Find Academic Supervisors

Michael A. BenderView profile

Professor

• Algorithms
• Data Structures
• Cache and I/O-Efficient Computing
• +7 more

Michael A. Bender is the John L. Hennessy Chaired Professor of Computer Science at Stony Brook University. His work spans both theoretical and applied domains in algorithms, data structures, and storage systems. He co-founded Tokutek, Inc., a database company acquired by Percona in 2015, and has held visiting positions at MIT and King's College London. Education: PhD in Computer Science from Harvard University (1998), DEA and Magistère in Computer Science from École Normale Supérieure de Lyon (1993), BA in Applied Mathematics from Harvard (1992). His research focuses on cache-oblivious algorithms , I/O-efficient computing , parallel systems , and scheduling . His work addresses scalability in large-scale data management and computational geometry, with applications in bioinformatics and robotics. The 15 most recent publications highlight advancements in list labeling , hash table design , graph algorithms , and memory optimization . These papers reflect his commitment to solving practical problems with rigorous algorithmic approaches. Awards include: PODS Best Paper Award (2024) ASPLOS Distinguished Paper Award (2023) USENIX FAST Best Paper Award (2016) Chancellor's Award for Excellence in Teaching (2015) R&D 100 Award (2006) He has led the Stony Brook Computer Science Honors Program and advised graduate students in algorithmic research. His grants portfolio includes 39 funded projects, emphasizing algorithm engineering and systems optimization.

Eleni EliaView profile

Senior Lecturer

• Biostatistics
• Artificial Intelligence
• Advanced Computational Methods
• +3 more

Eleni Elia is a Senior Lecturer in Statistics at Oxford Brookes University's School of Engineering, Computing and Mathematics, leveraging expertise from research positions at Harvard University, Boston Children's Hospital, and the University of Leicester to develop AI-driven healthcare solutions that improve patient outcomes through advanced computational methods and biostatistics. Her academic foundation includes: BSc (Hons) in Mathematics MSc in Statistics PhD in Statistics Dr. Elia's research bridges biostatistics and artificial intelligence with clinical applications, focusing on prognosis research and healthcare data analysis . She pioneers computational approaches to transform patient care, particularly in pediatric cardiology and epidemic surveillance, using real-world data to address critical medical challenges through interdisciplinary collaboration. Analysis of her 14 publications (2015-2024) reveals consistent application of statistical modeling to medical domains, with growing emphasis on AI integration in cardiology, biomarker analysis, and epidemic prediction. Her work demonstrates methodological rigor while addressing urgent healthcare needs through pediatric applications and refugee camp disease surveillance. Her scientific recognition includes: Enterprise Champion Award 2022/23 for 'Gamifying entrepreneurship education for mathematics students' Executive Coaching Award 23/24 as a 'Research Leader' Mardia Prize from the Royal Statistical Society for refugee-focused statistical workshops Dr. Elia actively mentors PhD candidates and leads significant research initiatives including 'Infectious disease surveillance in refugee camps', 'Biomechanics interactive database development', and 'Epidemic surveillance systems', while securing substantial university funding for healthcare AI innovation and entrepreneurship programs. She contributes to cutting-edge research through the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and Visual Artificial Intelligence Laboratory (VAIL), driving interdisciplinary projects that connect statistical theory with life-saving medical applications.

Nematollaah Shiri V.View profile

Associate Professor

• Database and Knowledge Base Systems
• Uncertain Data Management and Reasoning
• Query Processing and Optimization
• +2 more

Dr. Nematollaah Shiri V. is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. He supervises students in the Computer Science (MCompSc) and Computer Science (PhD) programs. His research focuses on database systems, uncertain data management, query optimization, and machine learning analytics. Education: PhD (details not specified in text) Research interests include advancing database and knowledge base systems, developing methods for managing uncertain data, optimizing query processing, and applying machine learning techniques for data analytics. His work bridges theoretical foundations with practical applications in database systems. No specific grants, labs, or recent publications are listed in the provided text. For further details, visit his personal website linked in the contact information.

