جست‌وجوی استاد و پژوهشگر

Nolwenn Le Meurمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Biology
• Biostatistics
• +۶ مورد دیگر

Nolwenn Le Meur is an Associate Professor at the École des Hautes Études en Santé Publique (EHESP) and a researcher at UMR CNRS 6051 ARENES and INSERM U1309. She leads the Département Méthodes quantitatives en santé publique (METIS) as Deputy Director since 2022. Her expertise spans bioinformatics, computational biology, and public health analytics, with a focus on healthcare system optimization and health disparities. Education includes a HDR (2023) from Université de Rennes, a PhD in Bioinformatics (2005), and engineering degrees from Ecole Supérieure de Microbiologie (1997–2000) and Université de Rennes I (2000–2001). Research interests include data-driven approaches for analyzing healthcare trajectories, spatial accessibility to care, and pandemic impact assessments. She coordinates projects like DISPARITE (EHESP) and PRINCEPS (USPC), focusing on healthcare equity and quality. Her work integrates administrative health databases and semantic web technologies for epidemiological insights. Teaching includes Biostatistics, R programming, and public health management at the Master’s level. She advises on medico-administrative data systems and has contributed to over 50 peer-reviewed publications, emphasizing methodological innovations in health analytics.

Naphtali David Risheمشاهده پروفایل

استاد

• Database Systems
• Geospatial Information Systems
• High-Performance Computing
• +۱۲ مورد دیگر

Naphtali David Rishe is a Full Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU), where he holds the inaugural title of Outstanding University Professor. He is the Founding Director of the NSF Industry/University Cooperative Research Center for Advanced Knowledge Enablement and leads the FIU High Performance Database Research Center and the Geospatial Laboratory. His work spans database systems, geospatial technologies, AI, and health informatics. Dr. Rishe earned his Ph.D. in Computer Science from Tel Aviv University (1981–1984), an M.Sc. from the Israel Institute of Technology (Technion) (1979–1981), and a B.Sc. Summa Cum Laude from the same institution (1975–1979). He has held academic positions at the University of California, Santa Barbara (Visiting Assistant Professor, 1984–1987) and Tel Aviv University (Instructor, 1981–1984). His research focuses on high-performance databases, semantic data systems, geospatial analytics, wearable sensors, and AI applications in health and transportation. He is the architect of the TerraFly project, widely recognized by media such as the New York Times and Nature. His work integrates advanced machine learning with real-world systems for environmental monitoring, urban planning, and threat detection. His recent publications reflect a strong trend in geospatial AI, including terrain mapping using LIDAR, anomaly detection in video, real estate trend modeling, and generative models for map synthesis. These works demonstrate a fusion of deep learning, spatial reasoning, and practical system design. Fellow, National Academy of Inventors (2021) Outstanding University Professor, FIU (2000) IBM Global University Program Academic Award (2021) First Prize, Miami Herald Business Plan Competition (2002) Cover Feature, NSF Breakthroughs Compendium (2014, 2016) Cover Feature, FIU Magazine (Fall 2016) Dr. Rishe has secured over $60 million in research funding as Principal Investigator from agencies including NSF, NASA, IBM, DoI, USGS, and DOT. He is currently leading a $2.6M NSF grant for a Geospatial System for Multimodal Environmental Observations (2020–2025) and co-PI on a $3.3M NSF grant for Alzheimer’s Disease research (2019–2025). He mentors numerous students and has graduated top performers recognized by FIU. He leads multiple research centers and serves on editorial boards and NSF panels. His labs include the High Performance Database Research Center and the Geospatial Laboratory, driving innovation in data systems and spatial intelligence.

