جست‌وجوی استاد و پژوهشگر

Mads Leth Jakobsenمشاهده پروفایل

دانشیار

• Public Leadership
• Leadership Development
• Bureaucracy and Debureaucratization
• +۴ مورد دیگر

Mads Leth Jakobsen is an Associate Professor at the Department of Political Science , Aarhus University, specializing in Public Leadership , Leadership Development , and Bureaucracy and Debureaucratization . He serves as a member of the King Frederik Center for Public Leadership management team, focusing on leadership for public value creation and handling complex social challenges like the climate crisis. Academic Rank: Associate Professor Primary Research: Public Leadership, Distributed Leadership, Climate Governance Teaching: Public Leadership and Governance on political science programs Key Projects: Public Value Leadership (2022), Distributed Leadership (2012-2016) His research explores paradox leadership , organizational responses to climate challenges, and leadership credibility. Recent publications examine municipal climate strategies, bureaucratic dynamics, and leadership development frameworks. He emphasizes the interplay between leadership practices and employee outcomes in public sector organizations. Scientific awards and student supervision details are not listed in the available information. His articles highlight a strong focus on reconciling performance regimes with organizational learning, leadership credibility, and innovative governance models in public sector contexts.

Ruli Xiaoمشاهده پروفایل

دانشیار

• Empirical Industrial Organization
• Micro-econometrics
• Nonparametric Identification
• +۳ مورد دیگر

Ruli Xiao serves as Associate Professor and Director of Graduate Studies in the Department of Economics at Indiana University Bloomington's College of Arts and Sciences. Her academic office is located in Wylie Hall (Room 349), with contact details including email rulixiao@iu.edu and phone (812) 855-3213. Her academic credentials include: B.S. in Statistics from Tongji University M.A. in Economics from Shanghai University of Finance and Economics Ph.D. in Economics from Johns Hopkins University (2014) Dr. Xiao's research program emphasizes Empirical Industrial Organization and Micro-econometrics , specializing in methodological solutions for complex economic modeling scenarios. Her work develops identification frameworks for finite action games where multiple equilibria coexist with unobserved market heterogeneity, advancing estimation techniques for real-world industrial applications. Her publication profile demonstrates consistent focus on econometric theory development, particularly in dynamic modeling with unobservables as evidenced by her 2017 Journal of Econometric Methods paper. Current research trajectories indicate continued innovation in nonparametric methods for structural industrial organization models. As Director of Graduate Studies, Dr. Xiao oversees all graduate programs including M.A., M.S., and Ph.D. tracks, guiding curriculum development and student progression through rigorous economics training.

Christiane C. Schubertمشاهده پروفایل

استادیار

• Medical Education
• Healthcare Systems
• Patient Work Systems
• +۶ مورد دیگر

Christiane C. Schubert is an Assistant Professor in Medical Education at the School of Medicine and an Assistant Professor in Interdisciplinary Studies at the School of Behavioral Health at Loma Linda University. Her dual appointments reflect her interdisciplinary approach to healthcare research and education, bridging medical education with behavioral health perspectives. Dr. Schubert's educational background includes: PhD from Loma Linda University (2008) MS from Northern Arizona University (1999) BS from Northern Arizona University (1997) Her research program focuses on understanding healthcare systems through multiple lenses. She investigates how patients with chronic illnesses navigate self-care within complex healthcare environments, with particular attention to macroergonomic factors that influence patient work systems. Dr. Schubert examines resilience in healthcare settings, exploring how both patients and providers adapt to challenges in clinical environments. Her work addresses cultural factors in healthcare, particularly examining how acculturation affects health-promoting behaviors among Arab Americans. Through her studies on novice-expert differences in emergency medicine, she contributes to understanding cognitive work processes in high-stakes clinical settings. Her interdisciplinary approach integrates insights from medical education, behavioral health, and systems engineering to address complex healthcare challenges. Dr. Schubert's scholarly output from 2012-2017 demonstrates consistent contributions across healthcare resilience, patient work systems, medical education, and cultural competence. Her publications reveal a trajectory of increasingly sophisticated systems thinking applied to healthcare delivery challenges, with a particular emphasis on understanding patient experiences and clinician cognition within complex healthcare environments. Dr. Schubert has maintained an active research and publication record since completing her PhD in 2008. Her work demonstrates a commitment to improving healthcare delivery through better understanding of patient experiences, clinician cognition, and system-level factors that influence healthcare outcomes. Her interdisciplinary appointments position her to bridge traditional academic boundaries in addressing complex healthcare challenges.

