جست‌وجوی استاد و پژوهشگر

Melissa Roderickمشاهده پروفایل

استاد

• Education Policy
• Urban Education
• Student Transitions
• +۴ مورد دیگر

Melissa Roderick is the Herman Dunlap Smith Professor at the Crown Family School of Social Work, Policy, and Practice. She co-directs the Chicago Consortium on School Research and specializes in the critical transitions from high school to college and urban education reform. Her work combines qualitative and quantitative methods to address dropout prevention, grade retention, and summer program efficacy. Focus on student transitions Co-director, Chicago Consortium on School Research Expert in mixed-methods research Grants from J. P. Morgan Chase, Spencer, and Wallace Foundations Roderick's research explores college readiness , noncognitive factors , and urban school reform . Her work emphasizes data-driven interventions and policy analysis, particularly in Chicago Public Schools. Recent publications highlight college match indicators , academic momentum , and systemic barriers in college access. Her scholarly contributions include analyses of: Advanced Placement program efficacy Noncognitive skill development Postsecondary outcomes for IB students Long-term impacts of ninth-grade interventions Scientific recognition includes: Crown Family School teaching excellence award Chicago Sun-Times '100 Most Powerful Women' (2004) Roderick has received funding from multiple foundations, including the J. P. Morgan Chase Foundation, Spencer Foundation, and Wallace Foundation. She currently chairs the board of North Lawndale College Preparatory Charter High School and has led initiatives to restructure urban school systems through longitudinal data analysis and curriculum reform.

Daniel Schnurrمشاهده پروفایل

استاد

• Machine Learning
• Uncertainty Quantification
• Data Economics
• +۴ مورد دیگر

Daniel Schnurr holds the Chair of Machine Learning, especially Uncertainty Quantification at the University of Regensburg since August 2022, where he conducts research at the intersection of artificial intelligence, data economics, and digital market regulation. Previously, he headed the Data Policies research group at the University of Passau, building his expertise in the economic and regulatory aspects of digital markets. His educational background includes a doctorate in business informatics from the Karlsruhe Institute of Technology (2016), where he also worked for three years as a research associate at the Institute for Information Systems and Marketing. He completed his undergraduate and master's studies in Information Systems at KIT (2007-2013), with international experience at Concordia University in Canada and Singapore Management University. Professor Schnurr's research focuses on the technical, economic, and social implications of new machine learning methods and data as a decisive competitive factor and driver of innovation in digital markets. His work examines how data functions as both an economic asset and regulatory challenge, particularly in contexts of market power, competition policy, and AI governance. He investigates uncertainty quantification in machine learning systems while considering their broader economic and societal impacts. His publication portfolio demonstrates consistent output in top-tier journals including Management Science, Journal of Information Technology, and Journal of Competition Law & Economics, with recent work increasingly focusing on AI regulation, data access remedies, and uncertainty-aware AI systems. The trajectory shows evolution from telecommunications infrastructure research to contemporary digital market and AI regulation issues. As a Research Fellow at the Centre on Regulation in Europe (CERRE) since 2022, he has authored numerous policy reports addressing regulation of cloud computing services, digital platforms, and data economy frameworks. His policy contributions bridge academic research with practical regulatory implementation, particularly regarding the European AI Act and Digital Services Act. His research program involves experimental approaches to understanding data markets, human-AI interaction dynamics, and regulatory effectiveness. Through his work at CERRE and collaborations with international scholars, he contributes to shaping evidence-based digital policy in the European context while maintaining strong connections to academic research communities in information systems and economics.

