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David DuffView profile

Professor

• Romanticism
• Genre Theory
• Literary Theory
• +3 more

Professor David Duff is a Professor of Romanticism at Queen Mary University of London's School of English and Drama, joining in 2016 after teaching at the University of Aberdeen. He holds a BA and DPhil from the University of York. His research focuses on Romanticism, genre theory, and literary history, with notable work on Shelley, the French Revolution's literary impact, and the history of the book. He co-founded the London-Paris Romanticism Seminar and the International Summer School of Romanticism, fostering international collaboration. Duff chairs the English Association and serves as a Trustee, advocating for English studies. His teaching spans Romanticism, literary theory, and genre history. He has published extensively, including the award-winning Romanticism and the Uses of Genre , and contributes to public engagement through lectures and media commentary. Education: BA (University of York) DPhil (University of York) Research Interests: Romanticism, genre theory, literary forms, Shelley studies, French Revolution impact, and the history of the book. Awards and Roles: ESSE Book Award (2009) Fellow of the English Association (FEA) Chair of the English Association Public Engagement: Keynotes for the Charles Lamb Society, Burns Club of London, and Royal College of Psychiatrists. Media contributions include op-eds in the Guardian and Daily Mail. Labs/Teams: Co-director of the London-Paris Romanticism Seminar and International Summer School of Romanticism.

Philbert TsaiView profile

Academic

• Quantitative Biology
• Neurovascular Imaging
• Biomedical Engineering
• +3 more

Philbert Tsai is an Associate Teaching Professor in the Department of Physics at the University of California, San Diego (UCSD). He has held roles as QBio Lab Coordinator/Project Scientist (2015–Present) and Associate Project Scientist (2011–2015), overseeing advanced laboratory setups and bio-imaging research projects. His work focuses on neurovascular systems, microscopy techniques, and cortical blood flow dynamics. Education: Ph.D., Physics, UC San Diego, 2004 Research Interests: Quantitative analysis of cortical microvascular networks Development of ultra-high-resolution imaging systems (e.g., STED, two-photon microscopy) Neurovascular coupling mechanisms and their impact on brain oxygen supply Biomedical engineering applications in neuroscience research Lab & Projects: QBio Lab: Advanced instrumentation including confocal microscopes, 3D printers, and wet-lab equipment Developed vectorized models of mouse brain vasculature and ultra-wide-field multiphoton imaging systems Grants & Awards: No specific awards listed in provided text Collaborations: Worked extensively with colleagues like Dr. David Kleinfeld and Dr. Berislav Zlokovic on neurovascular projects.

Do Young EunView profile

Professor

• Distributed Optimization
• Machine Learning
• Network Modeling
• +5 more

Do Young Eun is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State), with affiliations in Computer Science and Operations Research. He holds a Ph.D. from Purdue University and M.S./B.S. degrees from KAIST, Korea. His research focuses on distributed optimization for machine learning, network modeling, and algorithms for social/wireless networks, with applications in epidemic analysis and graph analytics. Education: Ph.D. in Electrical and Computer Engineering, Purdue University (2003) M.S. in Electrical Engineering, KAIST (1997) B.S. in Electrical Engineering, KAIST (1995) Research Interests: Distributed optimization and machine learning Network modeling and performance analysis Epidemic modeling and control Graph analytics and social network analysis Stochastic processes and algorithms Highlighted Awards: NSF CAREER Award (2006) Outstanding Paper Award, ICML 2023 Best Paper Awards at IEEE ICCCN (2005), IPCCC (2006), NetSciCom (2015) Best Student Paper Award, ACM MobiCom 2007 Advising & Grants: Current advisees include Jie Hu, Yi-Ting Ma, and Feiya Xiang NSF Grant (2024–2027): 'Toward Maximally Efficient Sampling and Optimization for Decentralized Learning' Supervised over 10 Ph.D. students, many in academic or industry leadership roles Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of networking, machine learning, and stochastic systems, with collaborations in academia and industry.

