Find Academic Supervisors

Nicolas LoizouView profile

Assistant Professor

• Large-Scale Optimization
• Machine Learning
• Federated Learning
• +4 more

Nicolas Loizou is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with a secondary appointment in Computer Science and affiliation with the Mathematical Institute for Data Science (MINDS). He leads the Optimization and Machine Learning Lab and is part of the Data Science and AI Institute. His research focuses on large-scale optimization, machine learning, federated learning, game theory, and distributed/decentralized algorithms. He holds a PhD in Operational Research from the University of Edinburgh (2019), an MSc in Computing from Imperial College London (2015), and a BSc in Mathematics from the National and Kapodistrian University of Athens (2014). Key achievements include the OR Society’s 2019 Doctoral Award (runner-up), IVADO Postdoctoral Fellowship, COAP 2020 Best Paper Award, and a Cisco Research Grant for developing open-source federated learning tools. His work bridges theoretical guarantees with practical applications, particularly in stochastic optimization methods like the Polyak step-size and extragradient frameworks. Research interests span federated learning architectures, sharpness-aware minimization, and multiplayer systems in AI. He has pioneered frameworks like Locally Adaptive Federated Learning and Multiplayer Federated Learning, addressing challenges in medical imaging and distributed model training. His recent event at JHU highlighted advancements in adaptive optimization algorithms for large-scale models. Education: PhD (Edinburgh), MSc (Imperial College), BSc (Athens University) Affiliations: MINDS, Data Science Institute Grants: Cisco Research Gift, Catalyst Award Labs: Optimization and Machine Learning Lab Recent work includes improving communication efficiency in federated systems and analyzing stochastic algorithms through control-theoretic lenses. His contributions are published in top venues, with a focus on both foundational theory and real-world impact in healthcare and distributed AI.

Juan Pablo Soto ZuluagaView profile

Associate Professor

• Supply Chain Management
• Reverse Logistics
• Green Logistics
• +7 more

Juan Pablo Soto Zuluaga is an Associate Professor in the Faculty of Management at Universidad de los Andes, Colombia, where he also serves as Vice Dean of Corporate Relations since 2017. His academic work is centered in the Department of Supply Chain Management & Technology, where he teaches and conducts research on sustainable and innovative logistics systems. Education: PhD in Economics and Management, Pompeu Fabra University, Spain (2006) Master in Economics and Business, Pompeu Fabra University, Spain (2001) Bachelor in Business Administration, National University of Colombia (1998) His research interests lie at the intersection of supply chain innovation and sustainability. He focuses on Reverse Logistics, Green Logistics, Closed-Loop Supply Chains, Sustainable Supply Chains, and Retail Logistics . His work integrates experiential learning methodologies, such as problem-based learning and gamification, to enhance pedagogical effectiveness in supply chain education. He has explored how businesses in emerging markets can adopt environmentally responsible logistics practices and has contributed significantly to understanding supply chain resilience during crises like the COVID-19 pandemic. The trends in his recent publications (2020–2025) show a strong emphasis on sustainability, experiential education, and crisis response in supply chains. His works span book chapters, journal articles, and case studies, often co-authored with colleagues such as Thiell M. and Camacho S. Topics include corporate sustainability modeling, reverse cross-docking, supplier management, and strategic reactivation of supply chains post-crisis. These contributions reflect a consistent commitment to both academic rigor and practical application in business contexts. Scientific Awards: No awards listed in the provided text. Juan Pablo Soto Zuluaga has advised several master’s students and directed academic projects, including theses on engineering project management at El Cerrejón and business model innovation at Ecopetrol. He has led teaching initiatives and workshops, such as the REPS program at EATON, and has been involved in curriculum development for experiential learning in supply chain programs. While specific grant details are not mentioned, his project with FEDEARROZ in 2019 indicates engagement in consultancy and strategic transformation advisory. He is actively involved in the research group Supply Chain Management and Technology , contributing to academic and industry knowledge transfer through webinars, publications, and collaborative case studies. Laboratories and Research Groups: Member of the Supply Chain Management and Technology research group at Universidad de los Andes.

