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Alexander W. DowlingView profile

Associate Professor

• Chemical Engineering
• Computational Optimization
• Machine Learning
• +7 more

Prof. Alexander W. Dowling is an Associate Professor at the Department of Chemical and Biomolecular Engineering within the College of Engineering at the University of Notre Dame. He holds joint appointments with the Department of Applied & Computational Mathematics & Statistics and is affiliated with the Center for Sustainable Energy at Notre Dame (ND Energy) and the Center for Nano Science and Technology (ND nano). His research combines chemical engineering, computational optimization, machine learning, and data science across three core themes: (1) molecular-to-systems (multiscale) modeling and optimization, (2) optimal design of experiments and statistical inference, and (3) machine learning for bridging timescales. Dowling earned his Ph.D. in Chemical Engineering from Carnegie Mellon University (2015) and a B.S.E. from the University of Michigan-Ann Arbor (2010). His research applications span energy markets and infrastructure, integrated energy systems, carbon capture and sequestration, circular economies for refrigerants, sustainable hydrogen production, critical minerals, and advanced separation technologies like membranes and ionic liquids. Education Ph.D., Carnegie Mellon University, 2015 B.S.E., University of Michigan-Ann Arbor, 2010 His recent publications focus on multiscale modeling frameworks, Bayesian hybrid models for uncertainty quantification, and machine learning applications in energy systems and materials design. These works emphasize techno-economic analysis, dynamic process optimization, and uncertainty mitigation strategies. Scientific awards include the 2025 James A. Burns, C.S.C., Award and the 2020 NSF CAREER Award (CBET-1941596). He actively mentors graduate students and postdoctoral researchers, with lab alumni including Dr. Bridgette Befort (2022), Dr. Xian Gao (2022), Dr. Ke Wang (2025), and Dr. Jialu Wang (2024).

İLKAY SİBEL KERVANCIView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Machine Learning
• +5 more

İLKAY SİBEL KERVANCI serves as an Assistant Professor in the Department of Computer Engineering at Gaziantep University's Faculty of Engineering. Her academic career spans teaching and research in artificial intelligence, machine learning, and data mining with practical applications across finance and bioinformatics sectors. PhD in Computer Engineering, Çukurova University (2023) MSc in Informatics, Kahramanmaraş Sütçü İmam University (2017) BSc in Computer Engineering, Kocaeli University (2004) Her research centers on machine learning applications for cryptocurrency price forecasting using LSTM and GRU networks, neutrosophic logic implementations in regression problems, and drug-target interaction prediction. Recent work demonstrates expertise in hyperparameter optimization, time series analysis, and handling imbalanced datasets through hybrid feature reduction techniques. She actively contributes to advancing neural network architectures for financial and biomedical challenges. Publication trends reveal consistent focus on Bitcoin price prediction (6 publications since 2017), expanding into drug-target interaction modeling and neutrosophic applications. Her work bridges theoretical machine learning with industrial applications in cement manufacturing, stock markets, and pharmaceutical research through recurrent neural networks and optimization frameworks. Dr. Kervanci teaches graduate courses including Introduction to Artificial Intelligence Methods and Introduction to Data Mining Methods, alongside undergraduate courses such as Artificial Intelligence in Engineering and Discrete Mathematics, demonstrating commitment to computational education across academic levels.

Serkan ÖzbayView profile

Associate Professor

• Electrical and Electronics Engineering
• Signal Processing
• Computer Vision
• +4 more

Serkan Özbay serves as an Associate Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, Turkey. With academic appointments since 2003, he progressed from Lecturer to his current position (2024), maintaining continuous affiliation with the institution where he also completed all his degrees. His career demonstrates deep institutional integration and vertical advancement within the same department. His academic credentials were entirely earned at Gaziantep University: Doctorate (2007-2015): Institute of Science, Electrical and Electronic Engineering Master's (2003-2006): Institute of Science, Electrical and Electronics Engineering (Thesis) Licence (1997-2002): Faculty of Engineering, Electrical and Electronics Engineering Özbay's research centers on signal processing and machine learning applications, with significant contributions to computer vision for medical diagnostics (sinus segmentation, lung cancer detection) and security systems (image/video forgery analysis). His work bridges theoretical algorithms with practical implementations in edge computing and biomedical devices, evidenced by projects like real-time fall detection systems and metamaterial antennas. Recent publications show intensified focus on deep learning since 2020, particularly convolutional neural networks for medical imaging and object tracking. His publication trajectory reveals strategic interdisciplinary expansion: early work (2007-2015) concentrated on spectrum sensing and wireless communications, while post-2020 output pivoted toward AI-driven medical applications and digital forensics. Approximately 70% of his 22 publications (2019-2023) involve deep learning implementations, with strong institutional collaboration patterns—85% co-authored with Gaziantep University researchers. Journal publications predominantly appear in Q2-Q4 SCI-indexed engineering journals, while conference papers target international venues in computer vision and biomedical engineering. As an academic advisor, Özbay has supervised 16 graduate theses (1 PhD, 15 Master's) between 2017-2025, with 11 completed in 2020-2023 alone. Current advisees include students developing Raspberry Pi-based plate recognition systems and real-time fall detection solutions. He additionally mentors 15 undergraduate students in senior design projects annually, as evidenced by 2024 EEE498/499 project meetings. Administrative leadership includes Deputy Head of Department (2016), Erasmus Coordinator (2015-2016), and Institute Board membership (2020-2023).

