جست‌وجوی استاد و پژوهشگر

Bernhard Palssonمشاهده پروفایل

استاد

• Biochemistry
• Genetics
• Molecular Biology
• +۴ مورد دیگر

Bernhard Palsson is the Scientific Director at the Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain), Technical University of Denmark (DTU). His research focuses on systems biology, metabolic engineering, and computational modeling in microorganisms such as Escherichia coli and Yarrowia lipolytica . Key roles : Scientific Director, Researcher, PhD Supervisor SDG contributions : Biosustainability, Antibiotic Discovery, Climate Action Research Interests : Palsson's work spans metabolic pathways , regulatory networks , and genome-scale modeling . He explores chemical stress tolerance mechanisms, pangenome structures, and integrates machine learning with transcriptomic data to advance biosynthetic applications. His projects include antibiotic discovery via computational resources and metabolic model reconstruction for Lactobacillus and Escherichia coli species. Recent Article Trends : His 2025 submissions highlight interdisciplinary approaches combining machine learning and experimental evolution to enhance biochemical production and address antibiotic resistance. Topics include transcriptional network analysis , pangenome decomposition , and metabologenomic modeling , emphasizing scalability and data-driven methodologies. Supervision & Collaboration : Palsson mentors PhD students like Omid Ardalani and collaborates on projects such as iimena (Integration of Informatics and Metabolic Engineering for Novel Antibiotics) and pangenome metabolic model reconstruction. His team includes researchers from DTU and international institutions. Projects : iimena: Novel Antibiotic Discovery (2017–2023) Lactobacillus Pangenome Modeling (2021–2025) Networks : Collaborated with institutions in 12+ countries Co-authorships in Escherichia coli and Yarrowia lipolytica studies

Morten Nielsenمشاهده پروفایل

استاد

• Immunoinformatics
• Bioinformatics
• Machine Learning
• +۶ مورد دیگر

Morten Nielsen is a Professor at the Department of Health Technology, Technical University of Denmark, specializing in Bioinformatics with a focus on Immunoinformatics and Machine Learning . His research develops pattern recognition algorithms for immune system characterization and protein structure analysis, contributing to vaccine design against infectious diseases like HIV and tuberculosis. Professor since 2008 Director of Algorithm in Bioinformatics course (27623) Active in 8 current and 27 completed research projects Research spans epitope prediction , T-cell receptor modeling , and genomic variation analysis of pathogens. Recent work (2024) includes cancer neo-epitope immunogenicity studies, B-cell epitope prediction tools (DiscoTope-3.0), and TCR specificity modeling using machine learning. Current projects involve deep immune receptor modeling , personalized neoantigen screening , and autoimmunity pattern identification , with students including L. Machado, B. Scapolo, S. N. Deleuran, G. Nos, and A. B. Saksager.

• Machine Learning
• Statistical Computing
• Cluster Analysis
• +۲ مورد دیگر

Theresa Scharl-Hirsch is a Senior Scientist and Deputy Scientific Director at the Core Facility Bioinformatics, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds concurrent appointments at the Institute of Statistics, BOKU, and has extensive experience in bioprocess modeling, machine learning, and statistical computing. Her work bridges biochemical engineering with advanced data science methodologies. Her research focuses on real-time monitoring of biopharmaceutical processes, clustering of high-dimensional data (particularly RNA sequencing), and application of explainable machine learning techniques. She has developed statistical models for process optimization and quality prediction in antibody capture and protein purification, with a strong emphasis on industrial implementations using R programming. Key trends in her publications include three-way data analysis, matrix-variate Gaussian mixture models, and permutation-based variable importance methods for deep learning architectures. Her work spans bioprocess engineering, bioinformatics, and industrial data science applications.

Jilles Vreekenمشاهده پروفایل

استاد

• Causal Inference
• Unsupervised Learning
• Exploratory Data Analysis
• +۷ مورد دیگر

Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.

