جست‌وجوی استاد و پژوهشگر

Feng Guمشاهده پروفایل

استاد

• Modeling and Simulation
• Complex Systems
• High Performance Computing
• +۴ مورد دیگر

Feng Gu is a Professor of Computer Science at The College of Staten Island, CUNY, and a doctoral faculty member at The Graduate Center, CUNY. He holds a BS in Mechanical Engineering from China University of Mining and Technology, MS in Information Systems from Beijing Institute of Machinery, and MS/PhD in Computer Science from Georgia State University. His research focuses on Modeling and Simulation , Complex Systems , High Performance Computing , and Bioinformatics . Notable contributions include work on wildfire spread simulation, particle filters, and machine learning applications in healthcare and genomics. Recent grants include a $563,411 NSF grant for crime analysis and a $40,000 CUNY-IRG grant for obesity modeling. He has received awards such as the TMS/DEVS 2018 Best Paper Award and the 2018 Emerald Literati Award. His work bridges computational methods with real-world challenges in environmental science, cybersecurity, and social policy. Professional activities include serving as a reviewer for journals like IEEE Transactions and conferences like Winter Simulation Conference . He has organized sessions at events such as the International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

• Machine Learning
• Artificial Intelligence
• Blockchain
• +۱ مورد دیگر

Dr. Ismail Alarab serves as a Lecturer in Computer Science at Bournemouth University, teaching core subjects including Artificial Intelligence, Databases, Big Data, and Programming. His research focuses on applied artificial intelligence with specialized expertise in blockchain technology and real-world machine learning applications, culminating in a December 2022 PhD from Bournemouth University. PhD in Novel Machine Learning Models Based on Uncertainty Estimation and Sequential Predictions for Blockchain Networks, Bournemouth University, 2022 MSc in Signal and Image Processing: Applications and Methods, Université Grenoble Alpes, France Diploma in Telecommunication Engineering (equivalent to MEng), Lebanese University, Lebanon Alarab's research centers on machine learning for blockchain systems, particularly uncertainty estimation in neural networks and graph-based models for cryptocurrency transaction analysis. His work bridges theoretical AI frameworks with practical financial security applications, emphasizing adversarial robustness and sequential prediction capabilities in distributed ledgers. Key methodologies include graph convolutional networks and LSTM architectures adapted for anti-money laundering systems. Analysis of his 14 publications (2020-2024) reveals consistent focus on machine learning applications in blockchain environments, with evolving sophistication from foundational transaction analysis (2020) to advanced uncertainty-aware sequential prediction models (2024). Dominant themes include adversarial attack methodologies for graph neural networks, thermal comfort optimization extensions, and specialized techniques for imbalanced financial datasets. No major scientific awards are documented, though professional development includes Leadership and Management Training (AshorneHill, 2023), Deploying Machine Learning Models in Production certification (DeepLearning.AI, 2023), and Microsoft Azure Fundamentals (AZ-900, 2023). Current advising activities and research grants are not specified in available materials. Dr. Alarab serves as reviewer for IEEE Transactions on Knowledge and Data Engineering and Elsevier's Neural Networks journal, indicating active peer engagement within academic communities. His research operates within Bournemouth University's Department of Computer Science, primarily collaborating with Dr. Prakoonwit and Dr. Nacer on blockchain-AI integration projects. While no dedicated lab is mentioned, his work demonstrates strong cross-institutional partnerships in financial technology security research.

Leticia Arco Garciaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Complex Networks
• Deep Learning
• +۵ مورد دیگر

Leticia Arco Garcia is a postdoctoral researcher at Vrije Universiteit Brussel working in the Department of Informatics and Applied Informatics with affiliations at Federated Labs AI and Robotics. Her research spans computational linguistics, complex network analysis, and more recently, interdisciplinary applications in archaeobotany. Her research interests focus on Natural Language Processing , Complex Networks , and Deep Learning methodologies. Early work centered on community detection in complex networks and customer behavior analysis, while recent publications demonstrate expansion into archaeobotanical applications using morphometric analysis of phytoliths. She employs advanced computational techniques including LSTM networks, case-based reasoning, and multi-objective optimization across diverse domains from smart power grids to electronic money systems. Analysis of her 15 most recent publications reveals an interdisciplinary trajectory moving from pure computer science applications toward biological/archaeological domains. The earliest works focus on network construction and community detection, while more recent papers apply similar analytical frameworks to botanical specimens, indicating methodological transfer between fields. Her work consistently applies computational rigor to complex data structures across diverse application areas. Her scientific contributions include: Development of novel network construction methodologies for large purchasing collections Advancements in overlapping community detection using rough clustering Application of deep learning to sentiment analysis and customer behavior prediction Recent innovative work applying morphometric analysis to archaeobotanical specimens Dr. Garcia actively presents her research at international conferences including INTERSPEECH and specialized meetings on phytolith research. Her collaborative network spans multiple institutions, with co-authorship on interdisciplinary projects connecting computer science with archaeology and plant science. While specific grant information isn't detailed in the available text, her consistent publication output across multiple domains suggests successful research funding.

