جست‌وجوی استاد و پژوهشگر

Vladimir Korepinمشاهده پروفایل

استاد

• Quantum Information
• Condensed Matter Physics
• Mathematical Physics
• +۷ مورد دیگر

Professor Vladimir Korepin is a renowned theoretical physicist and mathematician at Stony Brook University's C. N. Yang Institute for Theoretical Physics, specializing in condensed matter physics, quantum information, and integrable systems. He holds a PhD from Saint Petersburg State University (1977) and a Doctor of Sciences degree (1985). His research spans quantum gravity, entanglement entropy, and the Hubbard model. Key contributions include foundational work on quantum inverse scattering methods, Bethe ansatz solutions, and quantum algorithms. He has pioneered studies on entanglement scaling in spin chains and developed noise-resistant quantum search algorithms for NISQ devices. His courses include Quantum Information Processing and Statistical Mechanics. Notable articles explore entanglement entropy in deep inelastic scattering (2022), quantum search optimization (2022), and the Fredkin spin chain (2017). His work bridges theoretical physics with cutting-edge quantum technologies, emphasizing interdisciplinary applications in quantum computing and high-energy physics.

• Stellar Spectroscopy
• Elemental Abundances
• Star and Planet Formation
• +۳ مورد دیگر

Fan Liu is an Adjunct Research Fellow at Swinburne University of Technology. They earned a PhD from the Australian National University in 2016 and held a postdoctoral researcher position at Lund University, Sweden, for three years. Research Interests: High-precision stellar spectroscopy, elemental abundances in stars (particularly planet hosts, binaries, solar twins, and open cluster stars), connections between star and planet formation, Galactic chemical evolution, and Galactic archaeology. They utilize high-quality spectra from telescopes like Keck, VLT, and Gemini . Publications Trends: Focus on co-natal star pairs, lithium signatures from planetary ingestion, chemical homogeneity in moving groups, and constraints on fundamental constants like the fine-structure constant ( α ). Their work bridges astrophysics, exoplanet science, and precision cosmology. Supervision & Grants: Supervised PhD projects related to solar twins and fundamental constants. Contributed to an Australian Research Council (ARC) grant titled "Unravelling the history of the Milky Way Galaxy and exoplanet searches."

Salvatore Andolinaمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Artificial Intelligence
• Information Retrieval
• +۶ مورد دیگر

Salvatore Andolina is an Associate Professor at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, Italy. His research focuses on Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Information Retrieval, with a particular emphasis on designing human-centered AI systems for collaborative, social, and ubiquitous settings. He has held academic affiliations at the University of Palermo, Helsinki Institute for Information Technology HIIT, University of Helsinki, Aalto University, and served as a visiting scholar at Carnegie Mellon University's Human-Computer Interaction Institute. Key research areas include: Human-Centered AI Exploratory Search and Information Systems Crowdsourcing and Collaborative Creativity Augmented Reality and Wearable Technologies Entity-Centric Interaction Design Notable achievements include co-chair roles for the C&C conference (2025-2026), winning the Google Award for Inclusion Research (2023), and contributions to projects like the "Conversational Patterns for Inclusive Prompt Engineering" as a co-PI. He is an ACM Senior Member and has published extensively in top-tier venues including CHI, DIS, SIGIR, and TOIS. His work spans theoretical frameworks and applied systems, with recent focus on balancing sustainability in AI systems and advancing inclusive conversational interfaces.