• Negotiation
• Behavioral Decision Making
• Ethics in Business
• +۷ مورد دیگر

Max H. Bazerman is the Jesse Isidor Straus Professor of Business Administration at Harvard Business School, specializing in negotiation, decision making, and behavioral ethics. His academic career spans several decades during which he has become a leading authority on how cognitive biases and ethical considerations influence organizational decision processes. Professor Bazerman's primary research interests include negotiation strategies, ethical decision making, judgment heuristics, and bounded awareness. His work explores how individuals and organizations can recognize and overcome cognitive limitations to make more ethical and effective decisions. He has pioneered research on blind spots in ethical judgment, the psychology of complicity, and decision architectures that promote better choices. His publications reveal a consistent focus on practical applications of behavioral science to real-world business challenges. Recent work examines resource allocation during crises, ethical leadership frameworks, and the role of experimentation in improving organizational decision making. The research shows increasing emphasis on systemic ethical failures and how individuals contribute to complicity in unethical behavior. Honorary doctorate from the University of London Life Achievement Award from the Aspen Institute's Business and Society Program Distinguished Educator Award from the Academy of Management Academy of Management Career Award for Scholarly Contributions to Management Lifetime Achievement Award from the Organizational Behavior Division of the Academy of Management Ethisphere's 100 Most Influential in Business Ethics Daily Kos' Heroes recognition for whistleblowing on the Bush Administration's corruption of the RICO Tobacco trial Bazerman has advised numerous organizations including Abbott, Aetna, AIG, Alcoa, Allstate, Amgen, and many others across 30 countries. His work bridges academic research and practical application, with significant influence on how businesses approach ethical decision making and negotiation strategy. He has trained doctoral students who now hold positions at leading business schools worldwide, including Harvard, Wharton, Kellogg, and Stanford.

Eugene Feinbergمشاهده پروفایل

استاد

• Markov Decision Processes
• Stochastic Optimization
• Inventory Control
• +۳ مورد دیگر

Eugene Feinberg is a Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University's College of Engineering and Applied Sciences. He is renowned for his extensive contributions to Markov Decision Processes (MDPs), stochastic optimization, and inventory control. Research Interests: His work spans theoretical and applied aspects of Markov Decision Processes , stochastic optimization , inventory control , healthcare decision-making , and machine learning . He has particularly focused on solving complex decision-making problems under uncertainty, with applications ranging from operations research to medical decision-making. Scientific Awards: He has been honored with the title of Distinguished Professor , recognizing his outstanding contributions to his field. Advising and Grants: While specific details on students and grants are not provided, his prolific publication record and faculty status suggest active involvement in advising and securing research funding. Contact and Resources: His university webpage can be accessed at http://www.ams.sunysb.edu/~feinberg/ , and his Google Scholar profile is available at https://scholar.google.com/citations?user=LLt--pgAAAAJ&hl=en .

Shawn Gomezمشاهده پروفایل

استاد

• Systems Biology
• Network Pharmacology
• Translational AI
• +۷ مورد دیگر

Dr. Shawn Gomez is a Professor in the Lampe Joint Department of Biomedical Engineering at UNC-Chapel Hill and North Carolina State University and in the Department of Pharmacology at UNC-Chapel Hill. He serves as the Executive Director of FastTraCS, a component of the NC TraCS Institute funded through the NIH CTSA Program, and is a UNC Lineberger Comprehensive Cancer Center member. His educational background includes a PhD in Biomedical Engineering from Columbia University (1999), an MS in Aerospace Engineering Sciences from the University of Colorado, Boulder (1993), and a BS in Aerospace Engineering Sciences from the same institution (1990). He completed postdoctoral training in Bioinformatics and Computational Biology at the Judith P. Sulzberger Columbia Genome Center and Institut Pasteur in Paris. Dr. Gomez's research spans systems biology , network pharmacology , and translational AI , with a focus on understanding cell signaling architecture in human disease. His lab develops computational approaches for network pharmacology and targeted cancer therapies, along with machine learning methodologies to address clinical needs and enhance clinical decision making. The research integrates computational modeling with experimental approaches to improve diagnostic and therapeutic interventions. His recent publications reveal a strong focus on kinome research, particularly in pancreatic cancer and understudied kinases, with increasing integration of machine learning techniques for predicting clinical outcomes and kinase-substrate relationships. The work spans from fundamental systems biology to translational applications in cancer therapeutics and surgical outcomes prediction. Scientific Awards: Leadership Advanced Program, UNC-Chapel Hill 2017 Chancellor's Entrepreneurship Boot Camp 2015 ACCLAIM Scholar (Academic Career Leadership Academy in Medicine) 2013-2014 UNC Research Council Award 2011 Carl Storm URM Fellowship 2008 UNC Junior Faculty Development Award 2006 Florence Gould Scholar 2005 Pasteur Foundation Fellow 2002-2005 Dr. Gomez directs the Gomez Lab, which focuses on systems biology, network pharmacology, and translational AI. The lab maintains several key resources including Darkkinome.org, FAAS, and IAS servers for kinome research. His work bridges computational biology with clinical applications, particularly in cancer therapeutics and surgical outcomes prediction through machine learning approaches.