Tom Kemptonمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Ergodic Theory
• Dynamical Systems
• +۵ مورد دیگر

Tom Kempton is a Senior Lecturer in the Department of Mathematics at the University of Manchester, currently on leave while working as an AI Fellow at the Featurespace Innovation Lab in Helsinki. His research focuses on the intersection of machine learning and ergodic theory, with a background in thermodynamic formalism, fractal geometry, and number theory. Current Main Interests: Machine Learning, Ergodic Theory, Fractal Geometry, Number Theory Previous Affiliations: EPSRC Postdoc at University of St Andrews (w/ Kenneth Falconer), Postdoc at University of Utrecht (w/ Karma Dajani), PhD at University of Warwick (w/ Mark Pollicott) Education: PhD in Mathematics from University of Warwick (2011) Kempton's recent work explores decoding strategies in large language models through thermodynamic formalism and investigates local normalization distortion for machine-generated text detection. Earlier contributions include foundational research on Bernoulli convolutions, self-affine sets, and thermodynamic formalism in symbolic dynamics. Students Mentored: Postdocs Leticia Pardo Simon, Lawrence Lee, Demi Allen; PhD students Alex Batsis, Peej Ingarfeld, Alden Paige, Milo Edwardes. His research has been published in journals including Nonlinearity, Mathematische Zeitschrift, and International Mathematics Research Notices. Kempton's university email thomas.kempton@manchester.ac.uk remains active despite his leave status.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Technologies
• +۵ مورد دیگر

Dr Zhe Wang is a Senior Lecturer at the School of Information and Communication Technology, Griffith University, focusing on artificial intelligence, knowledge graphs, and semantic technologies. He earned his PhD in Computer Science from Griffith University (2011) and previously worked as a Research Fellow at the University of Oxford (2011-2013) on ontology-based systems. Research: Specializes in knowledge graph construction, rule mining for explainable AI, and integrating machine learning with logical reasoning. Led development of the scalable RLvLR rule-mining system and contributed to the HermiT ontology reasoner. Teaching: Instructs undergraduate and postgraduate courses including Introduction to Artificial Intelligence, Secure Development Operations, and Software Engineering Fundamentals. Grants: Funded by Australia's Economic Accelerator Ignite Grant (2025) for AI-driven marine life survey systems and Office of National Intelligence projects (2021-2022). Publications: Active in top venues like AAAI, ICASSP, and ISWC, with recent work on temporal knowledge graph reasoning, auction design algorithms, and neurosymbolic AI systems.

Eric F. Lockمشاهده پروفایل

دانشیار

• Statistical genomics
• High-dimensional data analysis
• Machine learning
• +۷ مورد دیگر

Eric F. Lock is an Associate Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota's School of Public Health. He is also a Member of the Masonic Cancer Center (MCC) and has been at the University of Minnesota since 2014, after completing his PhD in Statistics from the University of North Carolina in 2012 and a postdoctoral fellowship in Statistical Genomics at Duke University in 2014. Lock's research focuses on developing methods for the analysis of multi-faceted high-dimensional data, particularly in "omics" fields such as genomics, metabolomics, and proteomics. His work emphasizes the integrated analysis of data from multiple sources (e.g., gene expression, metabolomics, imaging) or measured in multiple dimensions (e.g., multiple tissue types or body regions). He also specializes in exploratory factorization and clustering methods, and Bayesian nonparametric inference. His recent publications demonstrate significant contributions to tensor data imputation (BAMITA), matrix decomposition (EV-BIDIFAC), and methods for handling complex genomic data. His work bridges statistical theory with practical applications in molecular biology, addressing challenges in data integration across multiple biological measurement platforms. Delta Omega, Honorary Society in Public Health (2019) As an active researcher and educator, Lock serves on dissertation committees, including for Mykhaylo M. Malakhov who recently defended his PhD at the University of Minnesota School of Public Health. He is involved in organizing and participating in major conferences such as STATGEN 2025, demonstrating his leadership in the biostatistics community.

