جست‌وجوی استاد و پژوهشگر

Kevin Jamiesonمشاهده پروفایل

دانشیار

• Machine Learning
• Active Learning
• Multi-Armed Bandits
• +۴ مورد دیگر

Kevin Jamieson is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering and an Adjunct Professor in the Department of Statistics at the University of Washington . His academic journey includes a B.S. (2009) , M.S. (2010) , and Ph.D. (2015) in electrical engineering from the University of Washington, Columbia University, and University of Wisconsin–Madison respectively. He completed a postdoc at UC Berkeley's AMP Lab before joining UW in 2017. Ph.D., Electrical Engineering, University of Wisconsin–Madison (2015) M.S., Electrical Engineering, Columbia University (2010) B.S., Electrical Engineering, University of Washington (2009) Jamieson's research lies at the intersection of interactive machine learning , active learning , and sequential decision making . His work focuses on: Adaptive sampling strategies in multi-armed bandits and reinforcement learning (RL) Developing instance-dependent optimal algorithms that adapt to problem difficulty Applications in robotics , human perception studies , and hyperparameter optimization Representation learning for large models and experimental design frameworks His 15 most recent publications (2025-2022) demonstrate expertise in bandit theory , contextual RL , and game-theoretic learning . Notable trends include sample-efficient optimization , adaptive A/B testing , and sim-to-real transfer in robotics. Jamieson has received: NSF CAREER award for foundational contributions Amazon Faculty Research award for innovation in learning systems He actively recruits graduate students and postdocs , emphasizing collaboration in areas like: Multi-agent RL and strategic actor learning Empirical process suprema and adaptive sampling theory Applications in robotics , large language model finetuning , and biomedical data analysis Jamieson leads the Washington AI Lab (WAIL) and develops open-source learning systems like the NEXT framework for real-world adaptive data collection. He serves as co-PI for the Institute for the Foundations of Data Science (IFDS) and co-organizes the Distinguished Seminar in Optimization & Data .

Xin Guoمشاهده پروفایل

استاد

• Mathematical Finance
• Financial Engineering
• FinTech
• +۷ مورد دیگر

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

• Distributed Systems
• Cryptocurrencies
• Computer Security
• +۷ مورد دیگر

David Mazières is a Professor at Stanford University , affiliated with the School of Engineering and the Department of Computer Science . He serves as a software engineer at the Stellar Development Foundation . His work bridges academic research and industry applications in distributed systems and security. University: Stanford University Academic Rank: Professor Email: dm@scs.stanford.edu Research Interests: David Mazières' research focuses on distributed systems , cryptocurrencies , and computer security . Key areas include consensus protocols (e.g., Stellar ), low-latency scheduling ( Syrup , Shinjuku ), and cryptographic techniques for privacy and security ( SafetyPin , CCFI ). Key Achievements: His notable works include: TCP-ENO (RFC 8547) for secure transport protocols Stellar Consensus Protocol for decentralized finance SOSP 1999 Best Paper for 'Separating key management from file system security' Teaching Contributions: He has taught core and advanced courses at Stanford since 2005, including CS212 (Operating Systems) , CS240h (Functional Systems in Haskell) , and CS251 (Cryptocurrencies and Blockchain Technologies) . Previously taught courses at NYU (2001-2005).

Renjie Fengمشاهده پروفایل

پژوهشگر ارشد

• Probability Theory
• Random Matrix Theory
• Machine Learning
• +۵ مورد دیگر

Renjie Feng is a Research Fellow in Mathematics and AI at the School of Mathematics and Statistics and the Sydney Mathematical Research Institute , University of Sydney. His work bridges probability theory, statistics, and applications in machine learning, deep learning, and artificial intelligence. His research interests focus on probability theory and its applications to machine learning , random matrix theory , and statistical physics . He investigates extreme value problems, spectral properties of random matrices, and topological features of random fields over Riemannian manifolds. Recent publications highlight trends in random matrix theory (GUE, GOE, GSE), extreme gap problems , determinantal point processes , and Wiener chaos . Collaborative works with F. Götze, D. Yao, and R. Adler emphasize U-statistics , multivariate linear statistics , and random topology inspired by Poisson point process studies.

