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Mattia SerraView profile

Assistant Professor

• Nonlinear Dynamics
• Biophysics
• Active Matter
• +4 more

Mattia Serra is an Assistant Professor in the Department of Physics at the University of California, San Diego. He holds a Ph.D. from ETH Zurich and specializes in data-driven methods and mathematical modeling for studying complex biological and physical systems. His research focuses on nonlinear dynamics, active matter, fluid dynamics, and embryonic development, with applications ranging from search-and-rescue optimization to morphogenesis control. Education : Ph.D., ETH Zurich Research Interests : Serra develops frameworks to analyze multi-scale chaotic systems, particularly in embryonic development (e.g., chick gastrulation) and fluid dynamics. His work integrates experimental data with computational models to uncover causal mechanisms and predict system behavior. Key areas include: Self-organization in developmental biology Control of morphogenesis and active matter Identification of coherent structures in fluid flows His TRAPs (Transient Attracting Profiles) framework aids in predicting material transport in oceanic and atmospheric flows. Awards & Honors : 2024: NSF CAREER Award 2024: HFSP Early Career Research Award 2023: Hellman Fellowship in Physics 2019: Swiss National Science Foundation Fellowship 2018: Schmidt Science Fellowship 2017: ETH Medal Award (outstanding doctoral thesis) Advising & Grants : Serra advises PhD students including Alex (NIH QBio fellow) and Sreejith (Lindau Nobel Laureate meeting participant). He leads the UC Active Matter Hub, a UC-wide initiative collaborating with UC Merced and UCSB. His research is supported by NSF, HFSP, and Hellman grants. Labs & Teams : His lab at UCSD integrates physics, biology, and engineering to study morphodynamics and active systems. Collaborations include projects with UCL and Harvard's Mahadevan lab on embryonic mechanics.

Richard RöttgerView profile

Associate Professor

• Bioinformatics
• Machine Learning
• Biomedical Data Analysis
• +3 more

Richard Röttger is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), part of the Faculty of Science. His research focuses on developing efficient algorithms for analyzing biological networks and large-scale biomedical datasets, particularly emphasizing unsupervised machine learning and integration of heterogeneous datasets. He aims to understand biological processes such as cell responses and environmental adaptation through computational methods. His work spans federated learning applications in healthcare, privacy-preserving analytics, and algorithm development for genomic and proteomic data analysis. Education: Not explicitly detailed in the provided text. Research Interests: Bioinformatics, machine learning, biomedical data integration, federated learning, and algorithm development for healthcare applications. His projects include optimizing AI-driven risk assessment systems, analyzing genomic instability in aging populations, and developing tools for rare disease diagnostics. Key Contributions: Over 15 recent publications (2023–2025) highlight his work in federated learning frameworks (e.g., FeatureCloud), clinical AI performance evaluation, and algorithmic solutions for spatial transcriptomics and proteomics. He also contributes to open-source tools like DiMmeR for differential methylated region analysis and ClustEval for clustering evaluation. Awards: None explicitly mentioned in the provided texts. Grants/Advising: No specific grants or advising roles listed, though his research aligns with EU projects like screen4Care for accelerating rare disease diagnosis through digital technologies. Labs/Teams: Part of the Bioinformatics at SDU group, focusing on collaborative, privacy-preserving biomedical data science initiatives.

Joseph WalkerView profile

Assistant Professor

• Computational Biology
• Phylogenomics
• Evolutionary Biology
• +2 more

Joseph Walker is an Assistant Professor in the Department of Biological Sciences at the University of Illinois Chicago, College of Liberal Arts and Sciences. His computational biology research examines genomic evolution through phylogenomic analysis, focusing on historical processes like whole-genome duplications and speciation events. Education includes a Ph.D. in Ecology and Evolutionary Biology from the University of Michigan, M.S. in Botany from Purdue University, and dual B.S. degrees in Genetics and Neurology/Physiology from Purdue. Research develops novel bioinformatics methods for large-scale genomic data analysis. Key areas include phylogenetic conflict resolution, gene family evolution, and adaptive genomic changes across plant lineages like Caryophyllales and Ericales. Publications demonstrate methodological innovations in phylogenomics and contributions to plant grafting physiology. No awards, students, or grants are detailed in available materials.