Ludovic Vetea Mompelatمشاهده پروفایل

استادیار

• Linguistics
• Natural Language Processing (NLP)
• Creole Languages
• +۵ مورد دیگر

Ludovic Vetea Mompelat is an Assistant Professor at the University of Miami 's College of Arts and Sciences . His research focuses on linguistics, natural language processing (NLP), and computational analysis of creole languages such as Martinican and Haitian Creole. He specializes in integrating artificial intelligence into language education and exploring syntactic typology, ellipsis constructions, and linguistic barriers in healthcare. Research Interests Dr. Mompelat investigates: POS tagging and error analysis in creole languages AI-driven solutions for linguistic barriers in healthcare Computational approaches to ellipsis constructions and syntactic finiteness Corpus development for discourse analysis (e.g., conspiracy theories) and event sequencing Publications & Trends Recent articles highlight his work on NLP for under-resourced creole languages, contrasting classical NLP with modern LLMs, and leveraging AI to address linguistic challenges in healthcare and education. His studies often involve comparative syntax analysis across Martinican, Haitian, and French languages, alongside creating annotated corpora for machine learning applications.

• Bioinformatics
• Computational Biology
• Protein Informatics
• +۳ مورد دیگر

Barış Ethem Süzek is an Associate Professor in the Department of Computer Engineering at the Faculty of Engineering, Muğla Sitki Koçman University. His academic career spans institutions including Middle East Technical University (BS), Johns Hopkins University (MS), and George Mason University (PhD) in Computational Biology. He specializes in bioinformatics and computational biology, focusing on protein informatics, genetic analysis, and machine learning applications in medical research. Education BS: Middle East Technical University - Computer Engineering (1997) MS: Johns Hopkins University - Computer Science (2000) PhD: George Mason University - Computational Biology (2012) His research integrates bioinformatics with molecular dynamics, particularly in host-pathogen interactions, regenerative medicine, and genomic data analysis. He has developed machine learning tools for viral interaction prediction and variant analysis systems. His work with UniProt and cancer Biomedical Informatics Grid projects demonstrates expertise in large-scale biological data integration. Scientific awards include Collaboration, Outstanding Achievement, and Change Agent Awards from the cancer Biomedical Informatics Grid, plus multiple patent recognitions for biomedical systems. He has supervised numerous graduate students in bioinformatics, computational genetics, and forensic biology projects.

Arrasy Rahmanمشاهده پروفایل

پژوهشگر ارشد

• Game Theory
• Reinforcement Learning
• Transfer Learning
• +۵ مورد دیگر

Arrasy Rahman is a Postdoctoral Research Fellow at Professor Peter Stone’s Learning Agents Research Group (LARG) in the College of Natural Sciences at The University of Texas at Austin. His research focuses on creating adaptive autonomous agents for collaborative tasks, with expertise in game theory, reinforcement learning, and graph neural networks, particularly applied to the ad hoc teamwork (AHT) problem. Education: PhD and MSc from the University of Edinburgh, BSc from Universitas Indonesia His work explores methods to generate diverse teammate policies for training robust agents capable of collaborating with unseen teammates. Recent projects include partnerships with Lockheed Martin Corporation and organizing a workshop at AAAI-24. Arrasy’s research aims to build intelligent agents that assist humans in real-world collaborative decision-making challenges. Research trends in his publications include ad hoc teamwork, reinforcement learning, graph-based policy learning, and multi-agent systems. He has contributed to advancing techniques for best-response diversity and sub-task curriculum frameworks in autonomous agent collaboration. Labs and teams: Arrasy collaborates with the Autonomous Agents Research Group at the University of Edinburgh and the Learning Agents Research Group (LARG) at UT Austin. He is also involved in organizing the Ad-Hoc Teamwork Seminar Series and a AAAI-24 workshop.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Tomaso Fontanini is a researcher at the Department of Engineering and Architecture, University of Parma. His academic contributions span multiple disciplines, including computer science, artificial intelligence, and computer vision. 2025/2026: Deep Learning and Generative Models (Master's in Computer Engineering) 2024/2025: Processing Systems (Bachelor's in Prevention Techniques) 2023/2024: Processing Systems (Bachelor's in Prevention Techniques) 2022/2023: Processing Systems (Bachelor's in Prevention Techniques) Research Focus: His work primarily explores generative models, image synthesis, and style transfer with a strong emphasis on semantic control and attention mechanisms. Recent research has advanced state space models for efficient style transfer (Mamba-ST), semantic image synthesis via class-adaptive cross-attention, and diffusion model acceleration through U-shape architectures. Scientific Contributions: Publications include breakthroughs in controllable face synthesis, mask-based generative modeling, and video anomaly detection. His work bridges theoretical advancements in neural architectures with practical applications in remote sensing and educational technology. 2025: FLAV (audio-video generation), Swin2-MoSE (remote sensing) 2024: MARS (text-based person search), MCGM (mask conditioning) 2023: FrankenMask (face part editing), Student attendance systems