En Caiمشاهده پروفایل

استادیار

• Immunology
• Cell Biology
• Biophysics
• +۳ مورد دیگر

En Cai is an Assistant Professor in the Department of Biological Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. His research focuses on immune cell dynamics, T cell receptor scanning mechanisms, and advanced microscopy techniques. Cai holds a Ph.D. from the University of Illinois at Urbana-Champaign and completed postdoctoral appointments at the same institution and the University of California, San Francisco. Education: Ph.D., University of Illinois at Urbana-Champaign Postdoctoral Training: University of Illinois at Urbana-Champaign; UCSF His work integrates biophysics and immunology to investigate how immune cells detect antigens and communicate through microvilli networks. Key areas include CAR-T cell engineering, tumor antigen transfer between dendritic cells, and the development of quantum dot-based super-resolution imaging tools. Recent studies explore the interplay between exhausted T cells and macrophages in cancer progression. Cai has pioneered techniques like FIONA (Fluorescence Imaging with One-Nanometer Accuracy) and utilizes blinking quantum dots for 3D cellular imaging. His publications span high-impact journals such as Science , Cancer Cell , and eLife , focusing on cellular dynamics at the nanoscale. While no scientific awards are listed, his contributions to microscopy and immunology are recognized through collaborative lab efforts. He maintains an active research group without listed advisees at this time.

Hari Balakrishnanمشاهده پروفایل

استاد

• Networking
• Mobile Computing
• Distributed Systems
• +۵ مورد دیگر

Hari Balakrishnan is the Fujitsu Professor of Electrical Engineering and Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads research in networking, mobile computing, and distributed systems, with contributions to protocols, congestion control, and edge-cloud integration. His work on systems like Cricket (indoor localization) and CarTel (mobile sensing) led to Cambridge Mobile Telematics, a global telematics provider. He holds a PhD from UC Berkeley and BTech from IIT Madras, with accolades including the Marconi Prize and IEEE Fellowships. Education: PhD in EECS, UC Berkeley (1998) BTech in Electrical Engineering, IIT Madras (1993) Research Interests: Networking and Protocol Design Mobile and Edge Computing Sensor Systems and IoT Resilient Distributed Systems Awards and Honors: 2023 Marconi Prize (Top in Communications Technology) IEEE Kobayashi Award (2021) SIGCOMM Lifetime Achievement Award (2021) ACM and IEEE Fellowships (2008, 2020) Advising & Impact: Mentored ~30 PhD students and 10 postdocs Co-founder of Cambridge Mobile Telematics (CMT) Recipient of multiple teaching awards at MIT Labs & Teams: Principal Investigator at MIT CSAIL Leading research groups in networking and mobile systems

• Big Geospatial Data Management
• Distributed Computing
• Machine Learning
• +۴ مورد دیگر

Prof. Martin Werner is a Professor of Big Geospatial Data Management at the Technische Universität München (TUM), within the TUM School of Engineering and Design. His research focuses on distributed computing, machine learning, quantum algorithms, and geospatial data analysis. He holds a doctorate in mathematics from Ludwig Maximilian University of Munich and has held positions at institutions including the German Aerospace Center and Bundeswehr University Munich. Education: Studied mathematics at the University of Bonn (B.Sc./M.Sc.), completed a Ph.D. at Ludwig Maximilian University of Munich (2014) on indoor navigation and geometric applications. Postdoctoral and senior researcher roles at LMU Munich, University of Hanover, and others. Research interests include geospatial data processing, parallel computing, and quantum computing applications. Notable work includes the GloBiMaps probabilistic data structure and contributions to trajectory analysis and anomaly detection in industrial systems. Awards include the ACM SIGSPATIAL GIS Cup (2015) and IPIN Best Paper Award (2014). Advising and grants: Supervised research in geospatial AI, quantum computing, and environmental monitoring. Collaborates with interdisciplinary centers like the Interdisciplinary Center for Applied Machine Learning (ICAML). Labs/Teams: Involved in the ICAML and leads projects such as the AtlasHDF framework and FOREST-2 mission collaborations.

