Find Academic Supervisors

Hajk-Georg DrostView profile

Senior Lecturer

• Computational Biology
• Machine Learning
• Bioinformatics
• +4 more

Dr. Hajk-Georg Drost is a Senior Lecturer and Principal Investigator in the Division of Computational Biology at the University of Dundee's School of Life Sciences. He leads the Digital Biology Group, focusing on integrating machine learning and high-performance computing with biological research to advance healthcare innovation. Previously, he established a Computational Biology group at the Max Planck Institute for Biology Tübingen (2019-2024) and conducted postdoctoral research at the University of Cambridge's Sainsbury Laboratory. His research explores: Evolutionary transcriptomics and phylotranscriptomic patterns across species Machine learning applications in genomics and proteomics Development of bioinformatics tools (DIAMOND, myTAI) for tree-of-life scale analyses Gene regulatory networks and transposable element dynamics His publications demonstrate a consistent focus on evolutionary constraints in development, with recent work expanding into single-cell resolution analyses of developmental diseases. Awards include: Royal Society Wolfson Fellowship (2024) Fellow, Cambridge Philosophical Society Postdoctoral Affiliate, Trinity College Cambridge He currently supervises PhD students including Stefan Manolache and leads projects funded by the Royal Society and others, focusing on protein alignment infrastructure and developmental disease research. His lab develops open-source software for genomic analyses and maintains active collaborations across Europe.

• Epidemiology
• Infectious Diseases
• Tuberculosis
• +5 more

Dr. Maru Castellanos Reynosa is a Senior Lecturer in Epidemiology & Communicable Disease Control at James Cook University (JCU), Australia. She holds a BSc in Chemical Biology (Universidad de San Carlos de Guatemala), a Master’s in Medical Microbiology (Liverpool School of Tropical Medicine), and a PhD in Epidemiology (University of Georgia). Her expertise spans infectious disease epidemiology, particularly tuberculosis, malaria, and HIV, with a focus on low-resource settings in Africa and Latin America. Research interests include: epidemiological study design, infectious disease transmission dynamics, and public health interventions. Her work emphasizes molecular epidemiology, co-infections, and climate impacts on pathogen spread. Key contributions include studies on TB social networks, HIV-TB co-infection outcomes, and malaria in pregnancy. Key Achievements: Recipient of the Schlumberger Faculty for the Future Program (2016) and Fulbright LASPAU Fellowship (2013) Teaching Excellence Award at JCU (2023) and Dissertation Completion Award (UGA, 2018) Over 35 peer-reviewed publications and 2 active research projects, including a capacity-building initiative for tropical disease control Professional Affiliations: Royal Society of Tropical Medicine & Hygiene, American Society of Tropical Medicine & Hygiene, and Organization for Women in Science for the Developing World (OWSD).

Nicole LudwigView profile

Assistant Professor

• Machine Learning for Energy Systems
• Probabilistic Machine Learning
• Probabilistic Time Series Forecasting
• +3 more

Nicole Ludwig serves as a Junior Professor and Principal Investigator of the independent research group "Machine Learning in Sustainable Energy Systems" within the Cluster of Excellence – Machine Learning for Science at the University of Tübingen. Education: PhD in Computer Science, 2020, Karlsruhe Institute of Technology MSc in Information Systems and Network Economics, 2016, University of Freiburg BSc in Economics, 2014, University of Freiburg Ludwig's research focuses on applying machine learning techniques to sustainable energy systems, with particular emphasis on uncertainty quantification and the complex relationships between weather, climate, and energy systems. Her work integrates probabilistic approaches with time series analysis to address challenges in energy forecasting and management under uncertain conditions. She actively contributes to the development of methods for probabilistic forecasting and reinforcement learning applications in energy contexts. Ludwig maintains an active presence in the machine learning research community, with contributions to open-source projects related to energy time series analysis. Her GitHub profile shows engagement with repositories focused on graph convolutional networks and energy time series motif discovery.

