جست‌وجوی استاد و پژوهشگر

M Christina Whiteمشاهده پروفایل

استاد

• Organic Synthesis
• Transition-Metal Catalysis
• C-H Bond Oxidation
• +۴ مورد دیگر

Professor M. Christina White is the William H. and Janet G. Lycan Professor in the Department of Chemistry at the University of Illinois at Urbana-Champaign. Her research focuses on organic synthesis and transition-metal catalysis, particularly enantioselective C-H oxidation methods. She has developed innovative strategies for direct functionalization of C-H bonds using small molecule transition metal complexes, aiming to achieve Nature-level efficiency in synthetic pathways. B.A. in Biochemistry, Smith College (1992) Ph.D. in Chemistry, Johns Hopkins University (1998) Postdoctoral Fellowship, Harvard University (1999-2002) Her work redefines reactivity principles for complex molecule synthesis through predictive mechanistic studies. Key collaborations include Novartis, Vertex, and Pfizer, with a strong emphasis on translating chemical innovation to pharmaceutical development. Recent projects highlight ligand-controlled regioselectivity and biocatalysis integration. 15+ major awards including AAAS Fellow, NSF CAREER, and ACS Synthetic Organic Chemistry Award Mentored 5+ graduates now in academic/pharma roles (e.g., Assistant Professor Wayne State University, Associate VP at Lilly) Leadership in the White Group, driving interdisciplinary collaborations and industrial partnerships

Sandy Iraniمشاهده پروفایل

استاد

• Quantum Computing
• Algorithm Design
• Computational Complexity Theory
• +۵ مورد دیگر

Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.

Trine Grønhaug Halvorsenمشاهده پروفایل

استاد

• Analytical Chemistry
• Mass Spectrometry
• Protein Analysis
• +۳ مورد دیگر

Trine Grønhaug Halvorsen is a Professor at the University of Oslo's Department of Pharmacy, within the Faculty of Mathematics and Natural Sciences. Her research focuses on developing innovative analytical methods for protein analysis in complex biological samples using mass spectrometry. She specializes in sample preparation techniques, smart sampling strategies, and biomarker quantification. Education: PhD in Drug Analysis (2003), Cand.pharm. (1998) Awards: Publication Award (2001) for pioneering liquid-phase microextraction work Research interests include molecularly imprinted polymers, antibody-based capture methods, and electromembrane extraction. Her work emphasizes improving analytical sensitivity and speed for low-abundance proteins in clinical and biomedical contexts. Recent articles highlight advancements in smart samplers, dried blood spot analysis, and LC-MS-based protein quantification. These studies address challenges in biomarker detection and clinical diagnostics. Labs/Teams: Pharmaceutical Analytical Chemistry Group, SmartProteinAnalysis@UiO Teaching: Courses in bioanalytical chemistry, drug analysis, and sample preparation

Mahmoud Zarepourمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Bayesian Inference
• Analysis of Random Variables with Infinite Variance
• +۳ مورد دیگر

Mahmoud Zarepour is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds an MSc from Shiraz University (Iran) and a PhD from the University of Toronto. His research focuses on advanced statistical methodologies including Time Series Analysis, Nonparametric Bayesian Inference, and Analysis of Random Variables with Infinite Variance. He has supervised numerous graduate students and postdoctoral researchers, including Nada Habli, Reyhaneh Hosseini, and Sicheng Huang, among others. Education: MSc (Shiraz University, Iran) PhD (University of Toronto) Dr. Zarepour's research interests emphasize Bayesian nonparametric techniques, stochastic processes, and robust statistical methods. His work bridges theoretical developments with practical applications in areas such as change point detection, multivariate analysis, and resampling schemes. Notable contributions include advancements in Dirichlet process-based methods, bootstrap techniques for complex distributions, and asymptotic theory for unstable time series. His publications span over two decades, with recent works addressing Bayesian bootstrapping, quasi-Bayesian change point detection, and nonparametric inference for spherically symmetric distributions. His research group is part of the Statistics and Probability cluster at the University of Ottawa. Dr. Zarepour has advised multiple students and contributed to collaborative projects in statistical theory and methodology. His work often explores the intersection of probability theory and applied statistics, with an emphasis on rigorous mathematical foundations.

• Nonlinear Optimization
• Stochastic Optimization
• Distributed Optimization
• +۱ مورد دیگر

Raghu Bollapragada is an Assistant Professor in Operations Research and Industrial Engineering at the University of Texas at Austin, with affiliations to the Oden Institute and Machine Learning Laboratory. His research designs algorithms for nonlinear optimization, including constrained, stochastic, and distributed methods with applications in machine learning. Supported by NSF, Argonne National Laboratory, and Lawrence Livermore National Laboratory, recent work (2024-2025) develops gradient tracking for decentralized systems, adaptive sampling techniques, and hessian averaging for nonconvex problems. He was elected Vice Chair of Nonlinear Optimization for INFORMS (2024-2026).

