جستجوی استاد راهنمای دانشگاهی

Eugene Tangمشاهده پروفایل

استادیار

• Quantum Information Theory
• Quantum Computing
• Quantum Error Correction
• +۳ مورد دیگر

Eugene Tang is an Assistant Professor in the Department of Mathematics and Physics at Northeastern University. His research focuses on quantum information theory and the theoretical limitations of quantum computing, particularly quantum error correction and efficient protocols using high-rate codes. He received his PhD from the California Institute of Technology in 2021. Dr. Tang's research interests include quantum error correction, the development of efficient quantum protocols surpassing conventional schemes, and the study of quantum algorithms such as QAOA. He explores the theoretical boundaries of quantum computing, with a focus on optimizing error detection and decoding methods for quantum LDPC codes and subsystem codes. His work also intersects with quantum gravity, particularly in the context of black hole interiors and bulk geometry construction through tensor methods. His recent publications highlight advancements in quantum error correction, including optimal locality in subsystem codes and efficient decoding strategies for quantum LDPC codes. His work on variational quantum optimization addresses challenges in scalability, such as QAOA's performance at large qubit scales and symmetry-related obstacles. Earlier contributions include research on superoscillations and hybrid quantum-classical algorithms for graph coloring. No scientific awards or grants are explicitly mentioned in the provided information. No specific labs or teams are associated with his work in the given data.

• Statistics
• Bayesian Statistics
• Computational Statistics
• +۳ مورد دیگر

Anirban Bhattacharya is a Professor at the Department of Statistics, Texas A&M University, and holds the Patricia R. Smith and Dr. William B. Smith Faculty Fellow for Statistics position. His research focuses broadly on statistical inference, Bayesian methodology, and computational statistics. Education Ph.D. in Statistics (2012) from Duke University Master of Statistics (2008) from Indian Statistical Institute Bachelor of Statistics (2006) from Indian Statistical Institute Scientific Awards Patricia R. Smith and Dr. William B. Smith Faculty Fellow for Statistics

Magnus Bakke Botnanمشاهده پروفایل

استادیار

• Topological Data Analysis
• Representation Theory of Quivers
• Computational Topology
• +۳ مورد دیگر

Magnus Bakke Botnan is an Assistant Professor at the Department of Mathematics, Vrije Universiteit Amsterdam, holding a VIDI career grant (€850,000) since 2018. His research bridges pure and applied mathematics within topological data analysis (TDA), focusing on multiparameter persistence, computational topology, and applications to sciences. PhD in Mathematics, Norwegian University of Science and Technology (NTNU), 2015 Postdoc at TU Munich, 2016-2018 His research group includes postdocs Hannah Rocio Santa Cruz Baur and Rui Dong, and PhD student Enes Devecioğlu. Recent work involves signed barcodes, rank decompositions, and stability of persistence modules. He co-authored the first comprehensive tutorial on multiparameter persistence with Mike Lesnick. Notable contributions include proving the NP-hardness of computing interleaving distance, establishing universality of bottleneck distance for extended persistence diagrams, and developing computational methods for non-branching complexes. Publications span journals like Foundations of Computational Mathematics , Discrete & Computational Geometry , and conferences SoCG, NeurIPS, and ICRA. Scientific Awards: VIDI Career Grant (€850,000) He has taught courses including Complex Analysis, Calculus, Topological Data Analysis, and seminars on analysis and dynamical systems. Actively organizes Applied Topology Days and collaborates on projects integrating TDA with physics, computer science, and statistics.

Torsten Wolfgang Kuhlenمشاهده پروفایل

استاد

• Virtual Reality
• Immersive Visualization
• Scientific Visualization
• +۵ مورد دیگر

