جست‌وجوی استاد و پژوهشگر

Sonia Petroneمشاهده پروفایل

استاد

• Statistical Learning and Prediction
• Bayesian Theory and Methods
• Bayesian Nonparametrics
• +۵ مورد دیگر

Sonia Petrone is a Full Professor in Statistics at Bocconi University , holding a joint affiliation with the College of Economics, Politics and Decision Sciences and the field of Business Data Analytics. She joined Bocconi in 2001 and became Full Professor in 2014, previously serving at the University of Pavia (1991-1998) and the University of Insubria (1998-2001). She has conducted extensive research visits globally and served as President of the International Society for Bayesian Analysis (ISBA) in 2014. Education : Graduated in Discipline Economiche e Sociali from Bocconi University PhD in Statistics from Università degli Studi di Trento Her research focuses on Bayesian nonparametrics , statistical machine learning , and modeling complex dependence structures , with applications in random graphs and dynamic systems . Recent publications emphasize predictive modeling , stochastic processes , and Bayesian computational methods , reflecting her contributions to foundational Bayesian theory and its applications in advanced statistical frameworks. Scientific Awards and Editorial Roles : IMS Medallion Lecture (2018) ISBA Foundational Lecture (2016) Editor of Statistical Science (2020-2022) Coleditor of Bayesian Analysis (2010-2014) She has directed the Bocconi Summer School in Advanced Statistics and Probability since 2017 and played leadership roles in the Bocconi PhD in Statistics as Vice-Director and Curricula Responsible (2004-2011) and Director (2011-2018).

• Statistics
• Probability
• Machine Learning
• +۶ مورد دیگر

Feng Yu is a Senior Lecturer in Statistics at the School of Mathematics, University of Bristol, focusing on probability and statistical methods. Research interests include Bayesian inference, statistical hypothesis testing, machine learning applications in medical research, and interdisciplinary work with biomedical engineering. Key contributions involve developing methods for multiple comparison procedures, dose-finding trials, and 3D printing parameter optimization using AI. Recent publications highlight trends in integrating statistical rigor with biomedical innovation, spanning clinical trials, dose-finding studies, and additive manufacturing for bone scaffolds and implants. Collaborations include work on hybrid systems for tissue regeneration. Projects include "Girsanov Transformation and the Rate of Adaptation" (2011-2013), exploring probabilistic models in evolutionary biology. Affiliations are centered on the University of Bristol, with no scientific awards mentioned.

• Data Science
• Big Data
• Natural Language Query Processing
• +۴ مورد دیگر

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

Andrew Suttonمشاهده پروفایل

دانشیار

• Theory of Randomized Search Heuristics
• Evolutionary Computation
• Parameterized Complexity
• +۲ مورد دیگر

Andrew Sutton is an Associate Professor at the Swenson College of Science and Engineering , University of Minnesota Duluth . His research focuses on theoretical computer science, particularly in randomized algorithms and evolutionary computation. Office: 311 Heller Hall Email: amsutton@d.umn.edu Phone: +1 218 726 7978 Office Hours: By email appointment Research Interests : Theory of randomized search heuristics Evolutionary computation Parameterized complexity Randomized algorithms Graph theory

Momoko Hayamizuمشاهده پروفایل

دانشیار

• Computational Biology
• Theoretical Biology
• Discrete Mathematics
• +۴ مورد دیگر

Momoko Hayamizu is an Associate Professor at Waseda University's Faculty of Science and Engineering, Department of Applied Mathematics . Her work bridges discrete mathematics with computational biology , focusing on phylogenetic tree/network analysis, single-cell RNA-seq trajectory inference, and algorithm development. She holds a Ph.D. in Mathematical Sciences from The University of Tokyo and an M.D. from SOKENDAI. Research Interests : She specializes in combinatorics, graph theory, and discrete algorithms for modeling biological processes like cell differentiation and viral evolution. Her research emphasizes phylogenetic network theory , tree-based models , and software development (e.g., Treefit). Academic Contributions : Her work includes structure theorems for phylogenetic networks , algorithms for network orientation , and quantitative trajectory analysis methods . Publications span journals like SIAM Journal on Discrete Mathematics and IEEE/ACM Transactions on Computational Biology . Awards & Recognition : Recipient of MEXT's Young Scientists’ Prize , multiple WASEDA e-Teaching Awards , and international scholarships like the Anita Borg APAC . She actively contributes to educational outreach via YouTube lectures and high school collaborations. Leadership & Collaboration : Organized ICIAM 2023 Mini-symposium on phylogenetics, collaborated with institutions like IMI Kyushu University and RIKEN , and developed open-source tools like Treefit for single-cell data analysis.

