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Shiqian MaView profile

Professor

• Optimization
• Machine Learning

Shiqian Ma is a Professor of Computational Applied Mathematics and Operations Research at Rice University. His research focuses on optimization, machine learning, and their applications. He holds a Ph.D. from Columbia University, an M.S. from the Chinese Academy of Sciences, and a B.S. from Peking University. Education: Ph.D. in Industrial Engineering and Operations Research, Columbia University (2011) M.S. in Computational Mathematics, Chinese Academy of Sciences (2006) B.S. in Mathematics, Peking University (2003) Research Interests: Dr. Ma specializes in optimization theory and algorithms, particularly in stochastic approximation, distributed/decentralized optimization, and applications in machine learning. His work addresses challenges in non-convex optimization, Riemannian geometry-based methods, and high-dimensional data analysis. Advising and Grants: While no specific advisees or grant details are listed, his academic role suggests involvement in mentoring graduate students and securing research funding in computational mathematics and operations research. Labs/Teams: Not explicitly mentioned, but his research likely involves collaborations within Rice's computational and applied mathematics community.

Maria-Florina BalcanView profile

Professor

• Machine Learning
• Artificial Intelligence
• Algorithmic Game Theory
• +3 more

Maria-Florina (Nina) Balcan is the Cadence Design Systems Professor at Carnegie Mellon University, holding joint appointments in the Machine Learning Department and the Computer Science Department within the School of Computer Science. Her research focuses on foundational aspects of machine learning, algorithmic game theory, and theoretical computer science. She has held leadership roles, including the Cadence Design Systems Chair since 2020, and previously served as an associate professor at CMU and assistant professor at Georgia Tech. Education includes a PhD (2008) and MS (2002) from Carnegie Mellon University, and a B.S. (1996-2000) from the University of Bucharest. Postdoctoral work followed at Microsoft Research (2008-2009). Her research bridges machine learning and game theory, addressing challenges like interactive learning, distributed algorithms, and algorithmic economics. Key awards include the ACM Grace Murray Hopper Award and Simons Investigatorship. Teaching spans advanced courses on machine learning foundations and game theory. Her work on revenue maximization in auctions and regret minimization in Stackelberg games reflects her interdisciplinary impact. Professional activities include organizing major conferences (ICML, NeurIPS) and serving on committees such as the AAAI/SIGAI AI Dissertation Award Committee. Her lab focuses on theoretical and applied machine learning, emphasizing algorithmic robustness and generalization.

• Network & Information Security
• Internet of Things (IoT) Security
• Attack Detection & Prevention
• +4 more

Dr. Anca Delia Jurcut is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), leading the DNS Research Lab. She holds a PhD in Security Engineering from the University of Limerick and a BSc in Computer Science and Mathematics from West University of Timisoara. With over 16 years of academic and industry experience, her research focuses on cybersecurity, including formal verification of security protocols, IoT security, and machine learning applications for threat detection. She has secured €1.5M+ in EU and national grants and authored over 120 peer-reviewed publications. Notable awards include the 2023 Best Paper Award from JNCA and inclusion in the World’s Top 2% Scientists (2024). Education: PhD (University of Limerick), BSc (West University of Timisoara) Research Interests: Cybersecurity, Attack Detection, Formal Verification, Blockchain, Quantum-Resistant Protocols Awards: World’s Top 2% Scientist, IEEE Senior Member, UCD Women in Science Award Research Highlights : Pioneered SDN attack datasets (InSDN), developed formal verification tools (CDVT/AD), and advanced ransomware classification frameworks. Current projects address quantum-resistant protocols, MEC security, and resilient IoT systems. Grants & Funding : Lead researcher on EU H2020 projects (e.g., AIDA), SFI grants (Machine Learning Labs), and NATO-funded studies on insider threats. Collaborates internationally on cybersecurity frameworks and edge computing security. Labs & Teams : DNS Research Lab (UCD), co-founder of the InSDN Dataset initiative, and contributor to the CDVT/AD formal verification tool.