Ruibang Luoمشاهده پروفایل

دانشیار

• Bioinformatics
• Clinical Informatics
• Genomics
• +۲ مورد دیگر

Ruibang Luo is an Associate Professor in the Department of Computer Science at the School of Computing and Data Science, University of Hong Kong, and serves as Assistant Director (Learning Experience & Student Enrichment). His research spans bioinformatics algorithms and clinical informatics, with over 80 publications including ten highly cited papers. \n His educational background includes: \n \n PhD in Bioinformatics from the University of Hong Kong (2010-2015) under Dr. Tak-Wah Lam \n Postdoctoral training at Johns Hopkins University (2016-2017) with Drs. Steven Salzberg and Michael Schatz \n \n Luo's research centers on bioinformatics and clinical informatics , developing algorithms for genomic data analysis. Key areas include variant calling, metagenomics assembly, pan-genome construction, and AI applications in precision medicine. His work bridges computational methods with clinical solutions for genomic surveillance and disease detection. \n Analysis of his recent publications (2021-2022) reveals strong focus on population genomics (Hong Kong Cantonese exomes, Chinese pan-genome), nanopore sequencing for epigenomics/transcriptomics, and AI-driven gene-disease relation extraction. Earlier work established foundational tools like MEGAHIT for metagenome assembly and SOAPdenovo for genome alignment. \n Luo has received significant recognition: \n \n Top 1% Scholars Worldwide by Clarivate Analytics (2019-2023) \n Baidu Research: Worldwide Top 150 Chinese Young Scholars in AI \n MIT Technology Review: Top 10 Innovators Under 35 Asia Pacific (2019) \n Forbes: 30 Under 30 Asia in Healthcare and Science (2017) \n \n As principal investigator, Luo secured grants including RGC GRF for cancer mutation detection using single-molecule sequencing and industrial collaboration with Oxford Nanopore Technologies. He contributed as Co-PI to cardiovascular risk prediction and automated karyotype analysis projects. His group actively translates bioinformatics algorithms into clinical applications through partnerships with medical institutions. \n Luo leads a research team within HKU's Department of Computer Science focused on next-generation bioinformatics tools for clinical genomics, with ongoing work expanding Chinese pan-genome resources and AI diagnostic systems.

Sebastian Manethمشاهده پروفایل

استاد

• Databases
• Automata Theory and Applications
• Data Compression
• +۳ مورد دیگر

Sebastian Maneth is a Heisenberg Professor at the Universität Bremen , affiliated with the Faculty of Mathematics and Computer Science and the Department of Computer Science . His research focuses on Databases , Automata Theory and Applications , and Data Compression , with significant contributions to tree transducers and XML processing. Editor of Theory of Computing Systems (TOCS) Associate Editor for Frontiers in Computer Science Editor of Algorithms His recent work explores the boundaries of tree transducers , regular expressions , and grammar-based compression , with decidable properties in formal language theory. He organizes major conferences like the Dagstuhl Seminar on Regular Expressions and serves on numerous program committees (CIAA, ICALP, FoIKS, etc.). Current projects include FO-Query Enumeration over compressed data, shape-preserving tree transducers , and user identification via eye-tracking data . His editorial and organizational roles highlight leadership in theoretical computer science communities.

Zilong Liuمشاهده پروفایل

استادیار بالینی

• Fintech
• Credit Risk Modeling
• Machine Learning Applications
• +۵ مورد دیگر

Zilong Liu is a Clinical Assistant Professor of Business Administration at the Gies College of Business, University of Illinois Urbana-Champaign, and a Deloitte Scholar. He holds a Ph.D. in Business Administration with a minor in Statistics from Kent State University. His research focuses on fintech, credit risk modeling, and financial innovation, with publications in journals like the Journal of Risk and Journal of Risk Model Validation . He has over eight years of industry experience in risk management at institutions like Discover Financial Services and KeyBank, specializing in credit risk modeling, machine learning applications, and regulatory compliance. Dr. Liu's teaching expertise spans Business Analytics, Big Data Infrastructure, and Database Design, consistently earning high student ratings. He has taught at both the University of Illinois and Kent State University. His honors include the Best Paper Award in Derivatives (2015) from the Eastern Finance Association and the Kauffman Promising Paper Award (2015). His industry work includes leading model validation teams, developing operational risk frameworks, and applying NLP tools for risk analysis. He emphasizes practical problem-solving in his courses and bridges academic research with real-world applications.

