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Mike LeeView profile

Professor

• Evolutionary Biology
• Paleontology
• Herpetology
• +4 more

Mike Lee is a Professor in Evolutionary Biology at Flinders University, jointly appointed with the South Australian Museum. His research spans Phylogenetics , Macroevolution , Herpetology , and Palaeontology , primarily focusing on reptiles (living and fossil), but also birds, mammals, and arthropods. He employs 3D imaging , anatomical analysis , and Bayesian phylogenetics to study evolutionary patterns such as limb loss, speciation rates, and phenotypic plasticity. Key Collaborations: Flinders Palaeontology Group, ARC-funded projects, and international researchers. Research Grants include multiple ARC Discovery Grants (2016-2025) totaling over $1.2 million, focusing on snake evolution, fossil integration with genomics, and neural adaptations in reptiles. Scientific Awards : Fulbright Fellow (1998) Edgeworth David Medal (2000) Tall Poppy Award (2003-2004) Top 10 Breakthrough of 2014 (Science/AAAS) Riversleigh Medal (2015) Verco Medal (2018) Media Engagement: Regular contributor to The Conversation , with topics ranging from human evolution to Australian megafauna extinction. Available as a media expert on Biodiversity , Palaeontology , Reptiles , and Zoology .

• Distributed Computing
• Self-stabilizing Algorithms
• Network Algorithms
• +1 more

Alain Cournier is a University Professor at the University of Picardy Jules Verne (UPJV), France, affiliated with the ALCO research unit (UR 4290) focusing on Algorithmic and Complexity. His office is located in room 401 with contact phone 5913. He maintains an active research profile with continuous publications from 1992 through 2024, primarily in distributed computing and theoretical computer science. Professor Cournier's research centers on fault-tolerant distributed algorithms, specializing in self-stabilizing systems for networked environments. His core contributions span leader election protocols, synchronous unison mechanisms, and message forwarding algorithms in directed and undirected topologies. He pioneered work in snap-stabilization and polynomial-time convergence guarantees, with recent focus on directed network challenges. Earlier career phases included modular graph decomposition algorithms (1990s), while 2018-2019 saw exploration of autoencoder neural networks for anomaly detection in connected buildings. Analysis of his 15 most recent publications reveals strong continuity in distributed algorithms research (12/15 papers), with 2023-2024 works exclusively addressing self-stabilization in directed networks using innovative metaphors like Roman infrastructure. The 2018-2019 neural network studies represent a brief interdisciplinary expansion before returning to core theoretical work. His publications consistently appear in premier venues including PODC, ICDCN, and Theoretical Computer Science, often collaborating with Altisen, Defalque, and Devismes. He operates within the ALCO research unit (UR 4290) at UPJV, which serves as the institutional home for his algorithmic complexity research. This unit facilitates collaboration across the French distributed computing community, evidenced by regular AlgoTel conference participation. While specific lab structures aren't documented, his publication patterns indicate sustained theoretical work with periodic applied explorations in cybersecurity contexts.

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +6 more

Maria Salamo Llorente is an Associate Professor and researcher in the Department of Mathematics and Computer Science at the University of Barcelona (UB) . She leads the Teaching Innovation Group 'Innovació Docent en Matemàtiques i Informàtica' and is a core member of the Language and Computation Center (CLiC-UB) . Her career spans from postdoctoral work at University College Dublin to extensive contributions in artificial intelligence.