• Applied Topology
• Dynamical Systems
• Algebraic Topology
• +۵ مورد دیگر

Robert Ghrist is the Andrea Mitchell University Professor at the University of Pennsylvania with dual appointments in the Department of Mathematics and the Department of Electrical and Systems Engineering. He serves as Associate Dean for Undergraduate Education for Penn Engineering. His educational background includes a B.S. in Mechanical Engineering from the University of Toledo (1991), and M.S. and Ph.D. degrees in Applied Mathematics from Cornell University (1994, 1995). Ghrist's research bridges pure and applied mathematics, focusing on applied algebraic topology , dynamical systems , and geometric methods in data science. His work extends to network theory, topological data analysis, and computational geometry, with applications spanning robotics, neuroscience, and social dynamics. Key innovations include developing sheaf-theoretic approaches for networked systems and persistence homology techniques for high-dimensional data. Analysis of his recent publications reveals a strong emphasis on lattice-theoretic frameworks , topological robotics , and network dynamics , with emerging applications in neural data interpretation and geometric computing. His research consistently integrates category theory with real-world engineering challenges. Significant scientific recognition includes: Presidential Early Career Award (PECASE, 2004) Scientific American 'Top 50' Research Leader (2007) Mathematical Association of America's Chauvenet Prize (2013) University of Pennsylvania Lindback Award for Distinguished Teaching (2015) DoD National Security Science and Engineering Faculty Fellowship (NSSEFF, 2015) Ghrist leads multiple federally funded research initiatives supported by AFOSR, DARPA, NSF, and ONR. He directs the development of educational tools including the Calculus BLUE/GREEN Project video series and custom GPTs for mathematical pedagogy. His open online courses have reached over 100,000 learners globally.

Per Krusellمشاهده پروفایل

استاد

• Macroeconomics
• Inequality
• Climate Economics
• +۵ مورد دیگر

Per Krusell is Professor of Economics at Stockholm University and holds the Torsten and Ragnar Söderberg Chair at the Institute for International Economic Studies (IIES). He is a Fellow of the Econometric Society and served as President of the European Economic Association in 2020. He is currently the Secretary of the Prize Committee for the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel. Education: PhD in Economics, University of Minnesota Research Interests: Per Krusell’s research spans macroeconomics , inequality , climate economics , labor markets , and political economy . He has made foundational contributions to understanding incomplete markets, wealth distribution, optimal taxation, and the macroeconomic effects of climate change. His work often integrates dynamic stochastic general equilibrium (DSGE) models with heterogeneous agents to study long-run outcomes and policy impacts. Publications Trend: His recent publications (2022–2025) reflect a strong focus on climate policy , integrated assessment modeling , and macro-labor dynamics . Key themes include carbon taxation, labor supply responses to productivity growth, fiscal multipliers in heterogeneous-agent settings, and the macroeconomic consequences of vaccination and epidemic control. Awards & Honors: Fellow of the Econometric Society Torsten and Ragnar Söderberg Chair in Economics President, European Economic Association (2020) Secretary, Nobel Prize Committee in Economic Sciences Advising & Grants: While specific student names are not listed, his leadership roles in global economic associations and policy committees indicate extensive academic advising and grant involvement in macroeconomic policy and climate economics. Labs & Teams: He is affiliated with the Institute for International Economic Studies (IIES) at Stockholm University, a leading research institute in macroeconomics and international economics.

Celestine Mendler-Dünnerمشاهده پروفایل

استاد

• Machine Learning in Social Context
• Algorithmic Collective Action
• Performative Prediction
• +۶ مورد دیگر

Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.

Paul Goldbergمشاهده پروفایل

استاد

• Algorithmic Game Theory
• Computational Complexity Theory
• Machine Learning
• +۳ مورد دیگر

Paul Goldberg is a Professor of Computer Science and Director of the MSc in Mathematics and Foundations of Computer Science (MFoCS) at the University of Oxford. He holds a BA in Mathematics from Oxford University and a PhD in Computer Science from the University of Edinburgh. His research focuses on algorithmic game theory, computational complexity, and machine learning, with notable contributions to equilibrium computation, complexity classes of total search problems, and decentralized systems. Affiliations: Department of Computer Science, Oxford; Editorial Board of ACM Transactions on Economics and Computation. Education: PhD in Computer Science (1993), University of Edinburgh MSc in Computer Systems Engineering (1989), University of Edinburgh and Université Paris-Sud BA in Mathematics (1988), Oxford University Research Interests: Algorithmic game theory, computational complexity (especially total search problems like CLS and PPAD), decentralized computation of equilibria, and applications in machine learning and AI. His work bridges theoretical computer science and economics, with a focus on algorithm design and complexity analysis. Publications and Awards: Over 120 papers, including influential work on Nash equilibrium complexity (2009), gradient descent (2023), and fair division algorithms. Notable awards include the ACM SIGecom Test of Time Award (2022) and a SIAM Outstanding Paper Prize (2011). Grants and Students: Leads EPSRC-funded projects on game theory and machine learning. Supervised 11 PhD graduates and currently advises Giannis Tyrovolas and others. Active in mentoring MSc and undergraduate projects. Labs/Teams: Part of the Algorithms and Complexity Theory group at Oxford, contributing to research on optimization, equilibrium dynamics, and fair division.