• Bioinformatics
• Computational Biology
• Phylogenetics
• +۳ مورد دیگر

Daniel Huson is a Professor of Algorithms in Bioinformatics at the University of Tübingen , affiliated with the Faculty of Science and actively contributing to the Computer Science Department . He has held this position since 2002 and previously served as Senior Staff Scientist at Celera Genomics (1999-2002) and Post-Doc at Princeton University and the University of Pennsylvania (1997-99). Education: PhD in Mathematics, Bielefeld University (1990, summa cum laude) Habilitation in Mathematics, Bielefeld University (1997) Studied Mathematics and Physics, University of Washington (1980-86) Research Interests: Designing algorithms for bioinformatics and computational biology Metagenomic data analysis using tools like MEGAN and SplitsTree Phylogenetic networks and evolutionary modeling Microbiome dynamics and industrial bioproduction optimization Development of interactive software for biological data visualization Exploring autocatalytic reaction networks in early biochemistry Scientific Contributions: Developed MEGAN, a widely used metagenome analysis tool Created SplitsTree for phylogenetic network analysis Published extensively on microbiome analysis, metagenomic binning, and evolutionary relationships Contributed to power-to-gas technology and bioelectrochemical systems Awards & Grants: Royal Society of New Zealand International Leader Fellowship (2020-22) PLOS Computational Biology Research Prize (2017) Technology Transfer Prize of the IHK Reutlingen (2016) Co-organizer of major conferences (GCB, RECOMB, ISMB, etc.) Leadership Roles: Head of Computer Science Department (2011-14) Founding member of Computomics (since 2012) Faculty member of IMPRS 'From Molecules to Organisms' (since 2011)

• Artificial Intelligence
• Data Mining
• Natural Language Processing
• +۴ مورد دیگر

Andrés Jonathan Abeliuk Kimelman is an Assistant Professor at the University of Chile's Faculty of Physical Sciences and Mathematics , affiliated with the Department of Computer Science . He holds a PhD in Computer Science (University of Melbourne, 2017) and a Civil Engineering degree in Computing (University of Chile, 2012). Research Focus : AI ethics, network analysis, natural language processing, and machine learning applications in social systems. Teaching : Leads undergraduate and postgraduate courses in Discrete Mathematics, Data Mining, and Computational Theory. Students : Advises multiple thesis and research projects, including works on AI-assisted urban planning, misinformation analysis, and algorithmic bias. Collaborations : Participates in the National Center for Artificial Intelligence (CENIA) as a co-investigator (2022-2027). Key Publications explore polarization detection, networked public spheres, and computational models for social systems. Current projects include unsupervised topic quantification and extreme multi-label classification in multilingual contexts.

• Machine Learning
• Non-parametric Methods
• Computational Methods
• +۶ مورد دیگر