Feng Guمشاهده پروفایل

استاد

• Modeling and Simulation
• Complex Systems
• High Performance Computing
• +۵ مورد دیگر

Dr. Feng Gu is a Professor of Computer Science at the College of Staten Island, The City University of New York (CUNY). He holds degrees from Georgia State University (Ph.D., M.S.), Beijing Institute of Machinery (M.S.), and China University of Mining and Technology (B.S.). His research focuses on modeling and simulation, complex systems, and high-performance computing, supported by NSF and NIJ grants. Key achievements include pioneering work in malware detection, adaptive particle filtering, and genomic data analysis. He previously served as Department Chair at Voorhees College (2012–2013). **Education:** Ph.D. and M.S., Computer Science, Georgia State University M.S., Information Systems, Beijing Institute of Machinery B.S., Mechanical Engineering, China University of Mining and Technology **Research Interests:** High-performance computing for large-scale systems Data assimilation in wildfire simulation and genomic analysis Machine learning applications in cybersecurity and healthcare Parallel/distributed algorithms for particle filters **Awards:** Best Paper Award (TMS/DEVS 2018) Outstanding Paper in 2018 Emerald Literati Awards Runner-up for ANSS 2015 Best Paper His work bridges computational methods with real-world challenges in environmental modeling, healthcare, and security. Active in interdisciplinary collaborations, he has published extensively in top conferences like IEEE, ACM, and Springer journals.

Arnold Meijsterمشاهده پروفایل

مدرس

• Computer Science
• Parallel Computing
• Algorithm Design
• +۲ مورد دیگر

Arnold Meijster is a Lecturer in the Faculty of Science and Engineering at the University of Groningen, specializing in Computer Science with focus areas in Parallel Computing, Programming, Signal Processing, and Algorithm Design. He teaches courses including Imperative Programming, Functional Programming, Signals and Systems, Compiler Construction, and Artificial Intelligence. His research spans parallel computing, image processing algorithms, and artificial intelligence, with particular expertise in developing efficient algorithms for morphological image processing and watershed transforms. His work frequently involves parallel algorithm design for shared-memory systems applied to large-scale image data. Publications from 1996 to 2018 demonstrate consistent focus on image processing and parallel computing, with recent work on hybrid shared-memory algorithms and machine learning applications. Early contributions centered on watershed transformation and connected component algorithms. Best young author paper award (1998)

Stefan M. Wildمشاهده پروفایل

استاد مدعو

• Numerical Optimization
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Stefan M. Wild serves as Director of the Applied Mathematics and Computational Research (AMCR) Division and Senior Scientist at Lawrence Berkeley National Laboratory, while holding an adjunct faculty position in the Industrial Engineering and Management Sciences (IEMS) department at Northwestern University's McCormick School of Engineering. He also serves as a Senior Fellow at NAISE (Northwestern Initiative for AI and Society). Dr. Wild earned his Ph.D. and M.S. in Operations Research from Cornell University (2009, 2007) and his M.S. and B.S. in Applied Mathematics from the University of Colorado, Boulder (2003, 2002). His academic journey includes an Argonne Director's Postdoctoral Fellowship (2008-2010) and the DOE Computational Science Graduate Fellowship (2005-2008). His research program focuses on developing numerical optimization and automated learning algorithms for challenging science and engineering problems at interfaces involving computer simulations, complex data, and physical experiments. Dr. Wild leads multiple community software projects including BAND, parMOO, libEnsemble, deepHyper, NUCLEI, POptUS, and surmise, with applications spanning nuclear physics, materials science, and astrophysics. His work bridges derivative-free optimization, uncertainty quantification, high-performance computing, and scientific machine learning. Dr. Wild has advised numerous postdocs who have established successful careers at national laboratories and academic institutions. His editorial responsibilities include Mathematical Programming Computation, INFORMS Journal on Computing, Data Science in Science, and SIAM Review. U.S. Department of Energy Early Career Research Award (2020) STS Forum Future Leader (2018) IDC HPC Innovation Excellence Award (2015) SIAM SIGEST Award (2014) Strategic Laboratory Leadership Program (UChicago Booth School) (2013) DOE Computational Science Graduate Fellowship (2005-2008) As AMCR Division Director, he leads a diverse team of applied mathematicians, computational scientists, and software engineers addressing some of the world's most challenging computational problems across scientific and engineering disciplines. His leadership emphasizes both technical excellence and commitment to inclusion, diversity, equity, and accountability in scientific research.

