جستجوی استاد راهنمای دانشگاهی

Craig J. Fennieمشاهده پروفایل

استاد

• Computational Materials Physics
• Condensed Matter Physics
• Solid State Chemistry
• +۴ مورد دیگر

Craig J. Fennie is Professor in the College of Engineering at Cornell University , where he has been on the faculty since July 2008. His appointment spans the interdisciplinary space between physics, materials science, and solid-state chemistry. Education: Ph.D. in Physics, Rutgers University (2006) M.S. in Electrical Engineering, Villanova University (1996) Research Focus: Fennie’s group pursues computational and theoretical materials physics , with a long-term goal of realizing a first-principles “materials-by-design” paradigm. By integrating microscopic Hamiltonians, symmetry analysis, and density-functional theory, the team explores how crystalline motifs—composition, symmetry, geometry, and topology—govern emergent phenomena such as multiferroicity, magnetoelectric coupling, and tunable dielectric response. Particular attention is paid to complex oxides, layered heterostructures, and materials under extreme conditions where chemical intuition can fail. Publication Trends: From 2011 to 2016, Fennie co-authored a series of high-impact papers in Nature family journals and Physical Review Letters . These works collectively advance the understanding and design of room-temperature magnetoelectric multiferroics, bulk magnetoelectric effects in hexagonal manganites, tunable microwave dielectrics through defect mitigation, and the fundamental scarcity of ferroelectric perovskites. The studies span synthesis, characterization, and theory, underscoring a collaborative, interdisciplinary approach. Awards & Honors: MacArthur Fellowship, John D. and Catherine T. MacArthur Foundation (2013) Presidential Early Career Award for Scientists and Engineers (PECASE, 2012) NSF CAREER Award (2011) Army Research Office Young Investigator Award (2010) Fellow of the American Physical Society (2015) Advising & Funding: While specific student names are not listed, Fennie leads a vibrant research program supported by NSF, ARO, and other agencies. His group trains graduate and post-doctoral researchers at the intersection of physics, materials science, and chemistry, preparing them for careers in both academia and industry. Labs & Teams: Work is conducted within Cornell’s shared high-performance computing ecosystem and leverages national user facilities such as the Cornell High Energy Synchrotron Source (CHESS) and collaborations with experimental groups worldwide.

Esther Dufloمشاهده پروفایل

استاد

• Development Economics
• Poverty Alleviation
• Health Economics
• +۴ مورد دیگر

Esther Duflo is the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics at the Massachusetts Institute of Technology (MIT), where she is affiliated with the Department of Economics within the School of Humanities, Arts, and Social Sciences. She co-founded and co-directs the Abdul Latif Jameel Poverty Action Lab (J-PAL) and holds the position of Chaire, Pauvreté et politiques publiques at the Collège de France. Duflo also serves as President of the Paris School of Economics, demonstrating her significant international influence in economic policy and research. Her educational background includes first degrees in history and economics from Ecole Normale Superieure, Paris, followed by a Ph.D. in Economics from MIT in 1999. This strong foundation has enabled her to become one of the most influential economists of her generation. Duflo's research focuses on understanding the economic lives of people living in poverty, with the explicit aim of informing effective social policy design and evaluation. Her work spans critical areas including health, education, financial inclusion, environmental sustainability, and governance. What distinguishes her approach is the rigorous application of randomized controlled trials to evaluate development interventions, bringing scientific precision to poverty alleviation efforts. Her research methodology has revolutionized how economists approach development questions, moving beyond theoretical models to evidence-based policy recommendations. Her scholarly output demonstrates remarkable breadth and impact, with publications spanning from technical academic papers to influential books for general audiences. Duflo's work consistently bridges the gap between academic research and practical policy applications, with recent publications addressing contemporary challenges including climate change adaptation, pandemic response, and economic policy for hard times. Her research trends reveal a deepening commitment to translating complex economic concepts for broader audiences while maintaining scholarly rigor. Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2019, with Abhijit Banerjee and Michael Kremer) Princess of Asturias Award for Social Sciences (2015) A.SK Social Science Award (2015) Infosys Prize (2014) John Bates Clark Medal (2010) MacArthur 'Genius Grant' Fellowship (2009) Member of the National Academy of Sciences (2017) Duflo has supervised numerous graduate students and collaborated extensively with researchers worldwide through J-PAL, which has grown into a global network conducting field experiments across multiple continents. Her grant portfolio includes substantial funding from major foundations and government agencies supporting large-scale development research. Beyond traditional academic advising, Duflo has mentored a generation of development economists through J-PAL's training programs and field research opportunities. Her primary institutional home is the Abdul Latif Jameel Poverty Action Lab (J-PAL), which she co-founded at MIT. J-PAL has grown into a major research center with offices worldwide, connecting academic researchers with policymakers to ensure that program evaluations inform real-world decisions. Duflo also leads research teams at the Collège de France and the Paris School of Economics, creating a transatlantic network focused on evidence-based poverty reduction strategies.

