جست‌وجوی استاد و پژوهشگر

Adam Leeمشاهده پروفایل

استاد

• Computer Security
• Privacy
• Distributed Systems
• +۵ مورد دیگر

Adam J. Lee is a Professor of Computer Science and Vice Provost for Undergraduate Studies at the University of Pittsburgh's School of Computing and Information. He holds a PhD (2008) and MS (2005) from the University of Illinois at Urbana-Champaign, and a BS (2003) from Cornell University. His research bridges computer security, privacy, and distributed systems, focusing on systems that enable secure cross-domain interactions while preserving user privacy. Notable work includes studies on IoT privacy, cryptographic access control, and user perceptions of privacy in smart environments. Education: PhD in Computer Science, University of Illinois at Urbana-Champaign, 2008 MS in Computer Science, University of Illinois at Urbana-Champaign, 2005 BS in Computer Science, Cornell University, 2003 Research Interests: Explores secure distributed systems, privacy-preserving technologies, and user-centric design for IoT. Recent efforts include tangible privacy controls for smart devices, context-aware messaging agents, and formal analysis of cryptographic enforcement architectures. Supported by NSF and DARPA grants, including an NSF CAREER Award in 2013. Recent Work: Focuses on balancing utility and privacy in sensing systems, with contributions in ephemeral communication, auto-response agents, and privacy-aware smart voice assistants. Recent studies address user perceptions of temporal data redaction and trusted platforms. Awards: NSF Career Award (2013) ACM CSCW 2022 Best Paper ACM DIS 2022 Best Paper Advising & Grants: Advises PhD students in privacy and security research. Active in grant-funded projects on access control, cloud security, and health technology interventions. Collaborates with the Institute for Cyber Law, Policy, and Security on policy-relevant cybersecurity issues. Teaching: Courses include Discrete Structures for Computer Science and Computer and Network Security , emphasizing foundational theory and practical applications. Known for curriculum development in privacy-aware systems and ethical computing practices.

Francisco Correia dos Santosمشاهده پروفایل

استاد

• Computer Science
• Evolutionary Biology
• Economics
• +۱۰ مورد دیگر

Francisco C. Santos is a Full Professor at Instituto Superior Técnico (IST), University of Lisbon, and currently serves as Vice-President of the Portuguese Science Foundation (FCT). He holds a PhD in Computer Science from Université Libre de Bruxelles (ULB), Belgium, where he was a Marie Curie Fellow. His research spans computer science, evolutionary biology, economics, and physics, focusing on cooperation dynamics, environmental governance, network science, and AI's societal impact. He leads the GAIPS and ATP research groups at INESC-ID, Lisbon. Education PhD in Computer Science, Université Libre de Bruxelles (ULB), Belgium (Marie Curie Fellowship) FNRS Senior Researcher at ULB's Machine Learning Group Research Interests His work bridges AI, complex systems, and social dynamics. Key areas include: - Evolution of cooperation and social norms - Climate governance and collective-risk dilemmas - Network science and urban planning - Machine learning and robotics applications Awards Young Scientist Award (German Physical Society) CGD/ULisbon Prize in Computer Science AAAI Blue Sky Award IST Teaching Excellence Awards (multiple) Advising & Grants Supervised over 30 PhD and MSc students, many of whom have gone on to prestigious postdocs and academic roles. Co-headed research groups and led projects funded by FCT-Portugal and EU initiatives. Labs & Teams Leads the Group of Artificial Intelligence for People and Society (GAIPS) and the ATP interdisciplinary group at INESC-ID. Collaborates internationally on AI ethics and environmental policy.

Ruitao Linمشاهده پروفایل

استادیار

• Bayesian adaptive design
• Robust Bayesian methods
• Meta-analytic trial design
• +۶ مورد دیگر