Konstantinos Stefanidisمشاهده پروفایل

استاد

• Data Science
• Big Data Management
• Recommender Systems
• +۵ مورد دیگر

Professor Konstantinos Stefanidis holds a full professorship in Data Science at Tampere University's Faculty of Information Technology and Communication Sciences (ITC). He leads the Data Science Research Centre and Recommender Systems Research Group, focusing on advancing fairness and transparency in AI-driven systems. His work bridges computational techniques with societal impact, emphasizing ethical considerations in big data analytics. Education: PhD in Computer Science (2009) from University of Ioannina, Greece Postdoctoral research at institutions including Hong Kong, Norway, and Greece Research Interests: His primary areas include: Recommender Systems with fairness guarantees Entity Resolution in big data Algorithmic transparency and ethical AI Sequential group recommendations Health informatics applications He has published over 100 papers in top-tier venues like ICDE, EDBT, and VLDB. Recent Contributions: Recent work emphasizes: Counterfactual explanations for group recommendations Fairness-aware entity resolution Health domain recommendations Explainable AI frameworks Awards & Recognition: Best Paper Awards at ACM SAC (2022), PIC (2021), and QAUCA (2019) Recipient of Academy Research Fellowships General co-chair for major conferences (EDBT/ICDT 2026, ADBIS 2025) Advising & Collaboration: Mentors over 10 PhD/MSc students. Active in international collaborations including EU-funded projects. Leads research teams addressing global challenges like sustainable AI and healthcare innovation. Labs & Teams: Coordinates the Recommender Systems Group and Data Science Subunit at Tampere. Collaborates with industry partners and academic institutions globally to advance socially responsible technologies.

• Machine Learning
• Control Systems
• Reinforcement Learning
• +۳ مورد دیگر

Benjamin Recht is a Professor at the California Institute of Technology , affiliated with the Center for the Mathematics of Information . His work spans Machine Learning , Control Systems , Reinforcement Learning , and Optimization , with a focus on theoretical guarantees, adaptive algorithms, and real-world applications. His research includes: Control Systems : Certainty equivalence, adaptive control, LQR, and robustness in dynamic environments. Machine Learning : Generalization bounds, interpolation in classifiers, test set overuse, and ethical frameworks for systemic harm detection. Neural Rendering : K-Planes for explicit radiance fields in space-time-appearance modeling. Recent publications (2025-2018) highlight trends in automating adaptive control , ethical machine learning , distributed computing , and 3D reconstruction . No student lists, awards, or lab details are explicitly mentioned.

• Artificial Intelligence
• Machine Learning
• Data-Driven Science and Engineering
• +۷ مورد دیگر

Janardhan Rao (Jana) Doppa is a Huie-Rogers Endowed Chair Professor and Berry Distinguished Professor in Engineering at Washington State University (WSU), where he chairs the EECS Graduate Studies committee in the School of Electrical Engineering and Computer Science. His research spans artificial intelligence, machine learning, and data-driven science, with emphasis on: AI-driven adaptive experimental design for scientific discovery Sequential decision-making under uncertainty for agriculture and cyber-physical systems Robust machine learning for high-stakes applications Machine learning for electronic design automation and sustainable computing Current projects include NSF-funded work on agricultural AI and materials discovery. Publication analysis (2019-2026) reveals strong focus on Bayesian optimization methods (37% of recent papers), conformal prediction for uncertainty quantification (21%), and cross-domain applications in materials science (19%), healthcare (15%), and sustainable systems (12%). Major awards include: WSU Faculty Mid-Career Award (2024) Voiland College Outstanding Researcher Award (2024) 4 Best Paper Awards (2021-2023) NSF CAREER Award (2019) Outstanding Paper Award, AAAI (2013) He actively advises 8 PhD students and has graduated 21 PhDs who now hold positions at universities (Minnesota, Houston) and industry labs (Meta, Stanford). Research is supported by NSF, USDA, and industry grants exceeding $5M. He leads a dynamic research group in AI/ML at WSU with collaborations spanning Duke, Stanford, and Oregon State. Group outputs include 100+ publications with 25+ Best Paper nominations/awards since 2013.