Aritra MitraView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Control Theory
• +6 more

Aritra Mitra is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from Purdue University (2020), an M.Tech. from IIT Kanpur (2015), and a B.E. from Jadavpur University (2013). Before joining NC State, he was a postdoctoral researcher at the University of Pennsylvania. His research focuses on enabling reliable, efficient learning and decision-making in large-scale distributed systems, addressing challenges like computation, communication constraints, and adversarial robustness. Key areas include control theory, machine learning, signal processing, and network science. Education: Ph.D., Electrical and Computer Engineering, Purdue University (2020) M.Tech., Electrical Engineering, Indian Institute of Technology Kanpur (2015) B.E., Electrical Engineering, Jadavpur University (2013) Research Interests: Dr. Mitra’s work bridges theoretical foundations with practical applications in distributed systems. He designs algorithms for federated learning, reinforcement learning, and adversarial robustness, with applications in control systems and networked environments. Recent efforts emphasize finite-time analysis of TD learning, heterogeneous federated systems, and resilient control under communication constraints. His contributions often integrate tools from stochastic approximation, optimization, and signal processing. Publications: His articles explore cutting-edge topics like federated TD learning, robust system identification under heavy-tailed noise, and distributed multi-agent optimization. Recent trends highlight advancements in asynchronous algorithms, delay-adaptive systems, and model-free control under communication bottlenecks. Grants & Labs: While specific grants are not detailed, his research aligns with themes in distributed computing and control, suggesting potential involvement in NSF or industry-funded projects. No lab-specific details are provided in the text.

Orlin D. VelevView profile

Professor

• Colloid Science
• Soft Materials Engineering
• Nanoscience
• +7 more

Orlin D. Velev is the S. Frank and Doris Culberson Distinguished Professor of Chemical Engineering at North Carolina State University (NC State), part of the College of Engineering. His research focuses on colloid science, soft materials engineering, nanotechnology, and sustainable nanocomposites. He leads the Velev Lab, pioneering innovations in self-propelling microdevices, environmentally benign nanomaterials, and responsive materials for energy and biomedical applications. Velev's academic journey includes a PhD in Physical Chemistry from Sofia University (1999), followed by M.S. and B.Tech degrees in Chemical Engineering from NC State. His work bridges chemistry, physics, and biology, with notable contributions to microfluidics, colloidal assembly, and battery technologies. He has supervised numerous graduate and undergraduate students, fostering interdisciplinary research and innovation. Key research areas include: Directed assembly of colloids using external fields Self-propelling microbots and active particles Sustainable biopolymer composites (e.g., lignin-based nanoparticles) Soft robotic components and smart materials Biodegradable electronics and energy storage solutions Honors include the Braskem Award, AIChE’s Andreas Acrivos Award, and multiple fellowships. His lab’s recent advancements include osmotic-capillary wearable patches for sweat analysis and high-performance lithium-sulfur battery separators using soft dendritic colloids. Velev collaborates extensively, with projects funded by NSF, industry partnerships, and federal grants. His work emphasizes translating fundamental science into real-world applications, such as biodegradable packaging, microplastic remediation, and wearable health monitoring devices.

Peter ChinView profile

Professor

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +8 more

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Holden LeeView profile

Assistant Professor

• Machine Learning
• Probability
• Theoretical Computer Science
• +3 more

Holden Lee is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, where he joined in 2022. His research focuses on the theoretical foundations of machine learning, probability, and their intersections with theoretical computer science. He explores probabilistic methods in modern machine learning, including deep learning-based generative models and convergence guarantees for sampling algorithms like Markov Chain Monte Carlo. Prior to JHU, he was a postdoc at Duke University and a Simons Fellow at UC Berkeley. He holds a PhD in Mathematics from Princeton University and degrees from MIT and the University of Cambridge. Education: PhD in Mathematics, Princeton University, 2019 MASt in Pure Mathematics, University of Cambridge, 2014 BSc in Mathematics, MIT, 2013 Research Interests: Machine Learning Theory Probabilistic Sampling Methods Generative Models Statistical Learning Theory His work emphasizes theoretical rigor, particularly in understanding the success and limitations of deep learning algorithms and designing efficient sampling techniques beyond classical log-concave settings. Articles Trends: Lee’s recent publications (2022–2024) focus on advancing sampling algorithms for complex distributions, analyzing generative models, and improving convergence guarantees for methods like MCMC and score-based diffusion. His work bridges theory and practice, with applications in multimodal data, text generation, and dynamical systems. Awards: Simons Fellow at UC Berkeley (2021) Advising & Grants: Lee has contributed to projects at NeurIPS and collaborates on research in AI efficiency and theoretical guarantees. He teaches courses on probability and applied mathematics at JHU and Duke. Labs/Teams: His research group focuses on theoretical machine learning and probabilistic methods, with ongoing projects on scalable sampling algorithms and generative model analysis.