Yifan Chen is an Assistant Professor in the Department of Computer Science and affiliate faculty in the Department of Mathematics at Hong Kong Baptist University's Faculty of Science. He joined HKBU in Fall 2023 after completing his PhD in Statistics from the University of Illinois Urbana-Champaign in 2023 under the guidance of Prof. Yun Yang. His educational background includes: B.S. in Statistics from Fudan University (2018), advised by Prof. Juan Shen and Prof. Chenghong Zhang Ph.D. in Statistics from University of Illinois Urbana-Champaign (2023), advised by Prof. Yun Yang Dr. Chen's research focuses on developing efficient algorithms for machine learning, with particular emphasis on non-parametric models and neural networks featuring intensive matrix operations. His work bridges statistical theory with practical computational challenges in modern machine learning systems, especially those involving Transformers (language models) and Graph Neural Networks (GNNs). He approaches machine learning from both theoretical and applied perspectives, seeking to understand statistical structures while addressing real-world computational constraints. His publication record shows consistent output in top-tier venues including ICML, NeurIPS, KDD, and EMNLP, with recent work spanning graph coarsening, optimal transport, efficient language model fine-tuning, and causal inference. His research demonstrates strong mathematical foundations combined with practical applications in AI systems. Among his notable achievements: NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme proposal grant (2024) ICML 2023 Grant Award ($1,500) Dr. Chen actively mentors students through his research group, supervising PhD students and visiting research assistants. He has successfully guided students who have gone on to PhD programs at institutions including Institute of Science Tokyo, HKU, Fudan, and NUS. His teaching includes COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning, which covers core machine learning concepts for AI application research, and COMP 2027 Applied Linear Algebra for Computing. His research group focuses on efficient machine learning algorithms, with current projects spanning graph neural networks, optimal transport, language model efficiency, and causal inference. He collaborates with researchers from institutions including UIUC, Fudan University, and industry labs like Amazon Alexa AI.

Simone Severiniمشاهده پروفایل

استاد

• Quantum Computing
• Machine Learning
• Quantum Information
• +۳ مورد دیگر

Simone Severini is a Professor of Physics of Information at the University College London , affiliated with the Department of Computer Science . He is a Royal Society University Research Fellow and contributes to multidisciplinary groups including Intelligent Systems , UCL CS Quantum , UCL Quantum Science and Technology Institute , and CoMPLEX . Research Interests: His work bridges Quantum computing Machine learning Graph theory Quantum information theory Computational biology with a focus on quantum algorithms, classical simulation of quantum systems, and mathematical frameworks for physical correlations. Scientific Contributions: Recent publications span quantum state learning, non-Markovian dynamics, adversarial quantum learning, and graph isomorphism. His projects include Quantum Computing, Information, and Algebras of Operators and the Distributed Information initiative . Awards: Royal Society University Research Fellowship Best Paper Award at FCT2017

Peter Florian Stadlerمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• RNA Biology
• +۴ مورد دیگر

Peter Florian Stadler is a Professor of Bioinformatics at the University of Leipzig since 2002 and Director of the Interdisciplinary Center for Bioinformatics (IZBI) since 2023. He holds affiliations with the Max Planck Institute for Mathematics in the Sciences (scientific member since 2009), the Austrian Academy of Sciences (corresponding member since 2010), and the Santa Fe Institute (external member since 1994). Doctorate in Chemistry (University of Vienna, 1990) Habilitation in Theoretical Chemistry (University of Vienna, 1994) His research focuses on Bioinformatics , Computational Biology , and Evolutionary Dynamics , with specific interests in RNA structure prediction , non-coding RNA , and genome-wide data analysis . Recent work involves developing ViennaRNA Package tools and studying translational control mechanisms in melanoma. Professional milestones include: Co-development of Vienna RNA Package , FRANz (phylogenetic tree reconstruction), code2aln (alignment tools) Key contributions to RNA gene finding algorithms and ncRNA family characterization Honors: Corresponding member, Austrian Academy of Sciences (2010) Honorary professor, Faculty of Science, Bogota (2018)