• Cardiogenomics
• Cardiovascular Disease
• Genetics
• +3 more

Anja Bye is a Professor and head of the interdisciplinary Cardiogenomics research group at the Norwegian University of Science and Technology (NTNU). Her primary affiliation is with the Department of Circulation and Imaging, which is part of the Faculty of Medicine and Health Sciences. She leads research initiatives within the Cardiac Exercise Research Group (CERG), focusing on precision cardiac care through genomic and genetic analysis. Her main research projects include CorFemina , addressing sex-specific disparities in cardiovascular disease (CVD) prevention and treatment for women, and CardioFit , investigating the genetic determinants of aerobic fitness and its causal role in CVD development. She emphasizes integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics) with clinical outcomes to better understand CVD pathogenesis and improve diagnostic and therapeutic strategies. Key Competencies: Cardiovascular disease, clinical research, genetic epidemiology, biomarkers, lipidomics, metabolomics, microRNA, and Mendelian randomization. Research activities include developing CVD risk prediction algorithms for women, exploring molecular mechanisms for gender-specific drug targets, and leveraging large-scale cohorts like the HUNT study. She advocates for personalized CVD prevention, especially targeting women's underrepresentation in clinical studies. She oversees the Cardiogenomics group within CERG, fostering interdisciplinary collaboration to advance genomic applications in cardiology. No specific grants or awards are explicitly listed in the provided text, though her work builds on foundational discoveries like the Nobel Prize-winning LDL receptor research referenced in her background.

Roberto GrossiView profile

Professor

• Algorithms and Data Structures
• Dynamic and External Memory Algorithms
• Algorithm Engineering
• +4 more

Roberto Grossi is a Professor of Algorithms and Data Structures at the University of Pisa's Dipartimento di Informatica. He is actively involved in editorial roles for journals like Theory of Computing Systems and RAIRO-Theoretical Informatics, and has served on program committees for conferences such as SWAT, IWOCA, ESA, FOCS, and SPIRE. His research focuses on algorithms, data structures, graph theory, and bioinformatics, with contributions to string processing, compressed data structures, and algorithm engineering. He advises Ph.D. students and collaborates on projects involving large-scale networks and community detection. Grossi has authored numerous publications, including works on clique enumeration, graphlet algorithms, and phylogenetic reconstruction. His teaching includes courses on Algorithm Design and Data Structures, and he maintains an active presence in academic networks through his research and editorial activities. Education: Not explicitly stated, but inferred from his role as a professor at a top Italian university. Research Interests: Dynamic algorithms, external memory methods, experimental algorithmics, pattern matching in strings and graphs. Grants/Awards: While no specific prizes are listed, his extensive publication record and editorial roles reflect sustained recognition in the field. Lab/Teams: Leads research groups focused on algorithmic foundations and applications, collaborating with international teams on projects like Zuckerli (compressed graph representations) and motif trie indexing. His work bridges theoretical computer science with practical applications in bioinformatics and network analysis.