Prof. Torsten Wolfgang Kuhlen serves as a Universitätsprofessor at RWTH Aachen University, leading the Teaching and Research Area for Virtual Reality and Immersive Visualization within the Department of Computer Science. He is affiliated with Chair of Computer Science 12 (High Performance Computing), the Visual Computing Institute, and remains an integral part of the RWTH IT Center where his research group operates one of the world's largest Virtual Reality laboratories including the 30 sqm aixCAVE visualization chamber. The group maintains strong connections with Computational Science & Engineering Division, National High Performance Computing Center for Computational Engineering Science (NHR4CES), and VR in Science and Industry Network NRW e.V. Prof. Kuhlen's research spans virtual reality, immersive visualization, and multimodal 3D user interfaces with applications across simulation science, production technology, neuroscience, and medicine. His work combines basic research on advanced methods and algorithms with interdisciplinary collaborations involving RWTH Aachen institutes, Forschungszentrum Jülich, and industry partners. Recent publications demonstrate strong focus on audiovisual perception, immersive analytics, collaborative virtual environments, and practical VR applications in education and manufacturing. His research group has produced significant work on listening effort in virtual environments, immersive authoring techniques, and VR applications for scientific visualization. Notable projects include VRScenarioBuilder for automated vehicle testing and applications in monitoring additive manufacturing processes. The group actively participates in major conferences including IEEE VIS and EuroVis, with several award-winning contributions. Prof. Kuhlen has advised PhD students including Martin Bellgardt who recently completed his doctoral degree on "Increasing Immersion in Machine Learning Pipelines for Mechanical Engineering". The research group maintains state-of-the-art VR infrastructure including the aixCAVE facility which is open to all RWTH research groups.

Carlos Fernandez-Grandaمشاهده پروفایل

دانشیار

• Machine Learning
• Medical AI
• Climate Science
• +۷ مورد دیگر

Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications

• Decision Support Systems
• Energy Management
• Environmental Analysis
• +۶ مورد دیگر

John Psarras is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, specifically within the Division of Industrial Electric Devices and Decision Systems. He serves as the Director of the Decision Support Systems Laboratory (DSSlab) and the University Research Institute of Communication and Computer Systems. He holds a Diploma in Mechanical Engineering (1982) and a Ph.D. in Electrical and Computer Engineering (1989), both from NTUA. His research specializes in decision support systems with applications in energy management, environmental analysis, and information systems. Key areas include: Multi-criteria analysis for energy policy and renewable integration AI-driven optimization of smart grids and building efficiency Sustainable finance mechanisms for green projects Blockchain applications in education and data security His recent publications (2023–2025) demonstrate a strong focus on AI-enhanced decision tools for energy transitions, smart infrastructure, healthcare diagnostics, and cross-border renewable cooperation, reflecting interdisciplinary innovation. He has supervised 22 PhD theses and coordinates EU-funded projects in energy policy, clean technology, and capacity building. No scientific awards are listed in available sources. He leads the Decision Support Systems Laboratory (DSSlab), advancing research in energy analytics, and directs the University Research Institute of Communication and Computer Systems, facilitating large-scale interdisciplinary collaborations.

Taylor Sparksمشاهده پروفایل

استاد

• Materials informatics
• Machine learning for materials discovery
• Sustainable engineering
• +۷ مورد دیگر

Taylor Sparks is a Professor of Materials Science and Engineering at the University of Utah, where he also serves as Director of Graduate Affairs for the John and Marcia Price College of Engineering. He holds a PhD in Applied Physics from Harvard University, an MS in Materials from the University of California, Santa Barbara, and a BS in Materials Science & Engineering from the University of Utah. His research focuses on advancing materials discovery using machine learning to streamline and optimize material design, with applications in energy materials, dental materials, and sustainable engineering. His work integrates big data and materials informatics to explore new synthetic techniques, structure-property relationships, and sustainable materials that balance performance with economic factors. The Sparks Research Group has secured funding from agencies including DOE, NSF, DOD, and various industry partners. Sparks' recent research output demonstrates a strong trend toward leveraging artificial intelligence and machine learning to accelerate materials discovery, with particular emphasis on large language models for materials science, Bayesian optimization for experimental design, and novel approaches to crystal structure prediction. His work bridges the gap between theoretical predictions and experimental validation in materials science. NSF CAREER Award Royal Society Wolfson Visiting Fellow Acta Materialia Outstanding Reviewer Award for 2020 Honorary Outstanding Faculty Teaching Award of 2020-2021 Materials Science & Engineering Department Research Award for 2023 John G. Francis Prize for Undergraduate Student Mentoring Sparks has advised numerous graduate students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, DOE, DOD, Army Research Office, and industry partners. His group has developed innovative tools including the Materialism Podcast, a materials science YouTube channel, and the Honegumi interface for Bayesian optimization, demonstrating his commitment to both research excellence and science communication. The Sparks Research Group operates multiple laboratories focused on materials characterization, synthesis, and informatics. They collaborate extensively with other institutions globally, host visiting researchers, and run outreach initiatives including the Materialism Podcast and YouTube channel to make materials science more accessible to broader audiences.

