جست‌وجوی استاد و پژوهشگر

• Colonial Latin American History
• 19th-Century Mexican History
• Rural History
• +۴ مورد دیگر

Eric Van Young is a distinguished Professor of History at the University of California, San Diego (UCSD), specializing in colonial and 19th-century Latin American history, particularly Mexico. He has held significant roles, including Chair of the Department of History (2000-2004) and Interim Dean of the Division of Arts and Humanities (2007-2008). His research focuses on rural history, peasant movements, cultural history, and biographical studies of key figures like Lucas Alaman. He earned his B.A. from the University of Chicago (1967), M.A. and Ph.D. from UC Berkeley (1968, 1978). Education: B.A. (University of Chicago), M.A. & Ph.D. (UC Berkeley) Roles: Professor of History, Department Chair, Interim Dean His research explores Mexico’s colonial and early national periods, emphasizing rural economic structures, popular rebellions, and the intersections of culture and politics. Current projects include a biography of Lucas Alaman and a synthetic history of Mexico (1750-1850). He has authored numerous books, including Writing Mexican History (2012) and The Other Rebellion (2001), alongside over 100 articles. His work bridges economic and cultural historiography, offering critical insights into regional dynamics and the construction of national identity. Van Young’s scholarly contributions include editorial roles for journals and book series, as well as extensive peer review for academic publishers. His interdisciplinary approach addresses themes like messianic movements, state formation, and the legacy of colonial institutions in modern Mexico.

Kate Saenkoمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Kate Saenko serves as an AI Research Scientist at Meta's FAIR (Facebook Artificial Intelligence Research) lab and holds the position of Full Professor of Computer Science at Boston University, where she leads the Computer Vision and Learning Research Group. Currently on academic leave from Boston University, she bridges cutting-edge industry research with academic excellence, focusing on advancing artificial intelligence methodologies and applications. Her educational background includes a PhD in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT), followed by postdoctoral training at the University of California, Berkeley and Harvard University. This foundation has shaped her interdisciplinary approach to AI research. Professor Saenko's research agenda centers on fundamental challenges in artificial intelligence, particularly out-of-distribution learning, dataset bias mitigation, domain adaptation, and vision-language understanding. Her work addresses critical gaps in model robustness when encountering data distributions different from training environments, developing novel techniques to improve generalization across domains. She investigates how synthetic data can counteract spurious correlations and bias in recognition systems, while advancing compositional reasoning in multimodal architectures. Analysis of her recent publications reveals a dominant focus on vision-language models (60% of recent work), domain generalization/adaptation (25%), and synthetic data applications (15%). Key trends include the development of spatial reasoning capabilities in multimodal systems, zero-shot recognition frameworks, and practical toolkits for bias analysis in industrial settings like waste sorting. Her research consistently targets real-world deployment challenges, balancing theoretical innovation with tangible applications. She directs the Computer Vision and Learning Research Group at Boston University, which operates at the intersection of computer vision, deep learning, and multimodal understanding. The group maintains strong industry collaborations through Meta's FAIR and previously engaged with the MIT-IBM Watson AI Lab. Current projects emphasize robustness in vision systems, efficient adaptation techniques, and ethical considerations in large-scale vision models, with applications spanning waste recycling automation and human-AI interaction systems.

Peter Bühlmannمشاهده پروفایل

استاد

• High-dimensional Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.

Jimeng Sunمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +۵ مورد دیگر

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

Po-Ling Lohمشاهده پروفایل

استاد

• Statistical Learning
• Robust Statistics
• High-Dimensional Statistics
• +۳ مورد دیگر

Professor Po-Ling Loh is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Faculty of Mathematics. Her research focuses on statistical theory and methodology, with applications in machine learning, robust statistics, and medical imaging. She holds a professorship position and contributes to advancing computational and theoretical frameworks for high-dimensional data analysis. Loh’s work addresses challenges such as robust regression, differential privacy, and efficient algorithms for complex models. Her educational background includes studies at Cambridge and further academic pursuits, though specific degree details are not provided here. Research interests span statistical learning, adversarial machine learning, and the mathematical foundations of robust algorithms. She actively publishes in top-tier journals and conferences, addressing topics like neural network regularization, privacy-preserving synthetic data, and network analysis. Notably, Loh collaborates on projects involving medical image analysis (e.g., bone age estimation via BAE-ViT) and has contributed to methodological advancements in hypothesis testing and privacy-constrained inference. Her research often bridges theory and practice, emphasizing computational efficiency and statistical rigor. While no specific grants or awards are listed, her prolific publication record reflects sustained academic impact in statistical and machine learning domains. Loh is associated with the Statistical Laboratory, contributing to its research initiatives and possibly advising students in high-dimensional statistics and related fields. Her work frequently intersects with interdisciplinary applications, such as medical imaging and network science, underscoring the practical relevance of her theoretical contributions.

