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Sergei MaslovView profile

Professor

• Microbial Ecology
• Epidemiology Modeling
• Systems Biology
• +3 more

Sergei Maslov is a Full Professor and Bliss Faculty Scholar in the Department of Bioengineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He also holds a non-active appointment in the Department of Physics (0%). His research integrates theoretical physics, computational biology, and microbiology to study microbial ecosystems, epidemic dynamics, and systems-level biological processes. Education: Ph.D. in Theoretical Statistical Physics from Stony Brook University (1996). Academic leadership includes founding roles in UIUC's and Illinois Governor's COVID-19 modeling teams, advising on mitigation strategies during the pandemic. He leads interdisciplinary initiatives, such as the 2024 Strategic Research Initiative on spatial transcriptomics funded by Grainger College of Engineering. Research Interests: Microbial community dynamics, computational epidemiology, origins of life, and systems biology. Key projects include modeling gut microbiome behavior, predicting epidemic waves, and developing tools like FUN-PROSE and DR-BERT for biological data analysis. Awards: 2021 University of Illinois Presidential Medallion, AIMBE Fellowship (2021), APS Fellow (2020), and PECASE (2002). His work bridges theoretical frameworks with real-world applications, such as SHIELD testing protocols and community-level ecological modeling. Grants and Service: Principal Investigator (PI) on multi-million-dollar projects, including SRI grants. Served on campus committees like the Math Bio Faculty Search Committee (2022). Lab collaborations span departments, with co-PI roles in electrical engineering, computer science, and statistics. Labs/Teams: Maslov Lab focuses on computational and systems biology. Recent work includes a Nature Communications paper on microbial resource preferences and eLife publications on pandemic modeling. The lab actively engages in international conferences and hosts visiting scholars, such as Griffin Chure from Stanford.

William KathView profile

Professor

• Quantitative Biology
• Computational Neuroscience
• Optics
• +4 more

William Kath is a Professor of Engineering Sciences and Applied Mathematics and holds a courtesy appointment in Neurobiology at Northwestern University's McCormick School of Engineering. He is the Margaret B. Fuller Boos Professor and Deputy Director of the National Institute for Theory and Mathematics in Biology. His research focuses on quantitative biological modeling, computational neuroscience, and optics, with notable contributions to circadian rhythms and neuronal circuit analysis. Education Ph.D. Applied Mathematics, California Institute of Technology S.B. Mathematics, MIT Research Interests Dr. Kath’s work spans quantitative biology, computational neuroscience, and applied mathematics. He develops models for circadian rhythms, neuronal circuits in Drosophila, and optical systems. His EMBEDR algorithm distinguishes signal from noise in single-cell omics data. He also studies stochastic dynamics, nonlinear optics, and parametric amplification in fiber communications. Awards and Recognition Fellow of the Optical Society of America (Optica) ASEE Mathematics Division Distinguished Educator and Service Award Mccormick School Teaching Award Advising and Grants He has advised numerous graduate students and collaborates with labs such as Gallio and Dombeck. His grants include funding for interdisciplinary training in quantitative biological modeling. Labs and Affiliations Affiliated with the Northwestern Institute on Complex Systems, he serves as Editor of Wave Motion and Deputy Director of the National Institute for Theory and Mathematics in Biology.

Sorin DraghiciView profile

Professor

• Computational Biology
• Data Integration
• Disease Subtyping
• +1 more

Sorin Draghici is a Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research specializes in computational biology, integrating multi-omics data to discover disease subtypes and repurpose drugs. He develops bioinformatics tools like ROntoTools and CPA for pathway analysis and single-cell data interpretation. Research Focus: Machine learning applications in oncology and infectious diseases, including COVID-19 proteomics and ovarian cancer recurrence prediction. Recent innovations include AI-driven data interrogation (LmRaC) and upstream regulator prediction (PURE). Publications emphasize scalable algorithms for big data challenges.

