جست‌وجوی استاد و پژوهشگر

William Chuehمشاهده پروفایل

استاد

• Energy Storage
• Renewable Energy
• Materials for Carbon-Neutral Energy
• +۳ مورد دیگر

William Chueh is a Professor in the Departments of Materials Science and Engineering and Energy Science & Engineering at Stanford University. He serves as Director of the Precourt Institute for Energy and Faculty Director of the Energy Innovation and Emerging Technologies Program. His research focuses on redox-active materials for energy storage, conversion, and carbon-neutral energy cycles. Education: PhD, Materials Science, Caltech (2010) BS, Applied Physics, Caltech (2005) Research Interests: Energy storage and conversion systems (batteries, fuel cells, electrolyzers) Multi-scale electrochemical and chemical reaction dynamics Materials design rules for redox-active solids Thermodynamic frameworks for sustainable energy Publication Trends: His work spans fundamental materials synthesis, electrochemical characterization, and modeling of redox reactions. Key themes include solar thermochemical cycles, ceria-based systems for CO2/H2O conversion, and advanced battery technologies. Scientific Honors: Outstanding Young Investigator Award (MRS, 2018) Camille Dreyfus Teacher-Scholar Award (2016) Sloan Research Fellowship (2016) CAREER Award (NSF, 2015) Advising: He advises students in energy technologies, materials science, and electrochemistry, including doctoral and master’s candidates. Contact: wchueh@stanford.edu

Brent Doironمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Networks
• Theoretical Neuroscience
• +۲ مورد دیگر

Brent Doiron is a Professor at the University of Chicago, holding appointments in the Departments of Neurobiology and Statistics, and serving on the Committee on Computational and Applied Mathematics (CCAM). His research integrates nonlinear dynamics and statistical mechanics to study neural circuit variability, focusing on mechanisms underlying neural coding and network learning through collaborations with experimentalists in sensory systems. Education: PhD in Physics (University of Ottawa, 2004) Postdoc: Center for Neural Science at New York University (2017) Previous Roles: Mathematics Professor at University of Pittsburgh (2007-2020), Co-Director of Neural Computation Program at Carnegie Mellon Neuroscience Institute Research interests center on neuronal population dynamics, recurrent circuit mechanisms, and computational neuroscience. Current work investigates correlated variability in cortical networks, inter-areal communication, and stochastic spiking models. Recent publications emphasize cortical stability/gain modulation, asynchronous/synchronous activity balance, and Bayesian inference frameworks. Key themes include sensory processing, network plasticity, and dimensionality reduction in neural coding. Scientific Awards Alfred P. Sloan Research Fellowship in Neuroscience Vannevar Bush Faculty Fellowship Chancellor’s Distinguished Research Award (University of Pittsburgh) Active grants include NIH R01 and R90/T90 awards for neuronal dynamics research and computational neuroscience training programs.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines

Tommi Jaakkolaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۷ مورد دیگر

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

Sanjiv Singhمشاهده پروفایل

استاد پژوهشی

• Aerial Robotics
• Robotics in Agriculture and Forestry
• Mining Robotics
• +۱۱ مورد دیگر

Sanjiv Singh is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science. His academic journey at CMU spans from Systems Scientist (1995-2001), to Senior Research Scientist (2001-2003), Associate Research Professor (2003-2007), and finally Research Professor since 2007. He also holds an adjunct faculty position in Mechanical Engineering since 2009. Singh serves as Editor-in-Chief of the Journal of Field Robotics, demonstrating his leadership in the robotics community. His educational background includes a Ph.D. and M.S. in Robotics from Carnegie Mellon University (1995, 1992), an M.S. in Electrical Engineering from Lehigh University (1985), and a B.S. in Computer Science from the University of Denver (1983). Dr. Singh's research focuses on three primary themes: Autonomous Navigation (developing motion planning and control for ground and air vehicles with applications in agriculture, exploration, and low-flying aircraft), Coordinated Multi-Robots (examining team-based tasks like structure assembly and search/rescue operations), and Forceful Interaction with the world (using physical models to enable robots to handle complex, high-force interactions). His work spans aerial robotics, agricultural and forestry robotics, mining robotics, 3D vision, sensing and perception, visual servoing, motion planning, and field service robotics. Analysis of his recent publications (2016-2020) reveals a strong focus on collision avoidance algorithms, sensor fusion techniques, and real-time navigation systems. His research demonstrates consistent advancement in SLAM (Simultaneous Localization and Mapping) technologies, particularly in GPS-denied environments, with increasing sophistication in handling complex aerial maneuvers and multi-robot coordination. Editor-in-Chief of Journal of Field Robotics Dr. Singh has advised numerous graduate students throughout his career, with current advisees including Matt Aasted (Ph.D), Andrew Chambers (M.S), Hugh Cover (M.S), Michael Dille (Ph.D), and Justin Haines (M.S). His past students include prominent researchers like Sebastian Scherer, Joe Djugash, Fred Heger, Geoff Hollinger, and Ji Zhang who have gone on to make significant contributions in robotics. His research has been supported through various projects including CASC (agricultural applications), Riverine, Transformer, Trestle, and Ember. His laboratory work focuses on developing practical robotic systems capable of operating in challenging real-world environments, with particular emphasis on agricultural applications, search and rescue operations, and coordinated multi-robot teams that can work effectively alongside humans.

