جست‌وجوی استاد و پژوهشگر

Omar A. Salehمشاهده پروفایل

استاد

• Biophysics
• Soft Matter Physics
• Polymer Physics
• +۵ مورد دیگر

Professor Omar A. Saleh is a distinguished physicist and materials scientist at the University of California, Santa Barbara, holding appointments in both the Materials and Physics Departments. Since summer 2023, he has served as Chair of the Materials Department and maintains a minority appointment in the Biomolecular Science and Engineering (BMSE) Program, where he previously served as Director from 2013-2017. His educational background includes a B.S. in Physics from MIT (1997) and a Ph.D. in Physics from Princeton (2003), supported by a Hertz Fellowship. Following postdoctoral work at École Normale Supérieure in Paris developing single-molecule techniques for motor protein/DNA studies, he joined UCSB in 2005. Saleh's research centers on fundamental principles of biomolecular behavior through experimental investigation of biopolymer elasticity and biomimetic organelles. His lab pioneers precision single-molecule stretching experiments to study entropic/energetic contributions in soft systems and creates life-like behaviors using reconstituted nucleic acid/protein assemblies. Key focus areas include DNA nanostar phase separation, liquid-liquid phase behavior, intrinsically disordered proteins, and non-equilibrium biomolecular systems. His publication trends reveal a strong emphasis on biomolecular condensates (2023-2025), with recurring themes in DNA nanotechnology, polyelectrolyte physics, and single-molecule mechanics. Recent work explores tension-mediated control of phase separation, transcriptional regulation of biomolecular liquids, and active matter principles in DNA systems. NSF CAREER Award (2008) Bessel Research Award from Alexander von Humboldt Society (2017) Fellow of the American Physical Society (2019) Saleh actively mentors graduate students and postdocs including Sam Wilken, Gabrielle Abraham, Anna Nguyen, and Aria Chaderjian, whose research spans DNA nanostar liquids, active droplets, and complex coacervation. His lab develops innovative instrumentation including high-speed magnetic tweezers and GPU-based tracking systems, supported by grants such as NSF/MCB-BSF: Direct force measurements of intrinsically disordered proteins. The Saleh Group operates at BioE 3006, focusing on creating quantitative models of biological function through physical reconstitution.

• Robotics
• Robot Learning
• Machine Learning
• +۷ مورد دیگر

Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.

John Lygerosمشاهده پروفایل

استاد

• Automatic Control
• Hybrid Systems
• Large-Scale Dynamical Systems
• +۴ مورد دیگر

John Lygeros is a Full Professor and Head of the Institute for Automatic Control at ETH Zurich's Department of Information Technology and Electrical Engineering. He received his B.Eng. (1990) and M.Sc. (1991) from Imperial College London, and Ph.D. (1996) from UC Berkeley. Before joining ETH Zurich in 2006, he held academic positions at the University of Cambridge and University of Patras. His research focuses on: Modeling and control of hierarchical hybrid systems Large-scale dynamical systems applied to biochemical networks Control of automated transportation systems via wireless networks Energy systems and advanced manufacturing control His research demonstrates strong emphasis on optimization methods, distributed control architectures, and machine learning applications in control systems, with significant contributions to real-time optimization algorithms and data-driven control methodologies. Major scientific awards include: HSCC Test of Time Award (2024) ERC Advanced Grant (2018) O. Hugo Schuck Best Paper Award (2018) IEEE George S. Axelby Paper Award (2016) Three Golden Owl teaching awards from ETH Zurich He leads the Automatic Control Laboratory at ETH Zurich and serves as Director of the National Centre of Competence in Research 'Dependable Ubiquitous Automation'. He has advised over 60 doctoral candidates since 2017, with research spanning optimization algorithms, power systems, autonomous systems, and machine learning applications.