Yanbei Chenمشاهده پروفایل

استاد

• Gravitational Wave Astronomy
• Astrophysics
• Quantum Gravity
• +۳ مورد دیگر

Yanbei Chen is a Professor of Physics at the California Institute of Technology (Caltech), affiliated with the Division of Physics, Mathematics and Astronomy. He holds a B.S. from Peking University (1999) and a Ph.D. from Caltech (2003). His academic career includes roles as Visiting Associate (2005–2007), Assistant Professor (2007–2010), Associate Professor (2010–2013), and Professor since 2013. His research focuses on gravitational wave astronomy, quantum gravity, and astrophysical phenomena, with significant contributions to the Laser Interferometer Gravitational-Wave Observatory (LIGO) collaboration. Chen teaches advanced courses such as Computational Physics Laboratory and contributes to the development of gravitational wave detection technologies. His work emphasizes probing fundamental physics through gravitational wave observations, including black hole spectroscopy, quantum limits in interferometry, and testing general relativity in extreme conditions. Recent research explores applications of quantum metrology to gravitational wave detectors and the interplay between quantum effects and spacetime curvature. Chen’s publications span topics like bumpy black hole spectroscopy, ultralight dark matter searches, and neutron interferometer-based detectors.

Jacob Shapiroمشاهده پروفایل

استادیار

• Socio-technical Systems
• Conflict Studies
• Political Science
• +۳ مورد دیگر

Jacob Shapiro is an Assistant Professor in the Department of Mathematics at Princeton University. His research focuses on socio-technical systems, conflict studies, and the intersection of technology with governance and public policy. He explores topics such as content moderation, misinformation dynamics, and the impact of political violence on societal structures. Shapiro’s work often combines quantitative methods with interdisciplinary approaches, addressing real-world challenges in digital governance, public health, and global security. He holds a position within Princeton’s mathematics department but engages with broader academic communities through collaborative research. His recent projects examine the efficacy of countermeasures against online influence operations, the role of algorithms in spreading unreliable information, and the socio-economic impacts of conflict in regions like Africa and South Asia. Shapiro’s publications analyze topics ranging from election denial content on YouTube to the monetization strategies of anti-vaccine websites. He has contributed to discussions on civilian harm in counterinsurgent operations and the long-term effects of forced displacement on education and income inequality. His research emphasizes evidence-based solutions for complex societal issues, leveraging data science and computational methods. Despite his prolific output, no scientific awards or named grants are explicitly mentioned in the provided materials. He advises no listed students but collaborates widely with institutions and industry partners on projects related to digital misinformation and conflict resolution.

• Robotics
• Autonomous Systems
• Mobile Autonomy
• +۷ مورد دیگر

Paul Newman is the BP Professor of Information Engineering at the University of Oxford, affiliated with the Oxford Robotics Institute within the Department of Engineering Science. He is a Fellow of the Royal Academy of Engineering and IEEE, recognized for contributions to robot navigation. His research focuses on advancing autonomy and navigation techniques for mobile systems, including vehicles, drones, and robots. As founder of Oxbotica, a spinout company specializing in Mobile Autonomy, he bridges academic research and industrial applications. His key research interests include radar-based localization, sensor fusion, autonomous navigation, and machine learning for robotics. He has contributed to datasets like the Oxford Offroad Radar Dataset (OORD) and pioneered methods like Bayesian Radar Cosplace for uncertainty-aware localization. Notable awards include the FREng and FIEEE titles, reflecting his global impact. His work spans both robotics and fundamental physics, as evidenced by his contributions to ATLAS detector experiments at CERN. Newman is also affiliated with Balliol College, Oxford, and his lab, the Oxford Robotics Institute, is a world leader in mobile autonomy.

