جستجوی استاد راهنمای دانشگاهی

• Climate Policy
• Long-Term Policy
• International Institutions
• +۳ مورد دیگر

Detlef F. Sprinz is a Senior Scientist at the Potsdam Institute for Climate Impact Research (PIK) and a Professor at the Faculty of Economics and Social Sciences of the University of Potsdam, Germany. During 2024-2025, he leads the project "AMaReNa - A Market for the Restoration of Nature" , focusing on carbon removals. His academic background includes a Ph.D. and M.A. in Political Science from the University of Michigan and an M.A. in Economics from the University of the Saarland. Research Interests: Climate policy at multiple levels, long-term policy design, evaluation of international/national institutions, European/international environmental policy, and modeling political decisions for sustainability. Notable Projects: Tandem Fellowship (2020-2022) for interdisciplinary course development, Visiting Researcher at University of Lund (2022-2023), and coordination of research at the Käte Hamburger Kolleg (2017). Publications emphasize climate cooperation under the Paris Agreement, effectiveness of environmental regimes, and predictive modeling for climate negotiations. His work spans empirical analyses of acid rain regulation, Kyoto Protocol implementation challenges, and bioenergy accounting errors. Scientific Awards & Memberships: Tandem Fellowship (German Stifterverband) Member, European Academy Co-founder, Ecologic Institute Teaching includes courses on global climate governance, political decision modeling, and environmental policy at the University of Michigan, Yale, University of Oslo, and University of Potsdam. He has contributed to IPCC’s Sixth Assessment Report as a Lead Author. Advisory Roles: Chairmanship of the European Environment Agency’s Scientific Committee (2009-2012), Senior Research Fellow at CICERO (2011-2013), and consultancy for governments, corporations, and think tanks.

Elette Boyleمشاهده پروفایل

دانشیار

• Cryptography
• Computer Security
• Coding Theory
• +۷ مورد دیگر

Elette Boyle is an Associate Professor at Reichman University (IDC Herzliya) and a Senior Scientist at NTT Research . She holds a Ph.D. in Mathematics from MIT (advised by Shafi Goldwasser and Yael Tauman Kalai) and an undergraduate degree from Caltech . Education Ph.D. in Mathematics, MIT B.S. in Mathematics, Caltech Her research focuses on cryptographic solutions for secure data processing , particularly in secure multi-party computation , function/homomorphic secret sharing , and distributed point functions . Recent work explores topology-hiding communication , memory checking complexity , and sublinear-communication MPC . Key trends in her publications include: Advancements in Function Secret Sharing for branching programs and sparse vectors. Efficient Secure Multi-Party Computation protocols with preprocessing. Information-theoretic and computational Topology-Hiding Broadcast schemes. Optimized Oblivious Transfer with constant computational overhead. Scientific Awards European Research Council (ERC) Award Israeli Science Foundation (ISF) Grant United States Air Force Office of Scientific Research (AFOSR) Grant Google Research Scholar Award International Association for Cryptologic Research (IACR) Recognition As Director of the Foundations & Applications of Cryptography (FACT) Research Center , she leads collaborative work with institutions like Technion Israel , Cornell University , and NTT Research . Her students include Pierre Meyer (Ph.D.) , Matan Hamilis (Ph.D.) , and D'or Banon (MSc.) .

• Statistical Genomics and Bioinformatics
• High-Dimensional Data Analysis
• Bayesian Statistics
• +۴ مورد دیگر

Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.

Damir Filipovicمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Machine Learning
• +۷ مورد دیگر

Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.

Katherine J. Franzمشاهده پروفایل

استاد

• Bioinorganic Chemistry
• Metal Homeostasis
• Antimicrobial Agents
• +۴ مورد دیگر