Johannes BrandstetterView profile

Associate Professor

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +5 more

Johannes Brandstetter is an Associate Professor at the Institute for Machine Learning at Johannes Kepler University Linz (JKU) where he leads the "AI for data-driven simulations" research group. He is also Co-founder and Chief Scientist at Emmi AI, bridging academic research with industrial applications in AI-driven physics simulation. Brandstetter earned his PhD after working at CERN's CMS experiment on Higgs boson physics. In 2018, he transitioned to machine learning, joining Sepp Hochreiter's research group in Linz. From 2021-2023, he worked at the Amsterdam Machine Learning Lab under Max Welling and Microsoft Research, developing expertise in Geometric Deep Learning and neural surrogates for partial differential equations. He returned to JKU in October 2023 to establish his own research group. His research spans Machine Learning, Deep Learning, and Physics-Informed Machine Learning with focus areas including Neural PDE solvers, Computational Fluid Dynamics, and Climate Modeling. Brandstetter believes AI is poised to revolutionize industrial-scale simulations, potentially saving thousands of compute hours across engineering domains. His work integrates computer vision, numerical simulation, and engineering components to advance data-driven approaches. Recent publications reveal a strong trend toward foundation models for scientific applications, particularly in atmospheric modeling (Aurora), geometric deep learning, and neural surrogates for complex physical systems. His interdisciplinary work spans computer vision, climate science, computational physics, and engineering, demonstrating the versatility of his research approach. Principal Investigator for "AlKa-DL: Alpine karst spring discharge prediction" (FWF-funded, 2024-2027) Principal Investigator for Cluster of Excellence "Bilateral Artificial Intelligence" (FWF-funded, 2024-2029) Co-PI for "Fast, efficient and flexible CFD simulation through generative AI" (FFG-funded, 2025-2026) As an educator and researcher, Brandstetter actively engages with the scientific community through invited talks at major conferences including presentations on "Closing the Gap Between Scientific Foundation Models and Real-World Applications" (March 2025) and "Scientific Machine Learning for Science and Engineering" (February 2025).

Juho LeeView profile

Associate Professor

• Bayesian deep learning
• Bayesian inference
• meta learning
• +5 more

Juho Lee is an Associate Professor at the Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST). Previously, he worked as a research scientist at AITRICS and completed his PhD in Computer Science & Engineering at Pohang University of Science and Technology (POSTECH) under Professor Seungjin Choi, followed by postdoctoral research at University of Oxford with Professor François Caron. His research focuses on: Bayesian deep learning Bayesian inference Meta learning Generative models Uncertainty quantification Graph representation learning Professor Lee's work bridges theoretical Bayesian methods with practical deep learning applications. His recent publications demonstrate significant contributions to neural processes, Bayesian optimization, and scalable inference methods. He has developed novel architectures like Set Transformer for permutation-invariant modeling and advanced techniques for uncertainty quantification in deep networks. His research shows a clear trajectory toward making Bayesian principles applicable to large-scale, real-world machine learning problems. Notable contributions include: Set Transformer: A framework for attention-based permutation-invariant neural networks (ICML 2019) Bootstrapping neural processes (NeurIPS 2020) Deep amortized clustering (NeurIPS 2019 workshop) Learning to pool in graph neural networks for extrapolation Professor Lee actively mentors graduate students and has advised numerous PhD candidates who co-author papers with him across NeurIPS, ICML, and ICLR. His research group SIML@KAIST develops scalable and interpretable machine learning methods with strong theoretical foundations.

Edman TsangView profile

Professor

• Catalysis
• Renewable Energy Systems
• Sustainable Materials
• +3 more

Professor Edman Tsang leads the Wolfson Catalysis Centre at the University of Oxford, focusing on catalysis and sustainable energy systems. His work includes developing novel catalysts for green ammonia production, renewable energy storage, and CO₂ conversion. He collaborates with Siemens on a £1.5M Innovate UK project for a wind-powered green ammonia plant at Rutherford Appleton Laboratory. Research spans photocatalytic solar cells, biofuel production from biomass, and hydrogen storage solutions. His team pioneered the e-Haber-Bosch process, reducing energy demands through renewable integration. Key innovations include patented catalysts for ammonia synthesis and advanced electrochemical systems. The group also explores plastic upcycling via mild heterogeneous catalysis and carbon capture technologies.

