Find Academic Supervisors

• Operations Research
• Market Design
• Algorithmic Game Theory
• +3 more

Pengyu Qian serves as an Assistant Professor in the Department of Operations and Technology Management at Boston University's Questrom School of Business, where he conducts research at the intersection of operations research, economics, and algorithmic systems design. Education PhD, Graduate School of Business, Columbia University, 2021 B.Sc., Peking University, 2015 Research Interests Dr. Qian specializes in theoretical and applied problems within matching markets, dynamic resource allocation, and queueing network optimization. His work develops algorithmic solutions for market inefficiencies using tools from game theory, stochastic modeling, and mechanism design. Current investigations focus on partner competition dynamics in two-sided markets, incentive structures for resource pooling, and blind control policies in closed networks where system states remain partially observable. Publication Trends His 2018-2024 publications demonstrate consistent output in top-tier venues including Management Science and ACM Economics & Computation conferences, with increasing emphasis on real-world market applications. The research trajectory shows progression from foundational queueing network control to sophisticated matching market analyses, maintaining strong theoretical rigor while addressing practical constraints like information asymmetry and dynamic partner competition. Scientific Awards No scientific awards, fellowships, or major honors are documented in the available profile information. Advising and Grants While specific student advisees and grant funding details are not disclosed in the current materials, Dr. Qian's publication record suggests active collaboration with leading researchers including Yash Kanoria and Itai Ashlagi, indicating participation in significant research initiatives within operations management and market design. Labs and Teams Though no dedicated laboratory is specified, his research profile aligns with Boston University's operations management research groups at Questrom, particularly those focused on algorithmic market design and stochastic optimization within business contexts.

Sophie H. YuView profile

Assistant Professor

• Operations Research
• Applied Probability
• High-Dimensional Statistics
• +4 more

Sophie H. Yu is an Assistant Professor of Operations, Information and Decisions at the Wharton School of Business, University of Pennsylvania. She completed her postdoctoral work in the Department of Management Science and Engineering at Stanford University before joining Wharton. Her academic journey includes a Ph.D. in Decision Sciences from the Fuqua School of Business at Duke University (2023), an M.S. in statistical and economic modeling from Duke University (2017), and a B.S. in Economics from Renmin University of China (2015). Ph.D. in Decision Sciences, Fuqua School of Business, Duke University (2023) M.S. in Statistical and Economic Modeling, Duke University (2017) B.S. in Economics, Renmin University of China (2015) Sophie's research focuses on high-dimensional statistics, algorithm design, and performance evaluation in large-scale networks and stochastic systems . Her work draws inspiration from real-world business, engineering, and natural sciences problems that can be modeled into large and complex networks. She has explored fundamental limits and efficient algorithms on graph matching, online platform policy design with bounded regret, and data confidentiality protection. Her research spans the intersection of operations research, applied probability, statistics, and computer science, with particular emphasis on network science and information theory. Sophie's publications demonstrate a strong focus on matching problems in networks, with significant contributions to understanding random graph matching, network correlation testing, and online matching algorithms. Her work shows a progression from fundamental theoretical questions to practical applications in resource allocation and market design. Recent papers indicate increasing focus on practical implementations of theoretical concepts in real-world matching markets. Thomas M. Cover Dissertation Award from IEEE Information Theory Society (2024) Best Dissertation Award from Fuqua George Nicholson Student Paper Competition finalist, INFORMS 2022 Sophie has been actively involved in academic service, presenting her work at numerous prestigious institutions including University of Texas at Austin, University of Toronto, London School of Business, and MIT. She has taught graduate courses in decision modeling and served as a teaching assistant for various statistics and operations courses during her doctoral studies at Duke University. Her research has been supported through academic appointments and likely research grants related to her work in network science and matching algorithms.

