James Manley is the Julian Clarence Levi Professor of the Life Sciences at Columbia University, with extensive research in gene expression regulation. His work spans transcription, RNA splicing, and polyadenylation mechanisms in human cells, connecting these processes to neurodegenerative diseases (ALS/FTD) and cancers. Affiliation: Columbia University, Department of Biological Sciences Contact: jlm2@columbia.edu Research Interests: Dr. Manley's laboratory investigates nuclear processes including: Transcriptional control via RNA polymerase II CTD modifications Alternative splicing regulation by hnRNP and SR proteins Polyadenylation dynamics in cell cycle and differentiation Disease mechanisms in spliceosome mutations (SF3B1, SRSF2) RNA-protein interactions in stress responses Publication Trends: Recent work focuses on disease-associated mutations affecting RNA processing, non-canonical RNA functions, and immune regulation via polyadenylation. Articles span molecular oncology, neurodegeneration, and RNA surveillance mechanisms. Scientific Recognition: Member, American Academy of Arts & Sciences Member, National Academy of Sciences Key Collaborations: Studies involve interdisciplinary work with neurology, cancer biology, and immunology teams. His lab employs biochemical assays, structural analysis, and genetic models to dissect RNA processing pathways.
Athanasios Liavas is a Professor at the Technical University of Crete in the School of Electrical and Computer Engineering , specializing in Signal Processing for Telecommunications and Information Theory . He has held administrative roles as Department Chair (2009-2011), Vice Chair (2011-2013), and Dean of the ECE School (2017-2021). Education: Diploma (1989) and PhD (1993) in Computer Engineering and Informatics from the University of Patras. Professional Background: Postdoctoral Marie Curie Fellow at INT, Evry (1996-1998); Lecturer at University of Ioannina (1999-2001); Assistant/Associate Professor at University of the Aegean (2001-2004) and Technical University of Crete (2004-present). His research focuses on Signal Processing for Telecommunications , Information Theory , and Tensor Decomposition . Recent work involves nonnegative tensor factorization , parallel algorithms , and fMRI data analysis , with applications in wireless communications and medical imaging . Articles show trends in optimization algorithms , LDPC code design , and MIMO system robustness . Scientific Awards include: Marie Curie Fellowship (1996-1998) Associate Editor, IEEE Transactions on Signal Processing (2005-2009) Elected Member, IEEE Signal Processing for Communications and Networking Technical Committee (2006-2011) He has taught courses like Telecommunications Systems II , Wireless Communications , and Information Theory , and supervised students such as Despoina Tsipouridou (PhD) and Alex Balatsoukas-Stimming (Graduate). He leads projects like Partensor (Parallel Tensor Toolbox) and COOPCOM (Cooperative Communications), and contributes to labs including the Telecommunications Laboratory .
Professor Damien Woods is a faculty member at Maynooth University's Faculty of Science & Engineering, specifically affiliated with the Department of Computer Science and the Hamilton Institute. He leads groundbreaking research in DNA computing, molecular programming, and optical computing, focusing on self-assembly, algorithmic design, and computational complexity. ERC Consolidator Grant: 'Computationally Active DNA Nanostructures' SFI ERC Support Award EIC Pathfinder Challenge Grant: 'DISCO - DNA Infrastructure for Storage and Computation' His research projects explore programmable DNA storage, molecular robotics, and robust self-assembly systems. Recent publications span diverse topics like algorithmic DNA tile assembly, thermodynamic stability, and computational universality in nanosystems. Awards include ERC and SFI grants, with a focus on bridging theoretical computer science and experimental molecular biology. Scientific Contributions include: 2022: 'Turning Machines' - Molecular Robotics 2019: 'Diverse Molecular Algorithms' in Nature 2017: 'A Cargo-Sorting DNA Robot' in Science
Professor Serdar Özoğuz is a full faculty member at the Department of Electronics and Communication Engineering , Istanbul Technical University . Holding a Ph.D. from ITU (2000) and a M.Sc. from ITU (1993) , he has taught courses like Active Network Synthesis , Basics of Electrical Circuits , and Scientific Research Ethics since 2014. His research focuses on Active RC filters Nonlinear electronic circuits Analog integrated circuit design Network synthesis . His recent publications emphasize machine learning applications in RF/microwave design , quantum computing for CAD tools , and emerging memory devices . The department's Devreler ve Sistemler Laboratuvarı Çok Geniş Ölçekli Tümdevre (VLSI) Tasarımı Laboratuvarı likely support his work. Despite no explicit awards listed, his 15+ recent articles in high-impact journals underscore his technical contributions.