Jianqiang ChengView profile

Associate Professor

• Stochastic Programming
• Robust Optimization
• Distributionally Robust Optimization
• +7 more

Dr. Jianqiang Cheng is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He is also a member of the Graduate Faculty and affiliated with the Applied Mathematics and Statistics Graduate Interdisciplinary Programs. His research is centered on optimization under uncertainty with applications in energy systems and logistics. Research Interests: His primary research areas include stochastic programming, robust optimization, distributionally robust optimization, semidefinite programming, and chance-constrained optimization. He applies these methodologies to challenges in power systems, renewable energy integration, microgrid design, and resilient supply chains. The recent publications (2020–2022) reflect a strong trend toward data-driven and computationally efficient methods in optimization. Key themes include distributionally robust optimization under moment and Wasserstein ambiguity, chance-constrained AC optimal power flow, and resilient supply chain modeling under disruptions such as the COVID-19 pandemic. His work frequently appears in top journals like INFORMS Journal on Computing , IEEE Transactions on Power Systems , and European Journal of Operational Research . Scientific Awards: Best Short Paper Award, INFORMS Workshop on Data Science (Fall 2022) NSF CAREER Award, National Science Foundation (Spring 2022) Science Foundation Arizona's 2017 Bisgrove Scholar (Spring 2017) Dr. Cheng has secured significant research funding, including the NSF CAREER Award, supporting his work in data-driven optimization. He collaborates extensively with researchers in energy systems and operations research, including K. Pan, M. Cheramin, A. M. Fathabad, and A. Lisser. While specific advisees are not listed, his role as a member of the Graduate Faculty indicates active supervision of graduate students in systems engineering, applied mathematics, and statistics. His research contributes to the development of advanced optimization models for real-world systems affected by uncertainty, particularly in energy and logistics. Though no specific lab is mentioned, his work implies involvement in computational optimization and energy systems modeling research groups within the College of Engineering.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +3 more

Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.

• Translational Cancer Research
• Cancer Biomarkers
• Tumor Biology
• +11 more

Lars Andreas Akslen is a Professor at the Department of Clinical Medicine, University of Bergen, and serves as the Centre Director of CCBIO (Centre for Cancer Biomarkers). He is based at Haukeland University Hospital and leads a major translational cancer research group focused on biomarker discovery and validation. Position: Professor, Centre Director of CCBIO Institution: University of Bergen Department: Department of Clinical Medicine Location: Haukeland University Hospital, Bergen, Norway Email: lars.akslen@uib.no His research is centered on translational oncology, with a strong emphasis on identifying and validating novel biomarkers for improved biological classification and grading of malignant tumors. His work spans breast cancer , malignant melanoma , prostate cancer , and gynecologic cancers . By integrating human tumor sample analysis with experimental cell and animal models, his team aims to enhance the clinical utility of biomarkers in predicting aggressive tumor behavior and guiding personalized treatment strategies. The recent publications highlight a consistent focus on tumor microenvironment, immune biomarkers, imaging mass cytometry, AI in diagnosis, and age-related phenotypes in cancer. There is a strong trend towards high-dimensional spatial profiling and integration of molecular and clinical data to refine prognostic and predictive models. Lars Andreas Akslen has no listed scientific awards in the provided text. He leads the Tumor Biology Research Group (established in 1995) and the CCBIO center, indicating significant mentorship and leadership in cancer research. While specific students are not listed, his extensive collaboration network suggests active supervision of PhD and postdoctoral researchers. No specific grants are mentioned, but leadership of a national research center implies substantial funding acquisition. He is affiliated with CCBIO and the Tumor Biology Research Group, both based at the Department of Clinical Medicine, University of Bergen, and operating from Haukeland University Hospital. These teams focus on translational cancer biomarker research using advanced molecular and imaging technologies.

• Atmospheric Aerosols
• Environmental Physics
• Climate Science
• +3 more

Professor Markku Kulmala (University of Helsinki) is a leading expert in atmospheric and environmental physics. As Academician of Finland and double ERC Advanced Grant holder, he leads the Institute for Atmospheric and Earth System Research (INAR) and ACCC Flagship. With ~1200 publications and a WoS H-index of 124, his work focuses on atmospheric aerosols, climate interactions, and air quality. Academy of Finland grants (2004-2009, 2011-2015) ERC Advanced Grant (2×) ISI Highly Cited Researcher His research team has published 4 groundbreaking studies in Nature and Science , including: Aerosol formation mechanisms Aerosol-cloud-climate interactions Atmosphere-land surface relationships Climate-air quality feedbacks With over 20 PhD/Master's students supervised, recent work includes Arctic aerosol studies ( Elementa 2025), Beijing air quality analysis ( Nature Communications 2025), and climate modeling applications. He has received multiple international awards including the Fuchs Memorial Award and honors from Stockholm and Tartu universities.