Agneta AnderssonView profile

Professor

• Marine Ecology
• Aquatic Microbiology
• Eutrophication
• +5 more

Agneta Andersson is a Professor at Umeå University's Department of Ecology and Environmental Science, and affiliated as Professor at the Umeå Marine Sciences Centre (UMF). She leads research projects in marine ecology and coordinates the strategic EcoChange program. Her work focuses on microbial ecology, eutrophication impacts, and climate change effects on marine ecosystems. She supervises PhD student Meifang Zhong and collaborates with postdoc Alef dos Santos and lab members Sonia Brugel, Kesava Ramasamy, Karolina Eriksson, and Li Zhao. Research Interests Her research explores microbial community dynamics, predator-prey interactions in aquatic systems, and the ecological consequences of terrestrial organic matter inflow. Key topics include mercury contamination in sediments, phytoplankton responses to environmental changes, and the role of dissolved organic matter (DOM) in coastal ecosystems. She investigates how climate-driven environmental shifts affect food web efficiency and toxin bioaccumulation patterns. Current Projects Scientific Coordinator for the strategic EcoChange program Leading a 2025–2026 project assessing eutrophication threats in the Bothnian Bay Developing DNA-based monitoring tools for marine phytoplankton Grants & Collaborations Her work is supported by grants funding studies on Baltic Sea microbial diversity and coastal DOM dynamics. She contributes to international initiatives like the CoastDOM database and collaborates with institutions globally to address climate change impacts on aquatic ecosystems. Labs & Teams Her research group focuses on microbial ecology and environmental monitoring. The lab emphasizes interdisciplinary approaches combining molecular techniques, field observations, and experimental mesocosm studies to address pressing environmental challenges.

• Artificial Intelligence
• Geospatial Data Science
• Knowledge Graphs
• +5 more

Professor Manolis Koubarakis is a faculty member at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on geospatial data science, knowledge graphs, entity resolution, and AI applications in Earth observation. He leads projects like ExtremeEarth and Plato, advancing semantic data cube systems and geospatial question answering engines. His work bridges AI and geospatial technologies, with contributions to frameworks like pyJedAI and Strabo 2 for managing big geospatial data. Research Interests include: Geospatial Question Answering Systems Entity Resolution and Entity Linking Ontology-Based Data Access (OBDA) Knowledge Graph Construction and Applications Earth Observation Data Analytics AI for Geoinformatics Notable Projects: ExtremeEarth: Combines Copernicus satellite data with machine learning for environmental analytics. Plato: Semantic data cube system enabling advanced querying of multidimensional datasets. GeoQA2: A geospatial question answering engine evaluated on large benchmarks. JedAI Family: Tools for scalable entity resolution in structured/semi-structured data. His publications span 20+ years, with recent emphasis on: AI-driven Earth observation systems Geospatial RDF benchmarking 3D geospatial interlinking Large language models for legal and health content delivery

Eoin O'GormanView profile

Senior Lecturer

• Global Change Ecology
• Food Web Structure and Stability
• Climate Change Effects on Ecosystems
• +3 more

Dr. Eoin O'Gorman is a Senior Lecturer in the School of Life Sciences at the University of Essex. His research investigates the impacts of global change across multiple levels of ecological organisation, from individual metabolism to ecosystem processes, with a particular focus on food web dynamics and body size as a functional trait. BSc, University College Cork (2004) PhD, University College Cork (2009) Current research projects include: Warming effects on trophic interactions Anthropogenic stressors in marine and freshwater systems Body size scaling in ecological networks Food web stability under climate change Recent work demonstrates how warming alters plankton body-size distributions (2025), simplifies freshwater food webs through multiple stressors (2025), and reduces trophic diversity in high-latitude ecosystems (2024). His studies span marine, freshwater, and terrestrial habitats, seeking universal responses to environmental change. Current grants include: 2024 WebDNA: Food web reconstruction through environmental DNA analysis (Leverhulme Trust) 2023 Predicting Impacts of Global Environmental Change on Ecological Networks (NERC) 2022 ORBIT: Offshore Renewables and Benthic Communities (NERC) Supervises PhD candidates studying: Zelin Chen: Offshore structures' impacts on North Sea biodiversity Patrick Eskuche Keith: Southern Ocean food web dynamics Anamika Poyil: Environmental biology of marine communities

• Cyber Physical Systems
• Risk/Reliability Analysis
• Deep Learning
• +3 more

Dr. Saptarshi Sengupta is an Assistant Professor in the Department of Computer Science at San José State University (SJSU), leading the Machine Intelligence and Complex Systems (MICoSys) Lab. He advises the ACM student club at SJSU and holds a 'Alien of Extraordinary Ability' visa (Einstein Visa) from USCIS. His work focuses on resilient cyber-physical systems, risk analysis, and deep learning applications in healthcare and industrial systems. Education: Ph.D. in Electrical Engineering, Vanderbilt University M.S. in Electrical Engineering, Vanderbilt University B.Tech. in Electronics & Communication Engineering, West Bengal University of Technology Research Interests: Cyber-Physical Systems Security Healthcare AI for Cancer and Chronic Disease Prediction Battery Prognostics and Energy Systems Machine Learning for Complex Systems Analysis Key Achievements: Dr. T.M.A. Pai Gold Medal Award for Healthcare AI contributions Recipient of multiple best paper awards at international conferences Author of over 30 peer-reviewed publications Labs & Teams: Leads the MICoSys Lab, developing AI solutions for healthcare diagnostics, industrial prognostics, and smart infrastructure systems. Collaborations include interdisciplinary projects with biomedical and engineering domains.