Ceren Budakمشاهده پروفایل

دانشیار

• Computational Social Science
• Social Media Analysis
• News Media Production and Consumption
• +۷ مورد دیگر

Ceren Budak is an Associate Professor at the University of Michigan School of Information and holds a joint appointment as Associate Professor of Electrical Engineering and Computer Science in the College of Engineering. Her work bridges computer science, statistics, and social sciences through computational social science approaches. Her educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a Bachelors degree in Computer Science from Bilkent University in Turkey (2007). Prior to joining the University of Michigan faculty, she was a Postdoctoral Researcher at Microsoft Research New York. Professor Budak's research centers on computational social science, with particular emphasis on analyzing large-scale datasets to address questions with social, political, and policy implications. Her work spans several interconnected domains: News Media Production & Consumption (examining bias in news outlets and reader preferences), Social Movements & Media (using social media data to study collective action), Social Networks (understanding information diffusion processes), and Measuring and Promoting the Quality of Online Discussions (developing tools to improve online conversations). She teaches SI 608 (Networks) and SI 618 (Data Manipulation and Analysis) at the School of Information. Her publication record demonstrates consistent contributions to understanding how online information ecosystems operate, with recent work focusing on AI-human collaboration, misinformation dynamics, social movement framing, and the application of computational methods to political communication. Her research shows a clear trajectory from foundational work on social network diffusion to increasingly sophisticated analyses of contemporary information challenges. Among her service activities, she has served as Registration chair for COSN (ACM Conference on Online Social Networks) 2015 and as Program Committee Member for numerous prestigious conferences including WWW, ICWSM, WebSci, AAAI, and others. She has also been involved in organizing the MSR NYC Data Science Seminar Series and instructing the Microsoft Research Data Science Summer School.

Rebecca Schulmanمشاهده پروفایل

دانشیار

• DNA Self-Assembly
• Smart Biomaterials
• Synthetic Biology
• +۷ مورد دیگر

Rebecca Schulman is an Associate Professor in the Department of Chemical and Biomolecular Engineering at the Whiting School of Engineering, Johns Hopkins University. She holds secondary appointments in Chemistry and Computer Science and is affiliated with multiple interdisciplinary institutes, including the Institute for NanoBioTechnology, the Hopkins Extreme Materials Institute, the Chemistry-Biology Interface Program, the Center for Cell Dynamics, and the Laboratory for Computational Sensing and Robotics. She currently co-directs the Passport to Future Technology Leadership program for PhD students. Research Interests: Schulman's research lies at the intersection of DNA nanotechnology, synthetic biology, and smart materials. Her group develops intelligent, adaptive biomolecular materials and nanostructures by integrating concepts from materials science, biochemistry, circuit design, and soft matter physics. The team focuses on engineering dynamic self-assembly processes using DNA to create reconfigurable materials, molecular circuits, and autonomous soft micro-robots. Key themes include self-healing nanostructures, feedback-regulated crystallization, programmable hydrogels, and synthetic genetic networks for materials control. Publication Trends: Her recent publications demonstrate a consistent focus on using DNA-based chemical reaction networks to program spatial and temporal behavior in materials. The work spans from fundamental mechanisms like catalytic polymerization and crystal growth regulation to applications in soft robotics, self-wiring circuits, and synthetic pattern formation. The research is highly interdisciplinary, combining synthetic biology with materials engineering to achieve life-like functionalities in non-living systems. Scientific Awards: AIMBE Fellowship Award Vannevar Bush Faculty Fellowship Award Hartwell Individual Biomolecular Research Award President’s Early Career Award in Science and Engineering (PECASE) DARPA Young Faculty Award DARPA Directors Fellowship NSF CAREER Award Turing Scholar Award DOE Early Career Award Advising and Grants: Schulman mentors graduate students and leads a vibrant research group focused on next-generation biomolecular engineering. Her work is supported by major federal grants, including the NSF CAREER, DOE Early Career, DARPA, and the Vannevar Bush Fellowship—a prestigious Department of Defense award for basic research. She is actively involved in training future leaders through programs like the Passport to Future Technology Leadership. Labs and Teams: The Schulman Lab at Johns Hopkins is a multidisciplinary team working on DNA-powered materials and molecular programming. The lab is embedded within several collaborative centers, enabling strong cross-departmental and cross-institutional research. Their work combines experimental biochemistry with theoretical modeling to design and implement complex molecular systems.

