , focusing on algorithmic design, dynamic pricing, and optimization in gig economy platforms. Education: Ph.D. in Operations Research, Stanford University (2010) BSc in Computer Engineering, Sharif University of Technology (2004) His research interests include stochastic optimization , submodular maximization , marketplace stability , and fair allocation . Recent work explores dynamic pricing in extreme value regimes , shared ride sustainability , and regulation of gig economy platforms . His scientific contributions span algorithmic game theory, combinatorial optimization, and resource allocation, with key publications in Management Science and Operations Research . Current projects analyze escrow payment mechanisms , shared mobility efficiency , and hotel reservation systems . Scientific Awards Best Paper Award, ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010 1st Rank in Iran’s National Graduate Entrance Exam in Computer Engineering, 2004 Silver Medals in Iranian National Olympiads in Informatics, 1999–2000 He serves on graduate and PhD committees at CUNY and has reviewed for journals including Management Science and Operations Research . His teaching includes courses like Decision Models and Analytics and Advanced Discrete Optimization .

• Logistics Management
• Production Engineering
• Industrial Safety
• +۳ مورد دیگر

Dr. Lilianna Wojtynek is a Lecturer at the Department of Logistics, Faculty of Production Engineering and Logistics, Opole University of Technology. Her academic career focuses on logistics, production engineering, and industrial safety. Current Position: Lecturer in Logistics Department: Logistics School: Faculty of Production Engineering and Logistics University: Opole University of Technology Research interests span logistics systems, quality management, and transportation safety. Key themes include: Lean methodologies (5S, supply chain optimization) Industry 4.0 applications in logistics Risk analysis in transportation and logistics Production process planning and decision modeling Articles trends emphasize industrial safety, logistics efficiency, and technology integration in transportation. Notable subfields include dynamic forklift testing, BRT systems, and hazardous material storage.

Lukasz Szpruchمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Analysis
• Theoretical Machine Learning
• +۱۰ مورد دیگر

Lukasz Szpruch serves as Professor at the University of Edinburgh's School of Mathematics and Programme Director for Finance and Economics at The Alan Turing Institute. He leads the FAIR research programme on responsible AI adoption in financial services and co-investigates the UK Centre for Greening Finance & Investment (CGFI), directing partnerships with the National Office for Statistics, Accenture, Bill & Melinda Gates Foundation, and HSBC. He maintains affiliations with the Oxford-Man Institute for Quantitative Finance. His research focuses on probability theory , stochastic analysis , and theoretical machine learning , with current investigations into deep learning foundations, mean-field models, reinforcement learning, game theory, multiagent systems, and computational optimal transport. These theoretical frameworks are rigorously applied to financial economics problems including market dynamics, risk modeling, and regulatory compliance, emphasizing mathematical precision in AI system design. Recent publications reveal a strategic shift toward responsible AI deployment in finance , addressing large language model governance, synthetic data privacy, and non-asymptotic sampling theory. His work consistently bridges abstract mathematics with financial sector applications, particularly through the FAIR programme's industry collaborations that translate theoretical advances into practical frameworks for trustworthy AI adoption. As Principal Investigator of FAIR and CGFI co-Investigator, Szpruch manages significant research funding streams focused on AI ethics in financial services and sustainable finance. His academic leadership drives cross-sector initiatives where theoretical research directly informs regulatory policy development and industry best practices, though specific student mentoring details remain unspecified in source materials. Szpruch operates at the nexus of three critical research ecosystems: the FAIR programme's industry partnerships, CGFI's sustainability-focused finance research, and the Oxford-Man Institute's quantitative finance initiatives. These interconnected teams combine mathematical rigor with real-world financial applications, developing frameworks for AI assurance, green finance metrics, and synthetic data validation that address systemic challenges in modern financial systems.

Daehyeok Kimمشاهده پروفایل

استادیار

• Computer Networks
• Operating Systems
• Distributed Systems
• +۶ مورد دیگر

Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.