Tianqi HongView profile

Assistant Professor

• Power Systems
• Power Electronics
• Renewable Energy Integration
• +4 more

Dr. Tianqi Hong is an Assistant Professor at the University of Georgia, affiliated with the School of Electrical & Computer Engineering and the Department of Electrical and Computer Engineering. He holds a B.Sc. from Hohai University (2011), an M.Sc. from Southeast University and NYU (201?), and a Ph.D. from NYU (2016). Prior to his academic role, he served as a Principal Energy System Scientist at Argonne National Laboratory and a Senior Research Scientist at Unique Technical Services, LLC. His research focuses on power systems, power electronics, renewable energy integration, and AI-driven solutions for smart grids. Dr. Hong’s work emphasizes cybersecurity for power infrastructure, nonlinear dynamics in microgrids, and advanced control strategies. He actively contributes to IEEE Transactions across multiple journals and chairs the IEEE IAS Industrial Power Converters Committee. His recent publications address topics like AI-based photovoltaic inverter modeling, cyberattack mitigation in smart grids, and voltage regulation using distributed optimization. He has authored over 60 peer-reviewed articles, spanning from 2014 to 2025, with a focus on energy system stability, renewable integration challenges, and data-driven methodologies. His professional service includes editorial roles in top-tier journals and leadership in industry-research collaborations.

• Digital Twins
• Smart Cities
• Blockchain Technology
• +5 more

Noel Crespi is a Professor and Director of Studies at Telecom SudParis, part of Institut Polytechnique de Paris. He leads research in the NeSS group and is affiliated with SAMOVAR, a prominent research laboratory focusing on networks, systems, and services. His work spans multiple domains in telecommunications, networking, and smart systems. His primary research interests include digital twins for smart cities, blockchain technologies, Internet of Things (IoT) security and applications, 5G/6G networking, and machine learning applications in network management. He has published extensively on these topics, with over 100 publications in top-tier journals and conferences. His recent work shows a strong focus on digital twin applications for urban management, particularly in traffic and air quality monitoring. He has developed modular frameworks for smart city digital twins that integrate real-time data from multiple sources. His research also explores blockchain applications for network security, access control, and service provisioning in next-generation cellular networks. Dr. Crespi has received significant recognition for his work on digital twins, with his 2020 paper "Digital twin in the IoT context" in Proceedings of the IEEE being highly cited. His current research explores the integration of AI with digital twin technologies for sustainable urban development. He has supervised numerous PhD students and collaborates extensively with researchers across Europe and internationally. His work often involves interdisciplinary approaches, bringing together computer science, networking, urban planning, and environmental science. Dr. Crespi has been instrumental in developing frameworks for network digital twins, with applications in traffic management, air quality monitoring, and resource allocation in smart cities. His research demonstrates practical implementations in cities like Madrid, showing measurable improvements in urban management.

• Data Storage Systems
• Active Storage
• Data Replication
• +5 more

Zhipeng Tan is an Assistant Professor at Huazhong University of Science and Technology, affiliated with the Data Storage and Application Lab. His work centers on innovative solutions in data storage technologies, particularly in active storage and replication mechanisms. His research interests lie in the domains of data storage systems, provenance management, and scalable storage architectures. He focuses on enhancing efficiency and scalability in large-scale storage environments, particularly through active storage frameworks and provenance data compression. The recent publications highlight a strong focus on provenance storage efficiency, active storage system design (e.g., Oasis), and scalable data management. Key themes include hybrid storage models, provenance graph compression, and compliance with T10 OSD standards, reflecting contributions to both theoretical and applied aspects of storage systems. Scientific Awards: No scientific awards mentioned in the text. Advising and Grants: There is no mention of student advising, research grants, or funding sources in the available information. Labs and Teams: Zhipeng Tan is associated with the Data Storage and Application Lab at Huazhong University of Science and Technology, a research group focused on advancing storage system technologies, including dynamic file systems and ultra-large scale data solutions.