Nina Gantertمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Large Deviations
• +۴ مورد دیگر

Prof. Dr. Nina Gantert is a distinguished Professor of Probability Theory at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology . She has held faculty positions at Karlsruhe Institute of Technology and the University of Münster prior to joining TUM in 2011. Her research focuses on probability theory , particularly stochastic processes , large deviations , and random media . She investigates random walks in random environments as models for transport in disordered systems and explores applications in physics and biology . Recent publications highlight her work on branching random walks , mixing times , biased random walks , and large deviation principles for complex stochastic systems. She has co-authored studies on random walks in dynamical percolation , interacting edge-reinforced processes , and extremal point processes in branching models. Scientific Awards: Elected fellow of the IMS (2016) Her academic career spans institutions including ETH Zürich, University of Bonn, Technical University of Berlin, and TUM. She has supervised numerous Bachelor’s and Master’s theses on topics ranging from mixing time analysis to percolation theory , often collaborating with international co-authors.

Jun-Kun Wangمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Sampling Methods
• +۳ مورد دیگر

Jun-Kun Wang is an Assistant Professor at the University of California, San Diego (UCSD), with a joint appointment in the Department of Electrical and Computer Engineering and the Halicioğlu Data Science Institute. He joined UCSD in July 2023, previously serving as a postdoc at Yale University. His research focuses on optimization, sampling, and machine learning, emphasizing acceleration techniques and theoretical guarantees. He explores connections between optimization and areas like no-regret learning, sampling, and hypothesis testing. Education: PhD in Computer Science from Georgia Tech (advised by Jacob Abernethy), M.S. in Communication Engineering and B.S. in Electrical Engineering from National Taiwan University. Research Interests: Acceleration in optimization and sampling, trustworthy machine learning, momentum methods, and algorithmic convex optimization. His work bridges theoretical foundations and practical applications, with publications in top-tier venues like COLT, ICML, ICLR, and NeurIPS. Teaching: Courses include ECE 174 (Linear/Nonlinear Optimization), ECE 273 (Convex Optimization), and DSC 211 (Optimization). His lectures cover topics such as gradient descent, duality theory, mirror descent, and non-convex optimization. Lab/Team: Leads the Optimization and Machine Learning Group, advising PhD students Can Chen and Maria-Eleni Sfyraki, and MS student Yi Liu. His group focuses on theoretical and applied aspects of optimization algorithms.

Rohil Prasadمشاهده پروفایل

پژوهشگر ارشد

• Geometry
• Topology
• Dynamical Systems
• +۶ مورد دیگر

Rohil Prasad is a Miller Research Fellow at the University of California, Berkeley, appointed in 2023, and will join Princeton University as an Assistant Professor in 2025. His research lies at the intersection of geometry, topology, and dynamical systems, with a particular focus on conservative dynamics, pseudoholomorphic curves, and Floer theory. Education: PhD in Mathematics, Princeton University (2018–2023), advised by Helmut Hofer Research Interests: Prasad’s work spans several deep areas of modern mathematics, including: Conservative Dynamics: Studying systems that preserve volume or symplectic structures. Symplectic Geometry: Exploring geometric structures preserved under Hamiltonian flows. Low-Dimensional Topology: Investigating the topology of 3- and 4-dimensional manifolds. Floer Theory: Using pseudoholomorphic curves to study periodic orbits and invariants. His research has led to significant advances in understanding periodic orbits, invariant measures, and the structure of area-preserving maps. Awards and Honors: 2024 Brin Dynamical Systems Prize for Young Mathematicians Contact: rrprasad@berkeley.edu | Office: 968 Evans Hall

• Machine Learning
• Physics-Informed Machine Learning
• Interpretable Machine Learning
• +۳ مورد دیگر

