جستجوی استاد راهنمای دانشگاهی

Sachin Jogمشاهده پروفایل

پژوهشگر ارشد

• Process Modeling
• Optimization
• Sustainable Chemical Processes
• +۳ مورد دیگر

Dr. Sachin Jog is a researcher at ETH Zürich, affiliated with the Department of Chemistry and Applied Biosciences and working within the Professorship for Chemical Systems Engineering. His research focuses on sustainable process systems engineering, with particular emphasis on developing innovative modeling and optimization techniques for chemical processes. His educational background includes: Doctoral Degree from ETH Zürich (2021-2025) under Prof. Gonzalo Guillén Gosálbez Master of Science in Process Engineering from ETH Zürich (2019-2021) Bachelor of Chemical Engineering from Institute of Chemical Technology (ICT), Mumbai (2015-2019) Dr. Jog's research interests span several critical areas in modern chemical engineering: Hybrid modeling techniques combining first-principles and data-driven approaches Optimization of integrated chemical clusters with heat, mass, and power integration Accounting for renewable energy intermittency in chemical process design Multi-objective optimization considering both economic and environmental metrics Sustainable process design aligned with Sustainable Development Goals (SDGs) His recent publications demonstrate a strong progression from fundamental modeling approaches to their application in specific sustainable chemical processes like methanol production from CO2. His work consistently bridges traditional chemical engineering with modern sustainability requirements, showing how renewable carbon technologies can be designed for true sustainability rather than focusing solely on climate change mitigation. Dr. Jog's scientific contributions include: Development of hybrid modeling methodologies for process optimization Frameworks for sustainable process design incorporating SDGs Optimization approaches for integrated chemical clusters using renewable energy His research is supported by the Swiss National Science Foundation (SNF) through the 'Bayesian symbolic learning to aid in the design of sustainable chemical clusters (LEARN-D)' project. Dr. Jog works within the Chemical Systems Engineering group at ETH Zürich, contributing to research on sustainable chemical processes and systems engineering approaches for the chemical industry's decarbonization.

• Probabilistic Machine Learning
• Causal AI
• Imprecise Probabilities
• +۲ مورد دیگر

Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.

Ingo Scholtesمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Graph Mining
• +۵ مورد دیگر

Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.

Matthew Wodrichمشاهده پروفایل

پژوهشگر

• Computational Chemistry
• Homogeneous Catalysis
• Organic Reaction Mechanisms
• +۴ مورد دیگر

Matthew Wodrich is a Research and Teaching Associate at the Institute of Chemical Sciences and Engineering (ISIC-GE) and Computational Molecular Design Laboratory (LCMD) under the School of Basic Sciences at Ecole Polytechnique Fédérale de Lausanne (EPFL) . His research focuses on computational chemistry, homogeneous catalysis, and asymmetric synthesis, with significant contributions to reaction mechanism modeling and enantioselectivity prediction. He actively contributes to teaching programs at EPFL's SCGC-ENS division. Key Research Areas: Computational Reaction Modeling, Enantioselectivity Prediction, Volcano Plot Analysis, Metal-Ligand Cooperative Catalysis, Photocatalysis, and Machine Learning in Chemistry. Recent Article Trends: His 2022-2025 work emphasizes computational catalyst optimization , solvent effects in reaction selectivity , and machine learning-driven enantioselectivity analysis , with applications in transition metal catalysis, hypervalent iodine chemistry, and CO 2 reduction.

Mark Robinsonمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Statistical Genomics
• +۴ مورد دیگر

Mark Robinson is a Professor at the Department of Molecular Life Sciences, University of Zurich, and affiliated with the Swiss Institute of Bioinformatics. He leads the Robinson Research Group, focusing on Computational Biology Bioinformatics Single-Cell RNA Sequencing Statistical Genomics His work bridges computational method development with applications in cancer immunology, epigenetics, and developmental genetics. Key research contributions include Development of bioinformatics tools like pubassistant.ch, scDblFinder, and DESpace Advancements in spatial transcriptomics and single-cell data analysis Studies on epigenetic aging and tumor microenvironment dynamics Notable collaborations span institutions in Switzerland, Germany, and international agricultural pest research groups. His recent publications (2023-2025) emphasize Spatial omics data interpretation Interdisciplinary collaboration frameworks Optimized tissue processing methods Computational benchmarks for reproducible research While no specific scientific awards are mentioned in the data, his software tools and methodological papers demonstrate significant impact on open science and bioinformatics communities.