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +7 more

Ümit Deniz Uluşar is a Professor in the Department of Computer Engineering at Akdeniz University's College of Engineering. He serves as Dean of the Manavgat Faculty of Social and Human Sciences and manages two research centers: the Computer Sciences Research and Application Center and the Remote Education Application and Research Center. Doctorate: University of Arkansas at Little Rock , Computer Science (2010) Postgraduate: Bogazici University , Systems and Control Engineering (2004) Undergraduate: Marmara University , Computer Engineering (2001) His research focuses on biomedical signal processing, IoT systems, machine learning, and smart city applications. Key subfields include fetal movement analysis, gastrointestinal monitoring, edge computing, and wireless sensor networks. Recent publications emphasize biomedical engineering applications, with studies on cortical plasticity in facial transplants, IoT-based healthcare systems, and comparative analyses of adaptive filters. He has co-authored works with researchers like G. Çelik and E. Türk, spanning journals like Scientific Reports and conferences including the International Conference on Computer Science and Engineering. Managed 8 funded projects since 2009, including TÜBİTAK grants on bioacoustic sensors and gastrointestinal motility monitoring Co-founder of Mavi İleri Bilişim Teknolojileri ArGe Yazılım Ltd. Şti. (2021)

Marian MachView profile

Associate Professor

• Symbolic Artificial Intelligence
• Evolutionary Computation
• Ontological Engineering
• +5 more

Marian Mach is an Associate Professor at the Department of Cybernetics and Artificial Intelligence, Technical University of Kosice. He holds a Ph.D. (1992) and habilitation in Artificial Intelligence (2001). His research focuses on symbolic AI, evolutionary computation, knowledge modeling, and ontological engineering. He has led national and EU projects like Modermed (2023-2027) and DL4VISION (2022-2025), addressing toxic behavior detection and deep learning for image processing. Education: Ph.D. in Technical Cybernetics (1992), TU Kosice Habilitation in AI (2001), TU Kosice M.S. in Technical Cybernetics (1985), TU Kosice Research Interests: Evolutionary algorithms Constraint satisfaction problems Knowledge representation Machine learning applications in social media analysis Robotics and intelligent systems Teaching: Courses include Machine Learning II, Evolutionary Algorithms, and Application of Logic in Intelligent Systems. He has developed over 20 courses since 1987. Grants & Projects: EU-funded initiatives: VICINITY (2016-2019), PICASO (2016-2019) National grants: APVV-22-0414 (2023-2027), VEGA 1/0394/22 (2022-2025) Labs/Teams: Involved in AI Center at TU Kosice and collaborative projects on cloud-based robotics and semantic web technologies.

Koushik KarView profile

Professor

• Computer Networks
• Congestion Control
• Resource Allocation
• +5 more

Koushik Kar serves as a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where he conducts interdisciplinary research bridging computer networks, energy systems, and machine learning. His institutional affiliation includes the Center for Future Energy Systems (CFES). His core research focuses on: Routing and traffic management in computer networks Congestion control and fair resource allocation Ad-hoc and sensor networks IoT energy management with renewable resources Machine learning applications for network automation Analysis of his 2023-2025 publications reveals a strategic convergence of AI and sustainability, with dominant themes in model-predictive control for IoT energy systems, automated network peering decisions, and reinforcement learning for safe autonomous systems. His work consistently addresses real-world challenges in energy-efficient networking and building systems. Scientific Awards: No awards documented in provided materials Advising and Grants: No student advisees or grant funding sources specified Labs and Teams: Center for Future Energy Systems (CFES) at RPI

