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Kirk CameronView profile

Professor

• High-Performance Computing (HPC)
• Green Computing
• Power-Performance Modeling
• +5 more

Kirk Cameron is a Professor of Computer Science at Virginia Tech and an IEEE Fellow. He holds roles such as Associate Vice President for Academic Affairs at Virginia Tech’s Innovation Campus and Director of the SCAPE Laboratory. His academic tenure includes leadership positions like Director of the stack@cs Center for Computer Systems and Associate Department Head for Research and Engagement. Education: Ph.D., Computer Science, Louisiana State University (2000) B.S., Mathematics, University of Florida (1994) Research Interests: Focused on power and performance efficiency in HPC systems, green computing (e.g., Green500, SPECPower), and innovative educational tools like the SeeMore kinetic sculpture. His work bridges HPC, energy efficiency, and art to engage broader audiences. Key areas include power-aware computing, variability management, and I/O performance optimization. Impact Highlights: Pioneered Green HPC research with NSF and DOE funding. Co-founded MiserWare, Inc., and developed tools like PowerPack and grano.la. Recipient of HPDC 2017 Best Paper Award and multiple industry awards. Awards & Recognition: NSF Career Award ACM Distinguished Member IEEE Fellow Labs & Projects: Leads the SCAPE Laboratory and oversees initiatives like the CSGenome Project and SeeMore, a kinetic parallel computing exhibit featured at SIGGRAPH and the Smithsonian.

Wu-chun FengView profile

Professor

• High-Performance Computing
• Data Analytics Systems
• Computational Biology
• +7 more

Wu-chun Feng is a Professor of Computer Science and Electrical & Computer Engineering at Virginia Tech, holding the Elizabeth & James E. Turner Jr. '56 Faculty Fellowship. He directs the SyNeRGy Laboratory and is affiliated with the School of Biomedical Engineering & Sciences, CHREC, VBI, and Wireless @ VT. His research focuses on high-performance computing, energy-efficient systems, bioinformatics, and heterogeneous architectures. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (1996) M.S. in Computer Engineering, Penn State University (1990) B.S. in Electrical & Computer Engineering and Music (Honors), Penn State University (1988) Research Interests: His work spans high-performance computing systems, green supercomputing (e.g., Green500 List, Green Destiny), computational biology (mpiBLAST), and parallel programming models. He emphasizes energy-efficient architectures and interdisciplinary applications in biomedicine and data science. Key Contributions: Green500 List: Ranks energy-efficient supercomputers globally mpiBLAST: Parallel implementation of NCBI BLAST Supercomputing in Small Spaces initiative Awards: Three R&D 100 Awards (Green Supercomputing, High-Speed Networking, Bioinformatics) Over 15 Best & Outstanding Paper Awards Service & Leadership: Editorial roles in journals like IEEE/ACM Transactions on Computational Biology Conference leadership (SC, ICPP, etc.) Director of SyNeRGy Lab and CUDA Research Center

Zhenyu KongView profile

Professor

• Manufacturing Systems Engineering
• Machine Learning for Manufacturing
• Cyber-Physical Systems
• +5 more