Sicun Gaoمشاهده پروفایل

دانشیار

• Algorithms and Optimization
• Automated Reasoning
• Cyber-Physical Systems
• +۳ مورد دیگر

Sicun Gao is an Associate Professor in the Computer Science and Engineering department at the University of California, San Diego. His research focuses on practical algorithms for NP-hard search and optimization problems in computational systems, emphasizing combinatorial perspectives in numerical and statistical contexts to achieve reliable autonomy. Research Interests: Automated reasoning, Hamilton-Jacobi reachability, safe reinforcement learning, control barrier functions, and optimization in cyber-physical systems. Teaching: Courses on AI search, optimization, and graduate research seminars. The 15 most recent publications highlight advancements in safe AI control, motion planning, and policy optimization, often integrating neural networks with formal verification. Awards include the IEEE Power & Energy Society Technical Committee Prize Paper Award and the IROS RoboCup Best Paper Award. He advises PhD students working on AI-driven control and robotics, with alumni placed at institutions like Seoul National University, Amazon, and Apple. Grants include NSF Career, Air Force Young Investigator, and DARPA Assured Autonomy funding. His lab develops tools like dReal for automated reasoning in nonlinear theories over the reals.

• Plasma Physics
• Fusion Energy
• Space Plasma Physics
• +۳ مورد دیگر

Peter Gingell is a Researcher at the University of Warwick's Physics Centre for Fusion, Space and Astrophysics. His work focuses on plasma dynamics in fusion and space environments, particularly ion-gyro-scale blobs in magnetically confined plasmas. He developed a 2D hybrid code for simulating plasma blob evolution, combining kinetic ion treatments with electron fluid models. His research addresses coherent plasma structures critical to understanding energy transport in fusion devices like tokamaks. Funded by the Engineering and Physical Sciences Research Council (EPSRC), his project is supervised by Prof Sandra Chapman and Prof Richard Dendy. He has published in Plasma Physics and Controlled Fusion , contributing to advancements in plasma simulation methodologies. His office is PS143, and he can be reached at p.gingell@warwick.ac.uk.

Nima Fazeliمشاهده پروفایل

استادیار

• Robotic Manipulation
• Embodied Intelligence
• Multimodal Representation Learning
• +۴ مورد دیگر

Nima Fazeli is an Assistant Professor of Robotics at the University of Michigan (2020–Present), holding courtesy appointments in Computer Science & Engineering (CSE) and Mechanical Engineering. He directs the Manipulation and Machine Intelligence (MMint) Lab, focusing on enabling dexterous robotic manipulation through multimodal representation learning, tactile sensing, and model-based reasoning. His work integrates mechanics, perception, controls, and planning to achieve autonomous interaction with uncertain environments. Education: PhD, MIT (2019); MSc, University of Maryland (2014); BSc, Amirkabir University of Technology (2011) Research interests emphasize embodied intelligence , including visuo-tactile fusion, contact dynamics modeling, and cross-modal learning. Recent work explores tactile shadows, deformable object manipulation, and language-guided robot control. His research is supported by the NSF CAREER grant and National Robotics Initiative, with applications in manufacturing, assistive robotics, and space systems. Publications span topics like tactile sensing hardware (e.g., GelSlim 4.0), visuo-tactile implicit representations (ViTaSCOPE), and failure recovery policies (Racer). His team’s work has been featured in outlets like The New York Times and BBC. Key Awards: NSF CAREER Grant (2024) Teaching includes Introduction to Robotic Manipulation . Collaborations involve cross-disciplinary projects with mechanical, electrical, and biomedical engineering groups.