Mark Sellkeمشاهده پروفایل

استادیار

• Spin glasses
• Machine learning theory
• High-dimensional statistics

Mark Sellke is an Assistant Professor of Statistics at Harvard University's Department of Statistics. He is affiliated with the Faculty of Arts and Sciences and is based at the Science Center in Cambridge, MA. His research interests focus on spin glasses, machine learning theory, and high-dimensional statistics, with contributions to optimization theory, statistical physics, and algorithmic analysis. Dr. Sellke's work bridges theoretical computer science, probability, and mathematical physics. His recent publications explore algorithmic hardness in combinatorial optimization, energy landscapes in spin glasses, and foundational questions in machine learning. Notable areas include computational lower bounds for partitioning problems, stability analysis of local optima, and universality phenomena in spin glass models. Education: Advanced degrees in Statistics/Computer Science (details not explicitly listed) His research has been recognized through publications in top venues such as FOCS, NeurIPS, and JASA. Collaborative projects include work on multi-species spin glasses and algorithmic approaches to complex systems. Current research trends emphasize the intersection of optimization, stochastic processes, and high-dimensional data analysis. Dr. Sellke maintains an active research group and contributes to the academic community through courses on statistical theory and machine learning. His lab is part of Harvard's broader efforts in data science and probabilistic modeling.

Linqing Wenمشاهده پروفایل

استاد

• Gravitational Wave Astrophysics
• Multi-messenger Astronomy
• High-performance Computing
• +۴ مورد دیگر

**Linqing Wen** is a Professor in the School of Physics, Mathematics and Computing at The University of Western Australia (UWA). He leads the Gravitational Wave Astronomy Group and holds roles such as Chief Investigator in the ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav). His work focuses on gravitational wave astrophysics, real-time detection, and multi-messenger astronomy, with contributions to the LIGO-Virgo Collaboration (LVC) and the SPIIR search pipeline. **Education**: PhD in Astrophysics from MIT (2001), MS in Physics and ME in Computer Science from PSU (1995), and BS in Condensed Matter Physics from USTC (China, 1992). Professional experience includes roles at Caltech, the Max-Planck Institute, and UWA since 2004. **Research Interests**: Gravitational Wave Astrophysics, High-performance Computing, Machine Learning for GW detection, and Early Warning Systems. Notable projects include SPIIR pipeline development, GW multi-messenger studies, and GPU acceleration for real-time analysis. **Awards**: 2016 Special Breakthrough Prize (shared), ARC Future Fellowship (2009), UPRF Fellowship (2007), and Rossi Fellowship (1995). **Grants & Projects**: Over $30M in funding, including ARC grants for GW detection, multi-messenger astronomy, and pulsar timing arrays. Active roles in OzGrav, UWA research clusters, and international collaborations like the LVC and CRAFT. **Teaching**: Courses include SCIE1121 (introductory astronomy) and specialized modules on Gravitational Wave Astronomy.

• Machine Learning
• Genetic Programming
• Engineering Informatics
• +۳ مورد دیگر

Dr Peter Rockett is an Honorary Lecturer at the School of Electrical and Electronic Engineering, University of Sheffield. He holds a PhD in Semiconductor Physics from the University of Manchester (1980) and has been affiliated with the University of Sheffield since 1990. His research focuses on physics-informed genetic programming, machine learning for engineering, and evolutionary algorithms applied to feature extraction, control systems, and computer vision. Key areas of expertise include multiobjective genetic programming, algorithm optimization, and model-predictive control for non-domestic buildings. He has contributed to the development of standardized genetic programming frameworks (e.g., GPML) and has explored applications in robotics, cybersecurity, and image processing. His work bridges theoretical foundations with practical engineering challenges, emphasizing cross-domain feature extraction and algorithmic innovation. Recent publications highlight advancements in pruning genetic programming trees using permutation tests, energy-efficient building control systems, and semantic-based local search methods. Rockett has collaborated extensively with researchers globally, producing influential papers in journals like Evolutionary Intelligence , Genetic Programming and Evolvable Machines , and IEEE Transactions on Evolutionary Computation . No scientific awards are explicitly listed, but his extensive publication record underscores sustained academic contributions. His advising and mentoring focus on PhD and Master’s students in machine learning and engineering informatics, with research teams often collaborating on interdisciplinary projects involving control systems and AI-driven solutions.