Andrea Youngمشاهده پروفایل

استاد

• Quantum Materials
• Two-dimensional Systems
• Graphene
• +۷ مورد دیگر

Andrea Young is a Professor in the Department of Physics at the University of California Santa Barbara (UCSB), where they lead the Young Lab. The lab investigates quantum materials through nanofabrication and electronic measurement techniques. Affiliation: University of California Santa Barbara (Department of Physics) Research Interests: Andrea Young's work focuses on the interplay of symmetry, topology, and correlations in low-dimensional systems, particularly exploring superconductivity, magnetism, and fractionalization in graphene and van der Waals heterostructures . They utilize cryogenic measurements and heterostructure engineering to probe thermodynamic properties of these materials. Publication Trends: Recent articles emphasize twisted bilayer/trilayer graphene , moiré superlattices , and fractional quantum Hall effects , with keywords spanning condensed matter physics, quantum materials, and nanofabrication. Scientific Awards: Moore Foundation grant (2020) NSF fellowship (2020) CAREER award (2017) Advising: Andrea Young has advised PhD students such as Dr. Haoxin Zhou (2021) and Dr. Marec Serlin (2021). Their lab also supports research fellows like James Ehrets, who received an NSF fellowship in 2020. Labs & Teams: The Young Lab at UCSB specializes in creating van der Waals heterostructures and developing techniques like picosecond transport and nanoscale interferometry to study fragile electronic states.

Dawn Songمشاهده پروفایل

استاد

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, affiliated with multiple research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Center for Responsible, Decentralized Intelligence (RDI). She holds leadership roles in AI safety, cybersecurity, and blockchain research. Her work bridges machine learning, security, and distributed systems, with notable contributions to formal verification, privacy-preserving technologies, and AI governance. Education: Ph.D. (2002) and M.S. (1999) in Computer Science from UC Berkeley and Carnegie Mellon University, respectively. She previously served as an Assistant Professor at Carnegie Mellon University before joining UC Berkeley in 2007. Research Interests: Dawn's work focuses on AI safety, cybersecurity, and the ethical implications of frontier AI. She explores topics such as large language model vulnerabilities, zero-knowledge proofs, blockchain security, and policy frameworks for AI governance. Her research combines theoretical rigor with practical applications, addressing challenges in secure systems, adversarial machine learning, and privacy-preserving computation. Publications: Her recent work includes groundbreaking studies on LLM memorization, smart contract decompilation, and AI agent cybersecurity evaluation. She also leads initiatives like the Singapore Consensus and California Report on AI safety priorities, emphasizing global collaboration in risk mitigation. Notable Awards: MacArthur Fellowship (2010), ACM Fellow (2019), IEEE Fellow (2019) Advising & Grants: She advises projects on AI policy and cybersecurity, securing grants from NSF, DARPA, and industry collaborations. Her lab develops tools like CyberGym for evaluating AI agents and zkPyTorch for secure machine learning. Labs & Teams: Her research groups at UC Berkeley focus on cutting-edge projects in AI safety, blockchain, and cybersecurity, collaborating with industry leaders and policymakers to advance both technical and ethical standards.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).

• Digital Marketing Effectiveness
• Long-Term Brand Building
• Sustainable Consumer Behaviour
• +۲ مورد دیگر

Christina Antonie Reh is a Lecturer at the Lucerne School of Business (HSLU), affiliated with the Institute of Communication and Marketing (IKM MM). Her roles include academic teaching and research in digital marketing and sustainability. She holds a PhD from WHU - Otto Beisheim School of Management and has professional experience in data science and marketing at companies like LOréal and SevenVentures. Education: PhD (tbc 2024), WHU; M.Sc. (2016), LMU Munich; B.Sc. (2013), LMU Munich Her research focuses on digital marketing effectiveness , sustainability in advertising , and consumer behavior . Notable projects include studies on long-term brand equity and sustainable product messaging. Recent work explores the impact of climate pledges on consumer trust. Publications highlight her contributions to media measurement modeling and online coupon strategies. Key awards include the Amazon Research Awards (ARA 2023) and MassChallenge recognition (2024). Advising: No formal students listed; however, co-founded baybies GmbH and Aiko Pet Foods GmbH. Grants: $28,000+ from Amazon Research Awards. Labs/Teams: Co-founder of startups baybies and Aiko Pet Foods, focusing on sustainable consumer products and pet food innovation.