• Linear Algebra
• Graph Theory
• Combinatorial Matrix Analysis
• +4 more

Kevin Vander Meulen is a Professor of Mathematics at Redeemer University, where he has taught since 1994. He also holds an Adjunct Professor appointment in the Department of Mathematics and Statistics at McMaster University. His academic background includes a Ph.D. and M.Sc. in Mathematics and Statistics from Queen’s University (1995 and 1991) and a B.Sc. Hon. in Mathematics from Calvin College (1989). His research focuses on Linear Algebra and Graph Theory , including combinatorial matrix analysis, sign patterns, inertia, eigenvalue problems, and vertex decomposability. He has secured research funding through an NSERC Discovery Grant (2016–2021). Key trends in his publications (1994–2025) include spectral properties of matrices, graph theory applications, and interdisciplinary work linking mathematics with philosophy. He frequently collaborates with students on research projects. Professional memberships include the Association for Christians in the Mathematical Sciences , Canadian Mathematical Society , International Linear Algebra Society , and Society for Industrial and Applied Mathematics . Kevin resides in Hamilton with his family and enjoys outdoor activities like canoeing, hiking, and cycling.

Tom JohnstenView profile

Professor

• Data Mining Algorithms
• Computational Biology
• Machine Learning Applications

Dr. Tom Johnsten is a Professor in the Computer Science Department at the University of South Alabama, part of the School of Computing. He holds a Ph.D. in Computer Science from the University of Louisiana at Lafayette (1998), an M.S. in Computer Science from Arizona State University (1992), and a B.S. in Mathematics from Oklahoma State University (1987). His research focuses on developing data mining algorithms, computational biology, and applying machine learning technologies to interdisciplinary challenges. His work bridges computational methods with biological systems, cybersecurity, and environmental data analysis. Notable contributions include advancements in DNA repair mechanisms, anomaly detection in data streams, and prescriptive maintenance frameworks. He has taught courses such as Digital Logic and Computer Architecture, Operating Systems, Programming Language Theory, and Data Mining. His office is located in Shelby Hall, Room 1119, with regular office hours on Tuesdays and Thursdays. Dr. Johnsten’s publications reflect a strong emphasis on cybersecurity, bioinformatics, and data-driven solutions. He has explored topics like mitochondrial DNA repair, robotic system security, and synthetic signal peptide design. While no scientific awards are explicitly listed, his extensive publication record highlights sustained academic contribution. Teaching and advising roles include mentoring students through courses like Research Methodologies and Database Design. His work integrates theoretical computer science with practical applications in diverse fields.

David CairnsView profile

Professor

• Clinical Trials
• Biostatistics
• Haematological Oncology
• +12 more

Professor David Cairns is a leading academic in clinical trials research at the University of Leeds, Faculty of Medicine and Health, School of Medicine. He serves as Professor of Clinical Trials Research, Deputy Director of the Leeds Cancer Research UK Clinical Trials Unit (CTRU), and Director of Late Phase Trials in the Cancer Division. His work bridges biostatistics and oncology, with a focus on designing and analyzing large-scale confirmatory trials. His educational background includes a PhD and MSc in Statistics from the University of Sheffield and a BSc in Mathematics from the University of Leeds. David Cairns specializes in the design and analysis of cancer clinical trials, particularly in haematological malignancies such as multiple myeloma and chronic lymphocytic leukaemia, as well as solid tumours including colon, gastro-oesophageal, and urological cancers. He is passionate about developing efficient trial designs that incorporate biomarkers to define treatment pathways and enhance personalization. He actively promotes the integration of translational research within clinical trials to improve scientific value and inform future study designs. The 15 most recent articles reflect a strong focus on methodological innovation in clinical trials, including platform trials, non-concurrent controls, biomarker-driven designs, and the integration of real-world data. His work combines statistical rigor with clinical relevance, particularly in oncology settings. His scientific recognition includes the MRC Career Development Award in Biostatistics. He is also actively involved in national research governance through membership in key panels: Fellow of the Royal Statistical Society NCRI Multiple Myeloma Subgroup (Member) NIHR Clinical Trial Fellowship Panel (Member) NIHR Development and Skills Enhancement Fellowship Panel (Member) NIHR Doctoral Research Fellowship Panel (Deputy Chair) Cancer Research UK Expert Review Panel (Member) David Cairns is deeply committed to student education and the training of future clinical researchers. He welcomes PhD applicants interested in clinical trials, biostatistics, and cancer research. He supervises postgraduate research and contributes to the academic development of junior researchers through mentorship and collaborative projects. His leadership roles indicate substantial grant oversight and management of large-scale research initiatives. He is embedded in the Leeds Institute of Clinical Trials Research, a multidisciplinary team dedicated to improving patient outcomes through high-quality trial design and execution. He is affiliated with several research groups and institutes, primarily the Leeds Institute of Clinical Trials Research (Populations) and contributes to national and international efforts in advancing cancer clinical research methodology.