Tiffany Yipمشاهده پروفایل

استاد

• Ethnic identity development
• Discrimination and mental health
• Sleep and academic achievement
• +۳ مورد دیگر

Tiffany Yip is a Professor of Psychology at Fordham University, located at Dealy Hall on the Rose Hill Campus. She holds a BA in Psychology from Cornell University (1997), and a MA/PhD in Psychology from New York University (2003), followed by postdoctoral training at the University of Michigan. Her research focuses on ethnic identity development, discrimination, sleep, and well-being among minority adolescents and young adults. Key areas include the interplay between ethnic identity and psychological adjustment, the impact of ethnic-specific stressors, and cultural diversity in developmental contexts. Dr. Yip’s professional roles include former Associate Editorships for Child Development , the Asian American Journal of Psychology , and Cultural Diversity and Ethnic Minority Psychology . She currently serves on the NIH MESH study section and is a Fellow of APA Divisions 7 (Developmental Psychology) and 45 (Culture, Ethnicity, and Race), as well as the Academy of Behavioral Medicine Research. Her work has been funded by NICHD, NIMHD, NIMH, and NSF. Her lab, Youth Development in Diverse Contexts , explores how cultural and social environments shape identity and well-being. She teaches courses including Multicultural Psychology, Research Methods, and Culture, Ethnicity, and Development. Collaborations include researchers at Princeton University, the University of Michigan, Arizona State University, and UCLA.

• Medical Imaging
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Madeleine Wyburd is an Associate Member of the Department of Computer Science at the University of Oxford. Her research focuses on advancing medical imaging technologies through deep learning and computer vision, particularly in the domain of fetal ultrasound analysis. She specializes in developing algorithms for 3D ultrasound reconstruction, anatomically plausible segmentation, and automated assessment of fetal brain development. Her work bridges computer science and clinical medicine, emphasizing applications in obstetrics and prenatal care. Key contributions include techniques like RapidVol for real-time 3D ultrasound volume reconstruction and TEDS-Net, a topology-preservation network for medical image segmentation. She also explores test-time adaptation methods to improve subcortical segmentation accuracy in fetal brain imaging. Wyburd's research integrates interdisciplinary methodologies from biomedical engineering and algorithm design. Recent projects analyze cortical plate development in second-trimester fetuses and compare 3D ultrasound with MRI volumetric measurements to enhance clinical diagnostic reliability. Her work aims to improve prenatal care through AI-driven tools that standardize fetal biometry assessment and reduce human error in clinical workflows. She collaborates with global institutions, as evidenced by her participation in the 34th World Congress on Ultrasound in Obstetrics and Gynecology. Her studies often address practical challenges like sparse-sampling in intrapartum ultrasound and normative brain maturation tracking up to 2 years post-birth.

Gerd Schmidtمشاهده پروفایل

استاد

• Business Management
• Accounting
• Taxation
• +۵ مورد دیگر

Prof. Gerd Schmidt holds dual professorships in Business Management/Human Resources and Production Management at Nordakademie University of Applied Sciences, where he also serves as Head of the MBA program. He teaches courses in strategic corporate management, group accounting, cost accounting, tax theory, and controlling. His academic career includes studies at the University of Mannheim and University of Oregon (USA), earning a Dr. rer. pol., MBA, and CPA qualifications. Research interests focus on auditing, taxation, corporate finance, and cross-border accounting challenges. Notable publications explore tax treatment of educational costs, sampling audit methods, auditor training, and cross-country accounting comparisons. Professional activities include corporate finance advisory, business rating, and insolvency analysis. He has held teaching roles at institutions including the University of Oregon, University of Mannheim, and Hamburg Chamber of Commerce. No scientific awards are listed, but his extensive publications demonstrate expertise in accounting and auditing practices. His teaching and academic leadership are central to his career, with continuous involvement in educational programs since 2004.

Farnoush Rezaei Jafariمشاهده پروفایل

پژوهشگر

• Explainable AI
• Efficient Machine Learning
• Generative Models
• +۳ مورد دیگر

Farnoush Rezaei Jafari is a Researcher and Ph.D. Candidate in Machine Learning at the Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). She is part of the Machine Learning / Intelligent Data Analysis group. Her M.Sc. in Computer Science was completed at TU Berlin in 2021. Her research interests focus on enhancing the transparency and efficiency of machine learning models, particularly in the areas of Explainable AI, Efficient Machine Learning, and Generative Models. She explores innovative methods to make AI systems more interpretable while maintaining computational efficiency. Farnoush has contributed to notable publications, including work on adaptive token sampling techniques for vision transformers and explainable sequence models. Her recent research explores symbolic XAI to bridge the gap between model outputs and human-understandable logic. No scientific awards have been explicitly mentioned in the provided information. She has no recorded advisees or grants listed. She is associated with the BIFOLD institute, contributing to collaborative research efforts in foundational AI and data science.

