جست‌وجوی استاد و پژوهشگر

Jinying Chenمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Health Informatics
• +۵ مورد دیگر

Jinying Chen, PhD is an Assistant Professor in the Department of Population and Quantitative Health Sciences at UMass Chan Medical School, specifically within the Division of Health Informatics and Implementation Science. She is affiliated with the T.H. Chan School of Medicine and contributes to the UMass Cancer Center's research initiatives. Her educational background includes: BS in Computer Science & Technology from Tsinghua University, Beijing, China ME in Computer Science & Technology from Tsinghua University, Beijing, China MS in Computer & Information Science from University of Pennsylvania, Philadelphia, PA, USA PhD in Computer & Information Science from University of Pennsylvania, Philadelphia, PA, USA Postdoctoral training in Health Informatics and Implementation Science from University of Massachusetts Medical School, Worcester, MA, USA Dr. Chen's research focuses on applying information technologies, particularly natural language processing and machine learning, to healthcare problems. Her work spans several key areas including electronic health record analysis, cancer prevention and care, smoking cessation interventions, and predictive modeling for patient outcomes. She develops methods to extract meaningful information from clinical notes, identify important medical terms for patients, and create systems that improve patient understanding of their health information. Her research has significant implications for improving healthcare delivery, patient engagement, and clinical decision support systems. Dr. Chen's publication history reveals a strategic evolution from foundational computational linguistics research toward increasingly healthcare-focused applications. Her recent work (2019-2021) emphasizes patient-centered NLP applications in electronic health records, with particular focus on understanding patient-reported outcomes, pain management, and chronic disease care. She has developed innovative approaches for medical term ranking, hypoglycemia detection from patient messages, and linking medical terminology to lay definitions, demonstrating both technical expertise and clinical relevance. She maintains active collaborations with prominent researchers including Thomas Houston, Rajani Sadasivam, and David McManus, with publications appearing in high-impact journals such as Journal of Medical Internet Research and BMC Health Services Research. Her research bridges the gap between advanced computational techniques and real-world healthcare challenges, with particular emphasis on making health information more accessible and actionable for patients and providers.

Renato Rennerمشاهده پروفایل

استاد

• Quantum Information Science
• Quantum Thermodynamics
• Foundations of Quantum Physics
• +۲ مورد دیگر

Renato Renner is a Full Professor of Theoretical Physics and Head of the Institute for Theoretical Physics at ETH Zürich. He specializes in Quantum Information Science, Quantum Thermodynamics, and the Foundations of Quantum Physics. Born in Lucerne, he earned his physics degrees from EPF Lausanne and ETH Zurich, with a PhD focused on quantum cryptography. After postdoctoral work at the University of Cambridge, he joined ETH Zurich in 2007, progressing through academic ranks to Full Professor by 2015. His research group, the Research Group for Quantum Information Theory, explores cutting-edge topics like quantum key distribution, device-independent protocols, and the thermodynamic limits of quantum processes. His work bridges theoretical physics and applied cryptography, with contributions to quantum security, entropy accumulation, and foundational questions in quantum mechanics. Notable projects include the Space QUEST mission proposal to test quantum decoherence due to gravity and the development of rigorous security frameworks for quantum communication. Renner’s publications span foundational quantum theory, cryptographic protocols, and interdisciplinary applications of quantum information principles.

John Shepherdمشاهده پروفایل

مدرس ارشد

• Data Management
• Information Retrieval
• Web Search
• +۴ مورد دیگر

Dr. John Shepherd is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at UNSW, Sydney, Australia. His research focuses on data/information/knowledge management, including data modeling, query processing, multimedia retrieval, web search, and text mining. Current projects include RAM (Research Area Mapper) for research data management, and educational tools like ORE (Online Role-Play in Education), CMap (Curriculum Mapping), and WebCMS (Course Management System). His work spans interdisciplinary areas such as database systems, information retrieval, and educational technology. Notable contributions include the TEXUS framework for PDF table extraction and understanding, and collaborative projects with industry partners. Dr. Shepherd’s publications (e.g., on popularity forecasting, automated table understanding) reflect his expertise in machine learning, data systems, and applied research. He actively engages in curriculum development and educational innovation, leveraging technology to enhance learning experiences.