Prof. Sven Rady is a leading academic at the Department of Economics at the Hausdorff Center for Mathematics , University of Bonn. He serves as a Hausdorff Chair for Mathematical Economics and Deputy Spokesperson for Collaborative Research Centre TR224. Research Interests include dynamic decision problems, equilibrium models, optimal learning, and strategic experimentation, with significant contributions to information economics and stochastic game theory. His work bridges mathematical modeling with economic theory, focusing on markets, learning dynamics, and policy implications. Scientific Awards include Fellow of the Econometric Society (2023) Teaching Awards at the University of Bonn (2021, 2022) CESifo Outstanding Referee Award (2013) Teaching Award of the State of Bavaria (2005) Key Collaborations involve interdisciplinary research at the intersection of economics and mathematics. He leads projects in the CRC TR224 and contributes to HCM initiatives on probabilistic modeling and information economics.
Associate Professor Ivan Guo is a faculty member at Monash University's School of Mathematics, where he leads research in mathematical finance and stochastic modeling. He obtained his PhD in Mathematics from the University of Sydney in 2014 and currently accepts PhD students. His work bridges theoretical mathematics and practical financial applications, with active projects spanning 2022-2026. Research Focus Dr. Guo's research centers on three interconnected areas: Optimal Transport Applications : Developing transport-based methods for financial model calibration and derivatives pricing Market Microstructure : Analyzing market-making strategies, liquidity, and high-frequency trading dynamics Sustainable Finance : Modeling green investment impacts and energy market transitions using game-theoretic approaches Active Projects Can green investors drive transition to a low-emission economy? (2022-2026) Integrating energy storage into electricity markets (2022-2024) Data61 CRP #46 - Risklab mathematical sciences (2020-2023) Efficient computational techniques for econophysics (2019-2021) The role of liquidity in financial markets (2017-2020) His research consistently addresses model uncertainty, volatility dynamics, and computational methods across 18+ publications since 2012.
Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.
Dr. Quirin Thomas Simon Vogel is a Senior Lecturer at the Department of Statistics, University of Klagenfurt. He previously held postdoctoral positions at the Technical University of Munich, New York University Shanghai, and served as an Interim Professor at Ludwig-Maximilians University of Munich. His research bridges probability theory with statistical mechanics and algorithmic applications. Current role: Senior Lecturer (2025) Previous roles: Postdoc (TUM, NYU Shanghai), Interim Professor (LMU Munich) His research focuses on: Random walks and their geometric/stochastic properties Randomized algorithms with applications in statistical models Quantum-inspired probabilistic systems (e.g. interacting bosonic loop soups) Large deviation theory for complex systems Percolation and phase transitions in particle models The articles reflect trends in probability theory, mathematical physics, and algorithmic applications. Key topics include high-dimensional percolation, Bose gas models, neural network theory, and stochastic geometry. The work combines rigorous mathematical analysis with interdisciplinary applications in physics and computer science. Scientific awards and functions cannot be determined from the provided data, as they describe other researchers. The department's research activities include projects on statistical learning, quantum models, and algorithmic probability, though Vogel's direct involvement in these specific funded projects isn't explicitly stated.
Jean-Luc Thiffeault is a Professor of Applied Mathematics at the University of Wisconsin-Madison, serving as Chair of the Department of Mathematics. His research spans applied mathematics, fluid dynamics, and topological chaos, with a focus on mixing mechanisms in viscous flows, biogenic mixing by microorganisms, and computational modeling. Key research themes include: Topology-driven fluid mixing via braid theory; Chaotic advection in low-Reynolds environments; Microswimmer interactions with boundaries and waves; Development of numerical tools for dynamical systems analysis. He has authored significant software packages like braidlab (braid analysis), rodent (ODE integration), and jlt lib (utility functions for scientific computing). Collaborative projects include studies on hagfish slime unraveling, burger flipping dynamics, and Brownian particle winding around vortices. His work is supported by NSF grants DMS-0806821 and CMMI-1233935, emphasizing interdisciplinary approaches combining mathematics, physics, and computational methods.