Katherine J. Franz is a Professor of Chemistry at Duke University, affiliated with the Trinity College of Arts & Sciences and the Duke Cancer Institute. She holds a Ph.D. from MIT (2000) and a B.A. from Wellesley College (1995). Her research focuses on bioinorganic chemistry, particularly metal ion coordination in biological systems, with applications in antimicrobial therapies, cancer metallomics, and neurodegenerative diseases. Key projects include developing prochelators targeting fungal and bacterial pathogens, studying copper's role in antifungal drug efficacy, and designing light-activated metal complexes for controlled drug release. Dr. Franz has received numerous awards including the Camille Dreyfus Teacher-Scholar Award (2009), Sloan Research Fellowship (2008), and the NSF CAREER Award (2005). She leads a lab with 6 current students/mentees and has secured grants from NIH, NSF, and the US-Israel Binational Science Foundation. Her lab's work spans from fundamental metalloprotein studies to translational drug development, emphasizing interdisciplinary approaches in chemistry and biology. Education: Ph.D. in Chemistry, MIT, 2000 B.A. in Chemistry, Wellesley College, 1995 Research Interests: Metal homeostasis in pathogens, copper's role in antifungal resistance, prodrug design for targeted therapy, and mechanistic studies of metalloproteins. Grants: Tri-Institutional Molecular Mycology Training Program (NIH, 2024–2029) Duke PREP Biomedical Sciences Program (NIGMS, 2022–2027) Copper-Mucin Interaction Study (BSF, 2022–2026) Her lab's publications (n=15+ since 2020) highlight breakthroughs in prodrug selectivity, copper-induced protein toxicity, and histatin antifungal mechanisms. The Franz Lab actively collaborates with clinicians and computational scientists to advance therapeutic strategies addressing unmet medical needs in infectious diseases and cancer.

Ahmad Afsahiمشاهده پروفایل

استاد

• Parallel Processing
• High-Performance Computing
• Network-Based Computing
• +۵ مورد دیگر

Dr. Ahmad Afsahi is a Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada. He leads the Parallel Processing Research Laboratory (PPRL) and chairs the Graduate Studies committee in ECE. His research focuses on parallel processing, high-performance computing (HPC), and network-based systems, with emphasis on communication runtime systems, accelerated computing, and deep learning infrastructure. Education: Ph.D. (Electrical Engineering, 2000) from University of Victoria; M.Sc. (Computer Engineering, Sharif University of Technology); B.Sc. (Computer Engineering, Shiraz University). Research interests include parallel programming models, MPI optimization, GPU-aware communication, network-aware algorithms, and power-efficient HPC systems. He is a Senior Member of IEEE, ACM member, and licensed Professional Engineer in Ontario. Key Awards: Canada Foundation for Innovation Award, Ontario Innovation Trust Award. Over 50 publications in top venues like SC, EuroMPI, IPDPS, and IEEE journals. Current teaching includes cluster computing and digital systems. Labs/Groups: PPRL, Queen's Collaborative Graduate Specialization in Computational Science and Engineering, Data, Analytics, and Computing (DAC) Research Group.

James Shorterمشاهده پروفایل

استاد

• Protein Folding and Proteostasis
• Neurodegenerative Diseases
• Prion and Amyloid Biology
• +۷ مورد دیگر

James Shorter is a Professor of Biochemistry and Biophysics at the Perelman School of Medicine, University of Pennsylvania. He is affiliated with multiple prestigious institutes, including the Institute on Aging (IOA), the Institute for Translational Medicine and Therapeutics (ITMAT), the Penn Center for AIDS Research (CFAR), the Chemistry-Biology Interface (CBI), and the Penn Institute for RNA Innovation. He mentors several training programs such as the Penn Summer Undergraduate Internship Program (SUIP), PennPREP, and the Translational Research Immersion Program (TRIP), and serves as a Primary Trainer at the Center for Neurodegenerative Research (CNDR). Ph.D. in Cell Biology, University of London, 2000 M.A. in Biology, University of Oxford, 1995 Dr. Shorter’s research focuses on protein homeostasis, particularly the mechanisms of protein disaggregation and the role of prion-like domains in neurodegenerative diseases such as ALS, Alzheimer’s, Parkinson’s, and frontotemporal lobar degeneration. His lab investigates the Hsp104 disaggregase from yeast and has engineered variants to combat human proteinopathies. They also identified the mammalian disaggregase system (Hsp110/Hsp70/Hsp40) and explore how small molecules and nuclear import receptors can reverse pathological phase transitions of RNA-binding proteins like TDP-43 and FUS. His work bridges structural biology, genetics, and translational neuroscience. His recent publications highlight trends in targeting TDP-43 and FUS proteinopathies, engineering Hsp104 for selective detoxification, understanding mitochondrial disaggregases like Skd3, and modulating phase transitions with nuclear import receptors. His research spans from fundamental mechanisms of protein folding to therapeutic development for neurodegenerative diseases. Faculty Member, Institute on Aging (IOA) Faculty Member, Institute for Translational Medicine and Therapeutics (ITMAT) Mentor, Penn Summer Undergraduate Internship Program (SUIP) Primary Trainer, Center for Neurodegenerative Research (CNDR) Faculty Member, Penn Center for AIDS Research (CFAR) Member, Penn Institute for RNA Innovation Mentor, Translational Research Immersion Program (TRIP) Dr. Shorter advises numerous graduate students and postdoctoral researchers through the Biochemistry and Molecular Biophysics, Pharmacology, Neuroscience, and Cell and Molecular Biology graduate groups. His lab receives funding from NIH and other sources to support research on protein disaggregation, phase separation, and neurodegenerative disease mechanisms. He has trained many scientists now active in academia and biotech. His lab, located in Stellar-Chance Laboratories, operates at the intersection of biochemistry, cell biology, and translational medicine, with active projects on Hsp104 engineering, mitochondrial proteostasis, and the role of RNA-binding proteins in disease. The lab collaborates widely across Penn and with international partners to advance understanding and treatment of protein misfolding disorders.