• Machine Learning
• Deep Learning
• Statistical Signal Processing
• +3 more

Tadas Žvirblis serves as an Associate Professor within the Interdisciplinary Statistical Research Group at Vilnius University's Institute of Data Science and Digital Technologies, maintaining his research office at Akademijos St. 4, room 604A in Vilnius. His academic profile bridges theoretical statistics with practical engineering and medical applications through advanced computational methodologies. His research program centers on machine learning and deep learning innovations for complex signal analysis, with dual specializations in biomedical diagnostics (EEG, NIRS, cardiovascular monitoring) and industrial systems (conveyor mechanics, gear fault detection, engine emissions). Key methodological contributions include novel data augmentation techniques for time series, generative modeling of vibration signals, and prognostic frameworks for reliability engineering, demonstrating consistent interdisciplinary collaboration across medical and engineering domains. Analysis of his 13 publications from 2023-2025 reveals a strategic research trajectory applying deep learning to data-scarce scenarios, particularly in biomedical signal interpretation and industrial predictive maintenance. His work shows increasing focus on clinical applications since 2024, including ECMO mortality prediction and aortic morphology studies, while maintaining strong industrial engineering output through IEEE conference publications on conveyor systems and engine diagnostics. Dr. Žvirblis actively supervises doctoral research as Senior Researcher for Gajane Mikalkėnienė's project (2023-2027) developing EEG-based depression diagnosis methods under Informatics field N 009. His grant portfolio includes multiple industry-collaborative projects evidenced by co-authorship with clinical researchers and engineering teams across Lithuania, Poland, and Germany. As a core member of the Interdisciplinary Statistical Research Group, he contributes to the unit's mission of advancing statistical methodologies for real-world data challenges, with particular emphasis on time-series analysis in non-stationary environments. His laboratory work integrates signal processing hardware with deep learning frameworks to address industrial automation and medical monitoring challenges.

Jong-Weon LeeView profile

Professor

• Augmented Reality
• Virtual Reality
• 3D Interaction
• +4 more

Jong-Weon Lee is a Professor in the Department of Contents Software at Sejong University and a researcher at the eXtended Reality Research Center. With a focus on immersive technologies and human-computer interaction, he actively contributes to advancements in Augmented Reality (AR), Virtual Reality (VR), and 3D interaction systems. Ph.D. in Computer Science (2002) from the University of Southern California M.S. in Computer Science (1991) from the University of Wisconsin-Madison B.S. in Computer Science (1989) from Ohio University His research explores the intersection of AR/VR, deep learning, and practical applications in agriculture and crisis response. He develops lightweight CNN architectures for plant disease detection in low-resource environments, while also creating multimodal frameworks like CBRCL to detect hate speech in text-image pairs during geopolitical crises. His work extends to VR-based emotion detection systems for healthcare, leveraging neural networks to alleviate negative emotional states in real-time. Recent publications highlight his expertise in optimizing YOLO models for fire detection via Bayesian hyperparameter tuning, enhancing detection accuracy in large-scale datasets. His collaborations span international institutions, reflecting a commitment to cross-disciplinary innovation.

Sébastien Le DigabelView profile

Professor

• Operations Research
• Mathematical Optimization
• Black-box Optimization
• +5 more

Sébastien Le Digabel is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds affiliations with GERAD (Groupe d'études et de recherche en analyse des décisions) and IVADO (Institut de valorisation des données), where he contributes to research in optimization and artificial intelligence. Professor Le Digabel's research focuses on black-box optimization and derivative-free methods, particularly the Mesh Adaptive Direct Search (MADS) algorithm. His work spans three main areas: development of optimization methods, application to engineering problems, and development of optimization software. He has made significant contributions to extending MADS for constrained optimization, mixed-variable problems, multiobjective optimization, and stochastic optimization. His recent publications (2023-2025) demonstrate continued innovation in handling complex optimization challenges including categorical variables, hierarchical constraints, and multi-fidelity approaches. These works appear in top journals like Computational Optimization and Applications, SIAM Journal on Optimization, and Optimization and Engineering. Professor Le Digabel actively supervises graduate students, having completed 8 PhD theses and 10 Master's theses with topics spanning multiobjective optimization, hyperparameter tuning, smart grid applications, and power systems optimization. His research is supported by diverse funding sources including NSERC, FRQNT, InnovÉÉ, IVADO, Hydro-Québec, Rio Tinto, and Huawei-Canada, reflecting the broad applicability of his work across academic and industrial domains. Professor Le Digabel leads the NOMAD Research Group and is the primary developer of the NOMAD optimization software, which implements the MADS algorithm and serves as a standard tool in the derivative-free optimization community. The software is available at www.gerad.ca/nomad and continues to evolve with new capabilities for challenging optimization problems.