• Algorithms
• Algorithmics
• Big Data
• +۷ مورد دیگر

Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.

Uwe Bergmannمشاهده پروفایل

استاد

• Ultrafast X-ray Science
• Synchrotron Radiation
• X-ray Free-Electron Lasers
• +۵ مورد دیگر

Uwe Bergmann is the Martin L. Perl Endowed Professor in Ultrafast X-Ray Science at the University of Wisconsin-Madison's Department of Physics. His research leverages advanced X-ray techniques to study nonlinear phenomena, real-time chemical reactions, and structural changes in both biological and material systems, with a focus on photosynthesis and ancient cultural heritage. PhD in Physics from Stony Brook University Former affiliations: National Synchrotron Light Source, European Synchrotron Radiation Facility, Lawrence Berkeley National Laboratory, Stanford Synchrotron Radiation Lightsource, Linac Coherent Light Source, Stanford PULSE Institute His work spans ultrafast X-ray spectroscopy, synchrotron methods, and the development of novel instruments like the X-FAST tabletop spectrometer. Research themes include: Photosynthetic water oxidation mechanisms using femtosecond X-ray crystallography Electronic and structural dynamics in 2D materials and metalloenzymes Imaging ancient fossils and manuscripts with X-ray techniques Advancing X-ray laser and synchrotron instrumentation Recent publications highlight attosecond X-ray pulses, catalysis under extreme conditions, and structural intermediates in photosystem II. His work intersects physics, chemistry, and paleobiology. Scientific recognition includes the Martin L. Perl Endowed Professorship. He leads the Bergmann Research Group, mentoring graduate students and postdocs in ultrafast X-ray science.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

• Algebra
• Category Theory
• Commutative Algebra
• +۳ مورد دیگر

Henrik Granau Holm is a Professor in the Department of Mathematical Sciences at the University of Copenhagen. His research focuses on advanced algebraic structures within category theory and commutative algebra, with significant contributions to derived categories and quiver representations. His primary research areas include Algebra, Category Theory, Commutative Algebra, Derived Categories, Quiver Representations, and Homological Algebra. His work explores the structural properties of derived categories, particularly Q-shaped derived categories, and their applications in representation theory. Recent research investigates compact and perfect objects in ring-derived categories, tensor embeddings in Grothendieck cosmos, and vanishing properties of Tor functors. Holm's publication record shows consistent output in top mathematics journals, with multiple 2024 publications including a book on derived category methods in commutative algebra. His research demonstrates strong international collaboration, particularly with researchers in representation theory and category theory. Holm has supervised PhD students and postdoctoral researchers as indicated by departmental records. His activities include editorial work for Mathematical Reviews and Zentralblatt MATH, along with numerous lecture presentations on cohomology and AC rings dating back to 2007.

Fangzhou Jinمشاهده پروفایل

استادیار

• Algebraic Geometry
• Pure motives
• Mixed motives
• +۵ مورد دیگر

Fangzhou Jin is an Assistant Professor at the School of Mathematical Sciences, Tongji University. His research focuses on advanced topics in Algebraic Geometry , particularly in Motivic Homotopy Theory , Algebraic K-theory , and Real Algebraic Geometry . He has extensive experience in collaborative mathematical research, having worked with prominent mathematicians such as Frédéric Déglise, Enlin Yang, and Marc Levine. Education : Ph.D. in Mathematics (2016, École Normale Supérieure de Lyon), M.Sc. in Pure Mathematics (2012, Université Paris-Diderot), Diploma of ENS (2013) His work bridges Algebraic Cycles with Étale Cohomology , exploring Weight Structures in mixed motives and Chern Classes in motivic contexts. Jin has contributed to the theory of Trace Maps and Local Terms in motivic homotopy, as well as the study of Flat Cohomology and Real Schemes . Key trends in his publications include: 2025: Cohomology of singular varieties via real cycle class maps 2024: Trace maps and local terms in motivic homotopy 2023: Moving lemmas in homotopy colevel theory 2022: Quadratic conductors, perverse homotopy, and pro-Chern classes 2021: Rational motivic homotopy and Künneth formulas 2020: Gersten complexes and real flat cohomology 2018: Algebraic G-theory in motivic categories 2016: Borel-Moore homology and weight structures Scientific Awards & Grants : Fundamental Research Funds for Central Universities (2021-2025) National Natural Science Foundation of China Grants 12101455 (2022-2024), 12471014 (2025-2028) National Key R&D Program of China 2021YFA1001400 (2022-2026) DFG Priority Programme SPP 1786 & ERC Project QUADAG (2018-2020) Advising & Teaching : Jin has supervised exercise classes and seminars at multiple institutions, including Universität Duisburg-Essen and Tongji University. His teaching spans Algebraic Topology , Étale Cohomology , and Algebraic Geometry . Labs & Collaborations : Active in international collaborations, he is a key member of the Algebraic Geometry and Ramification Seminar at Tongji University and has participated in DFG Research Training Group 2553 during his postdoctoral period.