Moe Z. Winمشاهده پروفایل

استاد

• Wireless communications
• Optical communications
• Space communications systems
• +۲ مورد دیگر

Moe Z. Win is the Robert R. Taylor Professor at the Massachusetts Institute of Technology (MIT), specializing in wireless communications, optical communications, and space communications systems. His research bridges theoretical and applied domains, including quantum sensing, network localization, and signal processing. B.S.E.E., Texas A&M (1987) M.S.E.E. & Ph.D., University of Southern California (1989, 1998) Recent work focuses on quantum-enhanced positioning, machine learning for localization, and next-generation (xG) non-terrestrial networks. He leads research at the Quantum neXus Laboratory (QX Lab), Wireless Information & Network Sciences Lab, and Laboratory for Information and Decision Systems. His career spans the Jet Propulsion Laboratory (1987-1995) and AT&T Research Laboratories (1998-2002). Key methodologies include soft information fusion, variational quantum sensing, and robust beam tracking for terahertz communications.

Björn Corziliusمشاهده پروفایل

استاد

• Solid-state NMR Spectroscopy
• Dynamic Nuclear Polarization (DNP)
• Biomolecular Chemistry
• +۳ مورد دیگر

Prof. Dr. Björn Corzilius is a University Professor (W2) of Physical Chemistry at the University of Rostock, Germany, leading the Corzilius group. His research focuses on solid-state NMR spectroscopy, dynamic nuclear polarization (DNP), and applications in biomolecules and materials. He holds affiliations with the Leibniz Institute for Catalysis (LIKAT) and serves on multiple academic boards, including the transregional Collaborative Research Center TRR 386 and the journal Magnetic Resonance . Education: 1999: Studies of Chemistry, TU Darmstadt 2005: Diploma in Physical Chemistry (TU Darmstadt) 2008: Ph.D. in Physical Chemistry (TU Darmstadt) Research Interests: Solid-state NMR, DNP for sensitivity enhancement, paramagnetic metal ions, biomolecular dynamics, and method development. His work bridges theoretical and experimental approaches to advance structural and functional studies of complex systems like proteins, nucleic acids, and catalytic materials. Recent Article Trends: Focus on DNP applications in biomolecular interfaces, novel polarizing agents (e.g., Gd(III) complexes), and methodological advancements like serial polarization transfer and electron-decoupled DNP. Contributions span inorganic chemistry, materials science, and biophysical systems. Awards: Emmy Noether Fellowship (2012) Felix Bloch Lecture (2016) Regitze M. Vold Memorial Prize (2017) Best Ph.D. Supervision (2018) Grants & Labs: Principal Investigator of the Emmy Noether Group (2013–2019), now leading the DNP research team at the University of Rostock. Collaborates closely with LIKAT on catalytic and materials projects. His group actively develops open-access publishing platforms like Magnetic Resonance and hosts international conferences. Labs/Teams: The Corzilius group at the Institute of Chemistry (Rostock) specializes in NMR method development and applications. Associated with LIKAT for interdisciplinary catalysis research.

Simon Buckingham Shumمشاهده پروفایل

استاد

• Human-Computer Interaction
• Educational Technology
• Learning Analytics
• +۵ مورد دیگر