Dr. Ruitao Lin is an Assistant Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center and an Adjunct Assistant Professor at Rice University. He holds a PhD in Biostatistics from The University of Hong Kong (2012–2016) and completed postdoctoral training at the University of Washington and MD Anderson. His research focuses on Bayesian adaptive designs, robust statistical methods, and clinical trial methodologies, particularly in oncology and precision medicine. He leads the development of novel Bayesian approaches for dose-finding in phase I/II trials, meta-analytic trial designs, and high-dimensional data analysis. Dr. Lin is an Associate Editor for Contemporary Clinical Trials , Biometrical Journal , and Pharmaceutical Statistics , and has contributed to software tools like the BOIN design suite and TITE-BOIN12. His work bridges statistical innovation with practical clinical challenges, including late-onset toxicities in immunotherapy and heterogeneous treatment effects. He has secured NIH funding (e.g., R01CA261978) and collaborated with academic and industry partners to advance trial design and analysis. His software platforms, including R packages and Shiny apps, enable clinicians to implement cutting-edge statistical methods in real-world settings. Education : PhD, Biostatistics, The University of Hong Kong (2016); Postdocs at University of Washington (2016–2017) and MD Anderson (2017–2019). Grants : NIH/NCI R01CA261978 (2021–2025), focusing on Bayesian methods for precision oncology trials. Teaching : Teaches clinical trial design courses at Rice University and UTHealth. Labs/Software : Developed the BOIN design suite, TITE-BOIN12, and other tools for dose-finding and phase I/II trials.

Ricardo Grau-Crespoمشاهده پروفایل

دانشیار

• Materials Science
• Computational Chemistry
• Energy Materials
• +۳ مورد دیگر

Dr. Ricardo Grau-Crespo is an Associate Professor of Materials Theory and Lead of the Chemical Sciences Research Division at the University of Reading. He is affiliated with the School of Chemistry, Food, and Pharmacy within the Department of Chemistry, focusing on computational materials science for clean energy applications. His research explores molecular-level simulations to investigate materials for energy storage and environmental technologies. Key projects include studies on spinel ferrites, thermal conductivity in pyrochlores, and nanofluids for thermal energy systems. He leads the GCMT Group (https://gcmt-group.github.io/) and is active in advancing computational methods for materials discovery, including machine learning applications. His work bridges theory and experiment, with notable contributions to photocatalysis, thermoelectrics, and interfacial phenomena in nanomaterials. Research Themes: Environment, Energy Materials, and Computational Materials Science. Collaborations include experimental partners for validation of computational models. Active in high-throughput screening and AI-driven material design, emphasizing sustainability and energy efficiency.

• Market Microstructure
• Fintech
• Decentralized Finance (DeFi)
• +۳ مورد دیگر

Dr Olga Klein is an Associate Professor in the Finance Group at Warwick Business School (WBS), University of Warwick. She is also a research fellow at the Gillmore Center for Financial Technology and co-organizes the annual WBS Gillmore Centre Conference on DeFi and Digital Currencies. Her work focuses on market microstructure, fintech innovations, blockchain applications, and regulatory challenges in decentralized finance. She has advised the UK Financial Conduct Authority (FCA) on digital finance policies and contributed to industry collaborations like her project with ChainSolid Labs investigating blockchain scaling solutions on Uniswap. Education: Ph.D. in Finance (University of Mannheim), MSc in Finance & Economics (University of Magdeburg), BSc in Economic Theory (Kyiv-Mohyla Academy) Teaching: Executive MBA, Accelerator MBA, MSc Mathematical Finance, and undergraduate modules in Financial Management, Advanced Trading Strategies, and Finance in Practice Affiliations: European Finance Association program committee member, former post-doctoral fellow at Frankfurt University's E-Finance Lab Her research explores how technology reshapes liquidity and price efficiency in both traditional and decentralized exchanges, with recent emphasis on blockchain scalability and regulatory frameworks for digital assets. Olga's interdisciplinary work bridges theoretical finance with practical fintech applications, addressing emerging trends in automated market making and DeFi infrastructure.