Krzysztof Olińskiمشاهده پروفایل

استادیار

• Discrete optimization
• Control optimization
• Optimal control
• +۶ مورد دیگر

Krzysztof Oliński serves as an Assistant Professor at the Department of Decision Systems and Robotics within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. His academic profile demonstrates a strong focus on control engineering with particular expertise in optimization methodologies for complex dynamic systems. Dr. Oliński's research centers on discrete optimization techniques applied to control theory, with significant contributions to optimal control strategies for nonlinear dynamic processes. His work bridges theoretical foundations with practical industrial applications, particularly in manufacturing and pipeline systems. He has developed innovative approaches including graph-based representations of state-space dynamics and agent-based optimization methods. His keyword profile reveals expertise in computational intelligence, swarm algorithms, and intelligent manufacturing systems, reflecting his interdisciplinary approach to solving complex control problems. An analysis of his publication history from 2007-2012 shows a clear research trajectory evolving from foundational work on fault-tolerant control systems to more sophisticated methodologies like the 'toolgraph' approach. His early publications focused on discrete optimization techniques for control planning, while his later work expanded to include agent-based strategies and integration of natural and artificial intelligence in production systems. His most impactful contributions involve novel representations of state-space dynamics through flow graph structures that enable more effective control strategy design. Dr. Oliński maintains an active research presence with publications in both Polish and international journals including Mathematical Problems in Engineering and the International Journal of Applied Mathematics and Computer Science. His work demonstrates consistent theoretical rigor combined with practical relevance to industrial applications, particularly in intelligent manufacturing systems and pipeline dynamics modeling.

Rodrigo Santamaría Vicenteمشاهده پروفایل

دانشیار

• Bioinformatics
• Information Visualization
• Microarray Data Analysis
• +۴ مورد دیگر

Rodrigo Santamaría Vicente is an Associate Professor in the Department of Computer Science and Automation within the Faculty of Sciences at the University of Salamanca. His academic career centers on bridging computer science with biological research through advanced data analysis and visualization techniques. He serves as director of the Diploma of Specialization in Bioinformatics and Computational Genomics, demonstrating leadership in specialized academic programming. Dr. Santamaría earned his PhD in Computer Science from the University of Salamanca in 2009 with his thesis "Visual analysis of gene expression data by means of biclustering," supervised by Dr. Luis Antonio Miguel Quintales and Dr. Roberto Therón Sánchez. His academic journey began with an M.S. degree in Computer Science from the same institution. His research primarily focuses on Bioinformatics and Information Visualization, with particular emphasis on integrating diverse data sources, analysis algorithms, and visual representations to enhance understanding of complex biological problems. His work spans multiple subdomains including gene expression analysis, biclustering algorithms, phylogenetic tree visualization, and collaborative tagging systems. Santamaría's research group affiliations include both the BIOINFORMÁTICA research group and the CaUSAL (Cultura Académica, Patrimonio y Memoria Social) research group at the Institute of Functional Biology and Genomics. His publication record reveals a consistent trajectory of scholarly output since 2006, with recent work showing increased focus on biological applications of computational methods. His most significant contributions appear in the integration of visualization techniques with bioinformatics analysis, particularly through tools like BicOverlapper and Voronto that facilitate interactive exploration of complex biological data. The evolution of his work demonstrates progression from foundational algorithm development to increasingly sophisticated applications in systems biology. Author of the book "How Machines Organize: An Introduction to Distributed Systems" Director of the Diploma of Specialization in Bioinformatics and Computational Genomics Active contributor to R programming ecosystem with the biclust package As an educator, Santamaría teaches across multiple programs including the Master's Degree in Computer Engineering and previously taught Bioinformatics for the Biotechnology degree program. His teaching portfolio spans Bioinformatics (from both computer science and biology perspectives), Distributed Systems, and Service-Oriented Architectures. He maintains an active research laboratory focused on developing visualization tools for biological data analysis, with current projects emphasizing integration of gene expression data with biological ontologies and advanced biclustering techniques.

Mathias Sablé-Meyerمشاهده پروفایل

پژوهشگر ارشد

• Mechanistic implementation of compositional mental representation
• Geometry perception in humans and non-human primates
• Human reasoning and cognitive fallacies
• +۵ مورد دیگر