• Data Mining
• High-Performance Computing
• Bioinformatics
• +۷ مورد دیگر

Zina Ben Miled is an Associate Professor of Electrical and Computer Engineering at IUPUI and holds a courtesy appointment as Associate Professor of Chemical Engineering at Purdue University's Davidson School of Chemical Engineering. She has been affiliated with Purdue since 1997, holding degrees in Computer Engineering from Purdue (Ph.D., 1997; M.S., 1990) and Oregon State University (B.S., 1988). Her research focuses on data mining, high-performance computing, bioinformatics, and cheminformatics. Notable contributions include developing integrated systems for biological and chemical data (BACIIS), blockchain-based supply chain solutions, and machine learning models for water demand prediction and health surveillance. She has also pioneered educational models for digital system design courses. Her awards include the NSF Career Award (2002), Abraham Max Outstanding Research Award (2001), and Violet Haas Outstanding Academic Women Award (1996). She is an IEEE Senior Member and has led numerous industry-funded projects with Eli Lilly and the Office of Naval Research. Grants: Over 10 industry and federal grants as PI/Co-PI, including NSF, ONR, and Eli Lilly initiatives. Labs/Teams: Leads interdisciplinary teams in Purdue’s Chemical Engineering and IUPUI’s ECE departments, focusing on database systems, blockchain, and health informatics.

Mo Liمشاهده پروفایل

استاد

• Condensed Matter
• Photonics
• Quantum Information
• +۵ مورد دیگر

Mo Li is a Professor jointly appointed in the Department of Electrical & Computer Engineering and the Department of Physics at the University of Washington (UW), part of the College of Arts & Sciences. He joined UW in 2018 after serving as faculty at the University of Minnesota, Twin Cities (2010–2018) and postdoctoral research at Yale University (2007–2010). His expertise spans condensed matter physics, photonics, and quantum information, with a focus on integrated photonic systems, optoelectronics, and quantum photonics. Education: Ph.D., Applied Physics, California Institute of Technology, 2007 M.S., Physics, University of California, San Diego, 2003 B.S., Physics, University of Science and Technology of China, 2001 Research Interests: Dr. Li leads the Laboratory of Photonic Devices, investigating hybrid nanoscale devices, optoelectronic materials, and quantum phenomena. His work integrates experimental and theoretical approaches to explore photon-electron-phonon interactions, with applications in photonic circuits, quantum communication, and biomedical sensing. Key areas include 2D materials, spintronics, and MEMS/NEMS systems. Publications & Patents: Over 80 peer-reviewed papers and 6 U.S. patents. Recent research emphasizes graph-based algorithms (e.g., Color framework), enterprise data integration (BEAVER benchmark), and causal relational learning. Lab & Team: The Laboratory of Photonic Systems at UW develops cutting-edge photonic technologies. Openings exist for postdocs (photonic interconnection projects) and graduate students in ECE/Physics programs.

Donald Siegelمشاهده پروفایل

استاد

• Advanced Materials Science and Engineering
• Clean Energy Technology
• Computational Engineering
• +۷ مورد دیگر

Donald Siegel is Professor and Chair of the Walker Department of Mechanical Engineering at the University of Texas at Austin. He holds the Temple Foundation Endowed Professorship and the Cockrell Family Chair for Departmental Leadership. As a computational materials scientist, his research focuses on energy storage materials, lightweight alloys, and solid-state battery technologies. He is a Core Faculty Member in the Texas Materials Institute and the Oden Institute for Computational Engineering and Sciences. Previously, he served at the University of Michigan (2009–2021) and Ford Research (prior to 2009). His work has led to over 100 publications, patents, and prestigious awards like the NSF Career Award and U.S. Department of Energy Secretary’s Achievement Award. Education: Ph.D. from University of Illinois Urbana-Champaign, postdoctoral training at Sandia National Labs and U.S. Naval Research Lab. He was a VELUX Visiting Professor at Technical University of Denmark (2015–2016). Research interests include hydrogen storage in MOFs, thermal energy storage via salt hydrates, and solid electrolyte design for lithium batteries. He actively advises federal energy initiatives, including co-chairing FreedomCAR Hydrogen Storage Technical Team and serving on JCESR Directorate. Scientific contributions span computational screening of materials, interfacial stability analysis, and machine learning applications. His grants and collaborations emphasize translating materials science into real-world energy storage solutions.

Samir Khullerمشاهده پروفایل

استاد

• Algorithm Design
• Graph Algorithms
• Discrete Optimization
• +۳ مورد دیگر

Samir Khuller is the Peter and Adrienne Barris Chair of Computer Science and a Professor in the McCormick School of Engineering at Northwestern University. His research focuses on algorithm design, particularly graph algorithms, discrete optimization, and approximation algorithms. He has authored over 200 papers and serves on editorial boards for journals like Algorithmica and SIGACT News. Khuller holds a B.Tech. from IIT Kanpur and M.S./Ph.D. from Cornell University. He has held leadership roles including Chair of SIGACT (2018–2021) and served on program committees for major conferences like SODA and STOC. Awards include the CRA Undergraduate Mentoring Award (2020), EATCS Fellowship (2021), and ACM Fellowship (2022). His work spans data management algorithms, scheduling, and interdisciplinary applications in computational biology. Khuller teaches advanced algorithms and has advised numerous students. He co-organized workshops on topics like network design and scheduling. His research addresses practical challenges in data storage, distributed systems, and energy-efficient scheduling.