• Environmental Health
• Climate Change
• Public Health Equity
• +۵ مورد دیگر

Dr. Daniel Carrión is an Assistant Professor of Epidemiology at the Yale School of Public Health, Department of Environmental Health Sciences. His work bridges climate science , energy transitions , and health equity , focusing on structural inequality’s role in exposure and health disparities. Education: PhD in Environmental Health Sciences (Columbia University, 2019); MPH in Environmental Health Sciences (New York Medical College, 2011); BA in Environmental Studies (Ithaca College, 2008). His research examines how home and neighborhood environments serve as intervention points for climate and health equity . Recent studies include modeling heat vulnerability in U.S. housing, geospatial analysis of lead water lines, and clean cooking interventions in Ghana. Articles span climate change , air pollution , and social determinants , with methodological focus on exposure science and case-crossover designs . Scientific honors include: Senior Fellow, Agents of Change in Environmental Justice (2022) Fellow, New York Academy of Medicine (2022) Senior Fellow, Environmental Leadership Program (2019) He contributes to community service as a member of the New York State Minority Health Council (2016–present) and the International Society for Environmental Epidemiology (2018–present). His work integrates satellite data , machine learning , and policy evaluation to address energy insecurity , racial segregation , and climate justice .

Véronique Perdereauمشاهده پروفایل

استاد

• Robotics
• Dexterous Manipulation
• Tactile Feedback Systems
• +۴ مورد دیگر

Véronique Perdereau is a Full Professor at Sorbonne Université's Institute of Intelligent Systems and Robotics (ISIR) , specializing in robotics and dexterous manipulation. She leads the ASIMOV research team and serves as a principal investigator for multiple European projects, including SOFTMANBOT, INDEX, and CORSMAL, focusing on tactile feedback, multimodal control, and human-robot interaction. Role: Full Professor (2024) Email: veronique.perdereau@sorbonne-universite.fr Office: H13, Campus Pierre et Marie Curie, Paris Research Focus Perdereau's work centers on tactile-driven robotic control systems, human-inspired manipulation strategies, and multimodal fusion for industrial automation. Her research bridges theoretical modeling (e.g., Cosserat mechanics, dual quaternions) with practical applications in manufacturing sectors. Key Themes: Tactile Feedback Systems Dexterous In-Hand Manipulation Human Motion Data Integration Autonomous Grasp Stability Flexible Object Handling Reproducibility in Robotic Experiments Notable Projects She has spearheaded projects like: SOFTMANBOT (2019–2023): Advanced robotic technology for handling soft materials in manufacturing. INDEX (2019–2022): Developing motor action dictionaries for robotic hands. CORSMAL (2019–2022): Multimodal fusion for collaborative object recognition. Scientific Leadership Perdereau has led work packages in 10+ international collaborations with institutions across Europe, including Decathlon, IIT, and Queen Mary University. Her research emphasizes safety, ethics, and industry 4.0 applications.