Yasutaka Furukawaمشاهده پروفایل

دانشیار

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +۵ مورد دیگر

Yasutaka Furukawa is a Principal Scientist at Wayve and an Associate Professor of Computing Science at Simon Fraser University (SFU). He leads the SFU Visual Computing Group, ranked 12th globally in Computer Vision and Graphics. His research focuses on 3D reconstruction, neural networks, and computer graphics, with notable contributions in multi-view stereo, CAD modeling, and diffusion models. Furukawa holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008) and has held roles at Washington University in St. Louis, Google, and the University of Washington. Education: Ph.D., Computer Science, UIUC, 2008 Postdoctoral Research, University of Washington (with Prof. Steven Seitz and Brian Curless) Research Interests: Furukawa’s work bridges computer vision and graphics, emphasizing robust 3D reconstruction techniques. His projects include PuzzleFusion++ (diffusion-based fracture assembly), BrepGen (B-rep CAD generation), and HouseDiffusion (floorplan synthesis). He explores applications in autonomous systems, AR/VR, and robotics. Key Projects: Generative Adversarial Networks for architectural design (House-GAN series) Neural networks for inertial navigation (RoNIN) Diffusion models for spatial puzzles and CAD modeling Awards: Recipient of the PAMI Longuet-Higgins Prize (2020), NSF CAREER Award (2015), and multiple Google Faculty Research Awards. Teaching: Courses include CMPT412 Computer Vision and CMPT762 Advanced Computer Vision at SFU. Active in supervising over 30 graduate students and postdocs.

• Gut-Brain Axis
• Functional Gastrointestinal Disorders
• Neuroscience
• +۳ مورد دیگر

Lukas Van Oudenhove is a Full Professor at the Faculty of Medicine, KU Leuven , affiliated with the Department of Chronic Diseases and Metabolism and the Translational Research in GastroIntestinal Disorders unit. He is a member of interdisciplinary institutes including the Leuven Centre for Health Humanities , KU Leuven Brain Institute , and Leuven.AI . His research focuses on the gut-brain axis , neuroinflammation , and psychosomatic mechanisms in functional disorders. Academic Rank: Professor Key Affiliations: Faculty of Medicine (KU Leuven), LCH², LBI, Leuven.AI His research spans functional brain imaging of visceral pain, stress-modulated fear memory, microbiome metabolite effects on cognition, and neuroinflammatory mechanisms in fatigue. He leads projects like The gut-immune-brain axis as key to IBD fatigue and Neuroinflammation in sleep-pain relationships . Recent work explores butyrate's epigenetic modulation of fear memory and erythritol's impact on reward systems . Scientific contributions include a psychobiological model for gut-brain disorders and insights into interoceptive differences in fibromyalgia. Awards: Poster Prize Award (4th European Stress Conference, 2025) He serves on governance bodies: Council of the Faculty of Medicine (senior academic staff) Council/Board of the Department of Chronic Diseases and Metabolism

Gang Chenمشاهده پروفایل

استاد

• Remote Sensing
• Environmental Sustainability
• Geospatial Big Data Analytics
• +۶ مورد دیگر

Dr. Gang Chen is a Professor in the Department of Earth, Environmental and Geographical Sciences at the University of North Carolina at Charlotte (UNCC). He leads the Laboratory for Remote Sensing and Environmental Change (LRSEC), established in 2012, which focuses on applying remote sensing and geospatial sciences for environmental management. His research interests include remote sensing technologies (LiDAR, hyperspectral imaging), environmental modeling, urban sustainability, forest disturbances, and machine learning applications in ecological studies. Dr. Chen’s work emphasizes interdisciplinary approaches, such as integrating multi-sensor remote sensing with species distribution modeling to assess forest disease impacts and developing AI-driven tools like UrbanWatch for high-resolution urban land cover mapping. He has advised numerous graduate students, including MA/PhD candidates focused on remote sensing applications in environmental disasters, urban ecology, and geospatial data analytics. His research has been published in top-tier journals such as Remote Sensing of Environment (ranked #1 in remote sensing), Environmental Research Letters , and ISPRS Journal of Photogrammetry and Remote Sensing . He holds an Honors Faculty designation at UNCC until June 2030. LRSEC collaborates on projects like UrbanWatch, providing free 1m-resolution land cover maps for 23 U.S. cities using deep learning algorithms. Current initiatives explore human-environment interactions, including the effects of small hydropower plants on Amazon deforestation and post-wildfire forest recovery patterns.