Patrick JailletView profile

Professor

• Online Optimization
• Machine Learning
• Sequential Decision Making Under Uncertainty
• +4 more

Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science at MIT's School of Engineering. He holds joint appointments with the Laboratory for Information and Decision Systems (LIDS), the Operations Research Center (ORC), the Operations Research and Statistics Group at MIT Sloan, and the Department of Civil and Environmental Engineering. Previously, he served as Head of Civil and Environmental Engineering at MIT (2002-2009) and Chair of the Department of Management Science and Information Systems at UT Austin (1997-2002). Dr. Jaillet's research focuses on online optimization and learning, sequential decision-making under uncertainty, and security and resilience in complex networks. His work spans theoretical foundations in optimization and machine learning with applications in transportation, online market analytics, and network security. He has developed mathematical frameworks for problems involving uncertainty, dynamic resource allocation, and strategic behavior in complex systems. His recent publications reveal strong trends in bridging theoretical optimization with practical machine learning applications. Key themes include Bayesian optimization for black-box functions, online learning with limited information, mechanism design for resource allocation, and network security applications. His work increasingly integrates large language models with traditional optimization techniques, reflecting the evolving landscape of AI-driven decision-making systems. Fulbright Scholar (1990) Fellow of the Institute for Operations Research and Management Science (INFORMS) Best Applications Paper Award at ICAPS 2019 Long-standing Associate Editor for top journals including Operations Research and Transportation Science Dr. Jaillet has advised over 40 doctoral students who now hold prominent positions in academia and industry, including faculty positions at MIT, Georgia Tech, and ETH Zurich, and research scientist roles at Amazon, Microsoft Research, and Google. His research has been consistently funded by major agencies including NSF, ONR, AFOSR, and international partners like Singapore NRF, with current projects focusing on learning algorithms for autonomous security and fundamental tradeoffs in optimization. He leads a vibrant research group spanning MIT's EECS department and ORC, with current funding supporting work on neural bandits, federated optimization, and network security applications. His research group operates at the intersection of theory and practice, with strong connections to industry through collaborations with IBM, Microsoft, Google, and various transportation and technology companies. The group maintains active partnerships with international institutions, particularly through SMART in Singapore, reflecting Dr. Jaillet's global research impact.

Stefan WildermannView profile

Professor

• Reconfigurable Systems
• Edge Computing
• Organic Computing
• +7 more

Stefan Wildermann is a Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Reconfigurable Computing Group within the Chair of Computer Science 12 (Hardware-Software Co-Design) in the Department of Computer Science. He has maintained continuous research activity at FAU since 2006, progressing from researcher to his current leadership position. Dr. Wildermann earned his Diploma degree in Computer Science from FAU in 2006 and completed his doctorate (Dr.-Ing.) in Computer Science at the same institution in July 2012. His academic career has been entirely rooted at FAU, demonstrating a strong institutional commitment and progression through the ranks. His research spans multiple cutting-edge areas in computer science and engineering, with particular emphasis on reconfigurable systems and hardware-software co-design. Wildermann's work in edge computing explores efficient processing at the network periphery, while his research in organic computing investigates self-organizing systems that can adapt to changing environments. His expertise extends to optimization techniques for embedded systems, applying game theory principles and convex optimization methods to solve complex resource allocation problems. More recently, he has integrated reinforcement learning approaches to enhance system adaptability and performance. His teaching portfolio includes courses on event-driven systems, computer engineering fundamentals, embedded systems, and hardware-software co-design. Analysis of Wildermann's publication record from 2021-2025 reveals a strong focus on hardware acceleration, security, and embedded systems. His work demonstrates consistent evolution from foundational research in reconfigurable architectures toward practical applications in IoT, robotics, and secure computing. A significant portion of his recent work addresses near-data processing using FPGAs for database acceleration, while maintaining parallel research streams in side-channel security analysis and energy-efficient embedded systems design. His publications frequently appear in top-tier conferences including DATE, FPL, ASP-DAC, and HOST, reflecting strong recognition within the computer architecture and embedded systems communities. Wildermann has held significant leadership roles including Head of the Reconfigurable Computing Group since 2015 and previously served as Head of the Self-organizing Systems Group (2012-2015) and Lab Leader of the Automotive Lab within the Embedded Systems Initiative (2016-2020). His research has been consistently funded through multiple projects investigating invasive computing, reconfigurable architectures, and embedded systems design methodologies. Currently based in Room 02.116 at Cauerstr. 11, 91058 Erlangen, Wildermann continues to lead active research in the Hardware-Software Co-Design group, supervising projects that bridge theoretical computer science with practical hardware implementation challenges.