• Data Warehousing
• E-Commerce
• IT Architecture
• +۷ مورد دیگر

Boris Jukic is a Professor and Director of Applied Data Science at the Reh School of Business , Clarkson University. With a PhD from the University of Texas at Austin, he teaches courses such as Visual Basic Programming for Business Applications and Development of Business Applications on the Internet. Dr. Jukic's research focuses on: Management and pricing of networks and telecommunication services Application of data management and presentation strategies in e-business and e-commerce IT architecture's impact on organizational success metrics His recent publications highlight trends in data warehousing , IT architecture , and business intelligence . Notable subfields include data modeling , incentive-compatible pricing , and process-data integration . Contact: Email: bjukic@clarkson.edu Phone: 315/268-3884 Office: 227 Bertrand H. Snell Hall, Clarkson University

• Software Architecture
• Microservices
• Cloud Computing
• +۷ مورد دیگر

Uwe Zdun is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice-Director of Studies for Computer Science and Head of the Research Group Software Architecture. His teaching portfolio includes core courses such as Software Engineering 2, Advanced Software Engineering, and Practical Software Courses for Bachelor's and Master's theses across multiple semesters (2024W-2025S). His research spans software architecture with emphasis on microservices, cloud computing, and DevOps. Key focus areas include architectural design decisions, infrastructure-as-code conformance, security in distributed systems, and the integration of machine learning operations (MLOps/RLOps). He investigates cognitive aspects of architecture practices through controlled experiments and develops model-driven approaches for quality assessment in complex systems. Recent publications (2024-2026) reveal three dominant trends: (1) Security and coupling analysis in infrastructure-as-code deployments, (2) MLOps/RLOps integration for Industry 4.0 cyber-physical systems, and (3) Performance optimization patterns for CI/CD pipelines and autoscaling. His work bridges theoretical architecture models with industrial practice, particularly in microservice ecosystems and reinforcement learning applications. Professor Zdun leads the Research Group Software Architecture at the University of Vienna's Faculty of Computer Science. The group focuses on empirical validation of architectural patterns, tool development for conformance checking, and advancing design decision methodologies in cloud-native and AI-driven systems.

Jim Luedtkeمشاهده پروفایل

استاد

• Operations Research
• Stochastic Optimization
• Integer Programming
• +۲ مورد دیگر

Jim Luedtke is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on operations research, integer programming, and stochastic optimization methods for solving discrete and uncertain decision problems. Educational Background: BS in Industrial Engineering from University of Wisconsin-Madison MS in Operations Research from Georgia Institute of Technology PhD in Industrial and Systems Engineering from Georgia Institute of Technology Postdoctoral Research at IBM T.J. Watson Research Center His work spans applications in power systems optimization, healthcare analytics, and network design, with particular emphasis on developing cutting-edge algorithms for chance-constrained and multistage stochastic programming problems. Recent publications demonstrate strong focus on Benders decomposition techniques, Lagrangian dual methods, and distributionally robust optimization frameworks. Scientific Awards: NSF CAREER Award (2010) for "Risk Management via Stochastic Programming: Models, Computation, and Applications"

David Newburnمشاهده پروفایل

دانشیار

• Environmental Economics
• Land Economics
• Water Resource Economics
• +۶ مورد دیگر