• Algorithms on strings
• Formal language theory
• Bioinformatics
• +3 more

Chiara Epifanio is a researcher (INFO-01/A) at the University of Palermo , based in the Department of Mathematics and Computer Science . She holds regular office hours on Tuesdays 14:30–17:00 in Room 104, first floor, Via Archirafi 34, and can be reached at chiara.epifanio@unipa.it . Over the past fifteen years she has designed and taught a diverse portfolio of courses spanning Programming , Bioinformatics , Advanced Programming , Teaching Methodologies and Techniques for Computer Science and Pattern Discovery for Life Sciences . These offerings serve degree programmes in Mathematics, Computer Science, Statistics & Data Science, and the recently launched curricula in Data, Algorithms & Machine Intelligence and Computer Science & Artificial Intelligence . Her research lies at the intersection of combinatorics on words , string algorithms , and bioinformatics . She has made sustained contributions to the design of alignment-free distances for biological data, the theory of Sturmian words and their associated graphs, suffix automata tolerant to mismatches, and compact data structures such as linear-size suffix tries. The work repeatedly draws on deep results from formal language theory, automata theory, and discrete mathematics to solve practical problems in sequence analysis. Publications trend: From 2004 onward she has produced a steady stream of peer-reviewed articles that move from foundational combinatorial results on Sturmian words and critical factorization theorems toward application-driven studies on approximate string matching and genomic data mining. A marked acceleration is visible in the 2023 publications addressing k-Hamming and k-edit distances, reflecting current demands in large-scale biological data analytics. Scientific awards: None explicitly mentioned in the provided material. Advising & grants: No PhD or Master’s students are listed in the supplied pages, and no funded project descriptions are available. Laboratories & teams: While no specific lab is named, her teaching and research are embedded within the Department of Mathematics and Computer Science, which hosts groups in algorithms, discrete mathematics, and bioinformatics, and provides access to the university’s ATeN Center and other research infrastructures.

• Matrix and Tensor Factorizations
• Data Fusion
• Machine Learning
• +3 more

Carla Schenker is a Postdoctoral Fellow in the Department of Data Science and Knowledge Discovery at Simula Metropolitan, specializing in advanced tensor decomposition methods for multi-modal data analysis. Her research bridges machine learning, optimization, and neuroimaging applications, with a focus on interpretable pattern discovery from complex datasets. Her educational background includes: PhD from Oslo Metropolitan University, Norway (Thesis: A Flexible Framework for Data Fusion Based on Coupled Matrix and Tensor Factorizations for Interpretable Pattern Discovery) Dr. Schenker's research centers on Matrix and Tensor Factorizations , where she develops constrained optimization frameworks for PARAFAC2 and coupled decompositions. Her work enables Data Fusion across dynamic and static sources, with critical applications in neuroimaging biomarker discovery and temporal pattern tracking . She pioneers methods for handling incomplete temporal data while maintaining model interpretability, advancing both theoretical foundations and real-world implementations in multi-way data analysis. Analysis of her 11 publications (2019-2025) reveals a clear evolution: early work established optimization frameworks for regularized tensor factorizations (2019-2021), while recent breakthroughs (2023-2025) focus on temporal dynamics, interpretable evolving patterns, and constrained PARAFAC2 variants. Her research consistently bridges Machine Learning theory with applications in neuroscience and signal processing, demonstrating increasing sophistication in handling heterogeneous, multi-modal datasets. No scientific awards are documented in available sources. Public records indicate no formal student advising or grant leadership, though her collaborative publications involve significant interdisciplinary partnerships with institutions like Oslo Metropolitan University and international research teams. As a core member of Simula Metropolitan's Data Science and Knowledge Discovery department, she contributes to Norway's national research infrastructure for digital engineering, working within teams focused on algorithmic innovation for complex data challenges in healthcare and industrial applications.