• Disaster Risk Reduction
• Sustainability
• Interdisciplinarity
• +۵ مورد دیگر

Giuliano Di Baldassarre is a Professor of Hydrology and Environmental Analysis at the Department of Earth Sciences, Uppsala University , Sweden. He serves as Head of Division for LUVAL (Air, Water and Landscape Sciences) and directs the Centre of Natural Hazards and Disaster Science (CNDS) (2016–2025). His work bridges water, environment, and society through interdisciplinary methods , focusing on disaster risk reduction, climate adaptation, and sustainable development. Education : Details not explicitly provided in the text. His research examines feedbacks between human activities and hydrological processes , including floods, droughts, and reservoir management . Key themes include social-ecological systems , inequalities in water crises , and policy implications of hydrological extremes . He has pioneered sociohydrology and human-water system modeling . Recent articles highlight global drought-flood interactions , urban water inequality , climate service maladaptation , and sociohydrological modeling . His work spans Nature Sustainability , Science Advances , and Hydrological Sciences Journal . Scientific Awards : International Hydrology Prize (Volker Medal) Plinius Medal (EGU) Witherspoon Lecture Award (AGU) European Research Council Consolidator Grant He led Panta Rhei - Everything Flows (2013–2022), IAHS’s global initiative on water-society interactions. Current efforts include transdisciplinary praxis and climate risk reduction frameworks .

• Quantum Algorithms
• Quantum Computing
• Bioinformatics
• +۴ مورد دیگر

Sergii Strelchuk is an Associate Professor of Computer Science at the University of Oxford, specializing in quantum computing and its applications. His research sits at the intersection of quantum information theory, computer science, and bioinformatics, with a focus on developing quantum algorithms for practical problems in genomics and beyond. Professor Strelchuk's primary research interests include quantum algorithms and their applications (particularly in bioinformatics), classical simulation methods for quantum computation, quantum complexity theory, and quantum learning theory. His work bridges theoretical quantum computing with practical applications, especially in the emerging field of quantum genomics and pangenomics, with significant implications for understanding human and pathogen genomes. His recent publications demonstrate a strong focus on applying quantum computing techniques to genomic data analysis, developing efficient fermion-qubit mappings for quantum simulation, and exploring fundamental aspects of quantum complexity theory. His research shows a clear trajectory toward making quantum computing practically applicable to biological data analysis and advancing our theoretical understanding of quantum computational models. Among his notable scientific achievements are: Royal Society University Research Fellow Leverhulme Early Career Fellow John and Delia Agar Research Fellow Professor Strelchuk leads several significant research projects including the Wellcome Leap "Human and Pathogen Quantum Pangenomics" project (2023-2026), which recently entered Phase 3 in April 2025, the EPSRC "Structure and symmetry in quantum verification" grant (2023-2025), and the "Quantum Algorithms for Quantum Field Theory" project (2022-2025). His research has attracted substantial funding for quantum computing applications in genomics. His work has received significant attention in both academic and popular science media, including coverage in Quanta Magazine and collaborations with institutions like the Sanger Institute to tackle complex genomic challenges using quantum computing approaches, with recent publicity about his leadership in the final phase of the Wellcome Leap-funded quantum pangenomics project.

Berenice A. Benayounمشاهده پروفایل

دانشیار

• Aging and Longevity
• Epigenetics
• Transcriptomics
• +۵ مورد دیگر

Bérénice Benayoun, PhD is an Associate Professor at the USC Leonard Davis School of Gerontology , with secondary appointments in the Department of Molecular and Computational Biology (USC Dornsife College of Letters, Arts and Sciences) and the USC Norris Comprehensive Cancer Center . Her research bridges aging biology , epigenetics , and sex differences using vertebrate models like the African turquoise killifish and machine learning . Education : École Normale Supérieure (BSc, MSc), Paris Diderot-Paris 7 University (PhD in Genetics and Cell Biology) Her lab investigates epigenome and transcriptome remodeling during aging , focusing on how biological sex influences these processes. Key themes include inflamm-aging , genomic instability , and immune senescence , with applications in neurodegeneration and reproductive longevity . Recent publications highlight sex-dimorphic gene regulation in neutrophils , macrophages , and brain aging , alongside novel insights into transposable elements and MOTS-c mitochondrial signaling . She pioneers the use of single-cell transcriptomics and multi-omics in aging research. Scientific awards include: 2024 Vincent Cristofalo Rising Star in Aging Research Award 2023 AGHE Rising Star Early Career Faculty Award 2023 USC Mentoring Award 2023 Rising Star in Reproductive Biology 2021 Nathan Shock New Investigator Award 2019 Rosalind Franklin Young Investigator Award Her editorial roles include Geroscience , Translational Medicine of Aging , and eLife . She mentors students across PhD programs in Biology of Aging , Neuroscience , and Molecular Medicine , as well as Master's and undergraduate trainees.

