جست‌وجوی استاد و پژوهشگر

Ruben Loaiza Mayaمشاهده پروفایل

دانشیار

• Copula Modelling
• Bayesian Estimation Methods
• Time Series Analysis
• +۴ مورد دیگر

Ruben Loaiza-Maya is an Associate Professor (Research) in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Econometrics from the University of Melbourne and an undergraduate degree in Economics from Universidad Nacional de Colombia (Medellin). His research focuses on Copula Modelling, Bayesian Estimation Methods, Time Series Analysis, and Macroeconomic/Financial Forecasting. Key contributions include advancements in variational inference, state space models, and robust forecasting techniques under model misspecification. He leads the active project 'Variational Inference for Intractable and Misspecified State Space Models' (2023–2026), funded as a Primary Chief Investigator. His work contributes to UN Sustainable Development Goals through methodological advancements in economic and financial analysis. Recent research emphasizes scalable Bayesian methods, hybrid variational approaches, and efficient computational techniques for high-dimensional models. Publications span prestigious journals like the International Journal of Forecasting, Journal of Econometrics, and Journal of Business and Economic Statistics. Notable collaborations include studies on copula-based time series forecasting and robust approximate Bayesian computation. His work bridges theoretical econometrics with practical applications in risk management and macroeconomic policy.

Aviad Rubinsteinمشاهده پروفایل

استادیار

• Algorithms
• Complexity Theory
• Game Theory
• +۳ مورد دیگر

Aviad Rubinstein is an Assistant Professor of Computer Science at Stanford University, specializing in theoretical computer science with a focus on algorithms, complexity, and game theory. He has taught courses such as Design and Analysis of Algorithms (CS161), Incentives in Computer Science (CS269i), and Topics in Intractability (CS354). His research interests include approximation algorithms, computational complexity, and fair division, with notable work on envy-free cake-cutting and prophet inequalities. He advises several PhD students including Joshua Brakensiek and Ruiquan Gao, and mentors postdocs like Soheil Behnezhad. His undergraduate mentoring includes students from Tsinghua and Berkeley. Rubinstein has received the Kalai Prize from the Game Theory Society and a FOCS Best Paper Award for his work on inapproximability of Nash equilibria. Rubinstein co-authored Algorithms for Toddlers with Mary Wootters, a book simplifying computational concepts for younger audiences. He also organizes workshops on topics like fine-grained complexity and early career mentoring in computer science. Beyond academia, he consults part-time for the blockchain startup Lava. His research frequently bridges theoretical foundations with practical implications, such as developing algorithms with real-world applications in auctions, mechanism design, and optimization under constraints.

Sinan Yıldırımمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Monte Carlo Methods
• Data Privacy
• +۳ مورد دیگر

Sinan Yıldırım is a Researcher in the Faculty of Engineering and Natural Sciences at Sabancı University, Turkey. His primary research focuses on Bayesian Statistics, Monte Carlo methods, and data privacy, with interdisciplinary applications in machine learning and signal processing. He holds a BSc and MSc in Electrical and Electronics Engineering from Boğaziçi University, followed by a PhD in Mathematical Statistics from the University of Cambridge. Postdoctoral research (2013-2015) at the University of Bristol’s School of Mathematics involved the EPSRC-funded project 'Intractable Likelihood: New Challenges from Modern Applications (i-like).' His work bridges theoretical statistics with practical problems in privacy, control systems, and energy optimization. Research interests emphasize Bayesian methodologies for privacy-preserving data analysis, dynamic modeling of complex systems, and stochastic optimization algorithms. Recent publications explore differential privacy in machine learning, Monte Carlo techniques for high-dimensional inference, and applications of Bayesian methods in robotics and energy systems. Advising and grants include contributions to multi-party resource sharing frameworks and privacy-aware algorithms. His work integrates computational methods with real-world challenges in engineering and policy modeling.