• Sleep Neuroscience
• Memory Consolidation
• Stroke Recovery
• +5 more

Anna Guttesen is a Postdoctoral Researcher at the Wellcome Centre for Integrative Neuroimaging within the Nuffield Department of Clinical Neuroscience at the University of Oxford, where she works under Prof Heidi Johansen-Berg and Dr Melanie Fleming in the Plasticity Group and OxCIN Neuroplastics Network. Her academic background includes a PhD in Psychology from the University of York, supervised by Dr Scott Cairney and Prof Gareth Gaskell, with research focused on sleep-dependent declarative memory consolidation. Dr. Guttesen's work centers on sleep neuroscience in clinical contexts, specifically investigating how stroke alters sleep-related memory consolidation processes and whether targeted memory reactivation can enhance recovery. Her research bridges cognitive neuroscience, clinical neurology, and sleep medicine through EEG-based studies of neural markers during sleep. She examines both fundamental mechanisms (e.g., neural signatures of learning) and clinical applications (e.g., sleep regularity as a biomarker for stroke rehabilitation). Her recent publications demonstrate consistent output in high-impact journals, with work spanning memory reactivation paradigms, sleep disturbances in neurological populations, and neural correlates of learning. As a postdoctoral researcher, she contributes to the Plasticity Group's grant-funded projects at the Wellcome Centre, focusing on translating sleep neuroscience discoveries into stroke rehabilitation strategies. Students collaborating with her gain expertise in EEG methodology, sleep monitoring, and clinical neuroscience research within Oxford's world-class neuroimaging infrastructure.

Amir H. BanihashemiView profile

Professor

• Digital Communications
• Wireless Communications
• Coding and Information Theory
• +5 more

Amir H. Banihashemi is a Professor of Systems and Computer Engineering at Carleton University, holding a P.Eng. designation. He earned his B.A.Sc. from Isfahan University of Technology, M.A.Sc. from Tehran University, and Ph.D. from the University of Waterloo. His academic career includes postdoctoral research at the University of Toronto and roles such as Director of the Broadband Communications and Wireless Systems (BCWS) Centre at Carleton until 2013. He has authored over 100 publications in top-tier journals and conferences, focusing on coding theory, digital communications, and signal processing. Notable awards include the 2017 IEEE Ottawa Section Outstanding Engineer Award and Carleton's Research Achievement Awards (2006, 2012). His research group actively explores LDPC codes, compressed sensing, and spectrum sensing in cognitive radios. Key contributions include error floor analysis, trapping set elimination, and efficient decoding algorithms. Education: B.A.Sc. Electrical Engineering, Isfahan University of Technology (GPA 19.4/20) M.A.Sc. Communication Engineering, Tehran University (GPA 18.74/20) Ph.D. Electrical and Computer Engineering, University of Waterloo Research Interests: Digital and Wireless Communications Coding and Information Theory Signal Processing Algorithms LDPC Codes Design and Analysis Recent Projects: Compressed Spectrum Sensing for Cognitive Radios (2021 FED Research Award) Error Floor Mitigation in LDPC Decoding Trapping Set Characterization Awards highlight his pioneering work in error correction coding and digital communications. His funded research includes NSERC Discovery Grants and industry partnerships. He has supervised numerous graduate students and advised postdoctoral fellows in coding theory and communications.