Ramesh SivanpillaiView profile

Professor

• Remote Sensing
• Environmental Science
• GIS (Geographic Information Systems)
• +4 more

Ramesh Sivanpillai is an Instructional Professor at the University of Wyoming's School of Computing, part of the Wyoming Geographic Information Science Center (WyGISC). He holds a PhD in Forestry from Texas A&M University and has extensive academic credentials including a GIS Professional (GISP) certification and a Certified Project Manager in disaster response. His research focuses on remote sensing applications for agriculture, flood mapping, land cover change, and machine learning integration. He teaches courses such as Remote Sensing for Agricultural Management and UAS Sensors and Platforms. Education: PhD, Texas A&M University (2002); MS, University of Wisconsin–Green Bay (1995); MPhil, Bharathiar University (1992); MSc, Cochin University of Science & Technology (1990); BSc, PSG College of Arts & Science (1987). Professional Affiliations: American Society for Photogrammetry & Remote Sensing (ASPRS Fellow 2021), Society of American Foresters, American Geophysical Union. Research Interests: Remote sensing of small crop lands, flood inundation mapping, phenology analysis, and machine learning for feature recognition in aerial imagery. Recent projects include rapid flood mapping with satellite images and identifying epiphytes using neural networks. Publications emphasize Landsat data applications, flood inundation techniques, and machine learning in environmental monitoring. Over 20 peer-reviewed articles since 2016 highlight his contributions to remote sensing science. Grants include ongoing AmericaView StateView Program funding for Wyoming, supporting Landsat data operations and education outreach. Professional service includes roles as Regional Director of ASPRS Rocky Mountain Chapter and editorial positions in Frontiers of Earth Science and Journal of Applied Remote Sensing . Advising spans undergraduate and graduate students in remote sensing research, with notable projects on flood mapping biases, water body segmentation, and UAV-based crop monitoring. His outreach includes K-12 educational programs integrating satellite imagery to teach Earth science concepts.

Keren LiView profile

Assistant Professor

• Distributed Learning
• Federated Learning
• Deep Learning
• +7 more

Keren Li is an Assistant Professor in the Department of Mathematics at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. She holds a PhD in Statistics from the University of Illinois at Chicago (2018), an MS in Mathematics from Louisiana State University (2004), and a BA in Mathematics from Nankai University (2001). Before joining UAB in 2022, she served as an Associate Scientist at UAB's Informatics Institute (2023–2024) and a Postdoctoral Fellow at Northwestern University’s NSF-Simons Center for Quantitative Biology and Department of Statistics and Data Science (2018–2022). Her research focuses on distributed learning, federated learning, deep learning, bioinformatics, and financial mathematics. Key projects include developing tools like DNAcycP (predicting DNA cyclizability) and RiboDiPA (analyzing ribosome profiling data). She also explores statistical methods for optimal design and generalized linear models. Her work bridges computational methods and real-world applications, with contributions to genomics, energy networks, and financial modeling. Her lab ( klilab ) emphasizes interdisciplinary collaboration and innovative algorithm development.

Francesca Marta Lilja Di LascioView profile

Associate Professor

• Copula-based stochastic models
• Clustering techniques for dependent data
• Statistical methods for spatial/time series data
• +5 more

Francesca Marta Lilja Di Lascio is an Associate Professor of Statistics at the Faculty of Economics and Management, Free University of Bozen-Bolzano. Her research focuses on copula-based statistical methods for complex data analysis, including dependence modeling, clustering algorithms, and applications in environmental science, energy economics, and medical statistics. She obtained her PhD in Statistical Methodology for Scientific Research from the University of Bologna (2008) and holds a National Scientific Qualification for Full Professor (2023-2034). Her academic roles include Vice-director of the Bachelor in Economics and Social Sciences, Coordinator of the BEMPS working paper series, and member of the Quality Committee. She has organized over 20 international conferences and workshops on statistical methods and data science, including the 2022 Workshop on Statistical Learning and Econometrics. Research interests span copula-based clustering, spatial-temporal modeling, and imputation techniques for missing data. She has secured grants totaling over €3.5M, including the €2M 'Agritech National Research Center' (PNRR funded) and the €182k 'Techno-economic methodologies for sustainable energy scenarios' project. She develops statistical software packages like CoClust (copula-based clustering) and PanelTM (dynamic threshold models). Teaching includes graduate courses on multivariate statistical methods and PhD-level quantitative research methods. She has published 40+ peer-reviewed articles in journals like Risk Analysis , Computational Statistics & Data Analysis , and Environmental and Ecological Statistics . Key Awards: Full Professor Qualification (2023), Associate Professor Qualification (2017) Recent Projects: EXTReme Events in Mountain Environments (€120k), Detecting Effects of Experimental Therapies (€82k) Professional Memberships: Italian Statistical Society (SIS), International Biometric Society (IBS), International Statistical Institute (ISI)