Sebastian Cioabaمشاهده پروفایل

استاد

• Spectral Graph Theory
• Algebraic Combinatorics
• Graph Theory
• +۳ مورد دیگر

Sebastian Cioaba is a Professor in the Department of Mathematical Sciences at the University of Delaware (UD), part of the College of Arts & Sciences. His research focuses on spectral graph theory, algebraic combinatorics, and their applications. He earned his Ph.D. from Queen’s University (2005) and joined UD in 2009 after postdoctoral work at UC San Diego and the University of Toronto. Cioaba has advised 8 Ph.D., 4 M.Sc., and numerous undergraduate researchers, with current advisees including John Byrne and Isabel Byrne. His work is supported by NSF, NSA, and international grants. Education - B.Sc. Mathematics & Computer Science, University of Bucharest (Undergraduate) - Ph.D. Mathematics, Queen’s University (2005) Research & Awards - 2024 College of Arts & Sciences Award - Co-editor of Discrete Mathematics and Linear Algebra and its Applications - Over 70 publications and two books: A Bridge to Advanced Mathematics (2023) and A First Course in Graph Theory and Combinatorics (2022, 2nd ed.) Teaching & Service - Organized conferences in discrete mathematics - Supervised over 25 undergraduate and high school students in research projects Advising - Current Ph.D. students: John Byrne, Isabel Byrne, Colby Sherwood - Notable past advisees include Vishal Gupta (Ph.D. 2025, Rochester) and Dheer Noal (Ph.D. 2022, Memphis postdoc)

Ram Mudambiمشاهده پروفایل

استاد

• International Business Strategy
• Global Innovation Networks
• Regional Innovation Systems
• +۷ مورد دیگر

Ram Mudambi is the Frank M. Speakman Professor of Strategy at Temple University’s Fox School of Business and Management. He holds a central role in strategic management and international business research, with prior faculty appointments at the University of North Carolina, Case Western Reserve University, and the University of Reading (UK). His work bridges economic geography, innovation studies, and global strategy. Research interests focus on the geography of innovation, emphasizing migration, ethnicity, gender dynamics, and global value chain governance. He has contributed to understanding how spatial factors influence innovation ecosystems, multinational enterprise strategies, and regional development. Mudambi’s interdisciplinary approach integrates microeconomic theories with macro-level institutional analysis. He serves as a Consulting Editor for the Journal of International Business Studies and the Journal of World Business, and as Area Editor for the Journal of International Business Policy. His research appears in top-tier outlets like the Journal of Political Economy, Management Science, and Strategic Management Journal, alongside practitioner-focused articles in Harvard Business Review and Sloan Management Review. Awards: Fellow of AIB (20XX), Fellow of EIBA (20XX) Teaching: Courses include Strategic Management, International Business Strategy, and Global Research Methodologies Media Engagement: Featured in The New York Times, The Atlantic, and NPR His recent work explores digitalization’s impact on globalization, global value chain resilience, and the strategic implications of restrictive immigration policies. Mudambi’s research also addresses sustainability linkages in global cities’ innovation systems.

John Willoughbyمشاهده پروفایل

استاد

• U.S. Economic History
• European Economic History
• Political Economy
• +۵ مورد دیگر

John Willoughby is a Professor of Economics at the College of Arts & Sciences, American University. He holds a PhD from the University of California at Berkeley, an MA from Cambridge University, and a BA in History from the University of Michigan. His research focuses on U.S. and European economic history, political economy, international economic policy, and the political economy of higher education in the Middle East and Gulf regions. Professor Willoughby has extensive international experience, including creating an undergraduate economics program at the American University of Sharjah in the UAE and teaching as a visiting professor at the American University in Cairo. His work critically examines capitalist structures, labor issues, and the intersection of education and geopolitics. He is affiliated with the Union for Radical Political Economics, the Review of Radical Political Economics Editorial Board, and the Middle East Studies Association. His publications explore topics such as authoritarian capitalist states, gender dynamics in labor markets, and the global expansion of higher education systems. Recent research trends include analyzing the political economy of the Gulf region, the impact of colonial legacies on language policies, and the role of worker empowerment in socialist models. He has also authored Remaking the Conquering Heroes: The Social and Geopolitical Impact of the Early American Occupation of Germany (2001).