Nikolaos Nikiforakisمشاهده پروفایل

استاد

• High Performance Computing
• Numerical Algorithms
• Computational Fluid Dynamics
• +۴ مورد دیگر

Prof Nikolaos Nikiforakis is a Professor at the University of Cambridge, leading the Laboratory for Scientific Computing at the Cavendish Laboratory. He holds roles including Director for Academic Programmes of the Centre for Scientific Computing, Course Director of the MPhil in Scientific Computing, and Deputy Director of the EPSRC Centre for Doctoral Training in Computational Methods for Materials Science. He is also a Fellow and Director of Studies in Mathematics at Selwyn College, Cambridge. He directs The Gianna Angelopoulos Programme for Science Technology and Innovation. He holds a BSc in Aeronautical Engineering from the University of Manchester, followed by an MSc in Aerospace Propulsion and a PhD in 'Evolution of Detonation Waves' from Cranfield Institute of Technology. His postdoctoral research at the University of Cambridge’s Department of Chemistry focused on computational models for stratospheric ozone depletion. He later founded the Laboratory of Computational Dynamics at the Department of Applied Mathematics and Theoretical Physics before joining the Cavendish Laboratory in 2008. His research focuses on numerical algorithms and High Performance Computing for multi-physics simulations involving complex systems of nonlinear PDEs. Applications span detonation dynamics, plasma physics, and materials science, with industry collaborations for software development. His work addresses multi-scale, multi-physics problems previously deemed intractable, with practical applications in aerospace, energy, and environmental fields. He leads academic programmes in scientific computing and supervises doctoral research through the EPSRC CDT. His contributions bridge fundamental science and industrial innovation, emphasizing computational methods for materials and fluid dynamics.

John Wrightمشاهده پروفایل

استادیار

• Quantum Computing
• Theoretical Computer Science
• Quantum Complexity Theory
• +۳ مورد دیگر

John Wright is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research centers on theoretical computer science with a focus on quantum computing, specifically quantum state learning, quantum complexity theory, property testing, and approximation algorithms. He is affiliated with the Simons Institute for the Theory of Computing. Education includes a Ph.D. in Computer Science from Carnegie Mellon University (2016), advised by Ryan O'Donnell, and a B.Sc. in Computer Science from the University of Texas at Austin. Research interests span quantum complexity, interactive proofs (e.g., MIP* = RE), quantum algorithms, and foundational aspects of quantum computation. His work bridges computer science, physics, and mathematics, with emphasis on understanding computational limits through quantum paradigms. Publications primarily explore quantum complexity, algorithms, and verification, with recent trends including quantum cryptography, tomography, and hardness proofs. Articles frequently involve collaborations with researchers like Thomas Vidick, Henry Yuen, and Ryan O'Donnell. Awards include the IEEE CS TCPAMI Young Researcher Award (2015). Teaching covers graduate and undergraduate courses such as CS 170 (Efficient Algorithms and Intractable Problems) and CS 294 (Quantum Complexity Theory). He advises students in quantum computing research and collaborates extensively across institutions. Labs/teams include the Quantum Computing group at UC Berkeley, with ties to the Simons Institute. Research support is managed by Amy Frithsen.

• Random Graph Theory
• Computational Complexity
• Statistical Inference
• +۴ مورد دیگر

Afonso S. Bandeira is affiliated with ETH Zurich (Swiss Federal Institute of Technology Zurich), where he conducts research at the intersection of theoretical computer science, statistics, and applied mathematics. His research focuses on fundamental questions about computational limits in statistical inference problems: Statistical-computational gaps in random structures Vertex colorings of random d-regular graphs Planting rare structures in random data Lower bounds for SK Hamiltonian Sparse Principal Component Analysis Matrix Restricted Isometry Property certification Dr. Bandeira's work examines situations where information-theoretically feasible problems become computationally intractable, particularly focusing on how to plant colorings with fewer colors than typically exist in random graphs while maintaining computational indistinguishability from uniformly random graphs. His research demonstrates significant collaboration across institutions, with joint work involving researchers from Berkeley, NYU, and the Santa Fe Institute.

