Find Academic Supervisors

• Financial Engineering
• High Frequency Trading
• Algorithmic Trading
• +3 more

David Lariviere is a Clinical Professor in the Industrial & Enterprise Systems Engineering (ISE) Department at the Grainger College of Engineering , University of Illinois at Urbana-Champaign. He is also the Founder and Director of the FinTech Lab at the university and affiliated with the Master of Science in Financial Engineering (MSFE) Program through both the Grainger College of Engineering and the Gies College of Business Finance Department. Research Interests include high-frequency trading technologies, algorithmic market microstructure, FPGA applications in financial trading, low-latency networking, and quantitative finance. His work bridges computer science, electrical engineering, and financial markets, focusing on real-world trading system design and optimization. Teaching Highlights : Directed 21 student groups in Spring 2025 with projects spanning agentic AI trading systems, FPGA development, crypto arbitrage, and GPS timing servers Received 8 teaching excellence awards (2019-2024) including Outstanding recognition for FIN556 Designed courses integrating cutting-edge technologies like OneTick databases, Strategy Studio backtesters, and PyTorch-based ML models Awards & Patents : 2007 Andrew P. Kosoresow Memorial Award 7 issued/filings including latency determinism enforcement and FPGA-based trading architectures

• Information and Knowledge Management
• Product Configuration Systems
• Supply Chain Management
• +5 more

Anders Haug serves as Associate Professor at the Department of Business and Sustainability (DBS) within the University of Southern Denmark's Kolding campus. Having joined the university in 2008 as Assistant Professor in the Department of Entrepreneurship and Relationship Management before transitioning to his current role in 2010, his academic career spans over 15 years of research and teaching in operations, supply chain, and digital transformation contexts. His work bridges theoretical rigor with practical industry applications, particularly in engineer-to-order manufacturing and logistics sectors. Education: PhD in communication, representation and automation of design knowledge (2005-2007) Haug's research centers on information and knowledge management systems, with deep expertise in data quality frameworks, knowledge-based configuration, and digitalization of business processes. His fingerprint reveals distinctive contributions to product configuration systems, digital twin applications, and supply chain resilience—particularly examining how configurators transform warehouse services, manufacturing processes, and product-service ecosystems. Recent work increasingly addresses sustainability through green dynamic capabilities frameworks and life cycle assessment tools, maintaining strong empirical grounding via case studies in Danish manufacturing. Analysis of his 2024-2025 publications shows converging trends: digital technologies (configurators, digital twins) are examined through operational performance lenses while addressing sustainability imperatives. These works span operations management, information systems, and strategic management disciplines but consistently prioritize practical implementation frameworks for manufacturing SMEs. The research demonstrates methodological diversity—from conceptual modeling to empirical case studies—with strong industry relevance in logistics, engineering-to-order contexts, and manufacturing digitization. Scientific Awards: Top read paper in Business 2017/18 (Wiley) (2019) Haug has supervised 34 teaching courses between 2018-2024 covering business information systems, digitalization projects, and supply chain management. His academic service includes extensive peer reviewing for conferences like NOFOMA and DRS, plus organizational roles in Nordic business research networks. While specific grant details aren't provided, his 175+ research outputs and industry collaborations (evidenced by consultant work since 2006) indicate substantial research funding engagement. Media contributions on 3D printing and business process efficiency demonstrate effective knowledge transfer to practitioners. Though no dedicated research lab is specified, Haug's extensive co-authorship network—including collaborations on projects like digital twin implementation and configurator development—reveals embeddedness in multiple research collectives. His industry-facing approach manifests through case studies with logistics providers, manufacturer partnerships, and practical frameworks for warehouse service design and supply chain resilience.