Henrik Sandbergمشاهده پروفایل

استاد

• Cyber-Physical Systems Security
• Power Systems
• Model Reduction
• +۶ مورد دیگر

Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm

• Machine Learning
• Artificial Intelligence
• Algorithms
• +۳ مورد دیگر

Zaid Harchaoui is an Adjunct Professor in the Department of Statistics at the University of Washington. His research focuses on machine learning, generative AI, and algorithmic optimization, with applications spanning ecology, neuroscience, and artificial intelligence. He explores learning under distributional shifts and develops tools for scalable generative models in language and vision domains. University: University of Washington Department: Statistics Research Focus: Learning from data with computational, inferential, and mathematical rigor; distributional shift adaptation; generative model scaling Email: zaid@uw.edu His recent work emphasizes generative AI applications in ecology and neuroscience, stochastic optimization for robustness, and algorithmic efficiency in large-scale learning. Key contributions include techniques for distributionally robust optimization, interpretable authorship obfuscation, and uncertainty quantification in behavior classification. Scientific awards and honors are not explicitly mentioned in the provided text. Collaborative efforts often intersect with nonlinear control algorithms, spectral analysis, and privacy-preserving machine learning frameworks.

Sarah Deanمشاهده پروفایل

استادیار

• Machine Learning
• Data-Driven Control
• Decision-Making Systems
• +۶ مورد دیگر

Sarah Dean is an Assistant Professor in the Computer Science Department at Cornell University, affiliated with the College of Engineering. Her research focuses on the interplay of machine learning, optimization, and dynamics in real-world systems, particularly in control theory, recommendation systems, and ethical AI. Education: PhD in EECS, University of California, Berkeley (2021) Postdoctoral Research, University of Washington (2021-2022) Research Interests: Data-driven control systems, reinforcement learning, recommendation systems, user dynamics, algorithmic fairness, and the societal impacts of AI. She emphasizes foundational understanding of how learning systems interact with human and social processes. Recent Work Trends: Her articles explore topics like bilinear system identification, user participation dynamics in recommendation platforms, and ethical considerations in AI development. Recent work includes harm mitigation strategies and mathematical modeling of AI-human feedback loops. Awards: AI2050 Early Career Fellow (2024) Best Paper at ICML 2018 (Delayed Impact of Fair Machine Learning) Best Student Paper in Imaging Systems (OSA Congress 2018) Advising & Labs: Advises over 15 graduate and undergraduate students. Leads research on interactive ML systems, with contributions to projects like the 'MSGD' repository for streaming data learning. Active in the GEESE group, promoting socially responsible computing.

Todd Squiresمشاهده پروفایل

استاد

• Fluid Mechanics
• Transport Science
• Microfluidics
• +۷ مورد دیگر

Professor Todd Squires is a distinguished faculty member in the Department of Chemical Engineering at the University of California, Santa Barbara, within the Robert Mehrabian College of Engineering. His research focuses on the fundamental principles of transport phenomena as applied to interfaces, membranes, and complex fluids, employing theoretical, computational, and experimental approaches to address significant challenges in micro-scale fluid mechanics. Dr. Squires' educational background includes: BS in Physics, UCLA (1995) BA in Russian Language and Literature, UCLA (1995) PhD in Physics, Harvard University (2002) His research interests span microfluidics and electrokinetics, active and nonlinear microrheology of complex materials, polymer dynamics and sensors, with particular emphasis on non-linear electrokinetic flows, interfacial rheology, and the self-assembly of nanostructured materials. His work bridges fundamental fluid mechanics with practical applications in microfluidic devices, energy storage, and biomedical systems, demonstrating the versatility of this fascinating field. Analysis of Professor Squires' recent publications reveals a consistent focus on interfacial phenomena, with particular attention to the rheological properties of fluid interfaces, particle dynamics in complex fluids, and novel microfluidic techniques for measuring and manipulating these systems. His research demonstrates strong interdisciplinary connections between chemical engineering, physics, and materials science, with applications spanning energy storage, biomedical engineering, and environmental systems. Professor Squires has received numerous prestigious awards and honors: 2018 Robert W. Vaughan Lecture in Chemical Engineering, Caltech 2015 Elected Fellow of the American Physical Society 2013 Mid-Career Award, American Electrophoresis Society 2012 The Dudley Saville Memorial Lecture at Princeton 2010 Pierre Gilles de Gennes Prize 2010 Allan P. Colburn Memorial Lectureship, University of Delaware 2009 Francois Frenkiel Award for Fluid Mechanics 2009 Camille Dreyfus Teacher-Scholar Award 2008 Beckman Young Investigator 2007 NSF CAREER Award 2005 'Rising Star' - Chronicle of Higher Education As principal investigator of the Squires Group, Professor Squires leads a dynamic research team that combines experimental, theoretical, and computational approaches to investigate transport phenomena at interfaces. His work has been supported by major funding agencies including the National Science Foundation, with his CAREER award indicating early recognition of his potential as both researcher and educator. While specific grant details aren't provided in the source material, his extensive publication record and prestigious awards suggest robust and sustained research funding. The Squires Group maintains state-of-the-art laboratory facilities for studying micro-scale fluid mechanics, including specialized equipment for microrheology measurements, microfluidic device fabrication, and interfacial characterization. Their research environment fosters collaboration across disciplines, with connections to materials science, physics, and biomedical engineering researchers at UCSB and beyond.