Shrideep PallickaraView profile

Professor

• Machine Learning
• Deep Learning
• Large-Scale Systems
• +12 more

Shrideep Pallickara is a Professor in the Department of Computer Science at Colorado State University, where he also directs the Center for eXascale Spatial Data Analytics and Computing (XSD) . His research is funded by the National Science Foundation, Department of Homeland Security, Environmental Protection Agency, Department of Agriculture, and the UK's e-Science program. Research Interests: His research lies at the intersection of machine learning and large-scale systems, focusing on: Spatiotemporal data management and analytics Extreme-scale storage systems Stream processing for IoT and cyber-physical systems Deep learning over petabyte-scale, high-dimensional datasets Model construction for forecasting natural and urban phenomena His work addresses challenges in computational tractability, resource utilization, and convergence in distributed environments. Systems developed in his lab are deployed in domains such as urban sustainability, agriculture, epidemiology, environmental monitoring, healthcare, and defense. Research Trends in Publications: His recent publications demonstrate a strong focus on scalable analytics for geospatial and environmental data. Key themes include deep learning for soil moisture and salinity prediction, efficient visualization of massive satellite datasets, spatiotemporal search and summarization, and model performance profiling across spatial domains. The work integrates scientific domain knowledge with machine learning and systems innovation. Scientific Awards: NSF CAREER Award Board of Governors Award for Excellence in Undergraduate Teaching OLIE Award N. Preston Davis Award Monfort Professorship Best Paper Award at IEEE/ACM CCGrid 2019 Best Paper Award at BDCAT 2023 Best Paper Award at IEEE Cluster 2012 Best Student Paper Award at IEEE CloudCom 2010 Shortlisted for ACM DEBS-2015 Grand Challenge Award One of the Six Best Papers at ACM/IEEE GRID 2005 Advising and Grants: He advises numerous graduate students, many of whom are co-authors on his publications. His research is supported by major grants from NSF, DHS, EPA, USDA, and UK e-Science, enabling the development of open-source systems such as Granules, NaradaBrokering, Galileo, Funnel, and Spindle. Labs and Teams: He leads the XSD Center, which develops and maintains large-scale open-source software systems involving over 2500 classes and a million lines of code. These systems are used in academic, commercial, and defense applications.

Alexander KurapovView profile

Adjunct Professor

• Ocean Modeling
• Data Assimilation
• Coastal Oceanography
• +7 more

Alexander Kurapov holds a Courtesy Appointment at Oregon State University within the College of Earth, Ocean, and Atmospheric Sciences, Department of Oceanography. He is actively engaged in research involving ocean modeling and data assimilation, with a focus on coastal and shelf processes along the U.S. West Coast and the Bering Sea. His research interests span oceanic data assimilation, coastal ocean modeling, wind-driven circulation, internal tides, shelf mixing, and nearshore flows. These areas are central to understanding coastal dynamics, climate variability, and improving ocean forecasting systems. The recent publications highlight a strong trend in high-resolution numerical modeling, variational and ensemble data assimilation techniques, and analysis of coastal and shelf-scale processes. Topics include internal tide dynamics, sea level variability, glider data integration, and wave-current interactions, primarily in the Pacific Northwest and Bering Sea regions. The work demonstrates consistent application of advanced modeling frameworks to real-world oceanographic problems. Alexander Kurapov has collaborated extensively with leading oceanographers at Oregon State University and beyond, contributing to major projects such as the US Integrated Ocean Observing System (IOOS). While no formal awards are listed, his body of work reflects significant contributions to coastal ocean science. He has advised or collaborated with numerous researchers, though specific students are not named. His work often involves model development, observational integration, and participation in multi-institutional efforts. Kurapov’s research is supported through collaborative grants and institutional resources, though specific funding sources are not detailed in the text. His work is closely tied to modeling teams at Oregon State University, particularly those involved in coastal ocean forecasting, data assimilation, and nearshore dynamics. He contributes to systems like the West Coast Ocean Forecast System (WCOFS) and participates in regional observational-modeling initiatives.