Paul M Thibado is a Professor in the Department of Physics within the College of Arts & Sciences at the University of Arkansas. With over 100 refereed publications and 51 patents worldwide, his work focuses on cutting-edge research in graphene physics and energy harvesting technology. He has secured over $12 million in external research funding from sources including NSF, DoD, and the Walton Foundation, with current support from the WoodNext Foundation. Education: Ph.D. in Physics, 1994, University of Pennsylvania, Philadelphia, PA B.S. in Physics, 1990, San Diego State University, San Diego, CA B.S. in Mathematics, 1990, San Diego State University, San Diego, CA Professor Thibado's primary research focuses on the physical properties of novel two-dimensional systems, particularly pristine freestanding graphene and chemically-functionalized graphene. His work investigates electronic, mechanical, electromechanical, spin-dependent tunneling, and transport properties. A significant portion of his recent research centers on developing multimodal energy harvesting technology using graphene, with power sources including kinetic, solar, thermal, ambient radiation, acoustic, and nonlinear thermal energy. His groundbreaking discovery that thermal fluctuations in graphene can be harnessed to generate usable electrical power represents a paradigm shift in nanoscale energy generation. Analysis of his recent publications (2023-2025) reveals a clear progression from fundamental studies of graphene properties to the development of functional energy harvesting devices. Key research themes include spectrum analysis of thermally driven curvature inversion in graphene ripples, transient thermal energy harvesting at single temperatures using nonlinearity, and creating arrays of graphene solar cells on silicon wafers. His work demonstrates how Brownian motion in two-dimensional materials can be converted into electrical energy through innovative device architectures. Scientific Awards: Senior Member of the National Academy of Inventors NSF CAREER Awardee ONR award recipient NSF MRSEC funding NSF FRG funding NSF MRI funding NSF REU funding NSF-EM funding NRC Post-doctoral Fellow, Naval Research Laboratory (1994-96) Master Researcher Award, Fulbright College (2014) Professor Thibado has successfully mentored numerous students and postdocs, including Dr. Vince LaBella who was elected APS Fellow for clicker development work. His research has been supported by over $12 million in external funding from diverse sources. His laboratory combines advanced scanning tunneling microscopy techniques with electrical measurements to study and harness the unique properties of two-dimensional materials. Future work appears directed toward scaling up graphene energy harvesting technology for practical applications and commercialization, with several patents recently granted for energy harvesting devices and sensors.
Oliver Kosut is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has worked since August 2012. He was promoted to Associate Professor in 2018 and received the NSF CAREER award in 2015. His research spans information theory, machine learning, cybersecurity, and power systems, with a focus on theoretical foundations and applications to privacy, security, and smart grid resilience. Education: B.S. in Electrical Engineering and Mathematics from MIT (2004), Ph.D. in Electrical and Computer Engineering from Cornell University (2010) His recent work explores differential privacy, adversarial robustness in decentralized networks, and information-theoretic approaches to cybersecurity. He advises graduate students with strong mathematical backgrounds, particularly those interested in fundamental theory for applied problems. Scientific accolades include the IEEE Information Theory Society Distinguished Lecturer (2023–2024) and NSF CAREER award. Key research areas: Information Theory, Privacy, Machine Learning, Power System Security Students: Obai Bahwal, Atefeh Gilani, Naima Tasnim (current); Nima Bazargani, Andrea Pinceti, Jingwen Liang, Zhigang Chu, Fatemeh Hosseinigoki, Nematollah Iri, Kousha Kalantari, Roozbeh Khodadadeh (former)
Santosh S. Vempala is the Frederick P. Storey II Chair and Professor of Computer Science at Georgia Institute of Technology's College of Computing with joint appointments in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and the School of Mathematics. He teaches courses including CS6150: Computing for Good (C4G) and CS6550/CS8803DAA: Continuous Algorithms: Optimization and Sampling. His research spans multiple interconnected domains: Algorithmic convex geometry and high-dimensional sampling Continuous optimization methods Computational models of brain function Randomized algorithms with applications to machine learning Vempala's recent publications reveal a strong focus on developing efficient algorithms for high-dimensional problems, particularly logconcave sampling and convex body integration. His work bridges theoretical computer science with practical applications in optimization and neuroscience, with increasing attention to the intersection of theoretical frameworks and brain computation models through his collaboration with Christos Papadimitriou. He leads the Computing for Good (C4G) initiative which applies computational approaches to social challenges, including projects like Safe and Easy Passwords!, LifeNet, C4G BLIS, and Shelter-to-Home that address problems in resource-constrained settings. Vempala currently advises PhD students Xinyuan Cao, Mirabel Reid, Max Dabagia, and Yunbum Kook, and has authored influential books including 'Spectral Algorithms' and 'The Random Projection Method' that have shaped research in algorithmic convex geometry. His tutorials at major conferences, including STOC 2015 on 'Sampling and Volume Computation in High Dimension' and FOCS 2020 on 'Computation in the Brain,' demonstrate his leadership in connecting theoretical computer science with broader scientific challenges.