Zhenyu "James" Kong is the Ralph H. Bogle Professor in the Department of Industrial and Systems Engineering at Virginia Tech's College of Engineering. With over 20 years of academic experience, he has established himself as a leading researcher in manufacturing systems engineering with a focus on smart manufacturing technologies. Dr. Kong received his Ph.D. in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2004, following Master's and Bachelor's degrees in Mechanical Engineering from Harbin Institute of Technology in China. His academic career includes professorial positions at Virginia Tech since 2006 and previously at Oklahoma State University. His research focuses on the intersection of machine learning, cyber-physical systems, and manufacturing engineering. Specifically, he investigates real-time sensing, advanced analytics, and process monitoring/control for smart manufacturing systems. His work on modeling, synthesis, and diagnosis for large and complex manufacturing systems has significantly advanced the field of additive manufacturing quality control, with particular emphasis on applying deep learning techniques to defect detection and process optimization. Dr. Kong's research has been supported by major funding agencies including the Department of Defense, National Science Foundation, Department of Energy, and Office of Naval Research. His publications demonstrate a consistent focus on applying machine learning to solve practical manufacturing challenges, particularly in additive manufacturing quality control and process monitoring. Fellow of Institute of Industrial and Systems Engineers (IISE), 2020 Dean's Award for Excellence in Research, College of Engineering, Virginia Tech, 2019 Multiple Best Paper Awards from IISE Transactions and INFORMS conferences (2015-2020) APM Outstanding Faculty Award, Virginia Tech, 2018 Halliburton Outstanding Faculty Award, Oklahoma State University, 2013 As an educator, Dr. Kong has taught courses including Advanced Topics in Manufacturing Systems Engineering, Sensing and Data Analytics for Complex Systems, and Statistical Quality Control. He leads the SMART Lab at Virginia Tech, where his research group develops innovative solutions for manufacturing challenges using cutting-edge data analytics and machine learning techniques. His research has been featured multiple times in ISE Magazine, highlighting its practical significance and impact on manufacturing industry practices.

Theodoros TheodoulidisView profile

Professor

• Electromagnetic Fields
• Nondestructive Testing
• Power Systems Engineering
• +3 more

Professor Theodoros Theodoulidis serves as Rector of the University of Western Macedonia and holds a Professorship in the Department of Mechanical Engineering at the Polytechnic School. He earned his PhD (1997) and Diploma (1992) in Electrical Engineering from Aristotle University of Thessaloniki (AUTH). His professional career includes roles as Professor at the University of Western Macedonia since 2013 and previous positions at Technical Education Institute of Western Macedonia (TEIWM). Research Interests: Focus on electromagnetic fields in power systems and nondestructive testing , with emphasis on eddy current techniques for material characterization, defect detection, and sensor design. His work integrates computational methods such as finite-element modeling, boundary-element analysis, and semi-analytical solutions. Publications: Recent work emphasizes high-frequency eddy current testing, superconducting magnet design, and uncertainty quantification in defect detection. Key trends include hybrid modeling approaches, sparse data techniques, and applications in industrial inspection (e.g., drill-bit tools, ferromagnetic welds). Earlier contributions address analytical solutions for coil interactions with conductive media and transient response calculations. Affiliations & Teaching: Teaches undergraduate courses in Electrotechnics , Electric Machines , and Nondestructive Testing . Active in academic leadership as Rector, overseeing institutional strategy and research initiatives. Labs & Teams: Research activities are conducted within the University’s mechanical engineering facilities, focusing on electromagnetic testing and computational electromagnetics. Collaborates with international institutions on projects like the WFNDEC benchmarks and ISEM conferences.

Sourav SahaView profile

Assistant Professor

• Mechanistic Computational Intelligence
• Computational Mechanics
• Computational Nanomaterials
• +5 more

Sourav Saha is an Assistant Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech. His research focuses on mechanistic computational intelligence, computational mechanics, and materials science, with a particular emphasis on integrating artificial intelligence with engineering challenges. He holds a Ph.D. from Northwestern University and M.S./B.S. degrees from Bangladesh University of Engineering and Technology (BUET). Key professional roles include a visiting position at Sandia National Laboratories and prior teaching at BUET. His honors include the Richter Memorial Fellowship (2023–24), NSF INTERN Award (2022–23), and NIST Additive Manufacturing Benchmark Challenge win (2022). He leads the IMMPACT Lab, which develops AI-driven tools for advanced manufacturing and materials innovation. Research interests span multiscale modeling, nanomaterials, and additive manufacturing. His recent articles explore topics like physics-informed machine learning, kernel-based neural architectures, and fatigue life estimation. He actively participates in professional societies such as the Minerals, Metals & Materials Society and the American Society of Thermal and Fluid Engineers.