Luis Nunes Vicenteمشاهده پروفایل

استاد

• Continuous Optimization
• Computational Science and Engineering
• Machine Learning
• +۴ مورد دیگر

Luis Nunes Vicente is the Timothy J. Wilmott '80 Endowed Faculty Professor and Chair of Lehigh University's Department of Industrial and Systems Engineering since August 2018. He previously served as a faculty member at the University of Coimbra's Department of Mathematics. His research focuses on Continuous Optimization, Computational Science and Engineering, and Machine Learning/Data Science. Education: Ph.D. in Applied Mathematics (Rice University, 1996), M.A. in Applied Mathematics (Rice University, 1994), B.S. in Mathematics and Operations Research (University of Coimbra, 1990). Honors include the Lagrange Prize (2015), SIAM Fellowship (2024), and Fulbright Scholarship (1996). He co-authored the influential book Introduction to Derivative-Free Optimization (2009). Research Interests: Development of optimization algorithms for derivative-free and stochastic scenarios, multi-objective optimization, machine learning applications, and computational methods for engineering problems. Key contributions include trust-region methods, bilevel optimization frameworks, and fairness-aware machine learning models. Grants and Leadership: Secured over $1M from the Office of Naval Research (2024) and the Air Force Office of Scientific Research (2023). Served as Editor-in-Chief of Portugaliae Mathematica (2013–2018) and on editorial boards of top journals like SIAM Journal on Optimization . Elected President of the Operations Research Association of Chairs (2024). Visiting Positions: IBM T.J. Watson Research Center (2002/2003), Courant Institute/NYU (2009/2010), and CERFACS/Toulouse (2010–2015). Active in international conferences, delivering plenary lectures at 13th French-German Conference on Optimization, ICCOPT III, and ISMP 2018.

George Hripcsakمشاهده پروفایل

استاد

• Biomedical Informatics
• Electronic Health Records (EHRs)
• Data Mining
• +۴ مورد دیگر

George Hripcsak is the Vivian Beaumont Allen Professor of Biomedical Informatics and Director of Medical Informatics Services at New York-Presbyterian Hospital, Columbia University. He holds affiliations with the Vagelos College of Physicians and Surgeons and the Data Science Institute (DSI). His expertise spans clinical informatics, electronic health records (EHRs), and medical knowledge representation standards. Hripcsak earned degrees in chemistry, medicine, and biostatistics, and is a board-certified internist. Research focuses on leveraging EHR data for clinical research and patient safety through data mining and causal inference techniques. Notable contributions include the Arden Syntax (a national standard for medical knowledge representation) and leadership in the Observational Health Data Sciences and Informatics (OHDSI) network. He chairs the AMIA Standards Committee and has advised federal health informatics policies under HIPAA. His academic awards include Fellowships in the American College of Medical Informatics (1995) and New York Academy of Medicine. Current projects emphasize federated learning, genomic risk prediction, and large-scale real-world evidence analysis through initiatives like LEGEND-T2DM and All of Us Research Program. Educations: MD, Biostatistics, Chemistry Labs/Teams: OHDSI, DSI, Medical Informatics Services Grants & Funding: Not explicitly listed in provided texts

Katherine Laniganمشاهده پروفایل

استاد

• Analytical Chemistry
• Environmental Chemistry
• Trace Metal Analysis
• +۳ مورد دیگر

Katherine Lanigan is Professor of Chemistry in the Department of Chemistry and Biochemistry at University of Detroit Mercy's College of Engineering & Science, teaching General Chemistry, Quantitative Analysis, and Instrumental Analysis since joining the faculty in 2001. Her educational background includes: Ph.D. in Chemistry, University of Iowa (1996) B.S. in Chemistry, University of Dayton (1990) Dr. Lanigan's research integrates analytical chemistry with environmental science through two primary avenues: trace metal analysis in ecological systems using FAAS, XRF, and UV/vis spectroscopy, and investigations of photocatalytic reactions involving nanoparticles for contaminant degradation. Her secondary focus examines adsorbed species at liquid/solid interfaces of metal oxide films using ATR-FTIR, with recent expansion into cloud point extraction methods for metal preconcentration in water samples. These efforts address critical environmental monitoring and remediation challenges. Analysis of her 2015-2024 publications reveals dual expertise in environmental analytical chemistry and pedagogical innovation, with environmental work emphasizing practical metal detection techniques and education research developing theme-based water quality activities across instructional levels. Her scientific recognition includes: National Institute of Health ReBUILDetroit Pilot Project Course Development Award (2017, co-PI) UDMPU Faculty Research Awards (2016, 2015) Dr. Lanigan has mentored 38 students since 2002, with alumni pursuing dentistry (University of Detroit Mercy, Case Western, Michigan, Pennsylvania), medicine, and chemistry careers. Her secured funding supports both research instrumentation and educational development initiatives. The active Lanigan Research Group currently comprises Jisoo Kim, Heewoong Kim, and Tina Lee, with Kelly Cho, Alina Varghese, and Abdel El Sayed joining for Fall 2025 projects on photocatalytic nickel oxidation and drinking water metal analysis.