Konrad Schindlerمشاهده پروفایل

استاد

• Photogrammetry
• Remote Sensing
• Computer Vision
• +۳ مورد دیگر

Prof. Konrad Schindler holds the position of Full Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. He is also the Head of the Institute of Geodesy and Photogrammetry (IGP), leading research and educational activities in geomatics and computer vision. His career spans roles as a Photogrammetric Engineer, scientific assistant, postdoc researcher, and academic faculty across institutions including Graz University of Technology, Monash University, and TU Darmstadt before joining ETH Zürich in 2010. Education: Undergraduate studies in Geodesy (1992–1995), Graz University of Technology, Austria MEng in Photogrammetry and Geoinformation (1995–1999), Vienna University of Technology, Austria PhD in Computer Science (2001–2003), Graz University of Technology, Austria Research focuses on Photogrammetry , Remote Sensing , Computer Vision , and Image Understanding with interdisciplinary applications in environmental monitoring, geospatial analysis, and disaster response. He develops computational methods for 3D reconstruction, fusion of multi-modal data, and AI-driven solutions for satellite imagery interpretation. His work bridges geomatic engineering and machine learning to address challenges in urban mapping, climate modeling, and biological systems analysis. Publications reflect expertise in geospatial AI, diffusion models, and benchmarking datasets for disaster resilience. Notable works include Marigold (image analysis adaptation) and BRIGHT (building damage assessment). His research emphasizes practicality and scalability, such as affordable depth estimation and global biomass datasets. He has received the 2013 Marr Prize Honourable Mention (IEEE) and the 2012 U.V. Helava Award (ISPRS), alongside several Best Presentation Awards. His contributions span technical leadership, editorial roles (ISPRS Journal), and service to Swiss remote sensing commissions. Advising and grants: While no specific advisee names or grant details are listed, his career trajectory includes mentoring postdocs and junior faculty. He teaches advanced courses in Photogrammetry , Image Interpretation , and Machine Vision , integrating cutting-edge AI techniques into curricula. His research group collaborates on global-scale projects like canopy height mapping and satellite-based climate variable assessments. Labs/Teams: As Institute Head, he oversees the IGP lab at ETH Zürich, with prior affiliations including the Digital Perception Lab (Monash University) and the Computer Vision Lab (ETH Zurich). His work often involves multi-institutional collaborations focused on geospatial AI and environmental science.

Jerrica Ty Rowlettمشاهده پروفایل

استادیار

• Social Media
• Identity
• Civic Engagement
• +۱ مورد دیگر

Jerrica Ty Rowlett serves as an Assistant Professor of Communication and Language Studies within the College of Arts and Sciences at Bryant University, where she examines digital communication's societal implications through research and teaching in digital culture and social media dynamics. Education Ph.D., Florida State University M.A., Clemson University B.A., Georgetown College Her research program investigates identity formation, civic engagement, and digital inequity through critical analysis of social media platforms. She explores how digital environments shape interpersonal relationships, political discourse, and community building while addressing power dynamics in online spaces, with particular attention to abusive contexts, gender representation, and emotional experiences. Analysis of her publication record (2015-2024) reveals a consistent trajectory examining social media's evolving societal impact. Early work focused on educational applications and youth culture, progressing to sophisticated analyses of political meme culture, relationship dissolution in abusive contexts, and emotional contagion online. Her scholarship demonstrates increasing attention to social justice dimensions within digital communication. Scientific Awards No awards documented in current sources Advising and Grants Student advising details and research grant information are not specified in available materials, though her extensive publication record indicates active scholarly mentorship and research productivity. Labs and Teams No dedicated research laboratories or formal collaborative teams are mentioned in the provided information.

Peichen Zhongمشاهده پروفایل

استادیار

• Materials Science
• Machine Learning
• Computational Modeling
• +۵ مورد دیگر

Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.

Kenji Kawaguchiمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

Aarti Singhمشاهده پروفایل

استاد

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
• +۷ مورد دیگر

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

Rob Timmermansمشاهده پروفایل

استاد

• Theoretical Particle Physics
• Quantum Mechanics
• Quantum Field Theory
• +۳ مورد دیگر

Prof. Rob Timmermans is a Professor of Theoretical Physics and Vice-Dean for Education at the University of Groningen (UG). He is affiliated with the Faculty of Science and Engineering and the Precision Frontier — Van Swinderen Institute for Particle Physics and Gravity. His research focuses on theoretical particle physics, quantum mechanics, and precision measurements, particularly in electric dipole moment (EDM) searches using molecules like BaF. His work includes developing methods for molecular beam manipulation, phase-space analysis, and symmetry violation studies. He has contributed to collaborations such as NL-eEDM, advancing techniques for EDM detection and precision physics. Prof. Timmermans has received nominations for teaching awards, reflecting his commitment to education. Research highlights include studies on nucleon decay, antinucleon-nucleon interactions, and chiral effective field theory. His lab activities involve collaborations on laser-cooled molecules and trapping techniques. Prof. Timmermans’ articles often address fundamental physics questions, such as Lorentz violation in beta decay and parity violation in molecular systems. Awards: Nominated for Faculty Teaching Award 2014, Teacher of the Year 2014-15. Grants/Advising: Leads projects on EDM searches and particle physics, with active roles in international collaborations. Labs/Teams: Van Swinderen Institute, Precision Frontier group.