• Computational Social Science
• Data Science
• Multivariate Social Networks
• +10 more

Termeh Shafie is a full-time Professor in the Department of Politics and Public Administration at the University of Konstanz, specializing in Computational Social Science and Data Science. With a strong statistical foundation, she develops advanced methodologies for analyzing multivariate social networks while bridging archaeological network reconstruction with modern data science techniques through projects like NEXUS 1492. Key Research Areas: Multigraph modeling, network entropy analysis, isotope geoprovenance, and hypergraph representations Projects: NEXUS 1492 archaeological network reconstruction Her 15 most recent publications demonstrate significant contributions to network methodology (random multigraph models, centrality index analysis), archaeological applications (Caribbean attack networks, Iroquoian settlement patterns), and data privacy frameworks. Articles span 2012–2025 with interdisciplinary focus on statistical sociology, archaeological theory, and computational modeling. Current teaching includes courses on social network analysis, data science, and statistical learning. Office hours available via ILIAS booking system. Contact details provided for both academic and administrative correspondence.

Sandeep SinghalView profile

Associate Professor

• Cancer Genomics
• Biomarker Discovery
• Radiogenomics
• +2 more

Sandeep Singhal is Associate Professor of Pathology and Biomedical Engineering at UND. His research develops multi-omics biomarkers for cancer prognosis/therapy response, focusing on breast cancer disparities and prostate radiation toxicity. Key contributions include PIK3CA mutation signatures for endocrine therapy response and DNA methylation-based immune classifiers. Recent work explores arsenic-related bladder carcinogenesis and radiogenomics models. Published in Nature Genetics and Journal of Clinical Investigation, he serves on Scientific Reports editorial board. Secured grants for clinical genomics and toxicity prediction. Holds adjunct appointments at Columbia University and directs bioinformatics for North Dakota INBRE.

Eric ChalmersView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Biomedical Engineering
• +3 more

Eric Chalmers is an Assistant Professor in the Department of Mathematics & Computing at Mount Royal University. He holds a BSc in Electrical Engineering and a PhD in Biomedical Engineering from the University of Alberta. His research focuses on understanding principles of intelligence and learning, applying insights from neuroscience to improve artificial intelligence and analytics. He has been recognized with the George Walker award for his doctoral thesis. Education: BSc in Electrical Engineering, University of Alberta PhD in Biomedical Engineering, University of Alberta Research Interests: Machine Learning, Artificial Intelligence, Biomedical Engineering, Neuroscience, and Healthcare Analytics. His work bridges biological insights with computational models, particularly in reinforcement learning, neurovascular dynamics, and mental health modeling. Articles Trends: Recent publications explore neurovascular coupling in health, hippocampus-inspired algorithms, depression-like behavior simulations in AI, and theoretical foundations of intelligence. His work emphasizes biologically inspired methods in AI development. Awards: George Walker Award for Best Doctoral Thesis Grants & Collaborations: His PhD research was supported by Alberta Innovates and the Women and Children’s Health Research Institute. He has collaborated across industries including healthcare, agriculture, and sports analytics. Labs & Teams: Active in interdisciplinary projects at Mount Royal University, though no specific lab is explicitly mentioned in the provided texts.