John Dohertyمشاهده پروفایل

دانشیار

• Aerodynamic CFD Optimisation
• Multidisciplinary Design Optimisation
• Conceptual Design and Innovation
• +۲ مورد دیگر

John Doherty is a Reader in Vehicle Engineering at the University of Surrey's School of Mechanical Engineering Sciences, affiliated with the Centre for Aerodynamics & Environmental Flow. His research spans aerodynamic and multidisciplinary design optimisation, novel air-vehicle conceptual design, propulsion installation aerodynamics, and hybrid/electric aircraft systems. With a career beginning at RAE Farnborough and QinetiQ, he has collaborated extensively with industrial partners like MOD, BAES, and Airbus, developing CFD-based optimisation tools critical for aerospace and motorsport applications. His research interests focus on: Aerodynamic CFD optimisation Multidisciplinary Design Optimisation Conceptual design and innovation Propulsion installation aerodynamics Electric ducted fan aerodynamics Recent publications emphasize transonic aerodynamics, wind tunnel correction techniques, electric propulsion integration, and surrogate-assisted optimization methods. Trends show consistent focus on improving aerodynamic efficiency, experimental validation, and computational methods for aircraft and propulsion systems. No scientific awards are mentioned in the provided text. He teaches courses including ENG3197 Vehicle Aerodynamics, ENG3162 Aircraft Design Group Projects, and ENG2087 Design, Make and Evaluate Projects. Grant involvement includes the EMPAS project (electric motor-powered aero-engine simulator) and collaborative EU research (e.g., Garteur AD/AG-52). Doherty leads research within the Centre for Aerodynamics & Environmental Flow and collaborates on international projects (e.g., EMPAS). His work involves experimental wind tunnel testing, CFD development, and multidisciplinary teams addressing next-generation aircraft design.

Daniel de Lengمشاهده پروفایل

استادیار

• Applied Human-Centered AI
• Explainable AI
• Autonomous Systems
• +۵ مورد دیگر

Daniel de Leng is an Assistant Professor at Linköping University (Sweden), affiliated with the Department of Computer and Information Science (IDA) and the Human-Centered Systems (HCS) division. He is part of the Semantic Web group and focuses on applied human-centered AI, particularly in autonomous systems and explainable AI. His work integrates symbolic and subsymbolic methods to address challenges in real-world AI deployment. Education: PhD (2019) and Licentiate (2017) in Computer Science from Linköping University, and MSc/BSc (2013/2011) from Utrecht University (Netherlands). Research Interests: Stream reasoning under uncertainty, autonomous system safety, and semantic integration in robotics. He developed the DyKnow-ROS framework for robot stream reasoning and contributed to the Stellar AIOps environment for AI research. His work emphasizes human-understandable explanations for AI decisions. Awards: Recipient of the SAIS Master's Thesis Award. Advising & Grants: Managed the AI Academy initiative, part of the AIOps ELLIIT Infrastructure and AI Academy management teams. Previously served as Saab Aeronautics' first Point of Contact for AI (until 2022). Labs & Teams: Active in the Reasoning and Learning Lab (ReaL), Semantic Web group, and Human-Centered Systems division. Collaborates on projects like Urdarbrunnen (AI for combat search and rescue) and multi-agent robotics systems.

Sodiq Adewoleمشاهده پروفایل

مدرس

• Machine Learning
• Medical Image Analysis
• Healthcare
• +۲ مورد دیگر

Sodiq Adewole is a Lecturer at the School of Data Science, University of Virginia. His research focuses on healthcare AI applications, particularly developing machine learning solutions for medical image analysis and video capsule endoscopy. Dr. Adewole's work bridges artificial intelligence and clinical medicine, specializing in few-shot lesion recognition, abnormality localization in endoscopic videos, and synthetic healthcare data generation. Recent publications demonstrate innovative approaches using graph convolutional networks, metric learning, and Bayesian methods to address challenges in gastrointestinal diagnostics. His research output shows consistent focus on medical video analysis, particularly capsule endoscopy interpretation. Key technical contributions include weakly supervised localization techniques, temporal segmentation methods, and few-shot learning frameworks adapted for medical imaging. No specific advising relationships, awards, or laboratory affiliations are detailed in available materials.