Wang Xiaoمشاهده پروفایل

استادیار

• Computer Security
• Privacy
• Cryptography
• +۷ مورد دیگر

Xiao Wang is an Assistant Professor of Computer Science at Northwestern University, specializing in applied cryptography with a focus on privacy-preserving systems, secure multi-party computation, and zero-knowledge proofs. He holds a PhD from the University of Maryland and completed postdoctoral fellowships at MIT and Boston University. His research bridges theoretical cryptography and practical applications in machine learning, databases, health informatics, and legal domains. Education: Postdoctoral Fellowship: MIT & Boston University (2018–2020) PhD in Computer Science: University of Maryland (2015–2019) B.E. in Computer Science: Hong Kong University of Science and Technology (2011–2015) Research Interests: Secure computation frameworks (MPC, ZK) Privacy-preserving machine learning Cryptographic protocol design Applications in healthcare and legal systems Publications Trends: Recent work emphasizes scalable MPC protocols (e.g., BitGC), privacy in machine learning (Confidential-DPproof), and cross-domain applications like secure reporting systems. Over 40 peer-reviewed papers in top venues (CCS, S&P, Eurocrypt, USENIX Security) reflect his focus on both theoretical rigor and practical usability. Awards: ACM CCS Best Paper (2017, 2021) CSLAW Best Paper (2025) ICLR Spotlight Award (2024) Multiple industry-recognized competitions (iDASH 2015) Advising & Grants: Supervises a dynamic PhD/postdoc team (10+ active researchers). Leads NSF CAREER project on MPC systematization. Collaborates with Chicago-area hospitals and VA on secure health data analytics. Co-develops tools like ObliVM and ZKSMT . Labs/Teams: Core member of Northwestern's Cryptography and Privacy Engineering Group . Co-leads the MPC Tall Order initiative for standardized MPC frameworks. Partner in NIST threshold cryptography projects.

Sheng Wangمشاهده پروفایل

عضو هیئت علمی

• Remote Sensing
• Precision Agriculture
• Environmental Sustainability
• +۳ مورد دیگر

Sheng Wang is a Research Assistant Professor at the Department of Natural Resources and Environmental Sciences (NRES) and a Research Scientist at the Agroecosystem Sustainability Center (ASC) at the University of Illinois Urbana-Champaign (UIUC). He leads the airborne sensing research team at ASC in collaboration with Dr. Kaiyu Guan. Dr. Wang holds a PhD from the Technical University of Denmark and has been at UIUC since February 2019. His research focuses on developing cross-scale sensing technologies integrating remote sensing, machine learning, and process-based models to quantify crop traits, soil properties, and farming practices. His work aims to advance understanding of ecohydrological processes in agroecosystems and provide actionable data for sustainable management. Key areas include precision agriculture, environmental sustainability, and ecohydrology. Dr. Wang has published over 30 papers in top journals and secured federal, state, and university grants as PI or Co-PI. His mission emphasizes leveraging airborne, satellite, and ground-based sensing to improve food security and environmental sustainability. Current projects involve crop residue cover detection, soil carbon quantification, and nitrogen management optimization using machine learning and remote sensing.

Xin Chenمشاهده پروفایل

استادیار مدعو

• Computer Vision
• Machine Learning
• Location-based Services (LBS)
• +۱۱ مورد دیگر