Christian Tetzlaffمشاهده پروفایل

استاد

• Computational Neuroscience
• Synaptic Physiology
• Neuromorphic Engineering
• +۴ مورد دیگر

Prof. Christian Tetzlaff holds a W2 Professorship at the Department for Neuro- and Sensory Physiology, University Medical Center Göttingen. He leads the Computational Synaptic Physiology group, focusing on integrating computational methods to understand neural systems' principles and translating these into technologies like robotics and neuromorphic engineering. His work spans scales from synaptic protein interactions to large neural networks, leveraging disciplines such as nonlinear dynamics and machine learning. Educational Background: 2009: Diploma in Physics, Georg-August University Göttingen 2013: PhD in Physics (Dr.rer.nat.), Georg-August University Göttingen Research Leadership & Grants: Principal Investigator in EU Horizon 2020 Projects (Plan4Act, ADOPD, Human Brain Project's FIPPA) Lead in DFG-funded SFB 1286 (Quantitative Synaptology) INRC member (Intel Neuromorphic Research Community) BMBF-funded KISSKI project (2022–present) Research Interests: His lab explores synaptic mechanisms underlying learning, memory consolidation via synaptic tagging-and-capture, and neuromorphic hardware implementations. Recent work emphasizes calcium signaling in synapses, memristive devices for synaptic working memory, and robust trajectory generation using neuromorphic chips like Loihi. Affiliations: Co-coordinator of Plan4Act (2017–2021) and active in the Göttingen Graduate Center for Neurosciences (GGNB). Collaborates globally with institutions like the Weizmann Institute and Columbia University. Laboratory: Visit tetzlab.com for lab activities and open-source tools like Plastic Arbor .

Zoltán Harmanمشاهده پروفایل

پژوهشگر

• Quantum Electrodynamics
• Atomic Physics
• Highly Charged Ions
• +۱۲ مورد دیگر

Zoltán Harman is a Leading Scientist at the Max Planck Institute for Nuclear Physics (MPIK) in Heidelberg, Germany, and is affiliated with Heidelberg University. He conducts cutting-edge research in atomic physics, quantum electrodynamics, and precision measurements, particularly using highly charged ions and Penning trap techniques. He holds a habilitation in physics from Heidelberg University and regularly lectures there on advanced topics such as quantum electrodynamics and theoretical physics. Research Interests: Quantum Electrodynamics (QED) in strong fields Precision spectroscopy of highly charged ions g-factor measurements and tests of fundamental physics Development of atomic clocks using highly charged ions Determination of fundamental constants (e.g., electron mass, fine-structure constant) Searches for new physics beyond the Standard Model, including fifth forces Neutrino mass determination via Penning trap measurements His recent publications (2024–2025) focus on high-precision g-factor measurements in ions like tin and beryllium, two-loop QED calculations, King plot analyses for new boson searches, and neutrino mass studies. These works appear in top journals such as Nature , Physical Review Letters , and Science , reflecting his leadership in precision atomic physics. Scientific Awards and Grants: Postdoctoral scholarship from the Max Planck Society (2005–2007) EMMI Visiting Professor Scholarship (2012, 2013) Erasmus student scholarship (1999–2000) Primary investigator of the ISOQUANT Collaborative Research Centre (SFB1225) Teaching and Advising: Harman has been a regular lecturer at Heidelberg University since 2008, teaching courses in quantum electrodynamics, theoretical physics, and experimental physics. He co-organizes advanced lecture series and supervises student research projects, including poster sessions and talks. He is also a lecturer in the International Max Planck Research School on Quantum Dynamics. Research Group and Collaborations: Harman is a key member of the quantum dynamics group at MPIK, led by Prof. Christoph H. Keitel. His work involves close collaboration with experimental teams at MPIK, GSI, and Heidelberg University, combining theoretical and experimental efforts in precision physics. He is actively involved in major projects such as PENTATRAP and ALPHATRAP, which aim to push the limits of atomic and nuclear measurements.

Dan Abramovichمشاهده پروفایل

استاد

• Algebraic Geometry
• Resolution of Singularities
• Logarithmic Geometry
• +۳ مورد دیگر

**Dan Abramovich** is a Professor in the Department of Mathematics at Brown University, affiliated with the College of Arts and Sciences. His research focuses on algebraic geometry, particularly in resolution of singularities, logarithmic geometry, moduli spaces, and birational geometry. He has authored numerous papers and contributed to foundational work in these areas, supported by NSF and BSF grants. **Teaching**: He teaches courses like Math 1540 (Galois theory and representations of finite groups) and has extensive past teaching records dating back to 2015. His seminars include Algebraic Geometry and Topology. **Research**: Abramovich's work bridges algebraic structures and geometric applications, with notable contributions to logarithmic geometry and moduli theory. His recent articles address weighted blow-ups, dynamical systems in resolution, and functorial monomialization. **Grants & Collaboration**: He collaborates internationally, organizing conferences like AGNES and BATMoBYle. His work is disseminated via arXiv, with over 50 publications since the 1990s.