• Data Compression
• Indexing Data Structures
• Algorithms
• +7 more

Giovanni Manzini is a Professor of Computer Science at the University of Pisa's Department of Computer Science. He holds a PhD in Mathematics from the Scuola Normale Superiore di Pisa and has held visiting roles at MIT, Johns Hopkins University, and the University of Melbourne. Previously, he served as Assistant Professor at the University of Torino and Associate Professor at the University of Eastern Piedmont. His research focuses on designing algorithms and data structures for data compression, indexing massive datasets, and computational biology. Notable contributions include advancements in the Burrows-Wheeler Transform (BWT), grammar-based compression, and matrix operations optimization. He received the ACM Paris Kanellakis Theory and Practice Award (2022) and the 2023 Test-of-Time Award from the European Symposium on Algorithms. His recent work explores two-dimensional string repetitiveness, BWT-based indexing, and green computing techniques for matrix operations. Manzini has contributed to over 100 conference program committees since 2020, including DCC, ALENEX, and SPIRE. His teaching spans courses on algorithms, programming languages, and numerical analysis. Current courses include Algorithms and Data Structures for Data-intensive Applications and an undergraduate laboratory course on programming fundamentals. He leads software projects such as MMRepair (grammar-compressed matrix multiplication), BigBWT (BWT construction via prefix-free parsing), and EZcount (microRNA quantification tools). He also mentors the Scuola Ortogonale under the Elicsir Foundation, promoting STEM education.

Huixia Judy WangView profile

Professor

• Quantile regression
• Extreme value theory
• Bioinformatics
• +4 more

Huixia Judy Wang is a Professor in the Department of Statistics at The George Washington University since 2017. Previously, she held positions at North Carolina State University from 2006 to 2017, advancing from Assistant to Associate Professor. Her expertise spans quantile regression, extreme value theory, bioinformatics, and longitudinal data analysis with applications in environmental science, health, and finance. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2006 M.S., Statistics, Fudan University, 2002 B.S., Statistics, Fudan University, 1999 Research focuses on developing robust statistical methods for analyzing extremes, high-dimensional data, and complex dependencies. Key themes include quantile-based approaches for spatial/temporal data, survival analysis with detection limits, and subgroup identification in clinical trials. She has contributed to methodological advancements in copula models, threshold regression, and Bayesian quantile inference. Her publications address applied problems such as climate variability impacts, biomedical data interpretation, and financial risk modeling. She maintains an active record of interdisciplinary collaboration across environmental science, genomics, and public health.

Kaiqiong ZhaoView profile

Assistant Professor

• Biostatistics
• Statistical Genetics
• High-Dimensional Data Analysis
• +4 more

Kaiqiong Zhao is an Assistant Professor in the Department of Mathematics and Statistics at York University, Toronto, Canada. Her research focuses on biostatistical methods development for complex genomic and clinical data analysis. Develops robust statistical frameworks for DNA methylation studies Specializes in multi-omics integration and Mendelian randomization Contributes to clinical outcome modeling in surgery and pharmacogenomics Her recent publications demonstrate expertise in: Overdispersion handling in genomic datasets EEG-based psychiatric outcome predictions Clinical decision support systems Causal inference from summary genetic data

• Quantum Field Theory
• Elementary Particle Physics
• Scientific Computing
• +3 more

Claudio Pica is a Professor of Computational Science and Head of Section at the University of Southern Denmark (SDU), affiliated with the Department of Mathematics and Computer Science (IMADA) and CP3-Origins. He holds leadership roles as CEO of DeiC National HPC Center and Head of the SDU eScience Center. With a PhD in Physics from the University of Pisa (2005) and a Laurea cum Laude in Theoretical Physics from the Scuola Normale Superiore (2001), his expertise spans quantum field theory, lattice gauge simulations, and high-performance computing. His research focuses on fundamental particle physics, including composite Higgs models, walking technicolor theories, and lattice QCD. He leads large-scale computational projects, such as scaling SU(2) simulations to 1000 GPUs and optimizing algorithms for supercomputers. His outreach initiatives include award-winning educational projects like 'KvanteBanditter' (Lovie Awards 2018) and the SDU Supercomputing Challenge. He has secured € millions in grants, including the €4M European ITN ETN 'EuroPLEx' (2018-2022) and Danish Lundbeckfonden funding (2013-2018). Pica has organized major conferences like the 'Origin of Mass' series and the 'Odense Winter School on Geometry and Theoretical Physics.' His work bridges theoretical physics with computational innovation, addressing challenges in conformal dynamics, dark matter, and supercomputing infrastructure. He is a frequent speaker at international forums and a reviewer for prestigious journals like Physical Review D and European Physical Journal C.