Henry H.Q. HengView profile

Professor

• Genomics
• Evolutionary Biology
• Cancer Research
• +3 more

Dr. Henry H.Q. Heng is a tenured Professor of Molecular Medicine and Genetics and Pathology at Wayne State University , affiliated with the Karmanos Cancer Institute . His research spans genomics, evolutionary biology, and cancer systems theory. Education: PhD from University of Toronto (1994) Key research areas: Genomics, Evolutionary Biology, Cancer Research, Precision Medicine Dr. Heng's groundbreaking Genome Architecture Theory challenges traditional neo-Darwinism by emphasizing genomic topology and system inheritance over gene-level mechanisms. His team introduced the two-phased cancer evolution model (punctuated macroevolution followed by microevolution), explaining rapid cancer adaptation through genome chaos and non-clonal chromosome aberrations. Recent publications focus on karyotype coding , therapy-induced genome chaos, and the limitations of precision medicine approaches that ignore genomic heterogeneity. His work reveals how stress-induced genomic instability drives cancer evolution and drug resistance. 2020 PROSE Award Finalist for 'Genome Chaos: Rethinking Genetics, Evolution, and Molecular Medicine' Dr. Heng's lab develops innovative genomic techniques like high-resolution Fiber-FISH and investigates Gulf War Illness through genomic instability lens. He actively debates foundational issues in cancer research through books like 'Debating Cancer: The Paradox in Cancer Research' (2015) and 'Genome Chaos' (2019).

• Algorithms for Big Data
• Parallel Computing
• External-Memory Algorithms
• +5 more

Ulrich Meyer is a Professor at the Institute for Computer Science at Goethe University Frankfurt. He serves as a prominent researcher in algorithms for big data, with extensive contributions to parallel and external-memory graph algorithms. His work spans theoretical foundations and practical implementations for processing large-scale data sets. Spokesperson of the DFG priority program (SPP 1736) on Algorithms for Big Data in Germany SEA23 Symposium on Experimental Algorithms, Steering Committee Chair ALENEX23 Algorithm Engineering and Experiments, Program Committee Member Professor Meyer's research interests focus on the theoretical and experimental aspects of processing large data sets on advanced computational models. His work particularly emphasizes parallel and external-memory graph algorithms, with recent focus on efficient large-scale network generation according to various stochastic models. His research has produced significant contributions including the parallel Delta-Stepping algorithm (which received the ESA Test of Time Award in 2019) and the first BFS approach with sublinear I/O. He has also explored more specialized topics like energy-efficient sorting (with records in the JouleSort competition 2009/10 and the Germany Land of Ideas Award) and fragile computing (which earned him a best-paper award at ESA 2019). His recent publications demonstrate a strong focus on graph algorithms, network generation, and parallel computing techniques. The research trends show consistent advancement in scalable algorithms for massive graphs, with particular emphasis on efficient sampling methods, shortcutting techniques, and communication-free distributed approaches. His work bridges theoretical computer science with practical engineering considerations for real-world big data applications. ESA Test of Time Award 2019 for Parallel Delta-Stepping algorithm Records in the JouleSort competition 2009/10 Germany Land of Ideas Award Best-paper award at ESA 2019 for fragile computing research Professor Meyer has made substantial contributions to the academic community through his leadership in the DFG priority program on Algorithms for Big Data, which has fostered significant research collaborations across Germany. His extensive publication record in top venues demonstrates sustained research productivity and impact in the algorithms community. While specific grant details aren't provided in the text, his role as spokesperson for a major DFG priority program indicates substantial research funding and leadership responsibilities. His work appears to be conducted within collaborative research environments focused on algorithm engineering and experimental evaluation. His research appears to be conducted within the Institute for Computer Science at Goethe University Frankfurt, likely involving collaborations with other researchers in the Algorithms for Big Data priority program. The extensive list of co-authored publications suggests active participation in research teams focused on parallel algorithms, graph processing, and network generation.