Xin Chen holds the title of Adjunct Assistant Professor at Northwestern University and Adjunct Professor and PhD Advisor at Illinois Institute of Technology. He leads the Central United States team in Amazon’s Machine Learning Solutions Lab and previously served as Director of Engineering at HERE Technologies, pioneering automated map creation using computer vision and machine learning. He earned a Ph.D. in Computer Science and Engineering from the University of Notre Dame in 2006. Research Interests: Xin Chen’s work spans Computer Vision, Machine Learning, and Geospatial Technologies. He focuses on applications such as automated map creation, LIDAR data analysis, biometric systems, and AI-driven solutions for transportation and navigation. His research integrates advanced image processing techniques with real-world challenges like ADAS (Advanced Driver Assistance Systems) and geospatial visualization. Key Achievements: Recipient of the 2010 and 2011 IMPACT Awards at NAVTEQ, multiple HERE Hack Week awards, and the 2015 HERE Berkeley Hackathon top prize. He has served on NSF panels evaluating multi-million-dollar AI projects and holds over 50 U.S. patents in geospatial data analysis and computer vision. Teaching & Advising: Teaches courses like “Geospatial Vision and Visualization” and “Biometrics” at Northwestern and IIT. Advises PhD students, including Xi Zhang at IIT. His academic leadership includes program chair roles at CVPR and ACM SIGSPATIAL conferences. Labs & Collaborations: Collaborates with institutions like Carnegie Mellon, Columbia, and Notre Dame on geospatial and computer vision projects. His work bridges academia and industry, emphasizing practical AI solutions for enterprise clients through Amazon and HERE technologies.

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۳ مورد دیگر

Lei Wang is a Professor at the School of Computing and Information Technology, University of Wollongong, Australia, and Founding Director of the Centre for Artificial Intelligence. He holds a PhD from Nanyang Technological University (2004) and B.Eng/M.Eng from Southeast University (1996/1999). His research focuses on machine learning, pattern recognition, and computer vision, with applications in medical imaging and AI. He leads the VILA group, emphasizing visual information learning and analysis. Key contributions include over 200 peer-reviewed publications, with citations exceeding 14,600 (Google Scholar). Notable grants include ARC Discovery Project (2021) and NHMRC Ideas Grant (2020). He serves as Action Editor for Transactions on Machine Learning Research, Associate Editor for ACM Transactions on Intelligent Systems and Technology, and holds leadership roles in top conferences like CVPR, NeurIPS, and ICCV. His work spans collaborative perception, medical report generation, and few-shot learning.

Anton Feenstraمشاهده پروفایل

دانشیار

• Structural Bioinformatics
• Protein Structure Prediction
• Computational Biology
• +۳ مورد دیگر

Anton Feenstra is an Associate Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Bioinformatics department, as well as AIMMS and Integrative Bioinformatics. He holds a PhD (dr.) and an engineering degree (ir.). His research focuses on structural bioinformatics, protein structure prediction, computational biology, and bioinformatics algorithms. Key interests include protein-protein interactions, molecular dynamics, and knowledge graph applications in health and microbiota studies. Feenstra leads projects such as ELIXIR-NL (Digital Research Infrastructure) and has contributed to initiatives like BIOEXE and ENFIN. He teaches courses including Algorithms in Sequence Analysis and Fundamentals of Bioinformatics. His work spans over 86 publications, with recent contributions on protein interface prediction (PIPENN-EMB), microbiota-gut-brain axis analysis, and structural bioinformatics tools. Notable achievements include developing the PRALINE alignment toolkit and advancing machine learning methods for protein function prediction. Collaborations include work on Mycobacterium tuberculosis and SARS-CoV-2 protein analysis. His research aligns with UN Sustainable Development Goals, particularly in health and innovation.

Eliezer Upfalمشاهده پروفایل

استاد

• Big Data
• Computational Statistics
• Data Mining
• +۵ مورد دیگر

Eliezer Upfal is the Rush C. Hawkins University Professor of Computer Science at Brown University, where he has served as Department Chair (2002–2007) and has been a faculty member since 1998. He holds a PhD (1983) and BS (1978) in Computer Science/Mathematics from the Hebrew University, and an MS (1980) from the Weizmann Institute. His research focuses on randomized algorithms, probabilistic analysis, and computational statistics, with applications in bioinformatics, genomics, and distributed systems. He has authored over 150 publications, including the textbook Probability and Computing (Cambridge University Press), and holds 13 patents. Notable awards include ACM Fellow (2005), IEEE Fellow (2002), and the Levinson Prize in Mathematical Sciences (1994). His funded research spans NSF grants on stochastic optimization and big data analytics, NIH grants for genomic studies, and industry collaborations with Goldman Sachs and Yahoo. Teaching includes advanced courses on probability, data science, and algorithmic methods. His editorial roles span major journals like the Journal of the ACM and SIAM. Research interests bridge theoretical computer science and practical applications in genomics, networks, and computational biology.