Ollie Ballingerمشاهده پروفایل

مدرس

• Geocomputation
• Remote Sensing
• Conflict Analysis
• +۴ مورد دیگر

Dr. Ollie Ballinger is a Lecturer (Teaching) in Geocomputation at the Centre for Advanced Spatial Analysis (CASA), University College London. His research focuses on leveraging computational methods and satellite imagery for conflict analysis, environmental monitoring, and humanitarian applications. He holds a PhD from the Oxford Department of International Development, where his work explored computational approaches to insurgent recruitment dynamics in Turkey. Education: PhD in International Development from the University of Oxford (focused on computational conflict analysis) Research Interests: Dr. Ballinger's work spans geospatial analysis, remote sensing, and open-source intelligence (OSINT). He develops novel algorithms for detecting conflict-related damage (e.g., pixel-wise T-test for SAR imagery), tracks illicit activities like ship-to-ship transfers and illegal mining, and creates tools for humanitarian and environmental accountability. His projects often involve collaboration with organizations like Bellingcat and the Alan Turing Institute. Key Projects: Pixel-Wise T-Test (PWTT) algorithm for open-access battle damage detection Analysis of ship-to-ship transfers in maritime trade Monitoring illegal rare earth mining in Myanmar Radar interference tracking for military surveillance systems Tools & Contributions: Developed Python libraries for image processing and network analysis Principal Investigator for projects with Global Witness and the Alan Turing Institute Contributor to Bellingcat's investigative reporting on conflict pollution and environmental crimes Grants & Collaborations: Collaborations include work with the United Nations Satellite Centre (UNOSAT) for damage assessments and partnerships with NGOs like Global Witness for environmental monitoring projects. His research is supported by grants focused on computational methods for humanitarian and environmental applications. Labs & Teams: Active within the Centre for Advanced Spatial Analysis (CASA) at UCL, where he contributes to interdisciplinary geospatial research initiatives.

Zijian Diaoمشاهده پروفایل

دانشیار

• Quantum Computation
• Quantum Information Theory
• Natural Language Processing
• +۷ مورد دیگر

Zijian Diao is an Associate Professor in the Department of Mathematics at Ohio University's College of Arts and Sciences, where he contributes to both teaching and research. He is based at the Eastern Campus in Shannon Hall and teaches a wide range of undergraduate mathematics courses. Education: Ph.D. in Mathematics, Texas A&M University, 2001 M.S. in Computer Science, University of Illinois, 2003 B.S. in Automation, University of Science and Technology of China, 1996 His research spans interdisciplinary areas with a strong focus on quantum computation and information theory , where he has made contributions to Grover's algorithm, quantum counting, and quantum circuit design. He has also worked in natural language processing , particularly in multilingual speech-to-speech translation systems like MARS. Additional interests include control theory , partial differential equations , and mathematical modeling . His recent and ongoing publications reflect a blend of theoretical mathematics and applied computational science. The works trend toward quantum algorithms and foundational mathematical proofs, with earlier contributions in NLP and control systems. His research bridges pure mathematics with practical implementations in quantum computing and language technologies. Scientific Awards: Ohio University Regional Higher Education Outstanding Professor Dr. Diao advises undergraduate and graduate students in mathematics and related fields, though specific student names are not listed. He has been involved in research projects supported by academic grants, particularly in quantum computing and interdisciplinary technology development. His curriculum vitae indicates sustained scholarly activity across multiple domains. He is associated with research groups and collaborations in quantum computing, having co-authored works with G. Chen, C. Huang, and others. His work in NLP was part of larger team efforts involving researchers from industry and academia, suggesting active participation in collaborative technical teams.