• Theoretical Computer Science
• Sublinear Algorithms
• Complexity Theory
• +۴ مورد دیگر

Elena Grigorescu is an Adjunct Associate Professor in the Department of Computer Science at Purdue University, where she has been a faculty member since Fall 2012. Her research program spans theoretical computer science with a focus on foundational algorithmic challenges in large-scale data processing and computational limits, maintaining strong connections to cryptography, communications, and optimization applications. Her educational background includes a PhD from the Massachusetts Institute of Technology (MIT), establishing her expertise in rigorous theoretical frameworks. Professor Grigorescu's research emphasizes designing algorithms that operate in sublinear time or space for massive datasets, analyzing complexity of error-correcting codes and lattices, and exploring information-theoretical computation limits. Current investigations integrate differential privacy with learning-augmented techniques to solve online optimization problems, network design challenges, and data stream processing bottlenecks. Her work bridges abstract theory with practical implementations in cryptographic systems and quantum computing paradigms, demonstrating consistent innovation in algorithmic foundations. Analysis of her recent publications (2022-2025) reveals a dominant focus on sublinear-time algorithms, particularly at the intersection with differential privacy and machine learning augmentation. Key contributions include novel spanner constructions for network design, privacy-preserving clustering frameworks, and breakthroughs in trace reconstruction and coding theory. A pronounced trend shows increasing integration of learning-based predictions to enhance classical online algorithms for packing/covering problems while maintaining theoretical guarantees, alongside sustained contributions to error-correcting code analysis and graph-theoretic foundations. No specific scientific awards or major fellowships were documented in the provided materials, though her publication record in premier venues like STOC, FOCS, and APPROX/RANDOM indicates significant peer recognition. Professor Grigorescu actively mentors graduate students in theoretical computer science research, guiding investigations in sublinear algorithms, complexity theory, and coding theory. Her collaborative projects involve interdisciplinary teams across institutions, focusing on cryptographic applications and quantum information theory, though specific grant details were not included in the source texts. Ongoing work suggests expansion into quantum algorithm design and privacy-preserving machine learning frameworks. While dedicated laboratory facilities were not specified, her research operates within Purdue's theoretical computer science group, leveraging university-wide computational resources and fostering collaborations through conference participation and workshop organization.

Herbert Terraceمشاهده پروفایل

استاد

• Animal Cognition
• Cognitive Psychology
• Primate Cognition
• +۳ مورد دیگر

Herbert Terrace is a Professor of Psychology at Columbia University, where he directs the Primate Cognition Lab within the Department of Psychology in the Faculty of Arts and Sciences. His research focuses on animal cognition, particularly primate cognition and the evolution of language. Dr. Terrace received his Ph.D. from Harvard University in 1961. His academic journey has been dedicated to understanding cognitive processes that do not require language, with a particular focus on rhesus monkeys' ability to learn serial tasks, numerical sequences, and social learning paradigms. His research interests span across animal cognition, cognitive psychology, primate cognition, and the evolution of language. Dr. Terrace has made significant contributions to understanding how non-human primates process numerical information, learn through observation, and develop cognitive skills that may represent precursors to human language. His work on the evolution of intelligence examines cognitive processes that can be performed without language, providing insights into what language adds to those fundamental cognitive abilities. Notably, he has written extensively on the evolution of language, particularly in his 2019 book "Why Chimpanzees Can't Learn Language and Only Humans Can," where he argues that separating the evolution of language into the origin of words and the origins of grammar makes an intractable problem more manageable. Dr. Terrace has taught courses including Evolution of Intelligence and Consciousness, Evolution of Cognition, and Evolution of Language at Columbia University. His laboratory, the Primate Cognition Lab, devises experiments where monkeys interact with touch-sensitive computer screens to earn food rewards, allowing researchers to analyze their cognitive performance and understand the pre-linguistic origins of human cognition. Among his notable scientific contributions are the development of the simultaneous chaining methodology for studying serial learning in animals, groundbreaking work on numerical cognition in non-human primates, and research on cognitive imitation that has implications for understanding autism spectrum disorders. His research has been published in top journals including Science, Nature, and Psychological Science. Dr. Terrace has mentored numerous researchers in the field of comparative cognition and has directed the Primate Cognition Lab at Columbia University for many years. His work continues to influence our understanding of the cognitive capabilities of non-human primates and their relevance to human cognitive evolution.