Charith MendisView profile

Assistant Professor

• Compilers
• Programming Languages
• Machine Learning for Systems
• +3 more

Charith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Computer Science, Electrical and Computer Engineering, and the Coordinated Science Lab. His research focuses on the intersection of compilers, program optimization, and machine learning systems. Dr. Mendis received his educational background from prestigious institutions: Ph.D. in Computer Science from Massachusetts Institute of Technology (2020) S.M. in Computer Science from Massachusetts Institute of Technology (2015) B.Sc. in Electronics and Telecommunication Engineering from University of Moratuwa (2013) His primary research interests center around compiler technology and machine learning systems. Mendis leads the ADAPT lab at UIUC, where his team works on creating high-performance ML optimization techniques and automated compiler construction using machine learning and formal methods. His work bridges the gap between traditional compiler design and modern machine learning approaches, with applications in tensor compilers, graph neural networks, and sparse computation. He has developed novel frameworks for optimizing deep learning workloads, verification of compiler transformations, and performance modeling for emerging hardware architectures. Mendis has established himself as a leading researcher in compiler optimization for machine learning systems, with a particular focus on tensor compilers, graph neural networks, and performance modeling. His recent publications demonstrate increasing sophistication in combining formal methods with machine learning techniques to solve challenging problems in compiler optimization and verification, with multiple papers accepted at top-tier conferences including OOPSLA, PLDI, POPL, and SIGMOD. His notable scientific achievements include: Google ML and Systems Junior Faculty Award (2025) DARPA Young Faculty Award (2024) NSF CAREER Award (2024) Distinguished Paper Award at POPL (2025) William A. Martin Thesis Award for Outstanding SM thesis, MIT (2015) Multiple teaching excellence awards at UIUC (2021-2023) Dr. Mendis actively mentors students through the ADAPT lab, offering research opportunities for undergraduates, master's students, and PhD candidates interested in compiler technology and machine learning systems. His research is supported by significant funding from the ACE center (part of JUMP 2.0), National Science Foundation (NSF), DARPA, IIDAI, and industry partners including Google, Intel, Amazon, and Qualcomm. He teaches advanced courses in compiler construction and machine learning for compilers. He leads the ADAPT lab at UIUC, which focuses on developing advanced compiler technologies for modern machine learning workloads. The lab maintains active collaborations with industry partners and has established itself as a leading research group in compiler optimization for AI systems. Current projects include tensor compilers, graph neural network optimization, and automated verification of deep learning systems.

Yikun BanView profile

Associate Professor

• Reinforcement Learning
• Data Mining
• Neural Networks
• +7 more

Yikun Ban is a tenure-track Associate Professor in the School of Computer Science and Engineering at Beihang University, where he is a member of the State Key Laboratory of Software Development Environment. He earned his PhD in Computer Science from the University of Illinois Urbana-Champaign (2023), MS in Computer Science from Peking University (2019), and BS in Software Engineering from Wuhan University (2016). PhD: University of Illinois Urbana-Champaign (2023) MS: Peking University (2019) BS: Wuhan University (2016) His research focuses on principled algorithms for reinforcement learning with human feedback, neural contextual bandits, and exploration-exploitation problems. He develops frameworks combining deep learning with bandit theory for applications in recommendation systems, disinformation detection, and dynamic graph learning. Recent publications address: Robust neural contextual bandits (NeurIPS 2024) Graph neural bandits (KDD 2023) Meta-learning for bandit scheduling (NeurIPS 2023) Clustering in contextual bandits (WWW 2021, AAAI 2021) Honors include the NeurIPS Scholar Award and recognition as ICML Outstanding Reviewer . His open-source LOCB repository provides Python implementations for contextual multi-armed bandit algorithms with local clustering, supporting applications in recommendation systems and online learning.