Nikita Zhivotovskiyمشاهده پروفایل

استادیار

• Mathematical Statistics
• Applied Probability
• Statistical Learning Theory
• +۵ مورد دیگر

Nikita Zhivotovskiy is an Assistant Professor in the Department of Statistics at the University of California Berkeley within the College of Letters and Science. His research spans the intersection of mathematical statistics, probability theory, and statistical learning theory with particular focus on high-dimensional data analysis and non-parametric inference. His research interests include mathematical statistics, applied probability, statistical learning theory, high-dimensional data analysis, non-parametric inference, and artificial intelligence/machine learning. Zhivotovskiy's work addresses fundamental questions in statistical learning theory, including risk bounds, algorithmic stability, and convergence rates, with applications spanning multiple domains including robust statistics and private learning. His recent publications (2021-2025) demonstrate significant contributions to theoretical machine learning, particularly in statistical learning theory, risk bounds, high-dimensional statistics, and algorithmic stability. These works appear in top venues including NeurIPS, COLT, and FOCS, reflecting the theoretical depth and importance of his contributions to the field. Among his notable achievements is a Best Paper Award at the Conference on Learning Theory (COLT) in 2020 for his work on 'Proper Learning, Helly Number, and an Optimal SVM Bound.' Zhivotovskiy has also contributed to the theoretical foundations of PAC learning, risk minimization, and statistical aggregation. Prior to his current position, Zhivotovskiy was a postdoctoral researcher at ETH Zürich (2021-2022) and Google Research (2019-2020). He completed his PhD at Moscow Institute of Physics and Technology in 2018 under the supervision of Vladimir Spokoiny and Konstantin Vorontsov.

Nicholas Matteiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Decision Making
• +۳ مورد دیگر

Nicholas Mattei is an Associate Professor of Computer Science at Tulane University and Co-Director of the Tulane Center for Community Engaged AI. He holds a Ph.D. from the University of Kentucky (2012) and researches artificial intelligence, machine learning, and decision-making systems. His work combines theory, data, and experiments to develop algorithms supporting individual and group decision-making. Dr. Mattei's research spans AI ethics, fairness in algorithms, computational social choice, and preference learning. He has published over 100 academic articles and received multiple grants from organizations including Google, IBM, and the National Science Foundation, including a 2024 NSF CAREER Award. He co-authored 'Computing and Technology Ethics: Engaging Through Science Fiction' from MIT Press. Prior to joining Tulane, he held research positions at IBM Research, Data61/CSIRO, and NASA Ames Research Center. His teaching portfolio includes courses on Discrete Mathematics, Data Science, Artificial Intelligence, and Multi-agent Systems.