• Organic Functional Materials
• Materials Informatics
• Structural Organic Chemistry
• +4 more

Takuya Taniguchi is an Associate Professor at the Center for Data Science, Waseda University, where he has been employed since 2019, first as an Assistant Professor (2019-2021) before promotion to his current non-tenure track position. His interdisciplinary research bridges organic chemistry, materials science, and data science, with a focus on developing and analyzing functional organic materials, particularly photo-responsive molecular crystals. He maintains active collaborations with research groups including Hideko Koshima and Toru Asahi, and serves on professional committees such as the Manufacturing Industry AI Promotion Association. Education Doctor of Engineering, Waseda University (2019) Graduate studies at Waseda University's Graduate School of Advanced Science and Engineering Undergraduate studies at Waseda University's School of Advanced Science and Engineering, Department of Life Science and Medical Bioscience Research Focus Professor Taniguchi's work centers on structural organic chemistry and physical organic chemistry , with particular emphasis on organic functional materials and the application of materials informatics to crystal engineering. His research investigates how molecular structure influences macroscopic mechanical properties, especially photo-mechanical behaviors in organic crystals. He has pioneered the use of machine learning techniques to predict crystal properties, optimize materials performance, and design novel functional materials with applications in soft robotics and energy conversion. His recent work demonstrates a clear progression from fundamental studies of photo-mechanical crystals toward increasingly sophisticated applications of machine learning in materials science. The 15 most recent publications reveal a strong focus on neural network potentials for crystal property prediction, optimization of photo-actuated materials, and exploration of phase transitions in molecular crystals. This research trajectory shows consistent innovation in combining experimental chemistry with advanced computational methods. Awards and Recognition Inoue Research Encouragement Prize (2019) Ono Azusa Memorial Award from Waseda University (2019) Student Presentation Award at Japan Chemical Society 99th Spring Annual Meeting (2019) Poster Presentation Award at Chirality 2017 Multiple poster and presentation awards at international crystallography conferences Research Leadership Dr. Taniguchi leads multiple research projects funded by JSPS, JST, and industry partners including ENEOS Corporation and Sumitomo Foundation. His current projects include 'Efficient crystal structure prediction for organic porous materials,' 'Machine learning simulation of HOF material stability,' and 'Machine learning application for organic solid-phase transition.' He has successfully secured competitive funding including JST ACT-X and JSPS Early-Career Scientist grants. His work has received significant media attention, with coverage in Nikkan Kogyo Shimbun and Kagaku Kogyo Nippo highlighting breakthroughs in light-driven organic crystals with 3.7x improved output force. Academic Contributions Beyond research, Professor Taniguchi teaches extensively in Waseda's Global Education Center, offering courses in data science, statistical analysis with R, and statistics literacy across multiple academic quarters. He has contributed chapters to technical publications on process informatics, Bayesian optimization, and crystallization process design. His scholarly impact is evidenced by an h-index of 11 (Google Scholar) with 581 total citations, demonstrating significant influence in the emerging field of materials informatics for organic crystals.

Anna MonrealeView profile

Associate Professor

• Privacy-Preserving Data Mining
• Big Data Analytics
• Social Network Analysis
• +7 more

Anna Monreale is an Associate Professor in the Department of Computer Science at the University of Pisa and a key member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group with the Information Science and Technology Institute of the National Research Council (ISTI-CNR) in Pisa. Her academic career is rooted in the University of Pisa, where she completed her Bachelor's, Master's, and Ph.D. in Computer Science. Her research focuses on privacy-preserving data analytics, with core interests in big data analytics, social network analysis, spatio-temporal mining, and explainable AI. She is particularly known for her work on privacy-by-design in data mining and evaluating privacy risks in analytical processes. Her research bridges technical innovation with ethical and legal considerations in data science. Her recent publications reveal a strong trend toward explainable AI, privacy in federated learning, and risk assessment in mobility and health data. She actively contributes to developing methods for explaining black-box models, assessing privacy exposure, and balancing privacy, utility, and fairness in AI systems. Privacy by Design Ambassador (2014) ISTI-CNR Young++ Researcher Award (2014) Monreale has advised and co-chaired several international workshops, including PriSMO, PinSoDa, and MoKMaSD, and serves on editorial boards such as Transactions on Data Privacy. She teaches advanced data mining, big data ethics, and database systems across multiple graduate and undergraduate programs. She is involved in major EU projects like SoBigData, XAI, TAILOR, and HumMingBird, reflecting her leadership in data science and AI ethics. She is affiliated with the KDD-Lab, a prominent research group focused on knowledge discovery, social mining, and big data analytics, contributing to both theoretical advances and real-world applications in privacy-aware data science.