Philipp FischView profile

Researcher

• Tissue Engineering
• Biofabrication
• Bioprinting
• +4 more

Dr. Philipp Fisch is a Researcher affiliated with ETH Zurich's Institute for Biomechanics, specifically within the Tissue Engineering and Biofabrication research group. His work focuses on advancing biofabrication techniques for complex biological structures, particularly in cartilage regeneration and auricular reconstruction. Key areas of expertise include 3D bioprinting, hydrogel development, and patient-specific tissue engineering solutions. Research Interests: Dr. Fisch's research integrates biomaterial science, cell biology, and engineering principles to create functional tissues. Central themes include: Development of biodegradable materials for cartilage and bone regeneration Optimization of bioprinting parameters for clinical translation Integration of patient-derived cells for personalized therapies Investigation of immune responses in transplanted biofabricated tissues Recent Work Trends: His publications emphasize translational applications, such as auricular reconstruction for microtia patients and immunocompetent animal model validation. Thematic clusters include hydrogel-based systems, anisotropic material design, and inflammation-resistant tissue engineering strategies. Labs/Teams: Active contributor to the Institute for Biomechanics' biofabrication initiatives, collaborating on projects involving eluting mold casting, multi-layered tissue transplants, and advanced scaffold development.

Jiangwen SunView profile

Assistant Professor

• Machine Learning
• Data Mining
• Medicine
• +6 more

Jiangwen Sun is an Assistant Professor in the Department of Computer Science at Old Dominion University (ODU), within the College of Science. He directs the ODU Computational Systems Medicine Lab and focuses on machine learning approaches for analyzing multi-dimensional biological data (phenome, genome, transcriptome, etc.) to advance precision medicine and its automation. His research is supported by ODU and federal agencies like NIH and NSF. Education: Ph.D., University of Connecticut M.E., Nanjing University, China B.M., Secondary Military Medical University, China Research Interests: Machine learning/data mining for medicine, health, drug discovery, and bioinformatics Multi-view bi-clustering and integrative analysis of genomic/phenotypic data Phenotype refinement and genetic association studies for complex diseases Applications in addiction medicine, cardiovascular biology, and bovine development Publications: Over 40 peer-reviewed articles in top venues like NIPS, ICML, Bioinformatics, and BMC Genomics. Recent work focuses on cryo-EM protein structure analysis, single-cell multiomics, and epigenetic modeling. Awards/Grants: NIH/NSF funding pending; previously supported by UConn's Health Informatics Lab and collaborations with University of Pennsylvania. Teaching: Courses include Machine Learning (CS722/822), Data Structures (CS361), and Deep Learning in Medicine (CS795/895). Labs/Teams: Leads the ODU Computational Systems Medicine Lab and collaborates with UConn Health Informatics Lab, Penn Medicine, and others.

Susana FrancoView profile

Researcher

• Regional Competitiveness
• Cluster Analysis
• Economic Development
• +2 more

Susana Franco is Research Director at Orkestra Basque Institute of Competitiveness and holds a PhD in Economics from the University of Nottingham. With prior academic experience at British and Latin American universities, she coordinates research on territorial competitiveness and regional development. Her research expertise spans: Quantitative analysis of regional competitiveness indicators Cluster development in evolving economies Employment-wellbeing relationships Policy evaluation methodologies Her publications primarily analyze competitiveness frameworks through longitudinal regional studies, with recent focus on inclusive growth and digital transformation impacts. As research director, she supervises doctoral candidates and manages international projects with UN agencies, facilitating knowledge exchange between academic and policy stakeholders.

Yulia GelView profile

Professor

• Uncertainty Quantification
• Artificial Intelligence
• Topological Data Analysis
• +6 more

Yulia Gel is a Professor in the Department of Statistics at Virginia Tech and serves as a Part-Time Program Director-Expert at the National Science Foundation (NSF). She holds a MSc (summa cum laude) and PhD in Mathematics from Saint Petersburg State University (Russia) and completed a postdoc in Statistics at the University of Washington. Her research focuses on uncertainty quantification in AI, statistical foundations of data science, spatio-temporal processes, and applications in climate science, healthcare, and blockchain analytics. She has received prestigious awards including the NSF Director’s Award (2023), ASA Distinguished Achievement Medal (2018), and TIES Abdel El-Shaarawi Award (2014). Gel has led grants on wildfire prediction, climate informatics, and blockchain data science. She serves on editorial boards of Statistica Sinica, Electronic Journal of Statistics, and Technometrics, and organizes workshops on AI for climate sustainability and fragile Earth systems. Her research group develops topological and geometric methods for graph neural networks, with applications to digital twins, environmental justice, and public health. Education: MSc (1997), PhD (2000) in Mathematics from Saint Petersburg State University; Postdoc in Statistics at University of Washington (2001–2003). Past roles include Professor at University of Texas at Dallas (2015–2024) and Associate Professor at University of Waterloo (2004–2014). Selected visiting positions include NASA Jet Propulsion Lab (2016–2017) and Isaac Newton Institute (2016–2017). She has pioneered statistical software packages like snowboot and funtimes for network inference and time-series analysis. Awards highlight her contributions to environmetrics and statistical methodologies. Current projects include NSF-funded research on AI-driven wildfire prediction and blockchain analytics for climate resilience. Her lab’s recent work emphasizes topological methods (e.g., zigzag persistence) for graph-based forecasting and adversarial robustness.