• Natural Language Processing
• Text Mining
• Software Engineering
• +3 more

Xueqing Liu is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology, part of the Charles V. Schaefer, Jr. School of Engineering and Science. His research focuses on text mining, natural language processing (NLP), and their applications to software engineering and cybersecurity, including automated machine learning and NLP-driven security tools. Research interests include developing NLP methods to address challenges in security vulnerability analysis, code retrieval, and automated model optimization. He has explored hyperparameter tuning for pre-trained language models, framework design for NLP testing (e.g., TestAug), and generative AI applications for vulnerability detection (e.g., VulLibGen). His work also addresses practical software engineering needs like build issue resolution and secure mobile app development. Professional service includes roles as a program committee member for conferences like ASE 2023 and AAAI 2023, and reviewer for top venues like ACL and NAACL. He actively contributes to institutional governance through DEI and faculty search committees. Teaching includes courses on data structures (CS284) and text mining/information retrieval (CS589). His research spans over 20 publications, emphasizing interdisciplinary approaches combining NLP with software engineering and cybersecurity.

Thorsten PapenbrockView profile

Professor

• Data Management
• Distributed Systems
• Data Profiling
• +4 more

Prof. Dr. Thorsten Papenbrock is a Professor in the Department of Mathematics and Computer Science at Philipps-Universität Marburg, leading the Big Data Analytics research group. He also holds an affiliation with the Hasso Plattner Institute (HPI) as a Senior Researcher. His research focuses on data management, scalable systems, distributed computing, and environmentally friendly applications. He earned his IT-Systems Engineering degrees from HPI, including a Ph.D. in 2016 on Data Profiling. His work includes projects like Metanome for big data profiling and TimeEval for anomaly detection benchmarking. Research interests span data profiling, distributed systems, and parallel computing, with contributions to functional dependency discovery, time series analysis, and actor-based database systems. He has advised numerous students on master’s theses, including topics in distributed duplicate detection and graph-based nearest neighbor search. Collaborations include the HPI and industry partners like SAP and BMW. Publications highlight advancements in holistic data profiling, anomaly detection, and distributed systems optimization. He teaches courses on distributed data management, database systems, and data profiling. His lab’s work emphasizes practical applications of theoretical insights to real-world challenges in big data analytics and system efficiency.

• Artificial Intelligence
• Neuroscience
• Cognitive Architecture
• +3 more

Prof Chris Huyck is a Professor of Artificial Intelligence at Middlesex University's Faculty of Science and Technology, Department of Computer Science, with 25 years of academic experience. His career spans institutions including the University of Sheffield, Microsoft, and the American University in Cairo, culminating in a PhD from the University of Michigan (1994). Dr. Huyck's research focuses on Cell Assemblies (CAs) grounded in mammalian neural circuitry, extending to cognitive architectures , neuromorphic hardware , and NLP applications . Key projects include modeling associative memory , category learning , and robotic control systems using spiking neural networks. Recent Publications : 2025 firefly algorithm tuning studies; 2024 emotion-integrated cognitive models; 2023 UAV path planning with coronavirus-inspired optimization Awards : No specific honors mentioned Students : Supervising Geethu Joy (PhD Enrolled, 2022-) and Babak Mohajer Ashjaei (PhD Enrolled, 2025-) Labs : Active in Middlesex's AI research group, integrating neuroscience with robotics and NLP