Timothy K. Mintonمشاهده پروفایل

استاد

• Gas-phase and gas-surface reaction dynamics
• Shock layer chemistry in hypersonic flows
• Material degradation in low-Earth-orbital environments
• +۳ مورد دیگر

Timothy K. Minton is a Professor in the Department of Aerospace Engineering Sciences at the University of Colorado, Boulder, and a member of the Aerospace Mechanics Research Center (AMREC). He holds a PhD from the University of California, Berkeley (1986) and a BS from the University of Illinois, Urbana-Champaign (1980). His research focuses on gas-phase and gas-surface reaction dynamics, particularly in hypersonic flow environments and space material degradation. He has held editorial roles at The Journal of Spacecraft and Rockets and The Journal of Physical Chemistry , and has been recognized with prestigious awards including Fellowships from the American Physical Society (2015) and American Association for the Advancement of Science (2012). Dr. Minton’s work emphasizes understanding atomic oxygen interactions with satellite materials, shock layer chemistry, and material durability in low-Earth-orbit environments. His innovations include the development of the Table-Top Shock Tunnel (TTST) for rapid material testing and durable coatings for space applications. He has also contributed to advancing models for carbon oxidation and nitridation processes. His awards highlight leadership in aerospace and chemistry, including the NASA Monetary Award (1995) for semiconductor etching innovations and the Charles & Nora Wiley Award (2002) for meritorious research. He maintains a courtesy appointment in the Department of Chemistry at CU Boulder and actively collaborates with industry (e.g., Skeyeon, Inc.) and international institutions.

• Food Safety
• Mycotoxin Contamination
• Circular Economy
• +۳ مورد دیگر

Ine van der Fels-Klerx is an Associate Professor in Business Economics at Wageningen University & Research, with a focus on food safety and risk management. She is also a Programme and Account Management lead, overseeing interdisciplinary projects. Her work integrates economic analysis with food safety challenges, particularly addressing mycotoxin contamination, climate change impacts, and circular economy solutions. Key collaborations include projects on early warning systems for mycotoxins and cost-effective monitoring strategies for contaminants in food supply chains. Her research spans topics such as alternative protein sources, insect-based feed production, and resilient supply chain designs. Notable projects include developing decision support tools for risk-based monitoring and assessing the economic feasibility of food safety interventions. She has advised multiple PhD candidates, including those studying mycotoxin management in maize and climate change effects on aflatoxins. Media contributions highlight her expertise in food safety, including interviews on predicting grain contamination and smart monitoring techniques. She actively participates in workshops and conferences, discussing digitalization in food systems and the role of artificial intelligence in food safety prevention.

• Algorithms
• Data Streams
• Machine Learning
• +۳ مورد دیگر

David P. Woodruff is a Professor in the Department of Computer Science at Carnegie Mellon University, part of the Theory Group within the School of Computer Science. He is actively involved in academic leadership roles, including chairing the CATCS (Conference on Theoretical Computer Science) and serving as PC chair for SODA 2024 and ICALP 2022. His research focuses on algorithms, data streams, machine learning, numerical linear algebra, sketching, and sparse recovery. He has been recognized with awards such as the Herbert Simon Award for teaching and the PODS Best Paper Award. Woodruff has advised numerous students and postdocs, including notable scholars like Ainesh Bakshi, Rajesh Jayaram, and Hongyang Zhang. His work often addresses foundational challenges in theoretical computer science, with contributions to distributed computing, streaming algorithms, and privacy-preserving techniques. He has published extensively in top conferences like NeurIPS, ICML, FOCS, and STOC, covering topics ranging from low-rank approximation to adversarial robustness in data streams. His teaching includes courses like Algorithms for Big Data and core algorithms courses, reflecting his commitment to both research and education. Collaborations span academia and industry, with applications in genomics and secure computation. Woodruff is a key contributor to the Foundations of Data Science program at the Simons Institute.