Laurent CondatView profile

Researcher

• Optimization
• Convex Relaxation
• Stochastic Algorithms
• +5 more

Laurent Condat is a Senior Research Scientist at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, where he conducts research in optimization algorithms and their applications. He is affiliated with the College of Engineering, Department of Computer Science, and has previously held research positions at CNRS in France, working at GREYC in Caen and GIPSA-Lab in Grenoble. Dr. Condat received his PhD in 2006 from Grenoble Institute of Technology, followed by a 2-year postdoc in Munich, Germany. He was recruited as a permanent researcher by CNRS in 2008 and has been on leave from CNRS since November 2019 to work at KAUST. In February 2025, he was promoted to 'chargé de recherche hors classe' (senior research scientist) by CNRS. His research focuses on deterministic and stochastic optimization algorithms, convex relaxations, and applications to machine learning, signal and image processing. His work spans theoretical foundations of optimization methods to practical implementations for distributed and federated learning systems. He has developed several influential algorithms including RandProx, TAMUNA, and LoCoDL that address communication efficiency in distributed optimization. His recent publications demonstrate strong trends in communication-efficient distributed optimization, with particular emphasis on federated learning, compression techniques, and local training methods. His work bridges theoretical optimization with practical machine learning applications, showing consistent innovation in algorithmic design for large-scale problems. Best reviewer award at AISTATS 2025 Meritorious Service Award from Mathematical Programming Stanford's list of world's top 2% most influential scientists Dr. Condat has co-supervised PhD students including Daniele Picone and Julien Baderot. He serves as an Associate Editor for IEEE Transactions on Signal Processing and has presented his work at numerous international conferences including plenary talks at major optimization workshops. His research is supported through KAUST funding and collaborative projects with researchers worldwide.

• Organizational Behavior
• Innovation Management
• Strategic Management
• +5 more

Prof. Dr. Torsten-Oliver Salge is a University Professor and Co-Director of the Institute for Technology and Innovation Management (TIM) at RWTH Aachen University, where he heads the Innovation, Strategy and Organisation (ISO) Group since April 2013. He also serves as a Fellow of Cambridge Digital Innovation at the University of Cambridge, maintaining strong international academic connections. His work bridges theoretical organizational research with practical applications across multiple sectors. Salge completed his business administration studies across multiple European countries including Germany, France, Italy, and England. He earned both his Ph.D. and M.Phil. degrees from the University of Cambridge. His academic journey includes positions as interim professor at the University of Duisburg-Essen (2012-2013), junior professor at the Ruhr-University Bochum (2009-2012), and post-doctoral research associate at Cambridge (2009). His research focuses on organizational behavior in innovation contexts , particularly examining collaboration across disciplinary, organizational, and institutional boundaries. He investigates how organizations learn from performance feedback, approach problem-solving, and implement strategic HRM practices that enhance employee well-being. A distinctive feature of his work is the examination of technology legitimation, adoption processes, and payoff realization in complex organizational settings. His research combines behavioral and institutional perspectives to understand innovation processes. Analysis of Salge's publication record reveals a strong emphasis on healthcare innovation management, with numerous studies examining hospital information systems, infection control, and healthcare service improvement. His work often applies dynamic capabilities theory to innovation processes and demonstrates methodological sophistication through both quantitative and qualitative approaches. The publications show consistent contributions to top-tier management journals including Academy of Management Review , MIS Quarterly , and Journal of Product Innovation Management . Scientific Recognition: Nominated for European Research Paper of the Year 2016 (CIONET) Multiple Best Paper awards at Academy of Management conferences across various divisions Shortlisted for Strategic Management Society (SMS) Best Conference Paper Awards Recipient of prestigious scholarships including ESRC, DAAD, and Cambridge European Trust funding Recognized with the Knut Holt Best Paper Award from ISPIM Throughout his career, Salge has maintained active collaborations with organizations across healthcare, central government, and financial services sectors. His international scholarly network is evidenced by visiting appointments at universities in Auckland, Buenos Aires, Cambridge, Oxford, and Philadelphia. At RWTH Aachen, he leads an interdisciplinary team within the TIME Research Area, which comprises over 100 senior and junior researchers working at the intersection of innovation, technology management, marketing, and entrepreneurship. As Co-Director of the RWTH TIM institute, Salge oversees a research unit with approximately 30 doctoral candidates, 5 postdocs, and more than 20 student researchers. The institute maintains strong industry connections and focuses on strategic technology management, open innovation, innovation culture, organizational change, business model development, and customer-oriented value creation.