Matthias K. GobbertView profile

Professor

• Scientific Computing
• Parallel Algorithms
• Machine Learning
• +5 more

Matthias K. Gobbert is a Professor of Mathematics at the University of Maryland, Baltimore County (UMBC) , affiliated with the Department of Mathematics and Statistics . His research focuses on scientific computing , parallel algorithms , machine learning , and numerical methods for applications in medicine, engineering, and biology. He has received the University System of Maryland Board of Regents' Faculty Award for Excellence in Mentoring in 2010. Education : Ph.D. in Mathematics, Arizona State University (1996) Postdoctoral Associate at the University of Minnesota’s Institute for Mathematics and its Applications (1996–1997) Research Interests : Parallel computing for large-scale simulations Multiscale modeling of chemically reactive flows Numerical methods for partial differential equations Applications in medical physics (e.g., proton therapy, prompt gamma imaging) Machine learning for big data in science and engineering Teaching : Develops courses using flipped classrooms, active learning, and technology integration (e.g., tablet laptops, Blackboard) Teaches Math 441 (Numerical Analysis) , Math 447/627 (Parallel Computing) , and software-focused courses like Math 426 (MATLAB) . Incorporates entrepreneurship training into curricula, linking computational methods to commercialization opportunities. Grants & Collaborations : Co-director of NSF-funded CyberTraining: Big Data + HPC + Atmospheric Physics . International collaborations with the University of Kassel (Germany) on parallel computing and HPC. Advisor to REU programs in big data analytics and high-performance computing. Labs & Teams : UMBC High Performance Computing Facility (HPCF) Center for Interdisciplinary Research and Consulting (CIRC)

Junfeng WenView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Junfeng Wen is an Assistant Professor at the School of Computer Science, Carleton University. He holds a Ph.D. in Statistical Machine Learning from the University of Alberta, where he worked with Prof. Dale Schuurmans and Prof. Russ Greiner. His research focuses on foundational machine learning algorithms, including reinforcement learning, transfer learning, federated learning, and domain adaptation. He teaches courses such as Reinforcement Learning and Machine Learning at Carleton University. Education: Ph.D. in Statistical Machine Learning (2013–2020), University of Alberta M.Sc. in Computing Science (2011–2013), University of Alberta Bachelor of Engineering (with Honors, 2007–2011), College of Computer Science and Technology, CKC College, Zhejiang University Research Interests: Dr. Wen’s work emphasizes advancing theoretical understanding and practical applications of machine learning. Key areas include federated learning frameworks for privacy preservation, reinforcement learning for decision-making systems, and multi-source domain adaptation techniques. His research bridges algorithmic innovation with real-world challenges in distributed systems and biological modeling. Publications: His recent articles explore federated learning optimization, decentralized collaboration strategies, and reinforcement learning applications in behavioral studies. Notable contributions include domain aggregation networks for cross-domain adaptation and proxy model-sharing techniques in decentralized learning. The work spans theoretical advancements and practical implementations, often addressing scalability and privacy concerns in distributed environments. Professional Contributions: Conference reviewer for NeurIPS, ICML, ICLR, AAAI, IJCAI, and ACML Journal reviewer for JMLR, Machine Learning Journal (Editorial Board member), and TPAMI Labs and Projects: He contributes to open-source projects like Domain Aggregation Networks (DARN) for multi-source domain adaptation and Frank-Wolfe for Covariate Shift . These repositories reflect his commitment to reproducible research and community engagement.

Yoshiyasu RaiView profile

Assistant Professor

• Theoretical Econometrics
• Statistical Modeling
• Econometric Theory
• +3 more

Prof. Yoshiyasu Rai is an Assistant Professor of Theoretical Econometrics and Statistics at the University of Mannheim's Department of Economics. His research focuses on econometrics and statistical modeling, with a strong emphasis on machine learning and knowledge graph embeddings. He holds a Ph.D. in Economics from the University of Wisconsin-Madison and degrees from Keio University, Tokyo. His work bridges theoretical econometrics with computational methods, addressing challenges in large-scale data analysis and distributed systems. Key contributions include advancements in knowledge graph embeddings, parameter server optimization, and scalable machine learning frameworks. Education: BA and MA in Economics (Keio University), Ph.D. in Economics (University of Wisconsin-Madison). Research Interests: Theoretical Econometrics Machine Learning Applications Knowledge Graphs and Embeddings Distributed Systems for Large-Scale Data Optimization Algorithms Articles trends focus on knowledge graph embeddings, parameter server design, and scalable machine learning techniques. Recent work emphasizes adaptive systems, dynamic resource allocation, and reproducible research through libraries like LibKGE. Labs/Teams: Involved in the Junior Professorship for Theoretical Econometrics and Statistics at the University of Mannheim, focusing on interdisciplinary data analysis projects.