Jonathan HenningerView profile

Assistant Professor

• RNA Biology
• Gene Regulation
• Cell Fate Determination
• +5 more

Jonathan Henninger is an Assistant Professor in the Department of Biological Sciences at Carnegie Mellon University (CMU), part of the Mellon College of Science. His research focuses on RNA's role in regulating gene expression and cell fate through molecular biology, soft-matter physics, and super-resolution imaging approaches. The Henninger Lab investigates RNA's interactions with proteins and condensates to uncover mechanisms underlying gene control and disease. Education: Ph.D. in Developmental and Regenerative Biology from Harvard University, followed by postdoctoral training at the Whitehead Institute for Biomedical Research, MIT. Key research interests include RNA-mediated transcriptional regulation, nuclear condensates, RNA structure-function relationships, and the development of RNA-based therapies. The lab combines experimental and computational methods to study how RNA defects contribute to human diseases like cancer and neurodevelopmental disorders. Recent work includes identifying how RNA synthesis patterns nuclear condensates and discovering roles for rRNA G-quadruplexes in nucleolar protein condensation. The lab received a Kaufman Foundation New Investigator Grant to explore RNA's role in directing transcription. Advising and grants: Jonathan mentors a diverse team of PhD, Master’s, and undergraduate students. Notable advisees include Haoran Wang, Lydia Phillips, and Arth Banka. Current projects involve synthetic transcriptional condensates and bioinformatic discovery of RNA regulatory elements. Labs and collaborations: The lab is equipped with state-of-the-art microscopy (e.g., Zeiss Lattice SIM 5) and collaborates with groups at CMU, Pitt, and NYU. Regular scientific events include the Steel City Condensate Club Symposium.

• Optimization
• Cryptography
• Artificial Intelligence
• +5 more

Yu Li is a Lecturer at the University of Picardie Jules Verne (UPJV) and a member of research unit UR 4290 “Optimization and Cryptography, AI – OCIA.” His office is located in room 302, reachable by internal telephone extension 5900. Research Interests Dr. Li’s research spans several inter-related domains: Optimization & Control Theory – developing dynamic optimization algorithms for industrial processes such as continuous casting in steel manufacturing. Cryptography & Security – investigating secure and dependable models for cloud and distributed systems. Artificial Intelligence & Robotics – integrating AI perception and decision-making into cloud-connected robotic platforms, including exoskeletons for rehabilitation and autonomous ground vehicles. Cloud & Fog Computing – designing middleware and domain-specific languages that seamlessly connect robotic devices with cloud and edge resources. Publication Trends Over the past decade, Dr. Li’s publication record reveals a clear evolution from foundational work in software architecture and component-based systems (2010-2016) toward cutting-edge applications in cloud/fog-enabled robotics and AI-driven cyber-physical systems (2017-2023). His studies increasingly emphasize real-world deployment, simulation-driven resource estimation, and human-centric interaction in rehabilitation robotics. Scientific Awards & Recognition No specific awards are listed in the provided materials. Advising & Funding No explicit information about supervised students or funded grants is available in the text supplied. Laboratories & Teams He carries out his research within the UR 4290 research unit “Optimization and Cryptography, AI – OCIA” at UPJV, focusing on collaborative projects that bridge mathematics, computer science, and robotics engineering.

Fatemeh PourahmadianView profile

Assistant Professor

• Geomechanics
• Engineering Mechanics
• Wave Propagation
• +3 more

Fatemeh Pourahmadian is an Assistant Professor in the Department of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder. Her research focuses on wave motion and inverse problems in engineering mechanics, particularly in complex materials such as metamaterials and porous media. Education: PhD in Civil Engineering, University of Minnesota (2016) MS in Geo-Engineering, University of Minnesota (2015) MS in Mechanical Engineering, Iran University of Science and Technology (2010) Research Interests: Wave propagation in heterogeneous materials Inverse scattering techniques Elastic-wave cloaking and nonlinear dynamics Development of computational methods for material characterization Key Themes in Publications: Recent work emphasizes machine learning-based approaches for ultrasonic imaging, fast Fourier transform solvers for micropolar composites, and inverse elastic scattering in time/frequency domains. Her studies bridge theoretical frameworks with experimental validations in geomechanics and material science. Professional Affiliations: Society of Engineering Science (SES) Engineering Mechanics Institute (EMI) American Society of Mechanical Engineers (ASME) American Rock Mechanics Association (ARMA)