Panayiotis Tsaparasمشاهده پروفایل

دانشیار

• Algorithmic Bias and Fairness
• Social Network Analysis
• Social Media Analysis
• +۷ مورد دیگر

Panayiotis Tsaparas is an Associate Professor in the Department of Computer Science & Engineering at the University of Ioannina, Greece. He is also a Collaborating Senior Researcher at the Archimedes Research Center since 2023. His academic journey includes a Ph.D. from the University of Toronto, postdoctoral work at the University of Rome (La Sapienza) and the University of Helsinki, and research experience at Microsoft Research, Search Labs. Education: B.Sc., University of Crete, Greece Ph.D., University of Toronto, Canada, supervised by Allan Borodin His research focuses on algorithmic fairness, social network and media analysis, and data mining . He investigates how opinions form and spread in networks, how bias manifests in algorithms like PageRank, and how to design fair recommendation systems. His work bridges theoretical algorithm development with practical applications in social computing. His recent publications, appearing in top venues like WWW, KDD, WSDM, and SDM, reveal strong trends in fairness-aware algorithms, opinion dynamics, polarization modeling, and temporal network analysis . He has pioneered work on fairness in PageRank and link recommendations, and on measuring and moderating polarization in online communities. Scientific Awards: Best paper award at ACM SIGMOD Workshop on Data Bases and Social Networks (DBSocial), 2013 Best paper award runner-up at ACM KDD, 2006 He leads a research group and has advised students on topics including internet review analysis and election prediction using Twitter. He has secured significant funding, notably a Marie Curie Reintegration Grant (JMUGCS) , which supported research on jointly mining user-generated content across reviews, social networks, and behavioral data. His teaching includes undergraduate and graduate courses such as Data Mining and Online Social Networks and Media . He collaborates extensively with researchers like Evaggelia Pitoura, Nikos Mamoulis, Aristides Gionis, and others, forming a strong network in the data mining and social network analysis community.

• Mathematical Analysis
• Fuzzy Set Theory
• Measure Theory
• +۵ مورد دیگر

Lect. dr. Alina Cristiana Gavriluţ is a faculty member at the Faculty of Mathematics, Al. I. Cuza University of Iaşi, Romania. Her research focuses on advanced topics in mathematical analysis, including set multifunctions, non-additive measures, fuzzy integrals, and their applications in physics and complex systems. She has authored/co-authored multiple books and over 30 peer-reviewed papers in journals like Fuzzy Sets and Systems , Entropy , and Reports on Mathematical Physics . Her work explores regularity properties of set multifunctions, integrability in non-additive settings, and interdisciplinary applications in areas like fractal information, quantum mechanics, and neurosciences. She has also contributed to theoretical frameworks connecting mathematical physics with complex systems, including studies on non-differentiable entropy and spacetime manifolds. Dr. Gavriluţ collaborates extensively with researchers in mathematics and physics, notably Maricel Agop and Anca Croitoru. Her research has implications for diverse fields, from image processing to neuronal network modeling. She actively participates in international conferences, presenting findings on set-valued integration, fractal systems, and transdisciplinary approaches to complex phenomena. Education: PhD in Mathematics from Al. I. Cuza University (year not specified). Key Areas: Mathematical analysis, measure theory, fuzzy set theory, complex systems, non-differentiable dynamics. Grants/Awards: Not explicitly listed in the provided texts, but her prolific publication record indicates sustained academic engagement. Labs/Teams: Affiliated with the Mathematics department at Al. I. Cuza University, contributing to research groups in functional analysis and mathematical physics.

• Statistical Mechanics
• Statistical Physics
• Error-Correcting Codes
• +۱۲ مورد دیگر