Luigi Ceruloمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Genomics
• Machine Learning
• +۳ مورد دیگر

Luigi Cerulo is an Associate Professor in Computer Science at the Department of Science and Technology (DST) , University of Sannio in Benevento. He co-directs the bioinformatics research group with Francesco Napolitano, focusing on integrating computational methods into life sciences. His teaching includes advanced topics like Computational Genomics and Statistics and Bioinformatics for Master's programs in Biotechnology and Biology. Academic Rank: Associate Professor Department: Science and Technology, University of Sannio Email: lcerulo@unisannio.it Research Interests: Driven by cross-disciplinary innovation, his work bridges machine learning and bioinformatics. Key areas include reverse engineering gene regulatory networks using deep graph neural networks, predicting ligand-receptor interactions via graph autoencoders, and exploring non-coding RNA functions with self-supervised learning. His team also investigates drug synergy prediction and AI applications in biological data. Recent Publications: His 15 most recent works span 2022 to 2007, emphasizing machine learning in gene network reconstruction (Chang et al., 2020), deep learning for RNA classification (Noviello et al., 2020), and foundational contributions to software engineering tools (Canfora et al., 2007-2008). Research Infrastructure: The group utilizes high-performance systems including a Superdome Flex server (224 CPU cores, 1.5 TB RAM) and a GPU cluster at Biogem Institute. Advising and Grants: He mentors PhD students in bioinformatics and leads projects such as GENOMA E SALUTE (EU-funded) and Non-Coding RNA Explosion (Ministry of Research grant). Collaborations span institutions like the University of Naples and Biogem.

• Corporate Finance
• International Corporate Finance
• Corporate Governance
• +۳ مورد دیگر

Dr. Ohaness Paskelian is a Professor in the Department of Finance, Information Systems, Economics, and Risk Management at the University of Houston Downtown (UHD), where he has taught since 2008. He holds a PhD in Financial Economics from the University of New Orleans, an MBA in Finance from Nicholls State University, and a Bachelor of Engineering in Computer Engineering from the American University of Beirut. His teaching focuses on Corporate Finance, Financial Markets, Investments, Derivatives, and Quantitative Financial Analysis . Dr. Paskelian's research explores Corporate Finance, International Corporate Governance, Financial Institutions, Investments, and Asset Pricing , with extensive work on Chinese markets, regulatory impacts, and behavioral finance. His publications analyze topics including ownership structures, dividend policies, banking performance, and market volatility using advanced methodologies like neural networks and regime-switching models. He holds the CFA (Chartered Financial Analyst) certification and co-authored the textbook Financial Markets & Institutions (14th Edition) . Dr. Paskelian actively serves on university committees including Grievance, Rank and Tenure, and Curriculum Development.

Melina Freitagمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +۶ مورد دیگر

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Paul J. Huمشاهده پروفایل

استاد

• Information Technology in Healthcare
• Artificial Intelligence
• Data-Driven Business Analytics
• +۴ مورد دیگر

Paul J. Hu is the E.R. Dumke Jr. Presidential Endowed Chair in Business and Assistant Dean for Programs in Asia at the University of Utah's David Eccles School of Business, where he serves in the Department of Operations and Information Systems. He holds a PhD in Management Information Systems from the University of Arizona (1998), following a Master's degree in the same field (1992) and a Bachelor's in Chemical Engineering from National Taiwan University of Science and Technology (1987). Dr. Hu's research spans information technology empowerment in health care, artificial intelligence, data-driven business analytics, technology implementation and management in organizations, digital transformation, and information technology-enriched learning and innovations. His work demonstrates exceptional impact, with multiple papers ranking among the most cited in information systems literature, including three papers in the top 10 most cited health IT papers published between 1985-2011. His recent publications reveal a strong focus on applying deep learning and blockchain technologies to healthcare challenges, energy systems, and social media analysis. This research trajectory shows increasing sophistication in handling complex data environments while addressing critical societal challenges in healthcare and infrastructure management. #9 most cited paper in 50-year history of Decision Sciences (2020) INFORMS Information Systems Society Design Science Award (2016) Faculty Research Award, David Eccles School of Business (2016) Among top 50 most productive IS authors globally (1999-2003) Multiple recognition as among most productive/published IS researchers Dr. Hu has secured significant research funding including an NSF CIF21 DIBBs grant ($1.5M) and contributed to the National Center of Excellence for Infectious Disease Informatics. He serves in editorial capacities for prestigious journals including Journal of the AIS and ACM Transactions on Management Information Systems, demonstrating his leadership in the academic community.