Qiang ZhuView profile

Professor

• Data Science
• Data Management
• Machine Learning
• +3 more

Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.

Maxime GobertView profile

Researcher

• Database Systems
• Hybrid Polystores
• Database Schema Evolution
• +4 more

Maxime Gobert is a researcher at the University of Namur's Faculty of Computer Science, specializing in database systems and software engineering. Having completed his PhD in March 2023 titled 'Design, Manipulation and Evolution of Hybrid Polystores,' Gobert has established himself as an expert in hybrid database systems, particularly focusing on the HyDRa framework for modeling and evolving polystores. His research interests span database systems, hybrid polystores, database schema evolution, software engineering, data-intensive systems, and static program analysis. Gobert's work bridges theoretical database concepts with practical applications, particularly in NoSQL databases like MongoDB and complex hybrid data storage environments. His publication record demonstrates a clear trajectory from his 2013 Master's thesis on database reverse engineering through to his recent work on database testing best practices and sign language processing applications. His research shows strong collaboration with colleagues at the University of Namur, particularly with Professor Cleve A., and extends to international collaborations as evidenced by his 2016 guest researcher position at the University of Geneva. Best New Idea and Emerging Results (NIER) Paper Award at the 20th IEEE Working Conference on Source Code Analysis and Manipulation (SCAM 2020) Jean Fichefet 2013 award Gobert has contributed significantly to the development of the HyDRa framework for hybrid polystore management and has extended his research into sign language processing through collaborative projects creating bilingual sign language dictionaries and parallel corpora. His work demonstrates both technical depth in database systems and a commitment to applying this expertise to accessibility-focused applications.

Manos AthanassoulisView profile

Associate Professor

• Data Systems
• Cloud Data Management
• Database Systems
• +7 more

Manos Athanassoulis is an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, Boston University. He is the Founder and Director of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, particularly cloud data management, hybrid transactional/analytical workloads, and integration with emerging hardware such as non-volatile memory and heterogeneous computing. His educational background includes a PhD from EPFL (2014), an MSc in Computer Systems Technology, and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Prior to BU, he was a Postdoctoral Researcher and Research Associate at Harvard University, supported by a SNSF Postdoc Mobility Fellowship. His research interests span data systems, database architectures, LSM trees, indexing, storage systems, and performance optimization. He explores how novel hardware can be leveraged to improve data management efficiency and scalability, especially in cloud environments. His recent publications (2021–2025) predominantly focus on LSM trees, covering topics such as compaction policies, Bloom filter tuning, DPU offloading, adversarial resilience, and sustainable caching. Earlier works include foundational contributions on access methods (RUM Conjecture) and optimal key-value stores (Monkey). The trend shows a consistent focus on data system efficiency, adaptability, and robustness under varying workloads and hardware constraints. Scientific Awards: NSF CAREER Award (2022) Facebook Faculty Research Award (2020) NSF CRII Award (2019) Best of VLDB 2017 and Best of SIGMOD 2017 SIGMOD Most Reproducible Paper Award (2017) Multiple ACM SIGMOD Distinguished PC Member recognitions (2018–2025) VLDB 2023 Best Demo Award RedHat Collaboratory Research Incubation Awards (multiple, 2021–2023) SNSF Postdoc Mobility Fellowship (2015–16) IBM PhD Fellowship (2011–12) Dr. Athanassoulis has advised numerous students and collaborators, evident from his co-authorship on works with researchers such as Niv Dayan, Stratos Idreos, and A. Ailamaki. His grants include major awards from NSF, Facebook, and RedHat, supporting research in robust data systems, hardware-software co-design, and learned cost models. He has also been recognized for teaching excellence at Harvard University. He leads the DiSC lab at Boston University, which focuses on data-intensive computing and systems research. The lab explores next-generation data architectures, particularly in cloud and hardware-aware environments. Collaborations with the BU MiDAS group enhance interdisciplinary research in data science and AI.