Steven X. WangView profile

Professor

• AI/Machine Learning
• Healthcare Applications
• Biomedical Signal Processing
• +3 more

Dr. Steven X. Wang is a full professor in the Department of Statistics at York University and currently serves as the Director of the undergraduate Data Science program. He holds a U.S. patent for Machine Learning and has been recognized with prestigious awards including the Queen Elizabeth II Platinum Jubilee Award (2023) and the Markham-Unionville Achievement Award and Medal (2025) for his contributions to AI in healthcare. Education: Postdoc in Data Science, Pacific Institute of Mathematical Sciences (2002) Ph.D. in Statistics, University of British Columbia (2001) M.Sc. in Applied Statistics, University of California, Riverside B.Sc. in Computational Mathematics, Beijing University of Technology Research Interests: Dr. Wang focuses on applying AI/machine learning to healthcare, particularly in pain analysis using EEG signals and financial time series modeling. His work bridges statistical theory with practical applications in biomedical engineering and medical data analysis. Publications: His recent articles emphasize advancements in neural networks for EEG analysis, financial portfolio strategies, and biomedical signal processing. His work consistently integrates interdisciplinary methods to solve real-world health and financial challenges. Awards: Queen Elizabeth II Platinum Jubilee Award (2023) Markham-Unionville Achievement Award and Medal (2025) U.S. Patent for Machine Learning Innovations Experience: With 30 years of consulting experience and 25 years in scientific computing, Dr. Wang leads research projects addressing critical challenges in healthcare and data-driven decision-making.

Younghyun ChoView profile

Assistant Professor

• Parallel Computing
• High-Performance Computing (HPC)
• Performance Engineering
• +5 more

Younghyun Cho is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University (SCU), leading the Systems & Performance Research Lab. He holds a Ph.D. from Seoul National University (2020) and previously served as a Postdoctoral Scholar at UC Berkeley. His research focuses on compiler/runtime-level performance optimizations, autotuning for high-performance computing (HPC), and emerging workloads such as quantum computing and machine learning. Key contributions include GPTune, a Bayesian optimization-based autotuning framework, and Dopia, an online parallelism management system for heterogeneous architectures. Education: Ph.D., Seoul National University, Electrical Engineering and Computer Science (2020). Research highlights include non-smooth Bayesian optimization for scientific applications and surrogate-based autotuning for randomized sketching algorithms. He has authored over 20 peer-reviewed publications in top venues like DAC, IPDPS, and SIAM Journal on Matrix Analysis. Awards include the SCU AI Scholarship Award (2025), Seoul National University's Excellence Research Award (2019), and a 2016 Open Source Software World Challenge prize for the SnuMAP profiler. He actively serves on program committees for PACT, LCTES, and IPDPS, and manages grants including AMD HPC Cloud resources (2025-2026).

Shih-Ting HuangView profile

Assistant Professor

• Precision medicine
• High-Dimensional Statistics
• Deep Learning
• +3 more

Shih-Ting Huang is an Assistant Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences. Her research focuses on precision medicine, high-dimensional statistics, and deep learning applications in healthcare. She holds a PhD and is affiliated with the University of Louisville, located in Louisville, KY. Education : PhD in a relevant field (specific details not provided in text). Research Interests : Dr. Huang's work emphasizes statistical methodologies for personalized medicine, robust machine learning algorithms, and high-dimensional data analysis in biomedical contexts. Her recent studies include developing sample-specific learning models for medical imaging and enhancing deep learning through median-of-means techniques. Publications : Her research spans topics like robust deep learning frameworks, tuning-free statistical models, and mathematical foundations for biological data analysis. Her 2021 work on tuning parameter calibration for medical predictions highlights her focus on clinical applicability. Awards & Grants : No specific awards or grants are listed in the provided text. Labs/Teams : No specific lab affiliations or team details are mentioned.