Monica Olvera de la Cruzمشاهده پروفایل

استاد

• Polymer Science
• Soft Materials
• Condensed Matter Physics
• +۵ مورد دیگر

Monica Olvera de la Cruz is the Lawyer Taylor Professor of Materials Science and Engineering, Chemistry, and Chemical & Biological Engineering at Northwestern University, with a courtesy appointment in Physics and Astronomy. She directs the Center for Computation & Theory of Soft Materials and serves as Deputy Director of the Center for Bio-Inspired Energy Science. Her research focuses on designing responsive materials, including polymers, electrolytes, and complex fluids, with applications in biotechnology and energy. She holds a Ph.D. from Cambridge University (1985) and a B.A. from UNAM (Mexico). Her research interests include self-assembly of heterogeneous molecules, ionic-driven assembly mechanisms, and functional materials design. Recent work highlights include modeling electrostatic effects in biomimetic systems and exploring superionic conductors. Awards include National Academy of Sciences membership (2012), APS Polymer Prize (2017), and American Philosophical Society membership (2020). Professional service roles: Gordon Research Conferences Board, DOE Basic Energy Sciences, Max Planck Institute advisory board Led over 150 publications since 2020, emphasizing soft matter physics and materials innovation Her group's innovations bridge theoretical physics and applied materials science, with notable achievements in bio-inspired materials and electrochemical systems.

Johan Chuمشاهده پروفایل

استادیار

• Management Science
• Organizational Behavior
• Strategy
• +۵ مورد دیگر

Johan Chu is the Sarofim Family Career Development Assistant Professor and an Assistant Professor of System Dynamics at the MIT Sloan School of Management. His research focuses on the dynamics of social power, corporate advantage, and competitive strategies in the digital age. He explores how technological advancements reshape markets, inequality, and organizational structures. Chu holds dual PhDs: a PhD in Physics (Artificial Life) from Caltech (1990s) and a later PhD in Management & Organizations from the University of Michigan Ross School of Business. Prior to academia, he consulted in the U.S., Korea, and China; led enterprise software ventures; and managed a global executive search firm's Asia-Pacific Consumer Practice. His research streams include: 1) durable dominance of firms/ideas; 2) mass attention direction via technology; and 3) evolving work/organizational power in the 21st century. Empirical methods include simulations, large datasets, social network analysis, machine learning, and qualitative interviews. His work bridges physics-inspired computational models with social science theory, addressing topics like scientific collaboration decline, corporate governance shifts, and elite dynamics. Recent insights highlight slowed scientific progress in large fields and the strategic role of attention economies.

Mahsa Ghasemiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Control Theory
• Active Perception
• +۳ مورد دیگر

Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.

Miaoyan Wangمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Matrix/Tensor Data Analysis
• High-Dimensional Statistics
• +۳ مورد دیگر

Miaoyan Wang is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, part of the School of Computer, Data & Information Sciences. She holds early tenure and is a faculty affiliate in the Mathematical Foundations of Machine Learning, Institute for Foundations of Data Science (IFDS), and Center for Demography of Health and Aging (CDHA). She is currently on sabbatical as a visiting associate professor at Stanford University and Lawrence Livermore National Laboratory. Education: PhD in Statistics from the University of Chicago (2015), BS in Mathematics from Fudan University (2010). Postdoctoral training included positions at UC Berkeley (Computer Science) and the University of Pennsylvania (Math+X). Research focuses on statistical machine learning, with emphasis on matrix/tensor data analysis, high-dimensional statistics, nonparametric learning, and applications in genetics. Her work bridges theory and practice, addressing challenges in computational efficiency and statistical optimality for complex data structures. Awards include the prestigious NSF CAREER Award (2022), multiple best paper awards (ASA, IMS, NEURIPS), and recognition from ASHJ and IGES. Her group has secured grants totaling $3.4 million, including NSF funding for foundational machine learning research and collaborative projects in population genomics. Advising includes PhD students Chanwoo Lee and Jiaxin Hu, with former students Yuchen Zeng and Zhuoyan Xu. She teaches advanced statistical methods and computational courses, emphasizing rigorous theoretical foundations and practical applications. Key collaborations include work on tensor decomposition algorithms, statistical genetics, and interdisciplinary projects with biology and computer science departments. Her lab contributes open-source software tools for data analysis, including packages for tensor block models and multiway clustering.