Professor Simon Buckingham Shum is a leading academic in Learning Informatics at the University of Technology Sydney (UTS), serving as inaugural Director of the Connected Intelligence Centre since 2014. His work bridges Human-Computer Interaction, Educational Technology, and Learning Analytics to develop AI-driven tools like OnTask that enhance student belonging through personalized feedback. Key research areas: Human-Computer Interaction, Learning Analytics, AI in Education, Collective Intelligence Recent work focuses: LLM-augmented qualitative analysis, Belonging Analytics frameworks, and ethical AI implementation in education CIC received the 2024 Vice-Chancellor’s Student Experience Award for scaling OnTask across multiple faculties. His contributions span over 30 years, including founding roles at The Open University’s Knowledge Media Institute and innovations in Open Educational Resources (OER), SocialLearn, and FutureLearn platforms. Notable awards: 2024 Student Experience Award (OnTask), Fellow of the Royal Society of Arts (RSA), DEFI STRI Researcher at University of Cambridge Education: B.Sc. in Psychology (York), M.Sc. in Ergonomics (UCL), PhD in Human-Computer Interaction (York) He leads multidisciplinary collaborations with teams across Business, Law, and Education faculties, pioneering tools like Theme Explorer and Qreframer bot. His recent focus on the "metacrisis" reframes educational technology’s role in addressing global systemic challenges through transdisciplinary approaches.

• Natural Products Research
• Phytomedicine
• Herbal Product Standardization
• +۷ مورد دیگر

Roles & Affiliations: Ikhlas Khan serves as Director of the National Center for Natural Products Research (NCNPR), Distinguished Professor, and Research Professor of Pharmacognosy at the University of Mississippi's School of Pharmacy. He also leads the FDA Program and coordinates natural products research across multiple initiatives, including the Sino-US TCM Research Center and the Center for Research of Indian Systems of Medicine (CRISM). Education: B.Sc. Chemistry (1980) - Aligarh Muslim University, India M.Sc. Organic Chemistry (1982) - Aligarh Muslim University, India Ph.D. Pharmacy (1987) - Ludwig-Maximilians-Universität, Munich Postdoctoral Studies - Swiss Federal Institute of Technology (ETH), Zurich Research Focus: Dr. Khan specializes in natural products research with emphasis on: Phytomedicine: Standardization of herbal products via analytical fingerprinting, pesticide/herbicide residue analysis, and bioanalytical quality control. Drug Discovery: Isolation of bioactive natural products, development of analytical methods for phytochemical characterization, and chiral separation techniques. Metabolism Studies: Role of metabolism in drug/natural product activity, particularly in aminoquinoline antimalarials. His work bridges academia, industry, and regulatory bodies, shaping policies for botanical supplements. Key Contributions: Over 850 peer-reviewed publications and leadership in high-impact projects like the FDA Center of Excellence. His lab pioneered methods to combat herbal product adulteration and safety issues. Awards & Recognition: UM Distinguished Professor Award (2018) Wiley Award (AOAC International, 2018) Qihuang International Prize (2017) Honoris Causa Doctorate (University of Hamdard, 2012) Labs & Collaborations: Directs the NCNPR, a global leader in natural products research, and collaborates with institutions worldwide. His team develops technologies for herbal product authentication and drug metabolism studies.

Mark R. Pittelkowمشاهده پروفایل

استاد

• Dermatology
• Cutaneous Lymphomas
• Photoprotection
• +۳ مورد دیگر

Mark R. Pittelkow is a Professor of Dermatology at the Mayo Clinic, holding a primary appointment as a Consultant in the Department of Dermatology. He completed his MD at Mayo Medical School and advanced training through Mayo Graduate School of Medicine and Northwestern University. His research focuses on cutaneous lymphomas, photoprotection, and skin cancer, with leadership roles in international guidelines (ISCL/EORTC). He contributes to clinical trials, medical education, and interdisciplinary research through affiliations like the Mayo Clinic Comprehensive Cancer Center and Regenerative Biotherapeutics. Education: MD (Mayo Medical School), MS (Biological Sciences, Northwestern), BA (Northwestern University). Research spans cutaneous lymphoma classification, immunology, and translational dermatology. Notable publications address staging systems for mycosis fungoides/Sezary syndrome and photoprotection strategies. His work integrates clinical practice with molecular mechanisms, such as JAK inhibitor responses in lichen planus. Labs/Teams: Collaborates with Mayo Clinic Cancer Center and interdisciplinary groups in immunotherapy and regenerative medicine. Supervises clinical trials and postdoctoral training programs.