Ming C. Wuمشاهده پروفایل

استاد

• Micro/Nano Electro Mechanical Systems (MEMS)
• Physical Electronics
• Silicon Photonics
• +۳ مورد دیگر

Ming C. Wu is the Nortel Distinguished Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Berkeley Emerging Technologies Research Center (BETR), and is affiliated with the NSF Challenge Institute for Quantum Computation. He earned his B.S. from National Taiwan University in 1983 and Ph.D. from UC Berkeley in 1988, following a postdoctoral stint at AT&T Bell Laboratories (1988–1992) and faculty role at UCLA (1992–2004). Research Areas: Silicon Photonics Optoelectronics Nanophotonics Optical MEMS Optofluidics Prof. Wu's recent publications focus on scalable photonic systems, including wafer-scale silicon photonic switches, MEMS-based LiDAR, and quantum technologies. His work bridges fundamental research and commercialization, exemplified by co-founding OMM, Inc. (MEMS optical switches) and Berkeley Lights, Inc. (optoelectronic tweezers). Scientific Awards: Paul F. Forman Engineering Excellence Award (OSA 2007) William Streifer Scientific Achievement Award (IEEE Photonics Society 2016) C.E.K. Mees Medal (OSA 2017) Robert Bosch MEMS Award (IEEE EDS 2020) Bakar Prize (UC Berkeley 2021) IEEE Fellow (2002) Packard Fellow (1992) He leads the Integrated Photonics Laboratory , which develops technologies for optical communication, sensing, and biomedical applications.

John Reynoldsمشاهده پروفایل

استاد

• Polymer Chemistry
• Materials Science
• Electroactive Polymers
• +۶ مورد دیگر

Dr. John Reynolds is a Professor of Chemistry and Biochemistry at the Georgia Institute of Technology with a 40-year legacy in polymer chemistry. He serves as founding Director of the Georgia Tech Polymer Network (GTPN) and a member of the Center for Organic Photonics and Electronics (COPE). Research spans conjugated polymers, electrochromism, organic LEDs, photovoltaics, and bioelectronics Expert in optoelectronic and redox properties of electroactive materials Co-editor of the Handbook of Conducting Polymers His group has published over 450 peer-reviewed papers and holds ~45 issued patents. Recent research focuses on: Advanced electrochromic materials for visible and infrared applications Next-generation organic solar cells with green processing techniques Supercapacitor and electrochemical transistor materials Space exploration polymer applications Scientific recognition includes: ACS Cope Scholar Award (2020) ACS Florida Award (2019) ACS Applied Polymer Science Award (2012) Fellowships from Royal Society of Chemistry, Materials Research Society, and PMSE (2013) His editorial contributions include serving on boards for multiple prestigious journals including ACS Central Science and Chemistry of Materials . The Reynolds Group actively trains PhD and postdoctoral researchers, with recent members advancing to positions at University of Michigan, ExxonMobil, Northwestern, and Intel.

Kate Crawfordمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Machine Learning
• Politics of Technology
• +۴ مورد دیگر

Kate Crawford is a Research Professor at the Annenberg School for Communication and Journalism, University of Southern California; Senior Principal Researcher at Microsoft Research New York City; and Honorary Professor at the University of Sydney. She serves as the inaugural Visiting Chair for AI and Justice at École Normale Supérieure in Paris, co-leading the international working group on Foundations of Machine Learning. Her research examines artificial intelligence through interdisciplinary lenses including politics, labor, environmental impact, and historical context. She investigates how large-scale data systems shape societal structures while emphasizing ethical implications and power dynamics in algorithmic systems. Her work bridges technical AI development with critical social theory. Dr. Crawford co-founded three major research initiatives: FATE (Fairness, Accountability, Transparency, Ethics) at Microsoft Research; AI Now Institute at New York University; and Knowing Machines at USC. She has advised policy bodies including the United Nations, Federal Trade Commission, European Parliament, Australian Human Rights Commission, and White House on AI governance. Her scientific recognition includes: Miegunyah Distinguished Visiting Fellowship (University of Melbourne, 2021)