Henry Chaiمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Bayesian Methods
• Probabilistic Numerics
• +۳ مورد دیگر

Henry Chai is an Assistant Teaching Professor in the Machine Learning Department at Carnegie Mellon University (CMU). His role focuses on teaching core machine learning courses such as Introduction to Machine Learning (10-301/601) and Bayesian Methods in Machine Learning (10-424/624). He co-teaches courses with faculty like Matt Gormley and Charlie Garrod. Chai holds a PhD from Washington University in St. Louis (2021), advised by Roman Garnett. His research explores Bayesian machine learning, probabilistic numerics, and active learning, aiming to address computational challenges in reasoning about intractable quantities. He has contributed to methods like BINOCULARS and Bayesian quadrature optimization, published in top venues like ICML and AISTATS. He actively engages in pedagogical research and K-12 CS education advocacy. His teaching responsibilities include advanced graduate courses (e.g., 10-701) and undergraduate introductory courses, with a focus on scalable Gaussian processes and Bayesian optimization. Office hours are held in GHC 8133.

Alina Eneمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorial Optimization
• Submodularity
• +۳ مورد دیگر

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University, within the Faculty of Arts & Sciences. She holds a BSE in Computer Science (High Honors) from Princeton University (2008) and a PhD from the University of Illinois at Urbana-Champaign (2013), advised by Chandra Chekuri. Her research focuses on algorithms, combinatorial optimization (submodularity, graph theory), and their applications to machine learning. She previously served as Assistant Professor at the University of Warwick, Faculty Fellow at the Alan Turing Institute, and postdoctoral researcher at Princeton's Center for Computational Intractability. Key research themes include distributed submodular maximization, streaming algorithms, optimization theory, and machine learning. Her work bridges theoretical guarantees with practical applications in data science and distributed systems. Notable contributions include frameworks for submodular maximization under constraints, randomized coordinate descent methods, and routing algorithms with balance considerations. Publications emphasize algorithm design for submodular functions, stochastic optimization, and graph problems. Recent work explores high-probability convergence in stochastic gradient methods, clipped gradient techniques, and online ad allocation strategies. Her 2016 FOCS paper on distributed submodular maximization remains influential in large-scale optimization. Professional service includes editorial work for conferences like FOCS/STOC/SODA and grants from NSF's CAREER program. No explicit scientific awards are listed, though her fellowships and postdoctoral appointments reflect scholarly recognition.

Paula Hammondمشاهده پروفایل

استاد

• Nanotechnology
• Chemical Engineering
• Drug Delivery
• +۱۲ مورد دیگر

Paula T. Hammond is the David H. Koch Professor of Engineering, head of the Department of Chemical Engineering, and a member of the Koch Institute for Integrative Cancer Research at the Massachusetts Institute of Technology. A pioneer in nanotechnology and polymer science, she develops layer-by-layer (LbL) nanoparticle platforms for cancer therapy, wound healing, vaccine delivery, and synthetic blood substitutes. Her work bridges chemical engineering, materials science, and biomedical applications with a focus on translational research. BS in Chemical Engineering, MIT (1984) MS in Chemical Engineering, Georgia Tech (1988) PhD in Chemical Engineering, MIT (1993) Research Interests : Hammond’s research spans nano-based drug delivery , immunotherapy , biomedical implants , and self-assembling materials . Her lab designs LbL nanoparticles for ovarian cancer , glioblastoma , and intractable cancers , enabling combination therapies (siRNA, proteins, chemotherapy). Recent work includes ML-driven nanoparticle design and two-component hemostatic systems for internal bleeding. Article Trends : Her recent publications (2025) emphasize modular LbL nanoparticles for hematopoietic stem cells , cancer immunostimulation , and multi-drug delivery . Broader keywords include Nanotechnology , Biomaterials , RNA Therapeutics , while sub-fields cover targeted drug release , immune activation , and synthetic oxygen transport . Scientific Awards : National Medal of Technology and Innovation (2024) Othmer Gold Medal (2024) Benjamin Franklin Medal in Chemistry (2024) James R. Killian Faculty Achievement Award (2023-24) MIT Institute Professor (2021) Three-time National Academy member (Sciences, Engineering, Medicine) Advising & Leadership : Hammond has mentored over 60 graduate students and postdocs , co-founded LayerBio, Inc. , and serves on scientific advisory boards for Moderna and Burroughs-Wellcome Fund . She chairs MIT’s Initiative on Faculty Race and Diversity and contributes to national science policy through PCAST. Lab & Collaborations : The Hammond Lab at MIT’s Koch Institute develops LbL nanoparticle systems with applications in cancer, regenerative medicine, and hemostasis. Collaborations include the MIT Institute for Soldier Nanotechnology and partnerships with clinicians, biologists, and engineers.