David Newburn is an Associate Professor in the Department of Agricultural and Resource Economics at the University of Maryland, College of Agriculture and Natural Resources. His research focuses on environmental and resource economics, particularly land-use regulations for farmland and forest preservation, and water quality in the Chesapeake Bay. He develops spatial models to manage urban sprawl and evaluates incentive-based programs for conservation practices. Research Areas: Land-Use Change and Urban Sprawl Water Quality Trading and Conservation Spatial Economic Modeling Payment for Ecosystem Services (PES) Collaborations: NSF-funded Baltimore Ecosystem Study (BES) National Center for Smart Growth Chesapeake Bay Program Scientific and Technical Advisory Committee Recent Publications: Highlight trends in household behavior, land-use policy effectiveness, and coastal adaptation to climate impacts. Articles span environmental economics, urban ecology, and interdisciplinary sustainability research. Education: B.S., University of Maryland, College Park (1991) Ph.D., University of California, Berkeley (2002)

Julian Millieمشاهده پروفایل

استاد

• Anthropology of Indonesia
• Islamic Culture and Society
• Religion and Politics
• +۷ مورد دیگر

Julian Millie is a Professor in Indonesian Studies at Monash University, Faculty of Arts, where he coordinates and teaches in the Indonesian Studies program. His academic work bridges anthropology, religious studies, and Southeast Asian studies, with a focus on the social and political dimensions of Islamic practice in Indonesia. His research centers on embodied Islamic routines, including preaching, ritual, and commemoration of regional Islamic figures. He has led major ARC-funded projects such as Preaching Islam: politics, performers and publics and Glocalisation and sub-national Islams , and currently leads the international project Islamic Bureaucracies and Pious Publics in Turkey and Indonesia . Anthropology of Indonesia Islamic Culture and Society Ritual and Religion Public Islam and Preaching Social Change in Muslim Societies Political Anthropology His recent publications (2023–2024) reveal a continued focus on Islamic consumerism, reformist critiques of consumption, ritual embodiment, and the politics of Islamic education. These works span journals like Asian Studies Review and edited volumes on preaching and Malay-Indonesian Islamic studies, showing strong interdisciplinary reach into sociology, cultural studies, and education policy. Julian Millie has contributed to scholarly discourse through editorial roles, including guest editorships for Asian Studies Review (2023) and International Journal of Cultural Studies (2013). His media engagements highlight his commitment to public anthropology, commenting on the state of Indonesian studies in Australia and Islamic education policy. He is actively involved in research grants, including current funding from 2020 to 2025, and has collaborated with scholars across Australia, Indonesia, and Turkey. While no formal advisees are listed, his leadership in research projects suggests mentorship and team coordination. He is a central figure in the study of contemporary Indonesian Islam, contributing to both academic and public understanding.

• Cancer Genomics
• Liquid Biopsies
• Machine Learning in Oncology
• +۷ مورد دیگر

Ash A. Alizadeh is the Moghadam Family Professor of Medicine, Oncology, and Hematology (by courtesy) at Stanford University, where he serves as leader of the Cancer Genomics Program at Stanford Cancer Institute. He holds multiple academic appointments including Professor in Medicine - Oncology, and membership in Bio-X, the Institute for Stem Cell Biology and Regenerative Medicine, and the Maternal & Child Health Research Institute (MCHRI). Dr. Alizadeh received his BS in Biochemistry from UCLA (1994), MD from Stanford Medical School, and PhD in Biophysics from Stanford. He completed additional training at the National Cancer Institute (NCI), the National Institutes of Health (NIH), and the Howard Hughes Medical Institute (HHMI). His primary research focuses on developing and applying genome technologies and computing (machine learning & data science) to problems in human disease, with special emphasis on cancer detection, classification, monitoring, and tumor immunology. His laboratory pioneers noninvasive cancer genomic techniques including CAPP-Seq, PhasED-Seq, and EPIC-Seq for "liquid biopsies" that analyze circulating nucleic acids for early cancer detection and monitoring therapeutic response. Using machine learning approaches, his group studies how cellular compositional variation impacts cancer behavior and therapeutic response, including anti-tumor immunity. His work spans molecular, cellular, organism and population levels of tumor behavior analysis. Dr. Alizadeh has received numerous prestigious awards including the Scholar Award from the American Society of Hematology (ASH), the Leukemia & Lymphoma Society (LLS), the V-Foundation, as well as awards from the American Red Cross, Damon Runyon Cancer Research Foundation, and Doris Duke Charitable Research Foundation. He is an elected member of the American Society for Clinical Investigation (ASCI) and serves on the Scientific Advisory Board of the Lymphoma Research Foundation (LRF). As an educator and mentor, Dr. Alizadeh advises numerous doctoral students, postdoctoral fellows, and medical scholars. He teaches in the Department of Medicine and Immunology and serves on various admissions panels at Stanford. His laboratory, the Alizadeh Lab, is a hub for interdisciplinary cancer genomics research that combines computational biology, molecular genetics, and clinical oncology to develop novel cancer diagnostics and therapeutics.