Eythan LevyView profile

Research Fellow

• Computational Archaeology
• Ancient Chronology
• Northwest Semitic Epigraphy
• +3 more

Eythan Levy serves as Senior Assistant in Digital Archaeology at the University of Zurich's Institute of Classical Archaeology within the Faculty of Arts and Social Sciences. Previously, he led an SNSF SPARK project at the University of Bern (2024) and conducted postdoctoral research on stamp seals from the Southern Levant (2022-2023). His research interests focus on computational approaches to archaeological problems, particularly: Computer applications and quantitative methods in archaeology Ancient chronology of the Iron Age Levant Northwest Semitic epigraphy and paleography Archaeology of the Southern Levant Ancient Egyptian archaeology and epigraphy His work bridges computer science and archaeology through innovative methodological frameworks. Levy's publication trends demonstrate consistent interdisciplinary output combining computational methods with archaeological analysis. Recent work focuses on chronological modeling tools, epigraphic analysis of Hebrew seals, multispectral imaging of ostraca, and computational approaches to ceramic typology. His research shows strong emphasis on developing formalized schemes for synchronizing archaeological data and creating specialized software solutions. Levy has developed several significant archaeological software tools : ChronoLog : For computer-assisted chronological modeling Scrypt : Web application for computer-assisted decipherment of ancient inscriptions TPQ Composer : For displaying stratigraphic termini post quem Artifacts Analyzer : For analyzing archaeological artifact datasets These tools represent his commitment to creating practical computational solutions for archaeological challenges. His academic background uniquely combines computer science and archaeology: PhD in Archaeology (Tel Aviv University, 2017-2021) PhD in Computer Science (Université Libre de Bruxelles, 2003-2009) Multiple MA degrees in Archaeology and Ancient Oriental Languages Teaching certificate for higher education This dual expertise enables his innovative approach to digital archaeology.

Ryozo NagamuneView profile

Professor

• Control Engineering
• Control of floating offshore wind turbines and wind farms
• Control of integrated solar thermal systems
• +6 more

Ryozo Nagamune is a Professor in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia (UBC). His research focuses on control engineering with specific expertise in floating offshore wind turbines, integrated solar thermal systems, and metal additive manufacturing processes. He maintains active collaborations with NSERC, MITACS, and industry partners including Ascent Systems Technologies. Dr. Nagamune received his B.Sc. and M.Sc. degrees from Osaka University, followed by a Ph.D. from the Royal Institute of Technology in Stockholm, Sweden. His educational background laid the foundation for his expertise in control systems theory and applications. His primary research interests center on control engineering, with particular emphasis on the control of floating offshore wind turbines and wind farms, integrated solar thermal systems, directed energy deposition metal additive manufacturing processes, engine aftertreatment systems, and data-driven modeling and control of dynamical systems. His work addresses critical challenges in renewable energy, manufacturing, and automotive applications, focusing on optimization, robustness, and efficiency improvements. The research spans theoretical developments in control algorithms to practical implementation in real-world systems. Analysis of Dr. Nagamune's recent publications reveals a strong focus on floating offshore wind turbine control, which constitutes approximately 40% of his recent work. Another significant portion (30%) addresses automotive control systems, particularly selective catalytic reduction for emissions control. The remaining publications cover diverse applications including haptic interfaces, spacecraft control, and precision manufacturing systems. His research demonstrates a consistent pattern of applying advanced control methodologies to solve practical engineering problems across multiple domains. Dr. Nagamune leads the Control Engineering Laboratory at UBC (located in KAIS 3104) and actively seeks collaborations with industry partners, research clusters, and interdisciplinary teams. His research is supported by major funding agencies including NSERC and MITACS, as well as industry partnerships. He is available for supervision of graduate students and expresses interest in working with undergraduate students on research projects. Dr. Nagamune welcomes interdisciplinary research opportunities and is particularly interested in collaborations that bridge multiple engineering domains.