Mohan SunthaView profile

Professor

• Radiation Oncology
• Head and Neck Cancer
• Esophageal Carcinoma
• +3 more

Mohan Suntha, MD, MBA, serves as Professor of Radiation Oncology at the University of Maryland School of Medicine and holds a secondary appointment in Otorhinolaryngology-Head & Neck Surgery. He concurrently serves as President and Chief Executive Officer of the University of Maryland Medical System (UMMS), a role he assumed in 2019 after previously leading the University of Maryland Medical Center since 2016. His educational background includes: BA in Biology from Brown University (1986) MD from Jefferson Medical College (1990) Residency in Radiation Oncology at University of Maryland School of Medicine (1994) Clinical Oncology Fellowship in Head & Neck Malignancies (1994-1995) MBA from The Wharton School (2009) Dr. Suntha's research focuses on carcinomas of the head, neck, and esophagus, with emphasis on optimizing combined modality approaches through investigator-initiated clinical trials. His work investigates concurrent chemoradiation protocols, neoadjuvant strategies, and surgical integration to improve therapeutic outcomes. Current investigations include tumor volume prognostic factors, molecular imaging applications, and quality indicators for surgical oncology procedures. His publications demonstrate consistent focus on head and neck cancer disparities, esophageal carcinoma management, and quality metrics in radiation oncology. Key trends include translational research connecting surgical pathology metrics with oncologic outcomes, prognostic biomarker identification, and health equity analysis in cancer care delivery. Award highlights include: Five-time 'Top Doctor' in Radiation Oncology by Baltimore Magazine (2007-2016) 2022 Influential Marylander Honoree in Healthcare by Maryland Daily Record Administratively, Dr. Suntha has held progressive leadership roles including Vice Chair of Radiation Oncology, Associate Director of Clinical Affairs at UMGCCC, Vice President for System Program Development at UMMS, and CEO of St. Joseph Medical Center. His current UMMS presidency encompasses oversight of Shock Trauma Center, Children's Hospital, and Greenebaum Comprehensive Cancer Center. While maintaining active clinical faculty status, his executive responsibilities focus on system-wide strategic development and healthcare delivery innovation.

• Computational Immunology
• Structural Biology
• Molecular Docking
• +3 more

Dinler A. Antunes is an Assistant Professor of Computational Biology at the Department of Biology and Biochemistry of the University of Houston, where he is also a member of the Center for Nuclear Receptors and Cell Signaling (CNRCS). His research focuses on computational immunology and structural modeling of protein-ligand complexes that play key roles in cellular immunity. Dr. Antunes received his educational training at the Federal University of Rio Grande do Sul (UFRGS) in Brazil, earning a Bachelor's degree in Biomedicine in 2008, a Master's degree in Genetics and Molecular Biology in 2011, and a Doctorate in 2014. He later completed postdoctoral studies at Rice University's Computer Science Department with a fellowship from the Computational Cancer Biology Training Program (CCBTP), collaborating with MD Anderson Cancer Center. His research interests center on developing computational methods for structural modeling of peptide-MHC complexes, with applications in personalized cancer immunotherapy and vaccine development. Dr. Antunes has pioneered several computational tools including DockTope, DINC, APE-Gen, and HLA-Arena, which enable researchers to model and analyze peptide-HLA complexes for immunotherapy applications. His work addresses the challenge of protein flexibility in molecular docking, particularly for large ligands like peptides binding to MHC receptors. Analysis of Dr. Antunes' recent publications (2023-2025) reveals a strong focus on computational immunology, with particular emphasis on peptide-MHC structural modeling, T-cell cross-reactivity prediction, and applications in cancer immunotherapy. His work spans structural biology, machine learning applications in immunology, and translational research connecting computational predictions with clinical applications, particularly in acute myeloid leukemia and rheumatoid arthritis. Dr. Antunes has received several scientific awards and recognitions: SCI Gold Oral Presentation Award at the 6th Annual SCI Summer Research Colloquium 2020 Immuno-Oncology Young Investigators' Forum (IOYIF) PhD Postdoc Awards Selected to represent the CCBTP program during the 30th Annual Keck Research Conference Fellowship from the Computational Cancer Biology Training Program Dr. Antunes is actively recruiting PhD students and postdoctoral researchers to join his team at the University of Houston. His laboratory develops computational tools for structural immunology, with current projects including HLA-Arena (a customizable environment for peptide-HLA complex analysis), DINC-COVID (a webserver for ensemble docking with SARS-CoV-2 proteins), and CrossTope (a structural database for cross-reactivity assessment).