Lorin M. Hittمشاهده پروفایل

استاد

• Information Technology and Productivity
• Economics of Electronic Commerce
• Information Systems and Organization
• +۶ مورد دیگر

Lorin M. Hitt serves as the Zhang Jindong Professor of Operations, Information and Decisions at the University of Pennsylvania's Wharton School. His distinguished career spans multiple domains at the intersection of information technology, economics, and business strategy. As a leading scholar in IT productivity and innovation, he maintains an active research agenda while teaching undergraduate and graduate courses in information systems and data analysis. Professor Hitt's research interests focus on the relationship between information technology and productivity, with particular emphasis on complementary factors such as organizational design and human capital that affect the value of IT investments. His current work explores the economics of IT labor mobility, enterprise software contracting, recommender systems' influence on consumer behavior, measurement of intangible assets, and pricing information goods. His research increasingly examines IT deployment in healthcare settings and the role of the IT workforce in relation to issues like offshoring and the H1-B visa program. His research publications demonstrate consistent contributions to top-tier journals, with recent work analyzing how data analytics mitigates post-IPO innovation decline, digital capital accumulation in superstar firms, and the relationship between analytics skills and firm productivity. His publication record shows sustained scholarly output across multiple domains of information systems research. Best Paper Runner-up – Management Science, 2014 Best Paper – Information Systems Research, 2013 Multiple Wharton Excellence in Teaching Awards (2003, 2007-2008, 2011) David Hauck Award for Distinguished Teaching, 1999 National Science Foundation Career Grant Recipient, 1998 Lindback Award for Distinguished Teaching, 1998 Professor Hitt teaches multiple courses including OPIM101 (Introduction to OPIM), OPIM105 (Data Analysis in VBA and SQL), OPIM469 (Information Strategy and Economics), and OPIM955 (Doctoral Seminar in IS Economics). His teaching focuses on information systems management, economics, data analysis, and advanced analytical methods. Beyond academia, he consults on IT outsourcing agreements and IT investment evaluation, and occasionally serves as an expert witness in technology-related litigation.