Claudio CasolaView profile

Associate Professor

• Evolutionary Biology
• Forest Genomics
• Genomics
• +3 more

Claudio Casola is an Associate Professor in the College of Agriculture & Life Sciences at Texas A&M University, affiliated with Texas A&M AgriLife Research. His research focuses on evolutionary biology and forest genomics, with a particular emphasis on understanding genome evolution, de novo gene origination, and plant-insect interactions. Casola holds a B.S., M.S., and Ph.D. in Biology and Molecular Biotechnology from the University of Pisa, Italy. His work integrates genomic, computational, and ecological approaches to study topics such as transposable element domestication, drought stress responses in conifers, and the genetic basis of adaptation in forest tree species. He leads the Casola Lab, whose activities include developing genomic tools like SECNVs for simulating genetic variants and collaborating on large-scale projects like the southern pine beetle genome assembly. Casola’s research spans diverse ecosystems, from savanna soil microbiomes to C4 plant chloroplast evolution, reflecting his interdisciplinary approach to solving complex biological questions. His recent studies highlight the importance of gene family dynamics and horizontal gene transfer in shaping genomic diversity across species.

Byung-Jun YoonView profile

Professor

• Artificial Intelligence
• Machine Learning for Science
• Optimal Experimental Design
• +5 more

Dr. Byung-Jun Yoon is a Professor in the Department of Electrical & Computer Engineering at Texas A&M University and a Scientist at Brookhaven National Laboratory's Computing and Data Sciences division. His research focuses on AI-driven scientific discovery, optimal experimental design under uncertainty, and bioinformatics. Yoon holds a joint appointment at BNL since 2019 and has served as a founding faculty member at Hamad bin Khalifa University (2014–2016). He earned his Ph.D. from Caltech (2007) and has received awards including the NSF CAREER Award and multiple best paper recognitions. Education: B.S.E. (summa cum laude) from Seoul National University (1998), M.S. and Ph.D. in Electrical Engineering from Caltech (2002, 2007). His theoretical interests include objective-based uncertainty quantification, machine learning, and signal processing. Application areas span bioinformatics, computational network biology, and AI-driven drug/materials discovery. Key contributions include advancements in optimal experimental design, Bayesian optimization, and neural network applications in scientific contexts. His work is published in top venues like IEEE Transactions and ACM conferences. He serves as an associate editor for multiple journals and chairs major bioinformatics conferences. Current projects include ARPA-H-funded vaccine design platforms and quantum chemistry benchmarking.

Ning Patricia NingView profile

Assistant Professor

• Stochastic Processes
• Markov Chains
• Time Series Analysis
• +7 more

Ning Patricia Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and Institute for Quantum Science and Engineering. She holds a B.S. from Shandong University, M.S. from USC, and Ph.D. from UCSB. Her research focuses on stochastic processes, Markov chains, Bayesian inference, networks, machine learning, and quantum computing. She has served as an Associate Editor for Bayesian Analysis, Journal of Computational and Graphical Statistics, and Statistics and Computing. Recent contributions include advancements in temporal interference stimulation modeling and applications of sequential Monte Carlo methods in machine learning. Her work bridges theoretical stochastic analysis with practical applications in epidemiology and finance. Notable publications include studies on MCMC optimal scaling and doubly reflected backward SDEs. She actively participates in academic events such as the Stochastic Numerics and Statistical Learning Workshop 2025 and the TAMIDS SciML Workshop. As an NSF panelist, she contributes to funding evaluations in mathematical sciences. Ning’s interdisciplinary approach integrates computational methods with real-world challenges, emphasizing scalable Bayesian inference and quantum algorithms. She has advised on multiple research initiatives and maintains a lab focused on stochastic processes and data science applications.