Shengyao Zhuangمشاهده پروفایل

مدرس

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Shengyao Zhuang is a Postdoctoral Research Fellow at CSIRO's Australian e-Health Research Centre in Brisbane and an Adjunct Lecturer at The University of Queensland (UQ) since 2023. His research focuses on large language model-based search systems for medical domains and Natural Language Processing in information retrieval. Education : PhD in Computer Science (UQ, 2023), Master of Information Technology (UQ, 2018), Bachelor of Electrical Engineering (Chongqing University of Science and Technology, 2016) Shengyao's work spans information retrieval , NLP , and zero-shot ranking with large language models. He develops neural rankers , efficient validation toolkits (Asyncval), and hybrid retrieval systems that combine dense and sparse representations. His contributions include contextualized exact term matching (TILDEv2) and counterfactual bias mitigation techniques for implicit feedback. Recent publications analyze LLM-based stemming , Vec2Text threats to retrieval systems, and green computing impacts of water consumption. His methods improve cross-lingual retrieval and federated learning efficiency while maintaining sub-100ms latency for CPU environments. Scientific Awards : SIGIR 2021 Top10 authors (unofficial) Rank #3 2022 HUAWEI DIGIX GLOBAL AI CHALLENGE Champion As a Tutor at UQ (2019-2022), he taught INFS7410: Information Retrieval and Web Search . He serves on program committees for major conferences including SIGIR, TheWebConf, and ECIR.

C. Mónica Capraمشاهده پروفایل

استاد

• Experimental Economics
• Behavioral Economics
• Neuroeconomics
• +۳ مورد دیگر

C. Mónica Capra is a Professor of Economic Sciences at Claremont Graduate University and Chair of its Department of Economics (2015-2016). Specializing in experimental and behavioral economics, she integrates psychology and neuroscience principles to analyze economic decision-making. Her research explores how personality, mood, emotions, and bounded rationality influence economic behavior, with particular focus on prosocial actions and volunteering motivations. BA from Franklin & Marshall College (1992) PhD in Economics from University of Virginia (1999) Her interdisciplinary work at the School of Politics and Economics examines neural mechanisms of decision-making through collaborations with neuroscientists and data scientists. Key trends in her recent publications (2018-2023) include: Application of behavioral economics to volunteering decisions and altruistic self-concept Integration of neuroeconomic methods in studying personality-driven probability distortions Development of online experimental platforms using Amazon Mechanical Turk Analysis of prosocial incentives in gig economy performance Exploration of AI-assisted research methodologies like GPT applications Investigation of genetic and hormonal influences on economic behavior Scientific recognitions include: Phi Beta Kappa Excellence in Teaching Award (Emory University, 2009) Fantastic Four recognition for young researchers (Emory Wheel Magazine, 2007) Top 2% publishing liberal arts assistant professors (1991-2001 ranking) Multiple invited lectures at international institutions and conferences As a BESTLab faculty member, she leads experiments combining economics with biological constraints. Her teaching portfolio spans graduate courses on Experimental Economics , Behavioral Economics , and Neuroeconomics , with notable course development in Grant Writing and Economic Games .

Yannis Velegrakisمشاهده پروفایل

استاد

• Big Data Management & Analytics
• Knowledge Discovery
• Graph Management
• +۱۵ مورد دیگر