Simon OyaView profile

Assistant Professor

• Privacy-enhancing technologies
• Computer Security
• Signal processing for security and privacy
• +4 more

Simon Oya is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Faculty of Applied Science. He holds a PhD in Information Technologies and Communications from the University of Vigo (Spain) and was previously a postdoctoral fellow at the Cryptography, Security and Privacy (CrySP) group at the University of Waterloo. His educational background includes: BSc, MSc, PhD from University of Vigo (Spain) Simon Oya's research focuses on designing and evaluating privacy-enhancing technologies with strong privacy and utility guarantees. He approaches privacy problems from a statistical perspective, using theoretical tools from signal processing and information theory to quantify privacy leakage and develop effective defenses. His primary research areas include: Privacy-preserving searchable encryption Machine learning privacy (particularly membership inference attacks) Anonymous communication systems Location privacy Differential privacy His publication record demonstrates a consistent focus on analyzing and improving privacy mechanisms across various domains. His recent work has particularly emphasized the intersection of machine learning and privacy, as well as advancing techniques for searchable encryption. His research methodology typically involves developing statistical models to understand privacy leakage and designing optimization-based approaches to improve privacy-utility tradeoffs. His notable scientific contributions include developing attacks against searchable encryption schemes to better understand their privacy limitations, and designing improved privacy mechanisms for location-based services. His work on statistical disclosure attacks against anonymous communication systems has also been influential in the field. As an educator, he teaches CPEN 442: Introduction to Cybersecurity at UBC. He actively seeks motivated graduate students interested in privacy research, particularly those with strong backgrounds in statistics, machine learning, or optimization.

• Combinatorial Optimization
• Algorithmic Discrete Mathematics
• Routing and Scheduling
• +2 more

Britta Peis is a Professor of Management Science at RWTH Aachen University since September 2013. She studied Mathematics and Sports Sciences at the University of Cologne and German Sport University Cologne, respectively. Her academic journey includes positions at TU Dortmund (2006-2007), TU Berlin (2007-2010), and a visiting professorship at Otto-von-Guericke University Magdeburg (2010-2011). Her research focuses on Combinatorial Optimization , Algorithmic Discrete Mathematics , Routing and Scheduling , Robust Optimization , and Algorithmic Game Theory . Her work spans theoretical and applied domains, including network flow analysis, auction algorithms, and strategic decision-making in complex systems. Recent publications (2025-2024) highlight advancements in dynamic auction mechanisms, Stackelberg game formulations, and train routing algorithms. Earlier works (2022-2018) explore matroid theory, packet routing with priority lists, and sensitivity analysis in polymatroid optimization. Key trends include algorithmic design for competitive networks and robustness in time-dependent flows. She is affiliated with the Graduiertenkolleg UnRAVeL (Aachen Institute for Discrete Mathematics and Logic) and contributes to the Chair of Management Science's research agenda in combinatorial optimization and algorithmic game theory.

• Empirical asset pricing
• Behavioral finance
• Investment funds
• +2 more

Miguel Antón is a full Professor in the Department of Financial Management at IESE Business School, University of Navarra, holding a PhD from the London School of Economics, a Master's from CEMFI, and dual degrees from the University of Navarra. He serves as a research associate in the LSE's Financial Markets Group and consults for private institutions. His academic credentials include: PhD in Finance, The London School of Economics and Political Science Master's in Economics and Finance, CEMFI Bachelor's and Master's degrees in Finance and Economics, University of Navarra Professor Antón's research centers on empirical asset pricing, behavioral finance, and systemic risk, with specialized focus on institutional trading impacts, investment fund performance, and credit market contagion. His recent work critically examines how common ownership structures influence corporate innovation, competitive dynamics, and governance mechanisms across global markets. Analysis of his 2021-2025 publications reveals dominant themes in common ownership economics, appearing in premier journals like Management Science and Journal of Political Economy. His research bridges academic theory and policy relevance, frequently addressing real-world implications through media outlets including Forbes and El Confidencial. His distinguished recognition includes: Outstanding End-of-Degree Award from University of Navarra Adjunct Professor Award from LSE Department of Finance Fellowships from Ramón Areces Foundation and Association of Friends of University of Navarra Professor Antón maintains active industry engagement through consulting roles and prior experience at Bank of New York and BBVA's research division. His collaborative work with scholars like F. Ederer and M. Giné demonstrates extensive research networking across institutions including Harvard and the Federal Reserve. As a research associate in the LSE's Financial Markets Group, he participates in high-impact collaborative projects examining financial system stability, with recent presentations at institutions spanning the Rotman School, HEC Paris, and Federal Reserve Bank of New York.