• Genome Engineering
• Synthetic Biology
• Biotechnology
• +5 more

Prof. Dr. Johann E. Kufs is a Professor at the Faculty of Engineering at University of Bielefeld, where he leads research in the Genome Engineering and Editing Group within the Center for Biotechnology (CeBiTec). His work focuses on utilizing unconventional biological systems for biotechnological applications, particularly using the amoeba Dictyostelium discoideum as a novel host organism for natural product biosynthesis. Dr. Kufs' research spans genome engineering, synthetic biology, and bioprocessing, with emphasis on developing amoeba-based systems for producing complex natural products. His laboratory has pioneered methods for engineering D. discoideum to serve as an alternative host for compounds that are traditionally difficult to produce through chemical synthesis or plant extraction. This includes significant work on cannabinoid precursors, psilocybin, and various polyketides, demonstrating the potential of amoebae as production platforms for pharmaceutical compounds. Analysis of Dr. Kufs' publication record reveals a strong emphasis on scaling up biological production processes from laboratory to industrial scales, with particular attention to the unique challenges of working with amoebae. His research has demonstrated the feasibility of using D. discoideum for cannabinoid precursor production at scales up to 300-L bioreactors, establishing this organism as a viable alternative to traditional bacterial or fungal hosts. Dr. Kufs has secured third-party funding from the Federal Government for his project "GBi1 M: Production of cannabinoids in Dictyostelium discoideum (ProDICAN)" (2023-2024), demonstrating the significance and potential impact of his research. His work bridges fundamental biological research on amoeba metabolism with practical industrial applications in pharmaceutical production. His laboratory actively collaborates with researchers across multiple institutions, with frequent co-authorship patterns indicating strong partnerships with Christin Reimer, Vito Valiante, Falk Hillmann, and Lars Regestein. The Genome Engineering and Editing Group maintains an active research program with regular publications in high-impact journals including Nature Biotechnology, mBio, and ACS Synthetic Biology.

• Machine learning
• Artificial Intelligence
• iOS

Eun Mi Kim serves as an Assistant Professor at Eastern Kentucky University, specializing in computational research at the intersection of artificial intelligence and biomedical applications. B.S. in Computer Engineering from Chonnam National University, South Korea M.S. in Computer Engineering from Chonnam National University, South Korea Ph.D. in Computer Engineering from Chonnam National University, South Korea Her research program centers on Machine Learning and Artificial Intelligence, with significant contributions to bioinformatics including protein-protein interaction modeling, cancer prognosis systems, and big data motif analysis. Key methodologies involve support vector machines, dynamic momentum optimization, and novel feature selection techniques for diagnosing abnormal protein sequences. Recent work extends into data commons infrastructure for biomedical research collaboration. Publication trends from 2006-2022 reveal an evolution from foundational machine learning algorithms toward applied biomedical informatics, with increasing focus on translational healthcare solutions. Her most recent work integrates AI with genomic data analysis, demonstrating consistent specialization in computational biology despite the explicit listing of iOS development in her research interests. No scientific awards or honors were documented in the source material. Information regarding graduate student mentorship, research funding, or grant awards is not provided in the available text. Dr. Kim maintains active research operations through eunmikimlab, evidenced by the GitHub repository (https://github.com/eunmikimlab/PPI) supporting her protein-protein interaction work, though formal lab structure or team composition details remain unspecified.