• Nanotechnology
• Materials Science
• Photovoltaics
• +۴ مورد دیگر

Professor Thomas Bein is affiliated with the Department of Chemistry at Ludwig-Maximilians-Universität München (LMU) , where he leads the Functional Nanosystems research group. His work focuses on synthesizing and characterizing nanostructured materials with applications in energy, catalysis, and biomedical delivery. Mesoporous nanoparticles for drug delivery Semiconductor nano-morphologies for photovoltaics Photoelectrochemical water splitting Metal-organic frameworks (MOFs) Electroactive networks His research emphasizes atomic-scale control of material architectures using self-assembly, hydrogen bonding, and covalent interactions, enabling precise tuning of electronic, optical, and catalytic properties. A review of his recent publications reveals cutting-edge investigations into covalent organic frameworks (COFs), perovskite-inspired solar materials, and functional nanoparticle systems. Key trends include optimizing energy conversion efficiency, enhancing stability in optoelectronic devices, and exploring bio-compatible nanocarriers for targeted therapies. Professor Bein’s group actively contributes to interdisciplinary projects at the intersection of chemistry, physics, and biomedical engineering, with ongoing collaborations in solar energy, sustainable materials, and nanomedicine.

Sean Caoمشاهده پروفایل

دانشیار

• Artificial Intelligence in Finance
• Machine Learning Applications
• Capital Market Research
• +۵ مورد دیگر

Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.

Harris H. Wangمشاهده پروفایل

دانشیار

• Systems Biology
• Synthetic Biology
• Genome Engineering
• +۱۷ مورد دیگر

Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.

Tal Malkinمشاهده پروفایل

استاد

• Cryptography
• Computer Security
• Complexity Theory
• +۸ مورد دیگر

Tal Malkin is a Professor of Computer Science at Columbia University, directing the Cryptography Lab and serving as inaugural chair of the Cybersecurity Center at Columbia's Data Science Institute. She holds a Ph.D. from MIT (2000), joined Columbia after AT&T Labs research experience, and focuses on cryptography, security, complexity theory with applications in secure computation, zero-knowledge proofs, and privacy-preserving systems . Education: B.S. in Math and Computer Science, Bar-Ilan University M.S. in Computer Science, Weizmann Institute of Science Ph.D. in Computer Science, MIT (2000) Research Interests: Malkin's work spans foundational and applied cryptography, including homomorphic encryption, lattice-based protocols, attribute-based encryption, and tamper-resilient systems . She explores intersections with machine learning and information theory , addressing challenges in multi-party computation , public-key encryption , and side-channel resistance . Scientific Contributions: Her publications include breakthroughs in non-malleable codes , secure computation , and privacy-preserving databases . Notable works involve continual leakage resilience , optimally-fair coin tossing , and garbled circuits for efficient cryptographic protocols. Awards & Grants: Recipient of the NSF CAREER award, IBM and Google faculty research awards, and the IACR Fellow designation. Her research is funded by NSF, NSA, DHS, NYSIA, IARPA, and industry partnerships with Google, IBM, Mitsubishi, and NEC. Professional Leadership: Former conference chairs at CRYPTO 2021 , CCS 2017 , and CT-RSA conferences. Active in program committees for over 20 leading cryptography and security events, including FOCS , Eurocrypt , and Real World Crypto . Advising: Supervised numerous Ph.D. students and postdoctoral researchers, including Marshall Ball , Chengyu Lin , and Negev Shekhel Nosatzki . Her lab has mentored graduates like Ghada Almashaqbeh (2019) and Fernando Krell (2016), focusing on decentralized networks , secure learning , and cryptographic primitives .

Bobak Mortazaviمشاهده پروفایل

دانشیار

• Medical analytics
• Machine learning
• Embedded systems design
• +۳ مورد دیگر

Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.

Maja J Matarićمشاهده پروفایل

استاد

• Socially Assistive Robotics
• Human-Robot Interaction
• Assistive Technology
• +۷ مورد دیگر

Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.

Olga Sorkine-Hornungمشاهده پروفایل

استاد

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +۵ مورد دیگر

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.