Yaojun Zhangمشاهده پروفایل

استادیار

• Biological Physics
• Biophysics
• Theoretical Physics
• +۳ مورد دیگر

Yaojun Zhang is an Assistant Professor in the Department of Physics & Astronomy and the Department of Biophysics at Johns Hopkins University. She earned her PhD in Physics from the University of California, San Diego (2015), followed by postdoctoral fellowships at the Princeton Center for Theoretical Science (2015-2018) and the Princeton Center for the Physics of Biological Function (2018-2021). Her research focuses on biological physics, particularly the complex behaviors of biomolecules and their assemblies across scales—from single-molecule folding to intracellular transport and biomolecular phase separation. She employs theoretical, mathematical, and computational tools to bridge biological questions with physical principles. Education PhD in Physics, University of California, San Diego (2015) Postdoctoral Fellowships: Princeton University (2015-2021) Research Interests Her group studies biomolecular condensates and liquid-liquid phase separation, exploring how microscopic interactions determine macroscopic properties of cellular compartments. Key areas include: Biomolecular condensate formation and dynamics Phase separation in cellular environments Interactions between biomolecules and cellular components Biophysics of intracellular transport Collaborations & Tools Zhang collaborates with experimentalists to validate theoretical models and develops frameworks for understanding condensate functions, such as surface tension, stoichiometry, and phase diagrams. Her work addresses challenges like condensate stability, molecular exclusion, and biological function regulation. Labs & Resources She leads the Zhang Lab , which integrates experimental and computational approaches. Her team’s research is supported by resources at the Bloomberg Center for Physics and Astronomy.

David Alvarez-Melisمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Optimal Transport
• +۴ مورد دیگر

David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.

Fatma Kılınç-Karzanمشاهده پروفایل

دانشیار

• Mathematical Optimization
• Convex Optimization
• Non-convex Optimization
• +۷ مورد دیگر

Fatma Kılınç-Karzan is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, with a courtesy appointment as Associate Professor of Computer Science. She is also affiliated with the Algorithms Combinatorics and Optimization (ACO) PhD Program and was a Visiting Scientist at Berkeley's Simons Institute for the Theory of Computing during Fall 2017. Her educational background includes a PhD from Georgia Institute of Technology's H. Milton Stewart School of Industrial & Systems Engineering with a minor in Mathematics, supervised by Prof. Arkadi Nemirovski. She earned her B.S. and M.S. degrees from the Industrial Engineering Department of Middle East Technical University with a minor in Information Systems. Dr. Kılınç-Karzan's research spans mathematical optimization with emphasis on convex and non-convex optimization theory, algorithms, and applications. Her work bridges theoretical foundations with practical implementations in optimization under uncertainty (robust optimization, chance constraints, distributionally robust optimization), machine learning (preference learning from limited data), and business analytics. She develops foundational theory for large-scale optimization problems with applications in decision making under uncertainty and high-dimensional statistical inference. Analysis of her recent publications reveals a strong focus on convex hull characterizations, semidefinite programming relaxations, distributionally robust optimization, and online convex optimization frameworks. Her work demonstrates increasing integration of optimization theory with machine learning applications, particularly in developing data-driven approaches for decision making under uncertainty. NSF CAREER Award (2015) INFORMS Optimization Society Young Researcher Prize (2015) INFORMS Junior Faculty Interest Group (JFIG) Best Paper Award (2014) BP Junior Faculty Chair (2014-2015) Faculty Giving Chair (2012-2013) Wimmer Fellowship (2012-2013) Dr. Kılınç-Karzan has successfully mentored numerous PhD students who have received prestigious awards, including the 2021 INFORMS Optimization Society Best Student Paper (1st prize) and multiple honorable mentions. Her research has been supported by significant grants including an NSF CAREER Award, an ONR grant (with S. Küçükyavuz), and an AFOSR grant. She serves on editorial boards for Mathematical Programming, Operations Research, Mathematics of Operations Research, and other leading journals, and has held leadership positions in professional societies including the Mathematical Optimization Society and INFORMS Computing Society. Through her affiliations with CMU's Tepper School, Computer Science Department, and ACO Program, she collaborates across disciplines to advance optimization theory and its applications. Her professional service includes committee chair roles for major INFORMS competitions and program committee leadership for international optimization conferences.