Mathias Sablé-Meyer is a Postdoctoral Researcher at University College London's Sainsbury Wellcome Centre, working in Tim Behrens's lab. He completed his PhD under the supervision of Stanislas Dehaene at PSL/Collège de France and NeuroSpin, CEA, where he investigated humans' ability to manipulate highly abstract structures, particularly focusing on the perception of geometry. His research spans cognitive neuroscience, computational modeling, and experimental psychology, with a focus on understanding the mechanistic implementation of compositional mental representations. He employs methodologies including MEG, fMRI, behavioral experiments, and computational models to investigate how humans mentally represent abstract structures like geometric shapes, language, and mathematical concepts. Sablé-Meyer's work reveals fascinating insights about human cognitive uniqueness, particularly in geometric perception compared to non-human primates. His recent research examines sequence processing mechanisms and aims to establish foundations for mechanistic implementations of Language of Thought models. He has published in top journals including Cognitive Psychology , Trends in Cognitive Sciences , PNAS , and Journal of Neuroscience . Glushko Dissertation Prize from the Cognitive Science Society (2023) As a supervisor, he co-advises Master's students including Maxime Cauté, who is exploring cross-modal representation of sequences, and is involved with several other students including Svenja Kuchenhoff, Amy Wong, and others. His lab follows open science principles, with code systematically published on the Open Science Framework alongside articles. His research program bridges cognitive science, neuroscience, and artificial intelligence, with implications for understanding both human cognition and developing more human-like AI systems. He maintains active collaborations across institutions including UCL, Collège de France, MIT, and École Normale Supérieure.

Yichi Zhangمشاهده پروفایل

استادیار

• Causal Mechanism and Causal Learning
• High-dimensional Models
• Large-scale Computations
• +۲ مورد دیگر

Yichi Zhang is an Assistant Professor in the Department of Statistics at Indiana University Bloomington. Previously, he was a Postdoctoral Scholar at Duke University with dual appointments in the Fuqua School of Business and Department of Biostatistics & Bioinformatics. His academic credentials include a Ph.D. in Statistics from North Carolina State University (2023) and a B.S. in Mathematics and Applied Mathematics from Sichuan University (2018). Professor Zhang's research centers on methodological innovation in statistical learning, with primary focus areas: Causal Mechanism and Causal Learning High-dimensional Models Large-scale Computations Randomized Algorithms Uncertainty Quantification His publication trajectory (2023-2025) reveals rapid advancement in theoretical statistics and machine learning, with increasing contributions to causal inference frameworks and computational methodologies. Recent work demonstrates particular strength in adapting statistical theory to AI challenges, including large language model evaluation and network analysis. His scholarly recognition includes: ASA Nonparametric Statistics Student Paper Award Finalist (2023) IMS Hannan Graduate Student Travel Award (2021) ENAR Distinguished Student Paper Award (2021) Mu Sigma Rho National Statistics Honor Society (2018) As an early-career faculty member, Professor Zhang is establishing his research program while teaching core statistics courses. His active publication pipeline suggests robust research momentum, though specific grant details aren't provided. Prospective students interested in theoretical-computational statistics should contact him directly regarding advising opportunities and current projects.

Olga Kalininaمشاهده پروفایل

استاد

• Bioinformatics
• Drug Discovery
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Olga Kalinina leads the Drug Bioinformatics research group at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS). She holds an M.Sc. in Mathematics from Moscow State University (2003) and a Ph.D. in Bioinformatics from the Russian Academy of Sciences (2007). Her academic journey includes postdoctoral positions at the European Molecular Biology Laboratory (EMBL) and University of Heidelberg, followed by establishing her independent research group at the Max Planck Institute for Informatics (2012-2018). Her research program integrates cutting-edge bioinformatics and computational approaches to address challenges in drug discovery and resistance mechanisms. Primary research domains include: Development of machine learning tools for predicting functional consequences of genetic variants and drug resistance phenotypes Structural bioinformatics approaches for protein-drug interaction analysis Graph theory-based investigation of drug-binding pockets Protein language models enhanced with 3D structural information Explainable AI for drug-target interaction prediction Her group maintains active research projects on: Predicting molecular mechanisms of genetic variants in cardiac/renal diseases (BMBF-funded) Investigating off-target drug binding mechanisms (BMBF-funded) Antimicrobial resistance discovery using explainable AI (HelmholtzAI-funded) Protein structure-informed drug affinity prediction Prof. Kalinina has received the EMBO Long Term Fellowship and currently supervises seven doctoral students. Her laboratory develops computational resources including novel methods for solvent accessibility estimation (SphereCon), variant annotation in structural context (StructMAn), and protein interaction analysis (DIGGER).