Gireeja Ranadeمشاهده پروفایل

استادیار

• Control Theory
• Information Theory
• Wireless Communication
• +۵ مورد دیگر

Gireeja Ranade is an Assistant Teaching Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She previously served as a Researcher at Microsoft Research AI in the Adaptive Systems and Interaction Group. Her educational background includes a PhD in Electrical Engineering and Computer Science from UC Berkeley and an undergraduate degree from MIT. Research Focus Prof. Ranade's research spans control theory, information theory, and machine learning, with applications in wireless communication, algorithmic fairness, and misinformation analysis. Her work addresses fundamental challenges in system stabilization under uncertainty, real-time control optimization, and equitable resource allocation. She maintains strong collaborations across disciplines, resulting in publications at premier venues like IEEE Transactions on Automatic Control, PNAS, and The Web Conference. Her recent publications demonstrate a consistent focus on robustness in control systems, fairness in algorithmic decision-making, and analysis of information propagation in online ecosystems. The work frequently combines theoretical rigor with practical implementations in robotics, networking, and social systems. Awards and Recognition 2017 UC Berkeley Electrical Engineering Award for Outstanding Teaching 2020 UC Berkeley Award for Extraordinary Teaching in Extraordinary Times Academic Leadership Prof. Ranade leads a dynamic research group including PhD candidates, master's students, and undergraduates. She has advised over 25 students on projects ranging from neural network controllers to fairness metrics in resource allocation. She founded the CalMentors program, which connects UC Berkeley students with K-12 learners for tutoring support during the COVID-19 pandemic. Educational Innovation She co-designed and teaches UC Berkeley's introductory EECS 16A/B sequence, integrating linear algebra with applications in machine learning and circuit design. She has also developed courses on optimization (EECS127/227A) and data science (Data 102), with publicly available lecture videos demonstrating her teaching methodology.

Dan A. Iancuمشاهده پروفایل

دانشیار

• Operations Research
• Finance
• Risk Management
• +۷ مورد دیگر

Dan A. Iancu is an Associate Professor of Operations, Information & Technology at Stanford Graduate School of Business, affiliated with the Emmett Interdisciplinary Program in Environment and Resources (E-IPER). He holds a BS from Yale, SM from Harvard, and PhD from MIT. His research bridges operations, finance, and sustainability, focusing on dynamic optimization under uncertainty and its applications in supply chains, FinTech, and healthcare. Notable work includes designing financing solutions for global supply chains and promoting ethical analytics. Education: BS in Electrical Engineering (Yale, 2004), SM in Engineering (Harvard, 2006), PhD in Operations Research (MIT, 2010). Professional experience includes a Goldstine Fellowship at IBM Research and teaching roles at Wharton and INSEAD. He has won multiple awards including INFORMS Optimization Society (2009) and JFIG (2013) prizes. Research emphasizes fairness and environmental impact, with recent projects on sustainable palm oil production and climate impacts of digital supply chains. His work appears in Management Science, Operations Research, and Mathematics of Operations Research. Awards include Srivani Faculty Scholar (2023–24) and teaching commendations at Harvard, MIT, and INSEAD. He advises on multi-disciplinary projects involving engineering, environmental science, and business students.

Brian C. Williamsمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +۴ مورد دیگر

Professor Brian C. Williams is a leading academic at the Massachusetts Institute of Technology (MIT) , holding the position of Professor of Aeronautics and Astronautics and directing the Autonomous Systems Laboratory (ASL) . He is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Space Systems Laboratory (SSL) . His work focuses on advancing model-based autonomy, enabling robots to operate independently in extreme environments such as space, underwater, and urban traffic. His contact details include email williams@csail.mit.edu and phone 253-2739 . Education : S.B., S.M., and Ph.D. in Computer Science and Electrical Engineering from MIT (1989). Williams' research spans risk-bounded decision making , collaborative robotics , and neural-symbolic learning . He has pioneered systems like Remote Agent, which demonstrated autonomous self-repair in NASA's Deep Space One mission, and Geordi, a risk-aware driver assistant. His work integrates symbolic reasoning, probabilistic methods, and machine learning to create resilient robotic systems for space exploration, manufacturing, and transportation. The most recent publications highlight trends in risk-aware planning , multi-agent robotics , and stochastic control . Key innovations include tube-based trajectory optimization, conflict-directed task allocation, and theory-of-mind guided interventions, reflecting his focus on robustness in uncertain environments. Scientific Awards : NASA Space Act Award (1999), AAAI Fellow, and multiple best paper awards from AAAI, IJCAI, ICAPS, CDC, HRI, and ECAI. Williams leads the Model-Based Embedded and Robotics Systems Group at CSAIL, collaborating with institutions like NASA's Jet Propulsion Laboratory. His projects include ASIST (Artificial Social Intelligence for Teams), RADMAX (Risk and Deadline Aware Planning), and Uhura (risk-aware personal assistants), addressing challenges in health care, manufacturing, and defense applications.