• Applied Mathematics
• Bayesian Inference
• Monte Carlo Methods
• +5 more

Simon Cotter is a Professor of Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, stochastic modeling, and computational methods, with applications in biological systems (e.g., tendon mechanics, placental development) and financial modeling. He contributes to the university’s Digital Futures research beacon and collaborates across disciplines including biomechanics, computational biology, and data science. Research Interests: Bayesian data assimilation and parameter estimation Monte Carlo methods (e.g., multi-index, adaptive importance sampling) Multiscale stochastic systems and reaction networks Inverse problems in material science and biological systems Recent work highlights include developing NuZZ (a numerical Zig-Zag algorithm for general models) and advancing hierarchical Bayesian methods for data selection. His projects address challenges in debt recovery forecasting, placental development modeling, and cell cycle heterogeneity analysis. He co-leads a multi-modal pregnancy research project aimed at understanding stillbirth mechanisms and is actively involved in initiatives promoting gender equity in academia.

Chloé ArsonView profile

Professor

• Rock Mechanics
• Computational Geomechanics
• Artificial Intelligence in Subsurface Exploration
• +4 more

Dr. Chloé Arson is a Professor in the Department of Earth and Atmospheric Sciences at Cornell University and an adjunct faculty member at Georgia Tech’s School of Civil and Environmental Engineering. She holds a Ph.D. in geomechanics from École Nationale des Ponts et Chaussées (2009) and has held academic roles at Texas A&M (2009–2012) and Georgia Tech (2012–2023) before joining Cornell in 2023. Research: Her work focuses on damage and healing in rock mechanics, AI-driven subsurface exploration, and bio-inspired geotechnical systems. Key areas include computational modeling of porous media, geothermal energy systems, and climate change mitigation through poromechanics. Her lab develops tools like the Burrowing Robot with Integrated Sensor System (BRISS) and investigates slime mold network dynamics for infrastructure adaptation. Teaching: Teaches mechanics-focused courses at Cornell and Georgia Tech, including 'Modern Structures,' 'Theoretical Geomechanics,' and 'Finite Element Method for Porous Media.' Awards: 2023 Susan G. and Christopher D. Pappas Professorship 2021 NSF BRITE Award 2016 NSF CAREER Award Service: Editorial roles in Scientific Reports and Open Geomechanics , leadership in ASCE committees, and director of the CEE Gateways to France program fostering Franco-American collaborations. Labs/Teams: Leads the Arson Lab at Cornell, focusing on computational geomechanics, AI integration, and bio-inspired engineering solutions.

Kevin LinView profile

Assistant Professor

• Single-cell Biology
• Statistical Genetics
• Biostatistics
• +3 more

Kevin Lin is an Assistant Professor in the Department of Biostatistics at the University of Washington, joining in Fall 2023. His research focuses on developing statistical methods for analyzing single-cell data to uncover cellular mechanisms in diseases like Alzheimer’s and immune resistance. He holds a PhD from Carnegie Mellon University and completed postdoctoral training at the University of Pennsylvania. His work bridges computational methods with biological questions, emphasizing matrix factorization, network modeling, and changepoint detection. Education: PhD in Statistics & Data Science (Carnegie Mellon University, 2020) Previous Role: Postdoctoral Researcher at University of Pennsylvania (Wharton Statistics) Research interests include high-dimensional data analysis, statistical genetics, and single-cell RNA-Seq. Notable contributions include methods like Tilted-CCA for multimodal data integration and eSVD-DE for cohort-wide differential expression analysis. His work has been recognized with awards such as the Wikimedia Foundation Research Award (2023). Lin’s lab collaborates on projects involving endolysosomal dysfunction in Alzheimer’s, yeast cell division dynamics, and lineage-aware machine learning. Key achievements include over 20 publications in journals like Nature Biotechnology , PNAS , and Biometrics . He advises on grants related to single-cell technologies and serves on the EDI subcommittee for mental health initiatives at UW. Outside academia, Lin enjoys zumba and cooking.