Stephan SmeekesView profile

Professor

• Econometrics
• Data Science
• Time Series Analysis
• +7 more

Stephan Smeekes is a Professor of Econometrics at the Department of Quantitative Economics, School of Business and Economics, Maastricht University. His research focuses on statistical analysis of time series data, blending econometrics, statistics, and data science. Specializing in modeling complex dynamic systems with multiple time series, he emphasizes uncertainty quantification using bootstrap techniques, applied to macroeconomics, finance, climate, environmental, and medical domains. He led significant projects including the NWO Veni project on Bootstrap Methods for Time-Varying Processes and NWO Vidi project on Inference for High-Dimensional Econometric Time Series. As a former member of the Dutch Young Academy (De Jonge Akademie), he contributes to interdisciplinary research and innovation. His teaching spans statistics, econometrics, and computational data science at both undergraduate and graduate levels. Smeekes develops open-source software like the bootUR R package for bootstrap unit root tests, desla for high-dimensional inference, and PAGFL for latent group structure estimation. His work bridges academic insight with real-world impact through collaborations across disciplines.

• Machine Learning
• Operations Research
• Optimization
• +3 more

Silvia Villa is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genoa (UniGe). Her teaching responsibilities include courses such as Game Theory, Machine Learning, Optimization and Operations Research, and Operations Research at both undergraduate and master's levels. Her research focuses on optimization theory and its applications in machine learning, inverse problems, and regularization methods. She has contributed to advancements in iterative regularization techniques, stochastic optimization algorithms, and convex optimization frameworks. Her work often bridges theoretical foundations with practical applications in signal processing and data science. Her research interests emphasize structured optimization problems, including low-complexity regularizers, bilevel optimization, and sparse recovery. Key methodologies in her studies include primal-dual dynamics, proximal algorithms, and variance reduction techniques. Recent trends in her publications highlight advancements in unrolled deep networks for sparse signal restoration, adaptive optimization strategies, and convergence analysis of stochastic methods. Villa's collaborative efforts span interdisciplinary projects involving applied mathematics, computer science, and engineering. While no specific awards are listed, her prolific publication record reflects her active role in the optimization and machine learning communities. She has advised multiple research projects but no formal student names are provided in the available data.

Bala KrishnamoorthyView profile

Professor

• Applied Algebraic Topology
• Geometric Measure Theory
• Mathematical Aspects of 3D Printing
• +4 more

Bala Krishnamoorthy is a Professor of Mathematics and Statistics at Washington State University (WSU) Vancouver, where he has been since 2014. Previously, he held positions at WSU Pullman (2004–2014). He earned his B.Tech from IIT Madras (1995) and a PhD in Operations Research from UNC Chapel Hill (2004). His research spans applied algebraic topology, geometric measure theory, optimization, and computational biology, with applications in 3D printing, biomedical analysis, and data science. He has secured grants from the NSF, Department of Energy, and WA State Attorney General’s Office, among others. Key research interests include topological data analysis (TDA), algorithmic optimization, and interdisciplinary collaborations with fields like surgery, chemistry, and criminology. Notable awards include the 2019 WSUV Chancellor’s Research Excellence Award, 2022 Yang Liu Teaching Award, and 2023 College of Arts and Sciences Excellence in Teaching Award. His work emphasizes practical applications, such as toolpath optimization in 3D printing, robust feasibility in optimization, and analyzing complex datasets like cancer gene expression. He advises numerous PhD students, many of whom have secured roles in academia, industry, and government. Current projects include steering committees for NSF-funded initiatives like DELTA and collaborations with institutions like Tohoku University and UC Davis. Teaching responsibilities include advanced courses on algebraic topology, optimization, and computational methods. His seminars and workshops foster interdisciplinary dialogue, such as the WSU Vancouver Math/Stats Seminar series and topological data analysis workshops at ACM-BCB and PSB conferences.