Victoria CrawfordView profile

Assistant Professor

• Approximation algorithms
• Optimization
• Machine learning
• +3 more

Victoria Crawford is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, where she has held this position since 2022. She holds a Ph.D. in Computer Engineering (2022), an M.S. in Mathematics (2016), and a B.S. in Mathematics (2012), all from the University of Florida. Her research focuses on approximation algorithms, optimization, machine learning, and submodular optimization, with an emphasis on scalable solutions for large datasets and theoretical guarantees. Her educational background includes advanced studies in mathematics and computer engineering, complemented by teaching experience in algorithms and theory courses at Texas A&M. Crawford has received prestigious awards such as the Gartner Group Graduate Fellowship (2019) and the Harris Fellowship (2017), and her work includes high-impact publications in venues like IJCAI, ICML, and NeurIPS. Crawford actively contributes to academic service, serving on committees including the Texas A&M Graduate Admissions Committee and the Data Science Hiring Committee. She also chairs and advises doctoral students such as Wenjing Chen and oversees research projects funded through initiatives like the Texas A&M Targeted Proposal Teams and the TAMIDS Seed Program for AI/Computing/Data Science. Her research spans theoretical and applied domains, including algorithm design for submodular functions, bandit optimization, and network resilience. Recent work emphasizes fairness in algorithmic design and scalable solutions for billion-scale networks.

Xiyu PengView profile

Assistant Professor

• Biostatistics
• Bioinformatics
• Computational Genomics
• +5 more

Xiyu Peng is an Assistant Professor of Biostatistics and Bioinformatics at Texas A&M University's Department of Statistics (College of Arts & Sciences). Her research focuses on integrating computational and statistical methods with biomedical data, particularly in genomics, proteomics, and immuno-oncology. She develops novel algorithms to analyze single-cell omics and spatial transcriptomics data, with applications in cancer immunotherapy and multiplexed imaging. Dr. Peng holds a Ph.D. in Bioinformatics and Statistics from Iowa State University and completed postdoctoral training at Memorial Sloan Kettering Cancer Center. Her methodology emphasizes model-based clustering and latent variable models to address challenges in large-scale biomedical data analysis. Her current projects include resolving temporal and spatial dynamics in single-cell data and developing scalable computational tools like the SpaTopic R package for spatial tissue architecture analysis. Her work spans collaborations in oncology, immunology, and microbiome research, with contributions to biomarker discovery, immune monitoring via flow cytometry, and denoising of amplicon sequencing data. She leads efforts to bridge statistical innovation with clinical and translational science.

Eric ChiView profile

Associate Professor

• Statistical Learning
• Numerical Optimization
• Biological Science Applications
• +3 more

Eric Chi is an Associate Professor in the Department of Statistics at Rice University, also affiliated with the Ken Kennedy Institute. He holds a B.A. in Physics from Rice University, an M.S. in Electrical Engineering from UC Berkeley, and a Ph.D. in Statistics from Rice University. His research focuses on statistical learning and numerical optimization, applied to large-scale data in biological and engineering domains. He has been recognized with awards including the NSF CAREER Award (2018) and the LeRoy and Elva Martin Teaching Excellence Award (2020). Chi's academic journey includes postdoctoral roles at UCLA and Rice, and prior faculty experience at North Carolina State University (2015-2021). He serves as an Associate Editor for the Journal of Computational and Graphical Statistics since 2016 and on the Editorial Board of Statistical Methods in Medical Research since 2011. His work emphasizes algorithmic development for complex data structures, including tensor decomposition, matrix completion, and convex clustering. Recent research trends include robust low-rank tensor decomposition, Bayesian inference via proximal MCMC, and convex-nonconvex regularization strategies. His publications span statistical computing, machine learning, and interdisciplinary applications. Chi advises current PhD students at Rice and has mentored multiple alumni now in academia and industry roles.