• Computer Networks
• Data Center Networking
• Software Defined Networking (SDN)
• +۵ مورد دیگر

Yashar Ganjali is a Professor in the Department of Computer Science at the University of Toronto , leading the Systems and Networking Group . His research spans computer networks , with a focus on data center networking , software-defined networking (SDN) , and congestion control . Education : Not explicitly detailed, but inferred from academic rank as a Professor. His work on flow consolidation , load migration in SDN controllers , and machine learning for network management has been influential. Recent projects include FORESIGHT (2025) for ML-driven scheduling and Meta-Migration (2023) to reduce switch migration latency. Scientific Awards include the IFIP Networking 2025 Best Paper Award . Collaborations with institutions like Google (2024) and Facebook (2019) highlight his industry impact. Advisees include Sepehr Abbasi Zadeh (PhD, 2024). Current projects integrate optical packet switching and eBPF-based network augmentation , aiming to address scalability, micro-bursts, and resource allocation efficiency in cloud environments.

George Cantwellمشاهده پروفایل

استادیار

• Network Science
• Complex Systems
• Statistical Inference
• +۴ مورد دیگر

Dr. George Cantwell is an Assistant Professor in the Department of Engineering at the University of Cambridge, affiliated with Cambridge Infectious Diseases. He specializes in computational methods for inference problems, particularly in disease spreading across networks. Education: PhD in Physics from the University of Michigan; postdoctoral fellowship at the Santa Fe Institute His research focuses on network science , complex systems , and statistical inference , with an emphasis on computational approaches. His work spans theoretical and applied domains, including: Message passing algorithms for heterogeneous networks Bias correction in social network analysis (friendship paradox) Statistical inference of network structure from noisy data Modeling judicial voting behavior through network interactions Computational cognitive neuroscience of category learning Recent publications highlight interdisciplinary applications in epidemiology, physics, and cognitive science. He actively mentors students in networks, complex systems, and statistical inference.

Mohamed F. Mokbelمشاهده پروفایل

استاد

• Database Systems
• Spatial Data
• GIS (Geographic Information Systems)
• +۳ مورد دیگر

Mohamed F. Mokbel is a Distinguished McKnight University Professor in the Department of Computer Science and Engineering at the University of Minnesota - Twin Cities , where he also serves as the Director of Graduate Studies. He is recognized as an IEEE Fellow and ACM Distinguished Member for his contributions to spatially- and privacy-aware systems. His research focuses on database systems , spatial data management , and GIS (Geographic Information Systems) , with significant work in spatiotemporal data, location-based services, and machine learning for spatial applications. His most recent publications address scalable BERT-based trajectory imputation , spatial logistic regression frameworks , and spatiotemporal big data decay techniques . Key Awards: Distinguished McKnight University Professor (2023) ACM SIGSPATIAL 10-Year Impact Award (2022) IEEE Fellow (2020) ACM Distinguished Member (2017) NSF CAREER Award (2010) Selected Conference Papers: Recathon (2015, IEEE MDM Best Paper) ST-Hadoop (2017, SSTD Best Paper) KAMEL (2023, ACM SIGMOD Demo) Academic Service: Editor-in-Chief, ACM Transactions on Spatial Algorithms and Systems (2024–) General Co-Chair, ACM SIGSPATIAL 2025 Past Chair, ACM SIGSPATIAL (2014–2017)

Toomas Laaritsمشاهده پروفایل

استادیار

• Asset Pricing
• Financial Intermediation
• Retail Investors
• +۷ مورد دیگر

Toomas Laarits is an Assistant Professor of Finance at the Leonard N. Stern School of Business, New York University, where he joined in 2019. His research lies at the intersection of asset pricing, financial intermediation, and monetary policy, with a focus on investor behavior, safe assets, and macroeconomic announcements. Education: PhD in Financial Economics, Yale University, 2019 MPhil in Financial Economics, Yale University, 2017 MA in Financial Economics, Yale University, 2016 AB in Mathematics, Harvard University, 2010 His research investigates puzzles in financial markets, such as the pre-FOMC announcement drift, retail investor behavior, and the role of safe assets in times of crisis. By combining empirical analysis with theoretical modeling, he explores how investors interpret public information, the hedging demand for Treasuries, and the impact of fiscal stimulus on equity markets. His interdisciplinary work also extends into financial history, examining the 1930 downturn and the evolution of financial architecture. The most recent research articles show a strong trend toward understanding decision-making under uncertainty, the role of information in asset pricing, and the behavior of retail investors using novel datasets such as browser activity. His work frequently appears in top finance journals and receives media attention from outlets like the Financial Times, Wall Street Journal, and The Economist. Scientific Awards: No awards mentioned in the text. Professor Laarits has advised or collaborated with researchers such as Marco Sammon and has been involved in multiple high-impact projects with leading scholars including Gary Gorton, Viral Acharya, and Robin Greenwood. While no formal grants are listed, the scope and publication record suggest active funding. He teaches Foundations of Finance at the undergraduate level and contributes to the academic life at NYU Stern through research and mentorship. Labs and Research Teams: No formal lab is mentioned. However, his extensive co-authorship network indicates active participation in collaborative research groups focused on financial economics, macro-finance, and market microstructure.