Stuart HamiltonView profile

Professor

• Remote Sensing
• Geographic Information Systems (GIS)
• Mangrove Ecology
• +4 more

Stuart Hamilton is a part-time Professor and Chair of the Department of Coastal Studies at East Carolina University (ECU) and the UNC Coastal Studies Institute. He holds a Ph.D. in Geography (GIS) from the University of Southern Mississippi (2012), an M.S. in Geography (GIS) from SUNY Buffalo (2003), and a B.S. in Geography and Applied Social Science from Canterbury University (1996). His research focuses on remote sensing and GIS analysis of nearshore environments, particularly mangrove forest change, hurricane impacts, and interactions between environmental degradation and socioeconomic outcomes.

Yoseph BarashView profile

Associate Professor

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

Yoseph Barash is an Associate Professor at the University of Pennsylvania, jointly appointed in the Perelman School of Medicine's Department of Genetics and the School of Engineering and Applied Science's Department of Computer and Information Science. He leads the BioCiphers Lab, integrating machine learning with experimental biology to decode RNA biogenesis and splicing regulation in human disease. Education: B.Sc. in Physics and Computer Science, Hebrew University Ph.D. in Machine Learning, Hebrew University (2006) His research spans Machine Learning , Computational Biology , and Bioinformatics , focusing on predictive models for RNA splicing and its role in diseases like cancer and neurological disorders. He pioneered the splicing code (Barash et al., Nature 2010) and extended it to genetic variations (Xiong et al., Science 2015). Recent publications emphasize RNA splicing variations in cancer, tool development (e.g., MAJIQ V3, MAJIQ-CLIN), and machine learning for drug discovery (e.g., trametinib sensitivity in AML). His work bridges computational innovation with wet-lab validation, enabling novel high-throughput assays. Scientific Awards: Lap-Chee Tsui Publication Award (2010) NSERC EWR Steacie Fellowship Canadian Institute for Advanced Research Fellowship He advises companies in RNA therapeutics and has licensed splicing quantification tools to Pfizer, GSK, and Biogen. His lab collaborates extensively, training students and postdocs in interdisciplinary approaches to RNA biology.

Robin CarpentierView profile

Research Fellow

• Data Privacy
• Information Management
• Hardware Security
• +3 more

Robin Carpentier is a Research Fellow at the School of Computing, Macquarie University . His work focuses on Data Privacy , Information Management , and Hardware Security , particularly in developing secure personal data management systems using Trusted Execution Environments (TEE) and SGX technology. Research Interests : Secure data processing with third-party code Privacy-preserving computation frameworks Hardware-based security for databases Dimensionality challenges in text privacy Resource-constrained privacy-preserving methods Recent Publications Trends : Robin's research over the past decade has explored data leakage mitigation, TEE-optimized database operations, and privacy-preserving mechanisms for large-scale data applications. His 2024 work extends these principles to secure AI/LLM interactions and advanced text privacy techniques.