John D. KubiatowiczView profile

Professor

• Computer Architecture
• Quantum Computing
• Distributed Systems
• +7 more

Professor John D. Kubiatowicz is a faculty member at the University of California at Berkeley in the Department of Electrical Engineering and Computer Sciences since 1998. He holds a PhD in Electrical Engineering and Computer Science (minor in Physics) from MIT (1998), an M.S. in EECS (1993), and a double B.S. in Electrical Engineering and Physics (1987) from MIT. His research interests span Quantum Computing Architectures Distributed Systems and Storage Network Security and Peer-to-Peer Protocols Introspective and Manycore Operating Systems Edge and Fog Computing Hardware-Assisted Security He has pioneered systems like OceanStore , a global-scale distributed file system, and Tessellation , a manycore OS with continuous adaptation. The scientific awards he has received include Presidential Early Career Award (PECASE, 2000) Scientific American 50 (2002) Diane S. McEntyre Teaching Award (2003) IEEE ICRA Best Paper (2025) George M. Sprowls Award for MIT PhD thesis (1998) Okawa Research Grant (1998) Best Paper at International Conference on Supercomputing (1993) His recent publications focus on Quantum Circuit Design and Optimization Edge/Fog Computing Architectures Secure Runtime Systems Distributed Garbage Collection Manycore OS Innovations Hardware-Assisted Security Mechanisms He leads the Quantum Architecture Research Center and co-founded the SWARM Lab at Berkeley, advancing a vision of self-adapting, secure systems from the chip level to internet scale.

Ewa SzostakView profile

Professor

• EU Policy Analysis
• Regional Development
• Innovation Economics
• +5 more

Prof. Ewa Szostak holds a professorial position at Wrocław University of Economics (UEW), serving as BIPS Program Coordinator. Her work focuses on EU policy analysis, regional development dynamics, innovation economics, and sustainable urban planning. Key research interests include Poland's EU integration trajectory, post-1989 educational reforms, energy transition strategies, and socio-economic challenges posed by aging populations. Her publications span topics such as territorial cohesion, smart specialization strategies, and the impact of migration on transportation networks. Notable works include analyses of Lower Silesia's regional infrastructure, smart city development challenges in Poland, and the decarbonization of inland navigation systems. Methodologically, her research combines policy evaluation with case studies examining institutional effectiveness and spatial planning. Laboratory/Team Affiliations: While specific lab names aren't listed, her role as a professor at UEW implies involvement in the university's economic policy research units. Her work frequently engages with EU-funded regional development projects and policy implementation frameworks.