• Algorithmic Game Theory
• Market Equilibrium Computation
• Fair Division
• +۵ مورد دیگر

Ruta Mehta is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign . Since 2016 she has led a vibrant research program in algorithmic game theory, market design, and fair division, while actively shaping the community through service roles such as Program Co-Chair of WINE 2020 and Area Chair of EC 2021. Education Ph.D. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2012. (Advisors: Prof. Milind Sohoni & Prof. Bharat Adsul; ACM India Doctoral Dissertation Award 2012) M.Tech. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2005 B.E. in Computer Engineering, Maharaja Sayajirao University (MSU) Baroda, 2003 Research Interests Mehta’s work lies at the intersection of theoretical computer science , mathematical economics , and social choice theory . She investigates the computability and complexity of equilibria—both market and Nash—under a variety of utility models, and designs provably efficient algorithms that are practical for real-world resource-allocation tasks. Current themes include: Algorithmic Game Theory: equilibrium computation, smoothed analysis, learning in games Fair Division: envy-freeness up to any good (EFX), mixed manna, competitive equilibrium with chores Interdisciplinary Applications: genetic evolution, machine-learning markets, climate-aware allocation Publication Trends Her 15 most recent works (2017–2025) span Operations Research , Mathematics of Operations Research , STOC, SODA, EC, ITCS, AAMAS, and NeurIPS. The articles cluster around three thrusts: (i) rigorous hardness and approximation results for market equilibrium in Leontief and PLC exchange economies, (ii) algorithmic advances toward guaranteed EFX allocations and competitive equilibrium with mixed manna, and (iii) novel game-theoretic analyses of genetic diversity and strategic resource allocation under budget constraints. Scientific Awards & Honors NSF CAREER Award (2018) Outstanding Post-Doctoral Researcher Award, Georgia Tech (2014) Rising Stars in EECS (2013) ACM India Doctoral Dissertation Award (2012) Google India Anita Borg Memorial Scholarship (2012) IBM PhD Award (2010) IBM PhD Fellowship (2009–2010) Grants, Advising, and Community Leadership Mehta currently mentors a growing group of graduate students and post-docs. She is PI on an NSF CAREER grant and has served on federal panels reviewing NSF CISE proposals. Beyond research, she founded the EC (AGT) Mentoring Workshop , co-located with the ACM Economics & Computation conference, to broaden participation of women and under-represented minorities in algorithmic game theory. Labs & Teams Her research group operates within the Theory & Algorithms cluster at UIUC, leveraging ties with the Decision & Control group and the Social & Algorithmic Thinking initiative. She is an active member of ACM SIGecom and regularly organizes reading groups on algorithmic game theory and fair division.

• Crystal structure determination
• Powder X-ray diffraction
• Solid-state NMR spectroscopy
• +۶ مورد دیگر

Professor Kenneth Harris is a Distinguished Research Professor in the School of Chemistry at Cardiff University, specializing in the fundamental properties of solids and the development of advanced experimental techniques for materials characterization. His work bridges the gap between traditional crystallography and modern analytical methodologies, with a particular focus on overcoming limitations in structural analysis of complex materials. His research spans three primary interconnected themes: the development of techniques for determining crystal structures of organic solids directly from powder X-ray diffraction data; the advancement of in-situ solid-state NMR strategies for monitoring crystallization processes in real time; and the investigation of structural properties of anisotropic materials using polarized X-ray beam techniques, including the pioneering development of X-ray Birefringence Imaging (XBI). This work has significant implications for pharmaceutical development, materials science, and understanding biological crystallization processes. Analysis of his recent publications reveals a consistent trajectory toward increasingly sophisticated multi-technique approaches to materials characterization. His work increasingly integrates 3D electron diffraction, powder XRD, solid-state NMR, and computational methods like DFT calculations to solve previously intractable structural problems. A notable trend is the application of these methods to biologically relevant molecules (xanthine, riboflavin, L-tyrosine) and the development of techniques to monitor dynamic processes like crystallization and phase transitions in real time. His research demonstrates a shift from purely structural determination toward understanding the dynamic processes that govern material formation and transformation. Distinguished Research Professor title at Cardiff University Key contributor to the development of X-ray Birefringence Imaging Significant contributions to NMR crystallography methodologies Extensive publication record in top chemistry and materials science journals Professor Harris leads a research group focused on developing and applying cutting-edge techniques for materials characterization. His work has significant implications for pharmaceutical development, where understanding crystal structure and polymorphism is critical for drug efficacy and safety. His group has developed innovative approaches to monitor crystallization processes in real time, which has applications in both industrial manufacturing and understanding natural biomineralization processes. The group maintains strong collaborations with researchers across multiple disciplines, including physics, biology, and engineering, reflecting the interdisciplinary nature of modern materials science research.