Yan Liu is a full professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), serving as Director of the USC Machine Learning Center within the Viterbi School of Engineering. He holds courtesy appointments in the Ming Hsieh Department of Electrical Engineering and the Quantitative and Computational Biology Department. Before joining USC in 2010, he was a research staff member at IBM's T.J. Watson Research Center. He earned his M.S. and Ph.D. from Carnegie Mellon University. His research focuses on machine learning for time series, physics-informed AI, and interpretable models, with applications in healthcare, sustainability, and social media. Notable projects include developing AI for surgical training, analyzing misinformation on social platforms, and predicting cancer treatment outcomes. He has held leadership roles in top conferences like ICLR and ACM KDD, and serves as Associate Editor-in-Chief of TPAMI and Board Member of ICLR. Education: Ph.D., Carnegie Mellon University Affiliations: USC Machine Learning Center, Viterbi School of Engineering Service: General Chair (ICLR 2023, ACM KDD 2020), Program Chair roles across multiple conferences His lab, the Melady Group, emphasizes foundational ML advancements and interdisciplinary applications. Recent work includes physics-aware neural networks and time-series foundation models.

Andre Wibisonoمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Sampling
• +۴ مورد دیگر

Andre Wibisono serves as Assistant Professor in Yale University's Department of Computer Science with a secondary appointment in Statistics & Data Science, joining the faculty in 2021 after postdoctoral research at University of Wisconsin-Madison and Georgia Institute of Technology. His educational background includes: Ph.D. in Computer Science, UC Berkeley M.A. in Statistics, UC Berkeley M.Eng. in Computer Science, MIT S.B. in Mathematics and Computer Science, MIT Wibisono's research focuses on algorithm design for machine learning through optimization, sampling, and game theory , leveraging dynamical systems and information theory to develop accelerated discrete-time algorithms from continuous dynamics. His work provides theoretical foundations for efficient machine learning systems with applications in generative modeling and constrained optimization. Recent publications (2023-2025) demonstrate consistent innovation in Hamiltonian-based optimization , constrained-space sampling , and min-max game convergence , characterized by rigorous mathematical analysis connecting continuous dynamics to discrete algorithms. Key trends include randomized integration for acceleration, phi-divergence convergence guarantees, and symplectic geometry applications to mirror descent. Scientific recognition includes: NSF CAREER Award for developing algorithmic frameworks bridging continuous and discrete dynamics He actively mentors current students (Siddharth Mitra, Kaylee Yang, Jane Lee, Qiang Fu, Peter Wang) and has guided two postdocs to faculty positions. Research is funded through the NSF CAREER award and collaborative CIF grants focused on Hamiltonian dynamics for sampling and optimization. His Yale research group develops theoretical foundations for next-generation machine learning algorithms, emphasizing mathematical rigor in optimization and sampling with applications to generative modeling and constrained inference problems.

Florian Schäferمشاهده پروفایل

استادیار

• Numerical Analysis
• Computational Mathematics
• Machine Learning
• +۹ مورد دیگر

Florian Schäfer is an Assistant Professor at the School of Computational Science and Engineering at Georgia Tech. His research spans numerical computation, statistical inference, and competitive games, with applications in materials science, turbulence modeling, computer graphics, and computational geometry. He will join the Courant Institute at NYU in September 2025. PhD in Applied and Computational Mathematics, Caltech Bachelor’s and Master’s in Mathematics, University of Bonn His work focuses on information geometric mechanics to design structure-preserving numerical methods for continuum mechanics. This includes: State-of-the-art solvers for elliptic PDEs via Gaussian elimination and conditional independence Efficient multi-agent optimization algorithms Information geometric regularization for compressible fluid dynamics Enabling the first compressible fluid simulation exceeding 100 trillion grid cells His recent research trends integrate: Machine learning for materials science (e.g., active learning, Bayesian approaches) Stochastic modeling of microstructures and phase-field problems Neural operators for super-resolution fluid dynamics Generative models for polycrystalline material datasets Optimal transport and diffusion models for conditional density transformations High-performance computing at extreme scales Florian collaborates with researchers including Houman Owhadi, Jessie Liu, Spencer Bryngelson, Tamer Zaki, and Ali Mani. He actively presents at conferences like SIAM and UCLA seminars, and is recruiting PhD students for work at the Courant Institute starting 2025.

Jiajun Wuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Robotics
• +۵ مورد دیگر

Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.