David SpiegelhalterView profile

Professor

• Public Understanding of Risk
• Bayesian Statistics
• Biostatistics
• +2 more

David Spiegelhalter is Professor of the Public Understanding of Risk at the University of Cambridge's Faculty of Mathematics. His research focuses on Bayesian statistics, public communication of uncertainty, biostatistics, and performance assessment in healthcare. He has contributed extensively to risk perception studies, particularly during the COVID-19 pandemic, and develops tools for statistical communication. Spiegelhalter's work bridges technical statistics with public understanding, emphasizing transparent communication of scientific uncertainty. His research interests include: Developing frameworks for trust in scientific advocacy Quantifying pandemic risks and public responses Creating accessible statistical tools for healthcare decisions His publications show consistent focus on: Public health communication strategies Philosophical foundations of probability Epidemiological modeling with recent emphasis on COVID-19 data interpretation.

Valentina RadicView profile

Associate Professor

• Glaciology
• Climate Science
• Atmospheric Science
• +7 more

Valentina Radic is an Associate Professor in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia. Her research focuses on quantifying the response of mountain glaciers to climate change, including projections of sea-level rise and regional hydrology impacts. She holds a PhD from the University of Alaska Fairbanks and has led major initiatives like the updated global glacier contribution projections featured in the IPCC Fifth Assessment Report. Education: PhD, Geophysical Institute, University of Alaska Fairbanks (2007–2008) Licentiate, Stockholm University, Department of Physical Geography and Quaternary Geology (2004–2007) MSc & BSc, University of Zagreb, Department of Geophysics (1998–2004) Research Interests: Glacier dynamics, climate modeling, energy balance processes, turbulent fluxes at glacier surfaces, and applications of machine learning in geosciences. Her work bridges field observations, numerical models, and data analysis to address uncertainties in glacier melt projections. Grants & Collaborations: Leads projects on glacier mass balance modeling, turbulent heat flux parameterization, and hydrological impacts of deglaciation. Ongoing collaborations involve PhD students in dynamical downscaling and katabatic flow modeling. Labs & Teams: Directs research groups focusing on glacier evolution modeling, surface energy balance studies, and interdisciplinary approaches combining physics-based models with machine learning.

Laks V.S. LakshmananView profile

Professor

• Data Management
• Graph Computing
• Machine Learning
• +5 more

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

Kyle KonisView profile

Associate Professor

• Building Science
• Sustainable Architecture
• Occupant-Centric Design
• +4 more

Kyle Konis is an Associate Professor of Architecture and Director of the Chase L. Leavitt Master of Building Science Program at the University of Southern California's School of Architecture. He holds a Ph.D. in Architecture (Building Science) from UC Berkeley, an M.Arch from Yale University, and a B.A. in Architectural Studies from the University of Washington. His research focuses on aligning building performance with human needs through participatory evaluation techniques, data-driven design tools, and evidence-based practices in sustainability and healthcare environments. Prof. Konis has led six externally funded research projects, including grants from the AIA Upjohn Initiative and California Energy Commission. His work emphasizes occupant-centric design, with notable contributions to daylighting strategies, circadian lighting in dementia care facilities, and energy-efficient building systems. He is a licensed architect in Washington and California. Education: B.A. Architectural Studies, University of Washington M.Arch, Yale University Ph.D., Architecture (Building Science), UC Berkeley His research interests include sustainable building practices, occupant well-being, and the application of technology to improve design outcomes. Notable projects include studies on thermal comfort databases, participatory sensing frameworks (TrojanSense), and circadian design tools. He has authored or co-authored over 30 peer-reviewed articles and a Springer-published book on daylighting. Awards: 2016 ARCC New Researcher Award 2015 ACSA New Faculty Teaching Award 2015 ACSA Housing Design Education Award 2015 BTES Emerging Faculty Award Prof. Konis teaches courses on architectural sustainability, environmental systems, and interactive computing in design. His lab integrates real-world data collection with computational modeling to bridge gaps between design intent and building performance. Ongoing work explores climate-responsive interventions for at-risk communities and the role of lighting in mental health contexts.