Ramya Korlakai VinayakView profile

Assistant Professor

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +2 more

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. She also holds affiliations with the Department of Computer Science and the Department of Statistics at UW-Madison. Her research program bridges theoretical machine learning with practical applications in data science and crowdsourcing. Education: PhD in Electrical Engineering from California Institute of Technology (Caltech), advised by Prof. Babak Hassibi B.Tech in Electrical Engineering with minor in Physics from Indian Institute of Technology Madras (IIT Madras) Dr. Vinayak's research focuses on developing theoretically grounded machine learning tools for reliable inference using data from human sources. Her work spans machine learning theory, statistical inference, and crowdsourcing systems, with particular emphasis on preference learning, metric learning, and robust dataset construction. She has pioneered methods for learning from limited pairwise comparisons, auto-labeling systems, and human-in-the-loop out-of-distribution detection. Her recent publications reveal a consistent trajectory toward building practical machine learning systems that incorporate human feedback while maintaining theoretical guarantees. The pattern shows increasing focus on ethical considerations in AI, particularly regarding bias in generative models and reliable human-AI collaboration frameworks. Scientific Awards: Faculty for the Future fellowship (2013-2015) from Schlumberger Foundation NSF CAREER Award American Family Funding Initiative Award with Fred Sala Dr. Vinayak leads an active research group with multiple PhD students across ECE and CS departments. She has secured significant research funding including an NSF grant for "Uncovering the cognitive and neural fingerprints that make each of us unique" in collaboration with Tim Rogers, Rob Nowak and Brad Postle. She is also co-organizing the MidWest Machine Learning Symposium and NeurIPS tutorials on dataset construction. Her research group operates at the intersection of theory and practice, developing mathematically grounded frameworks that address real-world challenges in dataset construction, human-AI collaboration, and reliable machine learning systems.

Ruben TaelmanView profile

Researcher

• Decentralization
• Linked Data Publishing
• Web Querying
• +3 more

Ruben Taelman is a postdoctoral researcher at IDLab , Ghent University – imec, specializing in decentralization, Linked Data publishing, and querying. He develops open-source JavaScript libraries like the Comunica engine for decentralized data access and focuses on intelligent infrastructure for data publication and retrieval. His research bridges academic and industrial perspectives, addressing challenges in decentralized knowledge graphs, policy negotiation, and data governance. Key projects include Triple Storage for versioned RDF querying and contributions to the Solid ecosystem for user-centric data control.

• Atmospheric Sciences
• Environmental Science
• Aerosol Research
• +3 more

Tuukka Petäjä is a Professor at the University of Helsinki's Faculty of Science and Department of Physics, currently serving as Docent and supervisor for the Doctoral Programme in Atmospheric Sciences. He is affiliated with the Institute for Atmospheric and Earth System Research (INAR). Research focuses on atmospheric and Earth system science Active in aerosol research and climate modeling Recipient of multiple scientific awards His research spans air pollution mapping, aerosol dynamics, and climate-relevant particle formation. Recent publications analyze aerosol sources in Europe, Arctic blowing snow events, and machine learning applications in atmospheric science. Notable scientific awards include: Aerosol Foundation Award (2022) Finnish Association for Aerosol Research Award (2010) Thompson Reuters Highly Cited Researcher in Geosciences (2014-2016) He supervises doctoral students and co-manages research infrastructure projects, including SMEAR Stations for Earth system-atmosphere relations. Current research involves international collaboration through initiatives like ACTRIS and PEEX-Academic-Challenge workshops.