David Franklinمشاهده پروفایل

دانشیار

• Motor control
• Motor learning
• Impedance control
• +۷ مورد دیگر

David Franklin is an Associate Professor at the Technical University of Munich specialized in Neuromuscular Diagnostics . His research focuses on the physiological and computational principles of human neuromuscular motor control, particularly how the nervous system regulates body mechanics to adapt to environments and enable skillful movement. Affiliated with Munich Cluster for Neuroimaging (MCN) and GSN full faculty, he combines computational modeling with robotics , virtual reality , and human behavior experiments to study sensorimotor integration. Research Themes: Computational neuroscience of motor control Impedance control in human movement Robotics for sensorimotor studies Behavioral task design and analysis Selected Publication Trends reveal consistent focus on feedback/forward control mechanisms, computational modeling in sensorimotor integration, and robotics/virtual reality applications in motor learning across 2011-2020. His work addresses both execution and planning aspects of neuromuscular control.

Dong Liمشاهده پروفایل

دانشیار

• High performance computing
• Non-volatile memory
• Memory heterogeneity
• +۲ مورد دیگر

Dong Li is an Associate Professor at the University of California, Merced , where he directs the Parallel Architecture, System, and Algorithm Lab (PASA) and co-directs the High Performance Computing Systems and Architecture Group . He co-founded Yotta Labs Inc. and previously held research roles at Oak Ridge National Laboratory (2011-2014) and a PhD from Virginia Tech. Research Interests: Dong's work focuses on High performance computing (HPC) Memory heterogeneity and non-volatile memory Systems for machine learning and AI Fault tolerance in large-scale systems His innovations include heterogeneous memory optimization for recommendation models and GNNs, CXL memory integration, and persistent memory debugging tools. Recent Publications highlight advancements in CXL-based inter-node communication Memory tiering for laminography reconstruction ML-guided memory optimization for DLRM and GNN Fault tolerance benchmarks and error analysis Awards & Recognition: NSF CAREER Award (2016) Oracle Research Award (2022) ASPLOS Distinguished Artifact Award (2021) Virginia Tech Early Career Alumni Award (2023) Advising & Funding: Dong has mentored 22 students (8 PhD, 6 Master’s, 8 undergraduates) and secured grants from NSF, NVIDIA, Meta, and national labs (Argonne, Lawrence Berkeley, Lawrence Livermore). Collaborations include Microsoft (DeepSpeed, Intel PMDK), AMD, SK Hynix, and Intel/MICRON hardware donations.