Matthias Grossglauserمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

Torsten Schwedeمشاهده پروفایل

استاد

• Structural Bioinformatics
• Protein Structure Prediction
• Computational Biology
• +۵ مورد دیگر

Torsten Schwede is a Professor for Structural Bioinformatics at the Biozentrum, University of Basel since 2018, and currently serves as President of the SNSF Research Council since 2025. Previously, he was Vice President for Research at the University of Basel (2018-2024), Director of the SPHN Data Coordination Center (2016-2019), and Scientific Director of sciCORE Center for Scientific Computing (2014-2019). He has been a Group Leader at the SIB Swiss Institute of Bioinformatics since 2002 and served as Associate Professor (2007-2018) and Assistant Professor (2001-2007) at the Biozentrum. Dr. Schwede earned his PhD in protein crystallography from Albert Ludwigs University, Freiburg, Germany (1995-1998), following diploma studies in biochemistry at Albert Ludwigs University (1991-1994) and University of Bayreuth (1988-1991). His early career included positions as a staff scientist at GSK GlaxoSmithKline R&D (2000-2001) and postdoctoral researcher at GWER GlaxoWellcome Experimental Research (1999-2000). His research focuses on computational methods for modeling and simulating three-dimensional protein structures, particularly through homology modeling. His work enables investigation of protein functions at the atomic level, with applications in understanding disease-causing mutations and structure-based drug development. Dr. Schwede is best known for developing SWISS-MODEL, an automated protein homology-modeling server that has become a standard tool in structural bioinformatics. His recent work centers on benchmarking protein structure prediction methods through CAMEO and developing high-throughput pipelines like AlphaPulldown2 for structural modeling. Dr. Schwede's research has been widely recognized, including being selected by ISI Thomson Reuters as having the highest cited Swiss paper during 1999-2009 for his work on SWISS-MODEL. In 2014, his Nucleic Acids Research manuscript on SWISS-MODEL achieved rank 6 in traditional impact measure according to a study by the Swiss National Science Foundation. His work on protein-ligand interactions and computational drug discovery has also received significant attention in the scientific community. President of the SNSF Research Council (2025-present) President of the SNSF Research Council (2025-present) Member of RCSB PDB scientific advisory board (2015-present) Member of CASP organizing committee (2011-present) Chair of ELIXIR board (2015-2016) Conference Chair for ISMB 2019 in Basel At the Biozentrum, Dr. Schwede leads a research group dedicated to advancing computational methods for protein structure prediction and analysis, with a particular focus on making these tools accessible to the broader scientific community through web-based platforms and standardized data formats. His team's work on ModelArchive and CAMEO has established critical infrastructure for the structural biology community.

Daniele Silvestroمشاهده پروفایل

پژوهشگر

• Evolutionary Biology
• Phylogenetics
• Macroevolution
• +۵ مورد دیگر

Daniele Silvestro is a researcher at ETH Zürich's Department of Biosystems Science and Engineering, working within the Computational Evolution group based in Basel, Switzerland. His research spans evolutionary biology, computational methods, and biodiversity science, with a focus on developing and applying novel analytical approaches to understand macroevolutionary patterns. Dr. Silvestro's research interests center on evolutionary biology and computational approaches to understanding biodiversity patterns through time. His work bridges micro- and macroevolutionary scales, with particular emphasis on phylogenetic methods, speciation processes, and the integration of fossil data with molecular phylogenies. He applies machine learning and artificial intelligence techniques to analyze large-scale biodiversity datasets, addressing questions about species diversification, extinction dynamics, and ecological interactions across deep time. His recent publications demonstrate a strong trend toward computational innovation in evolutionary biology, with increasing integration of artificial intelligence methods to tackle complex questions in biodiversity science. His work spans multiple biological systems, from plant-soil interactions to mammalian evolution, reflecting an interdisciplinary approach that combines theoretical modeling with empirical data analysis. Dr. Silvestro collaborates extensively with researchers across institutions and disciplines, contributing to major initiatives such as the 2030 Declaration on Scientific Plant and Fungal Collecting. His research has significant implications for biodiversity conservation, particularly in understanding how species and ecosystems respond to environmental change. His work on computational methods, including software development like DeepDiveR, demonstrates a commitment to creating practical tools for the broader scientific community. His research group at ETH Zürich appears to focus on developing and applying cutting-edge computational approaches to evolutionary questions, emphasizing the importance of integrating multiple data sources and analytical frameworks.

• Artificial Intelligence
• Decision Support Systems
• Logistics Modeling
• +۳ مورد دیگر

Andrea Emilio Rizzoli is a Professor at SUPSI's Department of Innovative Technologies and Director of the Dalle Molle Institute for Artificial Intelligence (IDSIA). With dual Swiss-Italian citizenship, he holds a PhD in Computer and Automatic Control Engineering from Polytechnic University of Milan (1993). His career includes international research positions at Université de Lausanne and CSIRO Australia. His research focuses on: Intelligent decision support systems Logistics and environmental systems modeling Sustainable transportation solutions Uncertainty management in computational models He leads projects like GoEco! for sustainable mobility and rail freight optimization systems. Honors include: Presidency of iEMSs (2012-2014) Membership in ZALF's Scientific Committee 200+ publications with significant citation impact (h-index 22/30) He teaches courses on dynamic systems modeling and simulation, serving as head of several academic modules at SUPSI.