• 3D Computer Vision
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Kwang Moo Yi is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), where he conducts research in computer vision and machine learning. He is affiliated with the Computer Vision Lab, CAIDA (Centre for Artificial Intelligence Decision-making and Action), and ICICS (Institute for Computing, Information and Cognitive Systems) at UBC. Education: B.Sc. from Seoul National University Ph.D. from Seoul National University under Prof. Jin Young Choi Post-doctoral researcher at École Polytechnique Fédérale de Lausanne (EPFL) with Prof. Pascal Fua and Prof. Vincent Lepetit Dr. Yi's research focuses on Visual Geometry with the goal of understanding local environments, adapting to them, and acting within them. His work spans applications in autonomous vehicles, drones, robots, and Augmented/Mixed Reality systems. He employs machine learning, particularly deep learning, as the primary tool for advancing computer vision capabilities. His recent publications demonstrate a strong focus on neural rendering techniques, especially 3D Gaussian Splatting and Neural Radiance Fields (NeRF). The research trends show increasing sophistication in handling occlusions, improving rendering quality, and developing more efficient training methods for neural fields. There's also significant work connecting computer vision with practical applications in industrial settings and energy systems. Dr. Yi serves as an area chair for top computer vision and machine learning conferences including CVPR, ICCV, ECCV, NeurIPS, ICML, and AAAI. He was part of the organizing committee for CVPR 2023. He supervises graduate students including Eric (who recently completed his PhD), Gopal (now at Samsung Research), and Jeong-Gi (joining as a postdoctoral fellow). His teaching includes CPSC 425: Computer Vision and CPSC 533Y: 3D Computer Vision with Deep Learning. Dr. Yi is actively involved with the Computer Vision Lab at UBC, collaborating with researchers across CAIDA and ICICS. His work bridges theoretical computer vision with practical applications in various domains including astronomy, industrial automation, and energy systems.

Stephen A. Vavasisمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science
• First-Order Methods
• +۴ مورد دیگر

Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.

Hanna Halaburdaمشاهده پروفایل

دانشیار

• Blockchain
• Cryptocurrencies
• Digital Currencies
• +۱۲ مورد دیگر

Hanna Halaburda is an Associate Professor of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where she joined in 2019. Her research lies at the intersection of economics, technology, and digital platforms, with a strong focus on blockchain, cryptocurrencies, and platform competition. She has published extensively in top academic journals and co-authored the seminal book Beyond Bitcoin: The Economics of Digital Currencies . PhD in Economics, Northwestern University MA in Economics, Warsaw School of Economics MA in Philosophy, Warsaw University Her research interests center on the economic implications of digital transformation. She investigates how blockchain technology reshapes trust, governance, and competition in digital markets. Her work explores token design, consensus mechanisms, smart contracts, and the strategic use of decentralization in platforms. She also studies platform competition under network effects, consumer choice, and omnichannel marketing. A recurring theme is how digital technologies alter traditional economic forces and business models. The most recent articles show a strong trend toward analyzing the governance, security, and economic design of blockchain systems. Her work combines rigorous theoretical modeling with empirical insights, often applying game theory and industrial organization frameworks. Topics include permissioned vs. permissionless blockchains, the role of cryptographic tokens in coordination, and the macroeconomic implications of digital currencies. She also contributes to debates on Web3, AI, and the future of digital platforms. Scientific awards and recognitions include: ISR Best Paper Published in 2022 Runner-Up Lead article in RAND Journal of Economics Best Paper Award at Tokenomics 2023 Best Paper Award at WISE 2023 Best Paper Finalist at WISE 2022 and WISE 2021 Hanna Halaburda has advised and collaborated with numerous researchers and institutions. Her co-authors include leading scholars from Harvard, NYU, and international universities. She has received research recognition through best paper awards and invitations to contribute to high-impact journals and policy discussions. Her work has been supported by academic and policy institutions, including the Bank of Canada, where she previously served as a senior economist. She frequently publishes in both academic and practitioner outlets, including Harvard Business Review and Nature Human Behavior , indicating strong translational impact. She is actively involved in research teams focused on digital assets, blockchain governance, and platform economics. While no formal lab is mentioned, her extensive list of working papers and collaborations suggests leadership in a dynamic research group at NYU Stern. Her recent work on DAOs, public crypto mining firms, and CBDCs indicates ongoing, forward-looking research programs with real-world policy and business implications.