Yannis Velegrakis is a Professor in the Department of Information and Computing Sciences at Utrecht University, where he holds the chair of Very Large Data Management. He leads the Data Intensive Systems research group and is the program leader for the Master’s in Data Science . He is also a part-time faculty member at the University of Trento and a Principal Investigator at the Archimedes AI and Data Science hub, Athena Research Center . Education: PhD in Computer Science, University of Toronto MSc in Computer Science, University of Crete BSc in Computer Science, University of Crete His research focuses on Big Data Management & Analytics , Knowledge Discovery , Graph Management , Data Integration , and Data Quality . His work combines theoretical rigor with practical system development, emphasizing human-centered approaches to data exploration and analysis. He has pioneered methods in example-based search, entity resolution, and dynamic graph analysis. His recent publications reflect a strong trend in knowledge graph exploration , data quality assessment , and dynamic network mining . These works often integrate machine learning with database systems to support interactive and intelligent data discovery. His research bridges foundational data management with applied AI, particularly in the context of real-world data challenges. Scientific Leadership and Awards: PC Chair, ICDE 2024 General Chair, VLDB 2013 PC Chair, EDBT 2021 He has advised numerous researchers and contributed to major projects in data integration and semantic search. His secondary activities include visiting positions at IBM Almaden , AT&T Research Labs , UC Santa Cruz , and University of Paris-Saclay . He has secured funding through collaborative research initiatives and has served on program committees of top international conferences. He leads the Data Intensive Systems group, which develops innovative tools for managing and exploring large-scale, heterogeneous datasets. The group emphasizes systems that support intuitive, example-driven interaction with complex data, aligning with the principles of human-centered AI.

• Computational topology
• Topological data analysis
• Machine learning
• +۷ مورد دیگر

Francesco Vaccarino is an Associate Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Polytechnic University of Turin and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is actively involved in research, teaching, and doctoral supervision across multiple programs, including Mathematical Sciences, Applied Mathematics, and Artificial Intelligence. Research Interests: Computational topology Topological data analysis (TDA) Machine learning and geometric learning Algebraic and geometric methods in data science Complex networks and higher-order interactions His recent publications reflect a strong interdisciplinary focus, combining topology with machine learning, neuroscience, and physics. Key themes include persistent homology, simplicial network models, and topological visualization of complex systems. He has made significant contributions to understanding brain networks, scientific collaboration patterns, and phase transitions through topological methods. Scientific Awards: The Best Science Visualizations of the Year (Wired.com, 2014) Advising and Grants: He supervises multiple PhD students in Mathematical Sciences and Artificial Intelligence. He has led research projects funded by competitive tenders (e.g., XTERA-FOOD) and commercial contracts (e.g., with FPT Industrial SPA and Stellantis), focusing on AI, digital transformation, and industrial data analysis. He has served as Scientific Director for corporate training programs in AI and data science. Conferences and Leadership: He has been Program Chair for several international events including AMTCS7, TOPDRIM, Toponets15, and ECCS 2013, promoting the application of topology in data science. Labs and Affiliations: He is a core member of SmartData@PoliTO, a leading data science laboratory fostering interdisciplinary research in big data and AI.

Vladimir Makarenkovمشاهده پروفایل

استاد

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Roles and Affiliations: Full Professor of Computer Science at Université du Québec à Montréal (UQAM), Director of the DESS in Bioinformatics program. Regular member of LaCIM and affiliate member of Mila (Quebec AI Institute). Served as Assistant Professor (2001–2006) and Associate Professor (2006–2011) before becoming Full Professor in 2011. Education: B.Sc./M.Sc. in Applied Mathematics and Computer Science, Moscow State University (1987–1992) DEA in Computer Science, University Paris V (1993–1994) Ph.D. in Computer Science and Mathematics, École des Hautes Études en Sciences Sociales (EHESS), Paris (1994–1997) Research Interests: Focuses on machine learning applications in bioinformatics, including clustering algorithms, phylogenetic analysis, and genomics. Specializes in CRISPR/Cas9 off-target prediction, SARS-CoV-2 origin analysis, and high-throughput screening (HTS) data correction. Develops tools like T-REX web server for phylogenetic analysis and DUNEScan for skin cancer uncertainty estimation. Integrates deep learning and reinforcement learning for clustering and medical diagnostics. Publications and Grants: Over 100 peer-reviewed articles spanning bioinformatics, AI, and computational biology. Key grants include projects on HTS data correction, phylogenetic networks, and machine learning for biomedical applications. Collaborates with institutions like Mila and Université de Montréal. Labs and Teams: Leads research teams at UQAM’s Department of Computer Science, focusing on interdisciplinary projects combining AI and life sciences. Maintains collaborations with international groups in computational biology and data science.