• Forecasting
• Time Series Analysis
• Applied Econometrics
• +۵ مورد دیگر

George Athanasopoulos is Professor and Head of the Department of Econometrics and Business Statistics at Monash University, a position he has held since 2022. He was appointed Professor in 2019 and has established himself as an internationally recognized expert in forecasting, time series analysis, and applied econometrics. He serves as Past President (since 2024) and former Director (2014-2024) of the International Institute of Forecasters, and is Associate Editor of the International Journal of Forecasting since 2014. His research focuses on hierarchical and grouped time series forecasting, where he has pioneered methods for forecast reconciliation and cross-temporal coherence. His work has significantly influenced forecasting practices across diverse fields including national statistics offices, energy markets, and public health. He is particularly renowned for his contributions to tourism forecasting and macroeconomic modeling in big data environments. Awarded the Australian Awards for University Teaching in 2022 for outstanding contributions to student learning, Professor Athanasopoulos has also received multiple Dean's Awards from Monash Business School for research excellence, teaching innovation, and publication quality. His research output includes over 49 publications and leadership of six major research projects, including the ARC-funded 'Macroeconomic forecasting in a Big Data world' and the RACE for 2030 CRC project on clean energy forecasting. His work contributes to UN Sustainable Development Goals through applications in economic forecasting, energy modeling, and sustainable tourism development. He has supervised numerous research students and collaborated extensively with institutions including Australian National University, Griffith University, and international partners across multiple continents.

Emmanuel Saezمشاهده پروفایل

استاد

• Income and Wealth Inequality
• Optimal Taxation
• Empirical Public Policy
• +۸ مورد دیگر

Emmanuel Saez is a Professor of Economics and Director of the Stone Center on Wealth and Income Inequality at the University of California, Berkeley , where he has been a leading figure in the Department of Economics within the College of Letters and Science. His research is foundational in the study of income and wealth inequality, optimal taxation, and empirical public policy. His academic journey began with a PhD from MIT in 1999. His work, particularly in collaboration with Thomas Piketty and Gabriel Zucman, has revolutionized how economists measure and understand economic inequality. He is best known for constructing long-term top income share series, which reveal the dramatic rise in U.S. inequality since the 1980s. His influential 2019 book, The Triumph of Injustice , co-authored with Gabriel Zucman, presents a compelling critique of the decline in U.S. progressive taxation and proposes bold policy reforms for the 21st century. Saez's recent research spans a wide range of topics including real-time inequality measurement, wealth tax enforcement, labor market dynamics for older workers, and macroeconomic models of unemployment. His publications, often appearing in top journals like the Quarterly Journal of Economics , American Economic Review , and Brookings Papers on Economic Activity , consistently focus on empirical analysis with direct policy relevance. His work shows a strong trend toward integrating macroeconomic models with distributional data and evaluating the real-world impact of fiscal and social policies. John Bates Clark Medal, American Economic Association, 2009 MacArthur 'Genius' Fellowship, 2010 Honorary degree from Harvard University, 2019 Saez is deeply involved in mentoring and advising, though specific student names are not listed in the provided texts. He has been instrumental in major data initiatives such as the World Inequality Database (WID.world) and Real-Time Inequality , which provide open-access, high-quality data on economic distribution. He has also secured significant research funding from foundations like the Bill & Melinda Gates Foundation, the Russell Sage Foundation, and the Alfred P. Sloan Foundation. He leads the Stone Center on Wealth and Income Inequality, fostering interdisciplinary research on economic disparity.