Tamar GutnerView profile

Professor

• Global Governance
• International Organizations
• International Political Economy
• +3 more

Tamar Gutner is Professor at American University's School of International Service (SIS), where she previously served as Associate Dean for Faculty Affairs and Graduate Education and directed the MAIR, GGPS, and IER MA programs. Her leadership extends to program development and faculty mentorship, recognized by the 2017 Outstanding Faculty Mentor award. Education: Ph.D. in Political Science, Massachusetts Institute of Technology M.A. in International Relations, Johns Hopkins University School of Advanced International Studies (SAIS) B.S. in Radio/TV/Film, Northwestern University Professor Gutner's research examines the performance and effectiveness of international organizations, particularly international financial institutions. Her work analyzes institutional collaboration between the IMF and World Bank, the design of the Asian Infrastructure Investment Bank (AIIB), and environmental governance in multilateral development banks. She employs theoretical frameworks from agency theory and institutional analysis to investigate mandate-performance gaps in global governance structures, with regional expertise in Central and Eastern Europe and emerging economies. Her publication trends reveal deepening focus on inter-organizational dynamics since 2020, shifting from single-institution analyses to collaborative mechanisms between multilateral entities. Recent work emphasizes evaluation methodologies for IO collaboration, with increasing attention to China's role in reshaping global financial governance through institutions like the AIIB. Scientific Awards: American University William Cromwell Award for Outstanding Teaching (2025) Darrell Randall Award for Outstanding Service (2014, 2024) Council on Foreign Relations International Affairs Fellowship (2019) Multiple AU SIS Faculty Research and Curriculum Development Grants Professor Gutner has secured significant research funding from the Council on Foreign Relations, Brookings Institution, MacArthur Foundation, and International Studies Association. As a mentor, she has guided numerous graduate students through AU's international relations programs, with former advisees now working in international organizations and policy institutions. Her 2019 fellowship at the IMF's Independent Evaluation Office informed both her scholarly work and practical contributions to institutional evaluation frameworks. Her professional engagement includes regular participation in major conferences like the American Political Science Association and International Studies Association, where she presents on MDB collaboration strategies and global governance innovations. Current projects examine evolving corporate strategies in multilateral development banks as accountability mechanisms.

Shah NawazView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Multimodal Systems
• +3 more

Shah Nawaz is an Assistant Professor at the Institute of Computational Perception , Johannes Kepler University Linz. His research focuses on multimodal systems, deep learning applications in healthcare, and cross-modal learning frameworks. He leads projects addressing challenges like missing modalities in machine learning, face-voice association, and medical image analysis. Key research interests include machine learning for medical diagnostics (e.g., breast cancer detection, skin lesion segmentation), speech recognition, and adaptive neural network architectures. He has contributed to frameworks like Chameleon for robust multimodal learning and the FAME challenge for face-voice association in multilingual environments. Publications emphasize practical applications, such as bilingual healthcare chatbots for pregnant women and light-weight speech recognition models for resource-constrained systems. His work bridges theoretical advancements and real-world deployment in healthcare and security domains. Shaw Nawaz actively participates in academic communities through workshops like DaQuaMRec@RecSys2025 and has developed open-source frameworks for image restoration and multimodal fusion. His lab focuses on scalable solutions for multimodal data challenges in both technical and clinical contexts.