• Bioengineering
• Artificial Intelligence
• Explainable AI
• +7 more

Gloria Menegaz is a Full Professor of Bioengineering at the Department of Engineering for Innovation Medicine, University of Verona, where she directs the BraiNAVLab. She has been serving in this position since 2021, following her appointment as Full Professor in Computer Science in 2017. Previously, she held Associate Professor and Assistant Professor positions at the University of Verona, University of Siena, and University of Fribourg (Switzerland). Her research spans multiple interdisciplinary fields including bioengineering, artificial intelligence, machine learning, and neuroimaging. She specializes in explainable AI methods applied to biomedical data, with particular focus on brain connectivity analysis using multi-modal neuroimaging data (fMRI, EEG) integrated with genetic information. Her work has resulted in over 160 publications in prestigious journals including IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging, and Frontiers in Neuroscience. Her recent research trends emphasize the development of explainable AI frameworks for clinical applications, particularly in neurological disorder diagnosis and treatment planning. She has pioneered approaches for integrating structural and functional brain connectivity data with genetic markers to identify numerical biomarkers for various neurological conditions. Senior Member of IEEE (2017) Chair of Steering Committee of IEEE Transactions on Medical Imaging Member of IEEE-SPS Challenges and Data Collection Technical Committee Vice-Chair of IEEE Bioimaging and Signal Processing Committee Recipient of Rita Levi Montalcini grant Throughout her career, Professor Menegaz has secured over 1 million euros in research funding from international institutions including the European Commission (H2020, FP7), European Science Foundation, MIUR (PRIN, SIR, FIRB), and ANR (France). She maintains extensive international collaborations with Georgia Tech University, INRIA (France), EPFL (Switzerland), Queen Mary University, and University College London. Her laboratory, BraiNAVLab, focuses on developing advanced computational methods for brain imaging analysis and navigation.

Bo ShenView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Additive Manufacturing
• +4 more

Bo Shen is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on machine learning, deep learning, additive manufacturing, and data analytics with applications in solar physics, healthcare, and materials science. He holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2022) and a B.S. in Statistics from the University of Science and Technology of China (2017). His work bridges theoretical advancements in AI with practical industrial and scientific challenges. Education: Ph.D., Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 2022 B.S., Statistics, University of Science and Technology of China, 2017 Research Interests: Prof. Shen’s research spans deep learning , optimization algorithms , additive manufacturing process analytics , and solar physics data modeling . He develops novel methods for model explainability, anomaly detection in manufacturing, and high-dimensional data analysis. His work often integrates physics-based principles with machine learning frameworks. Publications: His recent articles address topics like neural operator learning for fatigue prediction, Bayesian optimization in additive manufacturing, and deep learning-driven solar image reconstruction. These reflect a trend toward applying advanced AI techniques to solve interdisciplinary problems in engineering and science. Awards/Grants: No awards explicitly listed, but his prolific publication record suggests active research engagement. Details on grants or lab affiliations are not provided in the text. Labs/Teams: While specific lab affiliations are not mentioned, his work aligns with NJIT’s strategic research focus areas in data analytics and advanced manufacturing.

• Power Systems
• Electricity Markets
• Distributed Energy Resources
• +5 more

Subhonmesh Bose is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Lab and Health Care Engineering Systems Center . He holds the M. Stanley Helm Fellowship . His research focuses on power systems, optimization, machine learning, and their applications in energy markets and healthcare. Key areas include risk-sensitive market design, distributed optimization algorithms, and transient stability analysis using operator-theoretic methods. Education details are not explicitly provided in the text, but his affiliations suggest advanced training in electrical engineering and applied mathematics. His work bridges theoretical foundations (e.g., game theory, control systems) with practical challenges like pandemic management in schools and grid resilience. Research Trends : Recent articles emphasize adaptive control under uncertainty, incentive mechanisms in multi-agent systems, and data-driven approaches for power system stability. His work on test-to-stay programs for K-12 schools during the pandemic highlights interdisciplinary impact. Awards : NSF CAREER Award (2021) for Risk-Sensitive Market Design for Power Systems . Grants : Leads projects on AI for mitigating COVID-19, energy storage placement, and market design for distributed energy resources. Labs/Teams : Active in Coordinated Science Lab (CSL) and Health Care Engineering Systems Center (HCESC), collaborating on cross-disciplinary challenges.