Kenan LiView profile

Associate Professor

• Spatial Computation
• Environmental Health
• Community Resilience
• +12 more

Kenan Li, Ph.D., is an Associate Professor in the Department of Epidemiology and Biostatistics at Saint Louis University’s College for Public Health and Social Justice. He joined SLU in August 2022 and teaches courses such as Statistical Learning, R for Spatial Analysis, and Environmental Determinants of Health. His research bridges data science, GIS, and public health, focusing on spatial computation, environmental exposures, and community resilience. Ph.D. in Environmental Sciences, Louisiana State University M.S. in Environmental Sciences, Louisiana State University B.S. in Environmental Sciences and Applied Mathematics, Nankai University, China Dr. Li’s research interests lie at the intersection of spatial computation, environmental health, and community resilience . He develops geo-AI frameworks , integrated geo-cyber-infrastructures , and biostatistics algorithms using big data, deep learning, and sensor data. His work emphasizes understanding human-environment interactions, urban sustainability, and health disparities. His recent publications from 2023 to 2015 reveal a strong trend in spatial modeling of population dynamics , machine learning for environmental exposure analysis , and resilience assessment in vulnerable coastal regions. He has pioneered methods like Dynamic Time Warping Self-Organizing Maps and Wavelet-based Shapelet Discovery to extract meaningful patterns from high-frequency sensor data. His scientific awards include the Taylor Geospatial Institute Seed Grant (2023) , the Saint Louis University 2023 Health Research Grant , and selection for the Scholarly Undergraduate Research Grants and Experiences . He has secured funding from NSF, NIH, USC Keck School of Medicine, and the US Army Corps of Engineers. Dr. Li has advised and collaborated on numerous research projects, particularly in interdisciplinary teams studying the Mississippi River Delta and urban health interventions. He has been involved in NIH/NIBIB-funded projects and led research on emergency management of trail systems in Los Angeles County. He is actively involved in building research labs and teams focused on spatial data science and public health analytics , having previously worked at USC’s Spatial Sciences Institute and Population and Public Health Sciences Department.

Annette ByrneView profile

Professor

• Precision Medicine
• Molecular Imaging
• Angiogenesis
• +7 more

Professor Annette Byrne is a leading academic at RCSI University of Medicine and Health Sciences , where she serves as Professor of Physiology and Head of the Precision Cancer Medicine (PCM) Group. She has held this position since 2019 after progressing through roles as Lecturer (2008), Senior Lecturer (2013), and Associate Professor (2017). Her research focuses on precision medicine approaches for colorectal and brain cancers , integrating multi-modality molecular imaging , Next Generation Sequencing , and patient-derived xenograft models . PhD in Cell Biology (University of York, 1999) John Kerner Fellowship in Gynaecologic Oncology (UCSF, 1999-2001) Scientist at Pharmacyclics Inc. (2001-2003) Senior Scientist at Angion Biomedica Corp. (2003-2005) Principal Investigator at UCD Conway Institute (2005-2008) Her research interest lies in precision cancer medicine , particularly elucidating predictive biomarkers (genomic, transcriptomic, proteomic) and identifying novel therapeutic targets . Key methodologies include radiomics , fluorescence-guided surgery , and systems modeling of apoptosis pathways. She has pioneered Ireland's first Tumour Xenograft Facility and Translational In Vivo Imaging Centre . Recent publications highlight her work on cross-species radiomics , cell-free DNA analysis , and glioblastoma microenvironment subtyping . Her Marie Curie networks (Gliotrain, Glioresolve) and COLOSSUS project have trained 25+ PhD researchers in brain cancer therapeutics. Over €45M in national/international grants Member of Royal Irish Academy (2025) Highly cited in Cancer Discovery , Annals of Oncology , and Nature journals She supervises multiple PhD candidates and leads the RCSI Precision Cancer Medicine Group , which utilizes computational approaches and molecular imaging to improve cancer treatment outcomes. Her GLIORESOLVE and EDIReX projects focus on tumor microenvironment manipulation and distributed PDX infrastructure.

Selim YAZICIView profile

Professor

• Entrepreneurship
• Innovation Management
• Organizational Behavior
• +7 more

Prof. Selim YAZICI is a Professor at the Department of Business Administration, Faculty of Political Sciences, Istanbul University. He has been actively involved in academic and managerial roles since 1993, including multiple terms as Head of Department and Deputy Head of Department , and serves on ethics committees and academic boards. Education: PhD (1999) and Master's (1994) in Business Administration from Istanbul University; BSc (1992) in Mechanical Engineering from Yildiz Technical University. Research Interests span Entrepreneurship , FinTech , Business Ethics , Organizational Behavior , Project Management , and Healthcare Management , with a focus on cultural and cognitive factors influencing organizational resilience and technological adoption. Recent Publications highlight trends in FinTech ecosystems , organizational governance , and design thinking applications for business models. His studies often integrate cross-sectoral analysis, especially in healthcare and financial services. Thesis Advisory includes mentoring 18 doctoral and 21 non-thesis Master's students on topics ranging from project culture to entrepreneurial potential . He has contributed to Technopark research projects and scientific consultancies for institutions like TARSİM . International Collaborations include Erasmus lecturing at Universität Hamburg (2007) and conference presentations in Turkey, Sweden, and the USA.