• Climate variability
• Palaeoclimatology
• Hydrology
• +5 more

Dr. Danielle Verdon-Kidd is an Associate Professor in Earth Sciences at the University of Newcastle, specializing in climate variability and extreme event analysis. Her research integrates palaeoclimatology, hydrology, and climatology to understand and mitigate climate-driven risks. She has held roles in the School of Environmental and Life Sciences since 2003, after completing her PhD in Civil Surveying & Environmental Engineering and Bachelor of Engineering (Environmental Eng) at the University of Newcastle. Education: PhD (Civil Surveying & Environmental Engineering), University of Newcastle Bachelor of Engineering (Environmental Eng), University of Newcastle Research Interests: Her work focuses on climate extremes (droughts, bushfires, tropical cyclones), palaeoclimate reconstructions, and interdisciplinary approaches to risk assessment. She emphasizes leveraging long-term data (e.g., tree rings, coral records) to inform water management, infrastructure planning, and community resilience. Key projects include collaborations with governments and industries to develop tools like CoastAdapt for coastal risk management. Publications & Awards: Over 100+ publications, including peer-reviewed journal articles and conference contributions. Notable awards include the Women in Research Fellowship (2018) and Envisage Program mentorship (2019). Advising & Partnerships: Supervises undergraduate and graduate students, fostering the next generation of climate researchers. Collaborates with federal/state agencies (e.g., NSW Department of Planning), water authorities, and international partners (e.g., University of Melbourne, Hydro Tasmania) to bridge scientific research and practical applications. Labs & Teams: Leads projects in palaeoclimate reconstruction, stochastic modeling, and climate adaptation. Active in interdisciplinary teams addressing climate change impacts on agriculture, biodiversity, and urban planning.

Ismo LinnosmaaView profile

Professor

• Health Economics
• Social Economics
• Healthcare Cost-Effectiveness
• +3 more

Ismo Linnosmaa is a Professor of Health and Social Economics at the University of Eastern Finland (UEF), affiliated with the Department of Health and Social Management within the Faculty of Social Sciences and Business Studies. He also serves as Deputy Head of the Department. His research focuses on healthcare cost-effectiveness, aging population economics, long-term care markets, and pharmaceutical policy. Key research interests include evaluating the economic impact of health and social services for elderly populations, analyzing markets for healthcare and pharmaceuticals, and assessing cost-effectiveness of interventions. He leads the Health Economics Research Group and contributes to projects like the EXCELC project and SustAgeable initiative exploring social and healthcare effectiveness. Linnosmaa has published extensively on topics such as ASCOT (Adult Social Care Outcomes Toolkit) applications, cost-effectiveness of medical treatments (e.g., cochlear implants, parenting programs), and policy analyses of drug utilization and pricing. His work frequently involves cross-national comparisons (e.g., Austria, England, Finland) to evaluate quality-of-life outcomes and healthcare system performance. He has collaborated on major grants including the EU-funded Joint Action on Cardiovascular Diseases and Diabetes (2023–2027), MEDIFF (evaluating medication use in older adults), and GuideGap (analyzing guideline adherence in prescribing). His research bridges economic theory with practical healthcare policy, emphasizing evidence-based approaches to improve service efficiency and equity. Linnosmaa’s expertise includes economic modeling, health technology assessment, and outcomes measurement, with a focus on translating findings into actionable policy recommendations for aging societies. He has advised on healthcare reforms and participated in national surveys like the Finnish Meds75+ database initiative.

• Databases
• Data Management
• Provenance Analysis
• +6 more

Val Tannen is a Professor at the University of Pennsylvania, specializing in database systems, provenance analysis, and programming languages. His research focuses on data management, query languages, and systems like DBSP and ORCHESTRA. Collaborations include work with co-authors such as Zachary Ives, Susan Davidson, and Todd Green. Key research interests include provenance for databases, incremental view maintenance, and data integration. His work bridges theoretical foundations and practical applications in systems like DBSP for stream processing and ORCHESTRA for collaborative data sharing. Publications span provenance frameworks, query optimization, and distributed systems. While no awards are explicitly listed, his contributions to database theory and systems are widely recognized.