Helena HerrView profile

Researcher

• Marine Mammal Ecology
• Conservation Biology
• Population Dynamics
• +5 more

Dr. Helena Herr is a Postdoctoral Researcher and Marine Mammal Ecologist at the University of Hamburg, working within the Faculty of Mathematics, Computer Science and Natural Sciences, Department of Biology, specifically at the Institute of Marine Ecosystem and Fisheries Sciences. Her office is located at Große Elbstraße 133 in Hamburg, where she conducts critical research on marine mammal populations and their conservation. Her research interests focus on marine mammal ecology and conservation, with particular emphasis on population surveys to investigate abundance, distribution, and habitat use of whales. Dr. Herr specializes in large whales in the Antarctic, examining their population status and recovery from commercial whaling. Her work integrates field studies with advanced analytical methods to develop scientific foundations for marine conservation and management policies. She actively uses aerial surveys, satellite tagging, and ecological modeling in her research approach. Analysis of Dr. Herr's recent publications reveals a strong focus on fin whale ecology in the Antarctic region, with particular attention to population dynamics, feeding behavior, and recovery from historical whaling impacts. Her research employs innovative methods including drone imagery, machine learning for photo identification, and multi-source data integration for habitat modeling. A significant portion of her work examines anthropogenic impacts on whale populations while documenting natural ecological patterns and recovery trends. Member of the Scientific Committee of the International Whaling Commission Chair of the Southern Hemisphere Baleen Whales subcommittee German representative in the Southern Ocean Research Partnership (IWC-SORP) Leader of the 'Southern Hemisphere fin whales' focus area Guest scientist at Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung Dr. Herr leads a DFG-funded project on fin whale population status along the Antarctic Peninsula (SPP 1158), collaborating with the Alfred Wegener Institute. Her research has been supported by multiple third-party funding sources including the German Federal Ministry of Education and Research, the Federal Agency for Agriculture and Food, and the Federal Agency for Nature Conservation. She has participated in numerous Antarctic research expeditions aboard vessels including RV Polarstern and RV Maria S. Merian. She is part of the Marine Ecosystem Dynamics and Management research team at the University of Hamburg, collaborating with international researchers across multiple institutions. Her work contributes to global efforts in cetacean conservation and Antarctic ecosystem management through participation in international frameworks including the International Whaling Commission and CCAMLR.

Jie DingView profile

Associate Professor

• Artificial Intelligence
• Statistics
• Scientific Computing
• +6 more

Jie Ding is an Associate Professor at the University of Minnesota's School of Statistics with graduate faculty appointments in Electrical Engineering, Computer Science, and the Data Science Program. He serves as a core faculty member of the Data Science and AI Hub and holds an Amazon Scholar position with the Amazon AGI Team focusing on foundation model training. His educational background includes a Ph.D. in Engineering Sciences from Harvard University (2017), postdoctoral work at Duke University (2018), and a B.S. from Tsinghua University where he participated in both the Math & Physics Academic Talent Program and Electrical Engineering program. Ding's research sits at the intersection of artificial intelligence, statistics, and scientific computing, with focus areas including Agentic AI for autonomous data science workflows, AI Foundations for interpretability and trustworthiness, Scalable Modeling for broader AI accessibility, Decentralized and Collaborative AI systems, and AI Safety addressing privacy and security concerns. He developed the STAT 8931 Generative AI course with open-source materials available at genai-course.jding.org . His recent publications demonstrate strong activity across multiple AI subfields, particularly in value alignment (MAP framework), AI safety mechanisms, federated learning innovations, and statistical foundations for modern AI systems. The breadth of venues (ICML, ICLR, NeurIPS) indicates significant impact across the AI research community. NSF CAREER Award (2024) Army Early Career Program (Young Investigator) Award (2023) Cisco Research Award (2022-25) AWS Cloud Credits for Research (2021-22) Meta/Facebook Faculty Research Award (2021-22) UMN Thank-A-Teacher Teaching Award (2019-20) Ding leads the Agentic AI for Data Science Benchmark initiative, collaborating with University of Minnesota colleagues and Minnesota industry partners to evaluate AI agent capabilities across healthcare, insurance, retail, energy and other sectors. His research group actively recruits PhD students interested in AI/Statistics intersections, with focus on developing theoretically grounded yet practically impactful AI systems.