Ioanna ManolopoulouView profile

Professor

• Bayesian Statistics
• Causal Inference
• Health Data Science
• +4 more

Ioanna Manolopoulou is a Professor of Statistical Science at University College London (UCL), affiliated with the UCL ELLIS Unit. She holds the role of Associate Director of the HDRUK-Turing PhD programme. Her research focuses on developing Bayesian models for interpretable inferences in practical applications, with emphasis on causal inference, health data science, and retail analytics. She leads a research group advising numerous PhD students and collaborates with institutions like dunnhumby ltd and the Alan Turing Institute. Education: PhD in Statistical Science from the University of Cambridge (supervised by Prof. S. Tavaré and Prof. S.P. Brooks) Postdoctoral research at Duke University and SAMSI (Statistical and Applied Mathematical Sciences Institute) Research Interests: Bayesian nonparametric methods for causal effects and heterogeneous treatment estimation Interpretability in mixture models and topic models Applications in health economics, retail analytics, and immune cell dynamics Key Contributions: Her work bridges statistical theory and real-world applications, with notable contributions in causal machine learning, retail transaction modeling, and rater agreement analysis. Recent projects include Bayesian causal forests for treatment effects and segmented topic models for grocery retail analysis. Advising & Students: Supervises over 20 current and past PhD students, including Alberto Caron (postdoc at Alan Turing Institute) and Ilina Yozova (presenting at 2024 causal inference conferences). Labs & Affiliations: Active in UCL's Statistical Science department and the ELLIS Unit, collaborating with industry partners like dunnhumby ltd to advance applied statistical modeling.

• Statistics
• Data Mining
• Machine Learning
• +3 more

Dr. Nicos Pavlidis is a Senior Lecturer in the Department of Management Science at Lancaster University's Management School. His research focuses on developing novel methods for clustering and classification in high-dimensional and time-varying data environments, with applications in machine learning and statistics. He teaches courses such as MSCI 562 (Intelligent Data Analysis and Visualisation) and MSCI 331 (Data Mining for Direct Marketing and Finance), supported by DataCamp platforms. His research interests include statistics, data mining, and machine learning, with a particular emphasis on adaptive methods for dynamic data streams. Pavlidis has contributed to projects like STOR-i: DSI (2017–2021), which aimed to improve clustering efficiency in high-dimensional datasets. He is affiliated with the STOR-i Centre for Doctoral Training and the Centre for Marketing Analytics & Forecasting. His publications span topics such as spectral clustering, metric learning, and ensemble methods. He has advised PhD student Danielle Notice and has reviewed for journals like IEEE Transactions on Evolutionary Computation and Applied Soft Computing. Pavlidis actively participates in conferences and workshops, including the International Symposium on Intelligent Data Analysis and the IEEE Big Data Conference.

Marian CodreanuView profile

Associate Professor

• Wireless Networking
• Signal Processing
• Machine Learning
• +3 more

Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.

• Neuromorphic Computing
• Neural Networks
• Machine Learning
• +4 more

Dr. Denis Kleyko is a Visiting Research Fellow at La Trobe University's Business Analytics department. His work focuses on neuromorphic computing, hyperdimensional computing, and neuro-inspired algorithms. He has contributed to frameworks like NeuroBench for benchmarking neuromorphic systems and authored comprehensive surveys on vector symbolic architectures. Key research interests include neuromorphic reservoir computing, sparse randomized embeddings, and cognitive mapping through neural circuits. His publications span journals such as Nature Communications and IEEE Transactions, emphasizing interdisciplinary approaches in machine learning and neuroscience. Collaborations include international teams addressing challenges in seizure prediction, compositional learning, and high-dimensional data processing. Notable outputs include foundational work on HyperSEED and efficient optimization using Ising machines.