Charith AbhayaratneView profile

Senior Lecturer

• Signal Processing
• Machine Learning
• Multimedia Security
• +7 more

Dr. Charith Abhayaratne is a Senior Lecturer and EEE Foundation Year Tutor at the School of Electrical and Electronic Engineering, University of Sheffield. He leads the Communications Research Group and serves as the accreditation team lead for the school. With qualifications including a PhD from the University of Bath and a B.E. from the University of Adelaide, his research focuses on signal processing, machine learning, multimedia security, and video coding. His work explores blockchain for content protection, visual salience in robotics, human activity recognition, and advanced video coding techniques (HDR, UHD, 360° video). His research has been funded by Innovate UK, EPSRC, and industry partners. Education: B.E. (Electrical and Electronic Engineering), The University of Adelaide, Australia (1998) PhD (Electronic and Electrical Engineering), University of Bath, UK (2002) PGCertHE (Higher Education), University of Sheffield (2008) Fellow of the Higher Education Academy (FHEA), Member of the Institution of Engineering and Technology (MIET), Member of IEEE (MIEEE) Research Interests: His work spans multimedia security (data hiding, blockchain), computer vision (visual salience, object recognition), and video coding (HDR/UHD). Current projects include robotic vision applications, assisted living through activity recognition, and international standards development (JPEG/MPEG). He has contributed to scalable video standards and serves on technical committees for IEEE, EURASIP, and APSIPA. Awards & Service: Recipient of the Alain Bensoussan Fellowship (ERCIM, 2002) Associate Editor for IEEE Transactions on Image Processing, IEEE Access, and Elsevier JISA Member of EPSRC Peer Review College and British Standards Institution (BSI) Grants & Labs: Active grants from Innovate UK and EPSRC support projects in multimedia security and video coding. His lab leads interdisciplinary work in AI-driven visual analytics and secure media distribution frameworks.

David MobleyView profile

Professor

• Chemical Biology
• Physical Chemistry
• Computational Chemistry
• +3 more

David Mobley is a Professor in the Department of Chemistry at the University of California, Irvine (UCI), affiliated with the School of Physical Sciences. His research focuses on applying computational and theoretical methods to understand biological processes such as protein-ligand binding and solvation, with applications in drug design. He co-leads the Open Force Field Initiative, driving open-source molecular modeling tools and community efforts like the SAMPL Challenges and Open Free Energy project. Research Interests include chemical biology, physical chemistry, and computational methods for predicting molecular interactions. He emphasizes collaborative projects, including developing transferable force fields and advancing free energy calculation techniques. Recent work highlights advancements in force field parametrization, machine learning applications in molecular mechanics, and benchmarking methodologies. His lab's contributions include the Open Force Field v2.0, the Konnektor framework for free energy networks, and tools like alchemlyb for alchemical free energy calculations. Dr. Mobley’s research also addresses challenges in water sampling, binding free energy predictions, and collaborative initiatives such as the SAMPL blind challenges. His team works on improving simulation accuracy for industrial and academic applications.

• Dielectric Spectroscopy
• Electromagnetic Field Exposure
• Biomedical Engineering
• +3 more

Emanuele Piuzzi is an Associate Professor in Electrical and Electronic Measurements at the Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome. He holds an M.S. and Ph.D. in Electronic Engineering from Sapienza University, with a thesis on electromagnetic field exposure and a dissertation on human exposure to wireless telecommunication systems. His research focuses on dielectric spectroscopy, TDR applications, impedance pneumography, UWB radar for physiological monitoring, and electromagnetic field safety assessment. He is a reviewer for IEEE Transactions on Instrumentation and Measurement and a member of IEEE and the Italian Electrotechnical Committee. Teaching includes courses on 'Electrical Measurements' for Electronic and Biomedical Engineering students. Research interests span biomedical engineering, telecommunications, and medical imaging, with a strong emphasis on practical applications like non-invasive monitoring systems and material characterization. His work on radar systems for medical diagnostics and sensor networks for structural health monitoring demonstrates interdisciplinary innovation. Over 150 publications highlight contributions to electromagnetics, biomedical devices, and metrology.