Bjarte HannisdalView profile

Associate Professor

• Quantitative Paleobiology
• Geobiology
• Earth System Evolution
• +7 more

Bjarte Hannisdal is an Associate Professor in the Department of Geosciences at the University of Bergen, Norway, and affiliated with the Bjerknes Centre for Climate Research. He plays a key leadership role in iEarth, a national Centre of Excellence in geoscience education, serving as the iEarth Education Chair and Head of Focus Area 1, which aims to develop innovative frameworks for higher education in geosciences. Department of Geosciences, University of Bergen Bjerknes Centre for Climate Research iEarth – Centre of Excellence in Geoscience Education His research lies at the intersection of geobiology, paleontology, and Earth system science, with a strong focus on quantitative methods. He investigates Earth system evolution, causality in dynamical systems, and the co-evolution of life and the planet using advanced statistical and information-theoretic approaches applied to geological and fossil records. His work spans microbial ecology in deep-sea sediments, paleoclimatology through isotope analysis, and the detection of causal interactions in deep time. Hannisdal has made significant contributions through a high-impact publication record, with articles in top journals such as Science , Nature Geoscience , PNAS , and Physical Review E . His recent publications highlight trends in applying machine learning and information theory to geoscience problems, including microbial responses to oxygen, causality detection in incomplete records, and the calibration of geochemical proxies. These works reflect a strong interdisciplinary trend, integrating biology, physics, and computational methods into Earth sciences. He is actively involved in higher education, having developed and taught courses such as GEOV114 (Introduction to Geobiology) and GEOV302 (Data Analysis in Geosciences), and contributes to others like GEOV344 and BIO318. His educational research explores student-centered learning and computational skill development. He has supervised doctoral research, including Dario Blumenschein’s project on educational change. His work is supported by funding from the Research Council of Norway, Trond Mohn Foundation, and EU Horizon 2020. Hannisdal collaborates widely across institutions and disciplines, as evidenced by his co-authorship with researchers from Norway, the US, Germany, and others.

Hanna MeyerView profile

Professor

• Remote Sensing
• Spatial Modeling
• Machine Learning for Spatial Data
• +7 more

Prof. Dr. Hanna Meyer is a Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology, University of Münster (WWU). She leads the Remote Sensing and Spatial Modeling Group and is actively involved in teaching and research in geospatial data science, machine learning, and environmental monitoring. Her work is supported by multiple national and international funding bodies including the DFG, EU Horizon Europe, and internal university grants. B.Sc. Geography, Philipps University Marburg (2007–2010) M.Sc. Environmental Geography, Philipps University Marburg (2010–2013) Ph.D., Philipps University Marburg (2014–2018) Her research focuses on machine learning methods for spatial data, optical remote sensing, environmental monitoring, and spatio-temporal modeling. She develops and applies advanced statistical and machine learning techniques to satellite and drone-based data for mapping ecological variables, land cover, and environmental change. Her work emphasizes methodological rigor, model transferability, and uncertainty quantification in spatial predictions. The recent publications reflect a strong trend in developing and validating machine learning models for environmental mapping, with applications in soil science, peatland hydrology, forest ecology, and polar climatology. She contributes both to theoretical advancements in spatial model validation and to practical software tools in R for geospatial analysis. She has secured competitive research funding for projects such as PRISM, Carbon4D, Uebersat, and BEyond, focusing on spatial pattern recognition, carbon modeling, AI model transferability, and biodiversity prediction. She teaches courses on remote sensing, spatial data analysis with R, and environmental modeling, and supervises students and early-career researchers. She collaborates widely with researchers across institutions and leads a dynamic research group including postdoctoral researchers and students. Her open-source contributions, particularly R packages like CAST and uavRst, support reproducible research in geospatial machine learning.

Nur Arafeh DalmauView profile

Researcher

• Marine Ecology
• Marine Spatial Planning
• Climate Resilience
• +3 more

Nur Arafeh Dalmau is a marine community ecologist and marine spatial planner currently serving as a Postdoctoral Scholar at the University of California, Los Angeles (UCLA) and Stanford University. She is also an Honorary Fellow at The University of Queensland. Her research focuses on the impacts of marine heatwaves on kelp forest ecosystems and the role of marine protected areas (MPAs) in providing climate resilience. She collaborates with NGOs, parks, and fishers to support conservation initiatives and teaches decision-making tools like Marxan for marine spatial planning. She is deeply connected to her home region of Costa Brava, Spain, where she advocates for marine conservation. Her research interests span Marine Ecology Climate Resilience Marine Spatial Planning Kelp Forest Dynamics Conservation Biology Ecological Resilience with a focus on climate-smart marine protected areas and small-scale fisheries adaptation. Recent publications highlight trends in Climate resilience strategies for kelp forests Global marine heatwave impacts Systematic conservation planning Trophic dynamics in MPAs Data integration for fisheries resilience Transboundary marine management . These works emphasize climate adaptation, ecosystem-based management, and policy frameworks. Scientific recognition includes Honorary Fellow, The University of Queensland . Contact: nadalmau@stanford.edu