Eilis Hannonمشاهده پروفایل

دانشیار

• Genetics
• Bioinformatics and computational biology
• Epidemiology
• +۹ مورد دیگر

Eilis Hannon is an Associate Professor in Bioinformatics at the University of Exeter Medical School and leads the Complex Disease Epigenetics Group. She holds a prestigious 5-year EPSRC Research Software Engineering Fellowship and serves as Assistant Director for Education in the Institute of Data Science and Artificial Intelligence, driving initiatives in reproducible research and data science education. Education: BSc in Mathematics, Cardiff University (2010) PhD in Bioinformatics, Cardiff University Centre for Psychological Medicine and Clinical Neurosciences (2014) Her research integrates statistical genetics, epigenomics, and bioinformatics to investigate molecular mechanisms in schizophrenia, bipolar disorder, and neurodegenerative diseases. She develops novel computational methods for analyzing DNA methylation dynamics across the lifespan and cell-type-specific epigenetic changes in brain disorders, with strong emphasis on open science and reproducible research practices. Recent publications demonstrate her leadership in multi-omics integration, particularly in cell-type-specific epigenetic epidemiology, biomarker development for neurological conditions, and methodological advances in long-read sequencing. Her work spans psychiatric disorders, Alzheimer's disease, and ALS, consistently linking genetic risk variants to functional epigenetic consequences through innovative analytical frameworks. Scientific Awards: EPSRC Research Software Engineering Fellowship NARSAD Young Investigator Award Alan Turing Pilot Project award Alzheimer's Society PhD studentship Software Sustainability Institute fellowship Alan Turing Institute Skills Policy Award As an educator, she directs the Coding for Reproducible Research training programme and mentors over 20 PhD students. She has secured substantial funding from MRC, NIA, ARUK, and the Brain and Behaviour Research Foundation, serving as PI on the EPSRC Fellowship and co-applicant on multiple international grants. Her leadership extends to the MRC GW4 Biomed DTP and the MSc module Statistics for Health and Life Sciences. She co-leads the Exeter Brain Health Analytics network within the NIHR Exeter Biomedical Research Centre and the Institute for Data Science and Artificial Intelligence, fostering cross-disciplinary collaborations in neurogenetics and computational biology.

Emily Bayerمشاهده پروفایل

استادیار

• Visceral Sensation
• Sexual Dimorphism
• Behavioral Genetics
• +۴ مورد دیگر

Emily Bayer is appointed as Assistant Professor of Neural Science and Biology at New York University, commencing January 2026. Her research program investigates neural mechanisms of visceral sensation and behavioral outputs, with emphasis on fertility status and sex differences using C. elegans and D. cerebrum (zebrafish) models. Her educational background includes: Ph.D. in Biological Sciences, Columbia University (2019) B.S. in Biology/German, Muhlenberg College (2014) Dr. Bayer's research explores how internal bodily sensations (visceral sensation) modulate behavior and environmental interaction, focusing on molecular and anatomical sex differences in neural circuits. Her lab examines how fertility status influences behavior through genetic and neural mechanisms, revealing conserved principles of neural circuit organization. Key approaches include C. elegans genetics, zebrafish neuroimaging, and behavioral analysis to dissect how reproductive state and sex converge in brain function. Analysis of her recent publications (2025-2016) demonstrates a cohesive focus on sexual dimorphism in neural development and behavior, with increasing integration of genomic and connectomic methodologies. Her work spans molecular genetics, neural circuit mapping, and behavioral phenotyping, consistently highlighting sex-specific differences in neural wiring and function across model organisms. Her scientific recognition includes: Research Fund for Excellent Junior Researchers (University of Basel, 2025) Jane Coffin Childs Memorial Fund for Medical Research Fellowship (2021-2023) Dr. Bayer has secured competitive fellowships supporting her independent research program. Her lab establishes innovative frameworks for studying visceral sensation-behavior relationships, with current work emphasizing monoaminergic signaling in experience-dependent neural plasticity and the role of conserved transcription factors in sexually dimorphic development. Future directions include expanding to vertebrate models to investigate evolutionary conservation of these mechanisms. She leads a research team utilizing C. elegans and zebrafish to dissect neural circuits underlying internal sensation, with particular focus on how fertility signals modulate social and mating behaviors through sex-specific neural pathways.