David Saad is a Professor at Aston University, affiliated with the College of Engineering and Physical Sciences, the School of Computer Science and Digital Technologies, and the Applied Mathematics & Data Science group. He also leads the Aston Centre for Artificial Intelligence Research and Application and holds the 50th Anniversary Chair of Complexity Physics. His academic appointments include Professor of Information Mathematics since 1999 and former Head of the Mathematics Group (2006–2012, 2015–2019). His educational background includes: PhD in Engineering, Tel Aviv University (1993) MSc in Physics, Tel Aviv University (1987) BA in Physics and BSc in Electrical Engineering, Technion, Israel (1982) David Saad's research focuses on the application of statistical physics to complex computational and engineering problems. His primary interests include statistical mechanics of disordered systems, error-correcting codes, message-passing algorithms, learning in neural networks, and distributed optimization in networks such as smart grids and optical communication systems. He combines theoretical rigor with practical applications in machine learning, communications, and energy systems. His recent publications (2024–2025) demonstrate a strong trend in interdisciplinary research, bridging physics, computer science, and engineering. Key themes include neuromorphic computing, network inference from data, viral load estimation, and cascading failure modeling, indicating a continued focus on inference, optimization, and resilience in complex systems. His scientific recognition includes: Fellow of the Institute of Mathematics and its Applications Senior Fellow of the Higher Education Academy David Saad has supervised several research students, including H. F. Po, E. Harrison, M. Sheikh, B. Li, E. Manuylovich, and R. Sadeghi, many of whom appear as co-authors on recent publications. He has also been involved in research grants and projects related to smart grids, optical networks, and neuronal network analysis. His work involves collaborations with institutions across the UK and internationally, as evidenced by his research council membership and co-authored outputs. He leads research in the Aston Centre for Artificial Intelligence, focusing on theoretical and applied aspects of machine learning and complex systems. His team applies message-passing techniques to problems in energy distribution, communication networks, and biological data analysis, contributing to both foundational theory and real-world applications.

• Composite Materials
• Mechanical Engineering
• Structural Analysis
• +۳ مورد دیگر

Professor Albert Turon Travesa is a faculty member in the Department of Mechanical and Industrial Construction Engineering at the University of Girona (Spain), where he leads the Mechanics of Continuum Media and Theory of Structures research area. He serves as Head of Department and is affiliated with the AMADE research group. His research focuses on characterizing mechanical behavior of composite materials, developing non-deterministic modeling strategies for structural prediction, and advancing fatigue life estimation techniques. Key contributions include cohesive zone modeling, delamination growth analysis, and machine learning applications in materials science. He has authored over 100 top-tier journal papers, appears in Stanford's World Top 2% Scientists, and Research.com's Mechanical & Aerospace Engineering ranking. Awards include the 2022 ICREA Acadèmia grant and 2024 Air and Space Academy Medal for composite materials advancements in European aeronautics. His 15 most recent publications span fatigue modeling, delamination mechanics, and computational strategies, primarily applied to aerospace composites. Current work emphasizes probabilistic approaches, ply orientation effects, and residual strength prediction after damage accumulation. ICREA Acadèmia Award (2022) Air and Space Academy Medal (2024) Active collaborations with aeronautical industries include technology transfer projects on composite material implementation. His research combines experimental validation with numerical simulation to bridge theoretical modeling and industrial application.

Ramya Korlakai Vinayakمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +۲ مورد دیگر

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. She also holds affiliations with the Department of Computer Science and the Department of Statistics at UW-Madison. Her research program bridges theoretical machine learning with practical applications in data science and crowdsourcing. Education: PhD in Electrical Engineering from California Institute of Technology (Caltech), advised by Prof. Babak Hassibi B.Tech in Electrical Engineering with minor in Physics from Indian Institute of Technology Madras (IIT Madras) Dr. Vinayak's research focuses on developing theoretically grounded machine learning tools for reliable inference using data from human sources. Her work spans machine learning theory, statistical inference, and crowdsourcing systems, with particular emphasis on preference learning, metric learning, and robust dataset construction. She has pioneered methods for learning from limited pairwise comparisons, auto-labeling systems, and human-in-the-loop out-of-distribution detection. Her recent publications reveal a consistent trajectory toward building practical machine learning systems that incorporate human feedback while maintaining theoretical guarantees. The pattern shows increasing focus on ethical considerations in AI, particularly regarding bias in generative models and reliable human-AI collaboration frameworks. Scientific Awards: Faculty for the Future fellowship (2013-2015) from Schlumberger Foundation NSF CAREER Award American Family Funding Initiative Award with Fred Sala Dr. Vinayak leads an active research group with multiple PhD students across ECE and CS departments. She has secured significant research funding including an NSF grant for "Uncovering the cognitive and neural fingerprints that make each of us unique" in collaboration with Tim Rogers, Rob Nowak and Brad Postle. She is also co-organizing the MidWest Machine Learning Symposium and NeurIPS tutorials on dataset construction. Her research group operates at the intersection of theory and practice, developing mathematically grounded frameworks that address real-world challenges in dataset construction, human-AI collaboration, and reliable machine learning systems.