Paulina Łęska-Bayraktarمشاهده پروفایل

استادیار

• Syntax
• Comparative Linguistics
• Formal Semantics
• +۷ مورد دیگر

Paulina Łęska-Bayraktar is an Assistant Professor in the Department of English-Polish Comparative Linguistics at the Faculty of English, Adam Mickiewicz University in Poznań, where she conducts research and teaches in formal syntax and comparative linguistics. Her academic foundation is deeply rooted in the same institution, reflecting a sustained commitment to linguistic scholarship in Poland. Her educational background includes: B.A. in English Linguistics, Adam Mickiewicz University in Poznań (2012) M.A. in English Linguistics, Adam Mickiewicz University in Poznań (2014) Ph.D. in English Linguistics, Adam Mickiewicz University in Poznań (2019) Her research interests lie at the intersection of syntax, semantics, and cognitive grammar, with a strong focus on generative approaches to Slavic languages—particularly Polish. She investigates complex syntactic phenomena such as binding theory, pronoun distribution, quantifier scope, agreement, and the structure of relative and nominal clauses. Her work combines formal theoretical analysis with empirical data, often drawing on acceptability judgments and psycholinguistic methodologies. The trends in her recent publications reveal a sustained engagement with the syntax of Polish, especially in non-canonical constructions involving dative arguments, experiencers, and pronominal clusters. Her work contributes significantly to the understanding of how universal syntactic principles manifest in Slavic morphosyntax, and she frequently collaborates with leading scholars like Prof. Jacek Witkoś. She has received research funding from the Polish National Science Center through multiple grants, including OPUS, Beethoven, and Miniatura projects, which have supported her investigations into nominal expressions, pronouns, and anaphora in Polish. These grants underscore the national recognition of her research contributions. Paulina Łęska-Bayraktar actively participates in the international linguistics community, presenting her work at major conferences such as Formal Approaches to Slavic Linguistics (FASL), Formal Description of Slavic Languages (FDSL), and the Poznań Linguistic Meeting. She is also involved in collaborative research teams and has co-authored works with scholars from Humboldt-Universität zu Berlin and other institutions. Her academic presence is further strengthened by her profiles on ORCID and ResearchGate.

Eric Eatonمشاهده پروفایل

دانشگاهی

• Machine Learning
• Artificial Intelligence
• Lifelong Learning
• +۵ مورد دیگر

Eric Eaton is a Research Associate Professor of Computer and Information Science at the University of Pennsylvania , and a core member of the GRASP (General Robotics, Automation, Sensing, and Perception) Laboratory . His expertise centers on lifelong machine learning , transfer learning , and interactive AI , with impactful applications to robotics, precision medicine, and computational sustainability. Education & Affiliations Ph.D. in Computer Science, University of Maryland, Baltimore County (UMBC) – dissertation on selective knowledge transfer advised by Marie desJardins Former Visiting Assistant Professor, Bryn Mawr College Former part-time faculty, Swarthmore College and UMBC Two years as Senior Research Scientist at Lockheed Martin Advanced Technology Laboratories Research Interests Eaton’s research advances versatile AI systems that can learn multiple tasks over long lifetimes, transfer knowledge across domains, and interact effectively with humans and other agents. Core themes include: Lifelong & continual learning – continual acquisition and refinement of knowledge across tasks Knowledge transfer – selective, cross-domain, and zero-shot transfer techniques Interactive AI – human-in-the-loop learning and interpretable models Applications – autonomous service robotics, precision medicine, sustainability, and search & rescue Selected Scientific Awards IJCAI-16 Distinguished Student Paper Award for zero-shot transfer research IJCAI-15 Best Paper Nomination for autonomous cross-domain transfer ICML 2020 Workshop Best Paper Award for lifelong policy gradient learning Grants & Funding Current and past research is supported by the Office of Naval Research (ONR), the National Science Foundation (NSF), and Lockheed Martin, enabling large-scale projects in lifelong robotics and medical AI. Students & Postdocs Eaton has advised a diverse cohort of scholars including current PhD students David Isele, Seungwon Lee, Jorge Mendez, and Mohammad Rostami, as well as postdocs Boyu Wang and numerous alumni now in faculty positions or leading industry research teams. Labs & Teams He leads the Autonomous Service Robot Fleet within GRASP, creating low-cost robots that learn lifelong skills in university and office settings. His group also collaborates with clinicians for AI-driven precision medicine, and partners with defense and sustainability initiatives.

Sanjiv Singhمشاهده پروفایل

استاد پژوهش

• 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.