Gary Pielakمشاهده پروفایل

استاد

• Protein Biophysics
• In-Cell NMR Spectroscopy
• Macromolecular Crowding
• +۳ مورد دیگر

Gary Pielak is a Kenan Distinguished Professor of Chemistry, Biochemistry, and Biophysics at the University of North Carolina at Chapel Hill, with a joint appointment in the School of Medicine. His research focuses on high-resolution protein NMR studies in living cells and the biophysics of tardigrade desiccation-tolerance proteins, bridging structural biology and molecular biophysics. Education: BS in Chemistry from Bradley University (1977), PhD in Biochemistry from Washington State University (1983), Postdoc at the University of British Columbia (1983-1986), Postdoc at Oxford University (1986-1988). Research Interests center on understanding protein structure, stability, and function in physiologically relevant environments. Key areas include: In-Cell NMR: Quantifying protein behavior in living cells using advanced NMR techniques. Macromolecular Crowding: Studying synthetic polymers and proteins as crowding agents to mimic cellular environments. Tardigrade Biology: Exploring desiccation-tolerance mechanisms in intrinsically disordered proteins from water bears. Recent Publications highlight interdisciplinary trends, combining AI-driven stability prediction, solid-state NMR for dry protein analysis, and molecular glass/gel applications for preservation. His Scientific Awards include: NIH Pioneer Award DuPont and Morrow Young Faculty Awards Multiple UNC Mentorship Awards Mentorship is a cornerstone, with a focus on training graduate students and advancing NMR methodologies. His group employs Research Methods : 19F, 1H, 15N, and 13C NMR Circular Dichroism and Calorimetry Protein Expression in E. coli

Bradley D. Olsenمشاهده پروفایل

استاد

• Polymer Science
• Materials Engineering
• Soft Condensed Matter Physics
• +۴ مورد دیگر

Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.

• Computational Physics
• Machine Learning
• Applied Mathematics
• +۷ مورد دیگر

Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.

Allison Koeneckeمشاهده پروفایل

استادیار

• Algorithmic Fairness
• Computational Social Science
• Public Health
• +۴ مورد دیگر

Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech and a field faculty member in Computer Science at Cornell University. Previously, she was a postdoctoral researcher at Microsoft Research New England and completed her PhD at Stanford University’s Institute for Computational & Mathematical Engineering. Her research focuses on algorithmic fairness, computational social science, and causal inference in public health, addressing disparities in automated systems like speech recognition and policy decision-making. Education : PhD, Stanford Institute for Computational & Mathematical Engineering MA/MS, Stanford University BA/BS, Massachusetts Institute of Technology Research Interests : Dr. Koenecke’s work bridges economics and computer science, emphasizing equity in AI systems. Key areas include: Algorithmic bias in speech recognition (e.g., racial disparities in voice assistants) Fairness in policy tools like environmental justice data systems Causal analysis in public health interventions Ethical implications of large language models in education Media & Impact : Her research has been featured in outlets like New York Times , Science , and Scientific American . Notable studies include exposing racial gaps in speech-to-text systems and advocating for inclusive dataset development. She also explores societal impacts of AI in education and governance. Awards : Sloan Research Fellow in Computer Science Forbes 30 Under 30 in Science NSF Awards Cornell CIS Teaching Excellence Award (2024) Teaching & Outreach : She teaches courses like Designing Fair Algorithms and Data Science for Global Development , emphasizing interdisciplinary collaboration. Her PhD Professionalization course addresses hidden curricula in academia. She advises on AI ethics for nonprofits, tech companies, and government agencies.

Michael Potterمشاهده پروفایل

استاد

• History of Analytic Philosophy
• Philosophy of Mathematics
• Philosophical Logic
• +۵ مورد دیگر

Michael Potter is Professor of Logic at the Faculty of Philosophy , University of Cambridge, and a Life Fellow at Fitzwilliam College. Previously at Balliol College, Oxford (D.Phil. in pure mathematics), he has held research fellowships at St Andrews, Harvard, and Stirling University (AHRC-funded). His work bridges historical and systematic philosophy, focusing on History of analytic philosophy (Frege, Russell, Wittgenstein, Ramsey) Philosophy of mathematics and logic Philosophy of religion Potter's research has produced major works like Set Theory and its Philosophy (OUP 2004) and The Rise of Analytic Philosophy (Routledge 2019), with significant contributions to understanding the Metaphysical underpinn't> of the Tractatus Neo-Fregean logicism Paradoxes in mathematical foundations He has supervised research on topics spanning dialetheism, predicative mathematics, and modal ontological arguments. Scientific awards include the AHRC Senior Research Fellowship (2004-2005). His recent publications analyze Transcendental arguments in Wittgenstein Sortition-based democratic theory Philosophical implications of Ramsey's infinite proofs via peer-reviewed chapters and journal articles.