Silvia Santa Maria NewellView profile

Professor

• Nutrient Biogeochemistry
• Microbial Ecology
• Harmful Algal Blooms (HABs)
• +4 more

Silvia Santa Maria Newell serves as Director of Michigan Sea Grant and Professor at the University of Michigan School for Environment and Sustainability (SEAS), where she leads critical research on freshwater ecosystems. Her work bridges academic scholarship with practical environmental management through federal partnerships and stakeholder engagement initiatives focused on the Great Lakes region. Education Ph.D. in Geosciences, Princeton University M.A. in Geosciences, Princeton University A.B. in Biogeochemistry, Smith College Newell specializes in nutrient biogeochemistry and microbial ecology, with primary focus on how excess nutrients from agricultural and wastewater sources drive harmful algal blooms (HABs) in freshwater systems. Her research integrates field studies of nitrogen cycling, mercury methylation processes, and climate change impacts on eutrophication. She develops stakeholder-driven solutions for nutrient reduction in watersheds through interdisciplinary collaboration with farmers, policymakers, and environmental managers. Analysis of her 15 most recent publications reveals dominant research themes in Great Lakes HABs mitigation, nutrient cycling dynamics (particularly nitrogen-phosphorus interactions), and emerging contaminants like mercury. Her work consistently connects microbial processes to ecosystem-scale phenomena, with strong emphasis on practical applications for water resource management across North American and international freshwater systems. Scientific Awards Excellence in Grantsmanship (2022) for securing $1M+ research funding AGU Top 10 Most Downloaded Paper (2021) Limnology & Oceanography Outstanding Reviewer (2021) ASTRA Fellow, Estonia Education (2019-2020) Newell actively mentors the next generation of environmental scientists as primary advisor to PhD candidates Berk Durutürk and Ran Jin, and Master's student Monica Woodruff. Her current $1.2M+ research portfolio includes NSF-funded projects on Lake Erie HABs mitigation (DISES CHABS: $150K), mercury-nitrogen cycling interactions (GEOTRACES: $378K), and Ohio wetland monitoring (H2Ohio: $500K). She directs major collaborative initiatives including the Great Lakes Commission HABs Collaboratory and Lake Erie Area Research Network. As Director of Michigan Sea Grant, Newell oversees a federal-state partnership that funds research, outreach, and education programs addressing critical Great Lakes challenges. Her leadership connects university resources with community stakeholders through the Maumee River watershed engagement project and international collaborations studying nutrient pollution impacts from Lake Erie to Lake Taihu in China.

Andrea Di FalcoView profile

Professor

• Nano-photonics
• Metamaterials
• Plasmonics
• +6 more

Andrea Di Falco serves as Professor and Director of Impact at the School of Physics and Astronomy, University of St Andrews. He leads the Centre for Biophotonics and maintains an active research group focused on cutting-edge photonics research. His institutional affiliations include editorial work for Photonics and Nanostructures: Fundamentals and Applications journal since 2013. University of St Andrews - School of Physics and Astronomy (Current) Centre for Biophotonics (Current) Photonics and Nanostructures: Fundamentals and Applications (Editor since 2013) Professor Di Falco's research spans multiple photonics domains with particular expertise in nano-photonics, metamaterials, plasmonics, and biophotonics. His work bridges fundamental optical physics with practical applications, especially in metasurface technology and flexible photonics. The research demonstrates strong interdisciplinary connections between physics, materials science, and biomedical applications, contributing to multiple UN Sustainable Development Goals. His recent publication portfolio shows a clear trend toward increasingly sophisticated metasurface applications, with growing integration of machine learning techniques for photonic design. The research spans fundamental optical physics, novel material development, and practical biomedical applications, demonstrating remarkable breadth while maintaining technical depth in photonics. EPSRC Fellowship (2010) for flexible metamaterials and plasmonics research ERC Consolidator Grant (2019) for biophotonic applications of optically trapped photonic membranes Professor Di Falco actively supervises postgraduate research students and has led numerous significant research projects, including the current Holographic Integrated Photonic Platform project (2024-2025). His research program has secured substantial funding from EPSRC, European Research Council, and Medical Research Council, demonstrating strong recognition of his work's significance and potential impact. His group maintains the SynthOpt research website as a hub for their collaborative work. The SynthOpt research group operates within the School of Physics and Astronomy at St Andrews, maintaining strong connections with both theoretical and experimental photonics researchers. The team focuses on developing novel photonic structures with applications ranging from fundamental optics to biomedical sensing and human-computer interaction technologies.