Franjo CeceljaView profile

Professor

• Systems Engineering for Energy and Industrial Applications
• Optimisation and Decision Making
• Knowledge Modelling (Ontology Engineering)
• +3 more

Dr. Franjo Cecelja is a Reader in the School of Chemistry and Chemical Engineering at the University of Surrey. He holds a Dipl. Eng. from the University of Zagreb, an M.Sc. from Cranfield Institute of Technology, and a Ph.D. from Brunel University. His research focuses on systems engineering for energy and industrial applications, optimization, decision making, and semantic technologies. He has led projects such as the FP7 (Marie Curie LTN) initiative on renewable energy systems engineering (£425k, 2013–2018). His work spans ontology engineering applications in biorefining, waste valorization, and sustainable processing. Notable contributions include semantic frameworks for model and data integration in biorefineries and decision support systems for industrial symbiosis. Education: Ph.D., Brunel University (Optical Sensors for Electric Fields) M.Sc., Cranfield Institute of Technology (Control & Signal Processing) Dipl. Eng., University of Zagreb (Aerospace Technology) His research interests integrate ontology engineering with process systems engineering to address challenges in biorefining, industrial symbiosis, and sustainable resource management. Recent publications emphasize semantic technologies for waste valorization, PFAS treatment, and decision-making frameworks in biorefining. Dr. Cecelja’s FP7 project demonstrated leadership in renewable energy systems, leveraging semantic networking facilities and value chain optimization. His work bridges academic research with industrial applications, emphasizing circular economy principles and model-driven decision support. Labs/Teams: His research is conducted within the University of Surrey’s School of Chemistry and Chemical Engineering facilities, collaborating with interdisciplinary teams on biorefining and process systems engineering.

Traci J. HessView profile

Professor

• Human-Computer Interaction
• Decision-Making Systems
• Electronic Commerce
• +3 more

Traci J. Hess is the Douglas & Diana Berthiaume Endowed Professor of Information Systems and Senior Associate Dean at the Isenberg School of Management, University of Massachusetts Amherst. She holds a PhD (1999), MA (1997) in Information Systems from Virginia Tech, and a BS in Accounting from the University of Virginia (1988). Her academic career includes roles at Washington State University and industry experience as Senior Vice President in banking and auditing roles at Ernst & Young. Her research focuses on Human-Computer Interaction , Decision Support Systems , and Trust in Digital Technologies . She explores topics like online review dynamics, privacy mechanisms in health communities, and algorithmic labor platforms. Hess has published extensively in journals like Journal of the Association for Information Systems and MIS Quarterly , and her work bridges cognitive psychology with technology design. Hess has received prestigious awards including the Isenberg Outstanding Research Award (2012) and Transactions on Human-Computer Interaction Best Paper Award (2011). Her teaching spans Business Intelligence , Database Management , and Research Methods . She has advised numerous graduate students (not listed explicitly in texts) and contributes to digital strategy research. Her recent work (2023–2024) examines reviewer badges’ impact on consumer trust and human agency in algorithmic labor systems. She emphasizes user-centric design principles to mitigate negative emotional responses to technology (e.g., technostress).

Zhanfei LeiView profile

Assistant Professor

• User-generated content
• Biases and heuristics
• Electronic commerce
• +3 more

Zhanfei Lei is an Assistant Professor of Operations & Information Management at the Isenberg School of Management, University of Massachusetts Amherst . His research focuses on user-generated content, biases in decision-making, and electronic commerce, with an emphasis on understanding how online reviews and consumer behavior interact in digital markets. Education: Ph.D. in Information Technology Management, Georgia Institute of Technology M.S. in Information Sciences, University of Pittsburgh Bachelor of Management in Information Management and Information Systems, Nanjing University Lei investigates biases and heuristics in consumer decision-making, particularly exploring how cognitive processes influence trust in online word-of-mouth and selective exposure to reviews. His work bridges information systems, marketing, and behavioral economics, with applications in e-commerce optimization and digital strategy. Publications consistently examine themes like persuasive power of reviews , attentional focus effects , and consumer psychology . His research contributes to understanding dual-process theories in digital contexts and improving platform design for user engagement. No scientific awards are listed in the provided text. Advising and grants are not detailed here, though his CV may contain further details. He teaches courses in information systems, database management, and business analytics.

Evimaria TerziView profile

Professor

• algorithmic data mining
• network analysis
• recommendation systems
• +3 more

Evimaria Terzi is a Professor and Department Vice Chair at Boston University (BU), affiliated with the Data Management Lab@BU. Her research focuses on algorithmic data mining with applications in network analysis, recommendation systems, ranking, and clustering. She holds a PhD from the University of Helsinki and has held prior roles at IBM Almaden Research Center (2007–2009) and the Helsinki Institute for Information Technology (HIIT) before 2007. Her work spans theoretical and applied domains, including team formation algorithms, fairness in AI, and large language model evaluation. Notable contributions include studies on LSM tree optimization, counterfactual explanations for auditing fairness, and the dynamics of memorization in LLMs. Her recent publications emphasize flexibility in database systems and ethical AI practices. Evimaria’s research has been recognized through her contributions to conferences like WSDM and KDD, where she has served in organizing roles. The themes of her work consistently bridge algorithmic innovation with real-world applications in social networks, healthcare, and collaborative systems.