Claire Mathieuمشاهده پروفایل

استاد پژوهشی

• Algorithms
• Theoretical Computer Science
• Approximation Algorithms
• +۳ مورد دیگر

Claire Mathieu is a CNRS Research Director in the Computer Science department at École normale supérieure, specializing in algorithm design and analysis with emphasis on approximation schemes for NP-hard problems. Her academic background includes: Former student at École normale supérieure PhD in Computer Science from Paris-Sud University (1988) Dr. Mathieu's research focuses on theoretical foundations of algorithms, particularly developing local search methods for clustering problems where enlarged neighborhoods yield near-optimal solutions through separability structures. Her work bridges theoretical guarantees with practical applications in combinatorial optimization, advancing approximation techniques for computationally intractable problems. Prior to her current position, she held research and faculty appointments at CNRS, Paris-Sud University, École Polytechnique, and Brown University, demonstrating extensive international experience across French and American academic institutions.

Katherine Newhallمشاهده پروفایل

استاد

• Applied Mathematics
• Stochastic Differential Equations
• Dynamical Systems
• +۵ مورد دیگر

Katherine Newhall is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill, where she maintains an active research program in stochastic modeling and dynamical systems. Her office is located in Phillips Hall 308, and she can be reached at knewhall@unc.edu. She serves as a member at large of the GSNP (Group on Statistical and Nonlinear Physics) board, a position she assumed in April 2024. Dr. Newhall earned her educational credentials from Rensselaer Polytechnic Institute, including a B.S. in Applied Physics and Applied Mathematics (2004), an M.S. in Mechanical Engineering (2006) with thesis entitled 'Turbulent Boundary Layers: A look at Skin Friction, Pressure Gradient and Surface Roughness,' and a Ph.D. in Mathematics (2011) with dissertation 'Synchrony in Stochastically-Driven Neuronal Network Models.' Following her doctoral work, she completed postdoctoral research at New York University's Courant Institute of Mathematical Sciences from 2011 to 2014. Her research focuses on developing new tools for analyzing large and infinite dimensional stochastic systems to understand large-scale and long-time dynamics of physical and biological systems. Rather than relying on traditional Fokker-Planck formulations that become intractable with increasing complexity, her work builds on concepts of statistical mechanics to create macroscopic descriptions from individual unit statistics. This approach extends the usefulness of energy landscapes even in non-gradient systems, enabling explanations of experimentally observable phenomena while exposing fundamental mechanisms responsible for system behavior. Her work spans applications from granular materials and chromosome dynamics to biological systems and metamaterials. Dr. Newhall's publications demonstrate consistent advancement in stochastic modeling techniques, with recent work (2023-2025) focusing on hyperuniformity in biological structures, energy landscape sampling methods, and the role of weak transient interactions in biological systems. Her research shows a clear trajectory from fundamental mathematical developments toward increasingly sophisticated biological applications. Outstanding Referee of the Physical Review journals (2019) NSF grant DMS-1816394 DMREF grant ($2M NSF Grant to Revolutionize Materials, 2023) Member at large of the GSNP board (2024) Dr. Newhall has successfully mentored numerous PhD students to completion, including Anna Coletti (2024), Daftari (2023), Moakler (2021), Ben Walker (2021), and Yuan Gao (2019). Her research is consistently supported by competitive grants, most notably the $2M NSF DMREF grant awarded in 2023. She maintains active collaborations across disciplines, particularly in applying mathematical techniques to biological problems such as chromatin organization and organ transplantation risk assessment. Her laboratory work focuses on developing computational methods for analyzing complex stochastic systems, with particular emphasis on the hydra string method for exploring high-dimensional potential energy surfaces. The research group maintains strong connections with both theoretical and experimental collaborators working on granular materials, chromosome dynamics, and biological systems.