Mathias Beiglboeck is a Professor at the Faculty of Mathematics, University of Vienna. His research spans Probability, Optimal Transport, and Mathematical Finance, with a focus on martingale constraints and stochastic modeling. Doctorate in Mathematics (2004, TU Vienna) Diploma in Mathematics (2003, TU Vienna) His work bridges geometric and probabilistic methods in finance, addressing problems like the Skorokhod Embedding, Weak Martingale Transport, and applications to financial institutions. Preprints and publications highlight advancements in Wasserstein distances, causal transport, and stability analysis under martingale constraints. Notable projects and awards include the OeNB-Anniversary-Fund Project (2024-), FWF-Project on Mimicking Processes (2022-), a START-Prize (2014-2022), and early recognition for his Master's thesis (2003). Teaching roles at University of Vienna and TU Vienna cover Stochastic Processes, Financial Mathematics, and Mathematical Finance courses. 2024: OeNB-Anniversary-Fund Project 18983 (250,000 EUR) 2023: Mentor in Daniel Bartl's Esprit-Project (280,000 EUR) 2022: FWF-Project on Mimicking Processes (400,000 EUR) 2014-2022: START-Prize (1,000,000 EUR) 2012: Austrian Mathematical Society Prize 2003: Best Master's Thesis Award, Austrian Mathematical Society His research trends integrate adapted Wasserstein distances, disease modeling, and financial applications, with collaborations across institutions like TU Vienna, Bonn University, and MSRI Berkeley. Grants emphasize geometric and entropic transport methods in finance and public health contexts.
Ningchuan Xiao is a Professor of Geography at The Ohio State University's Department of Geography. His work bridges Geographic Information Science (GIScience) with computational methods, emphasizing spatial optimization, cartography, and machine learning integration. Education: Ph.D. in Geography from The University of Iowa (2003). Courses taught include GIS fundamentals, cartography, and Python-based spatial analysis. Research Interests: Spatial Optimization: Developing algorithms for land acquisition, redistricting, and resource allocation. Machine Learning & Cartography: Exploring AI-driven map interpretation and ethical visualization of complex data. Census Data: Innovating privacy-preserving techniques while maintaining data utility, including temporal/spatial modeling. Open Source Tools: Authored GIS Algorithms (2016) and maintains GitHub repository 'gisalgs' for accessible code. Publications: Recent work (2023-2025) highlights advancements in synthetic microdata generation, privacy-utility tradeoffs in census aggregation, and AI-driven cartographic recognition. His 2022 studies include traffic camera analytics and choropleth map QA systems. Awards: Not explicitly listed in the provided texts. Advising & Grants: Collaborated with researchers like Y. Lin, J. Li, and S. Bao. Projects include the Sustainable Columbus Observatory (SCO) for urban sustainability metrics. Research is supported through academic partnerships and computational initiatives.
Dan Sheldon is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, holding a Five College joint faculty position with Mount Holyoke College. His research focuses on developing algorithms to address environmental challenges using large datasets, emphasizing computational sustainability. Key areas include spatial optimization for endangered species conservation, continent-scale bird migration modeling, and interpreting weather radar data for ecological insights. Methodologically, his work leverages probabilistic inference, network modeling, and machine learning. Sheldon earned a PhD in Computer Science from Cornell University and an AB in Mathematics from Dartmouth College. His postdoctoral training at Oregon State University was supported by an NSF Bioinformatics Fellowship. He co-leads the BirdCast project, an NSF-funded initiative applying novel machine learning to avian migration studies. His research affiliations include the Center for Data Science and the Computational Social Science Institute. Research interests span computational biology, machine learning, and data privacy. Notable contributions include algorithms for ecological decision-making, differentially private synthetic data techniques, and Gaussian process applications in environmental forecasting. Awards include an NSF Fellowship in Bioinformatics. Current projects integrate radar data analysis, biodiversity tracking, and privacy-preserving statistical methods. Grants include the BirdCast NSF grant and collaborations in computational sustainability. His work bridges theoretical computer science with applied ecological challenges, emphasizing interdisciplinary approaches to global-scale environmental problems.