Matthew Joseph KusnerView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +5 more

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

Raghav GnanasambandamView profile

Assistant Professor

• Scientific Machine Learning
• Surrogate Modeling
• Uncertainty Quantification
• +2 more

Raghav Gnanasambandam is an Assistant Professor in the Industrial & Manufacturing Engineering department at Florida A&M University-Florida State University College of Engineering. He holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2024) and a dual bachelor's/master's degree in Mechanical Engineering from IIT Madras (2019). His research focuses on Scientific Machine Learning, Surrogate Modeling, Uncertainty Quantification, and Metal Additive Manufacturing. Educational Background: Ph.D., Industrial and Systems Engineering, Virginia Tech (2024) Dual Degree (B.S./M.S.), Mechanical Engineering, IIT Madras (2019) He develops physics-informed machine learning techniques for digital twins in manufacturing, with applications in laser scan path optimization, thermal modeling, and multi-physics simulations. His work combines deep learning with physical constraints to improve process efficiency and predictive accuracy. Scientific Awards: ISE Outstanding Doctoral Student, Virginia Tech, 2023 As principal investigator of the M2M Research Group, he leads projects supported by Amazon Web Services. His teaching includes courses in manufacturing systems and machine learning applications.

• Machine Learning
• Deep Learning
• Time Series Analysis
• +4 more

Dr. Jurgen van den Hoogen serves as a Researcher at Tilburg University's Department of Computational Cognitive Science within the Tilburg School of Humanities and Digital Sciences. Having recently completed his doctoral studies with thesis publication in January 2025, he represents an emerging researcher in the field of machine learning applications for time series data. His research focuses on innovative applications of Convolutional Neural Networks for time series analysis, with particular emphasis on industrial fault detection and seismic activity monitoring. Dr. van den Hoogen's work demonstrates how specialized CNN architectures can effectively process raw sensor data with minimal computational requirements, making them suitable for edge computing applications. His doctoral research made significant contributions to the field, particularly in developing wide-kernel CNN architectures optimized for time series data processing. The research demonstrated that processing multivariate time series in a univariate manner with separate inputs yields optimal results, and that graph-based architectures significantly improve performance in seismic domains characterized by extensive sensor networks. Dr. van den Hoogen's methodological expertise includes architectural optimizations through adaptive input layers and residual learning, hyperparameter analysis specifically tailored for time series CNNs, and transfer learning applications for both classification and regression tasks in time series analysis.

Marenglen BibaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Marenglen Biba is a Full Professor and Dean of the Faculty of Engineering and Architecture at the University of New York Tirana, where he also chairs the Department of Computer Science. His academic journey includes a Ph.D. in Computer Science from the University of Bari, Italy (2009), and a Laurea degree cum laude from the same institution (2004). His research spans: Core AI/ML : Machine learning algorithms, statistical relational learning, pattern recognition Language technologies : NLP for Albanian, stemming, morphological analysis Applied domains : Computational biology, social network mining, emotion detection Recent publications (2018–2021) focus on NLP for low-resource languages, deep learning surveys, and emotion-aware data mining. Award highlights include: ICT Academic of the Year 2012 (Albania) Best Paper Award at ILP 2008 Multiple University of Bari research grants He has led EU-funded projects like ADRIATinn (€4.6M budget) and maintains collaborations with the University of Washington, University of Bari, and Asian universities. Administrative leadership includes decade-long oversight of UNYT's Computer Science department and current deanship.

• Energy Systems
• Smart Grids
• Demand Response
• +3 more

Mikko Nykyri is a Post-doctoral researcher at the Department of Electrical Engineering within the School of Energy Systems at Lappeenranta-Lahti University of Technology (LUT). His research focuses on energy systems with particular emphasis on smart grids, energy communities, and demand response mechanisms. His work bridges theoretical approaches with practical applications in the energy sector, aligning with LUT's strategic focus on energy transition. Dr. Nykyri's research interests center around the intersection of energy systems and digital technologies. He investigates how machine learning, particularly deep reinforcement learning, can optimize energy efficiency in buildings and industrial processes. His work on energy communities explores economic models, blockchain-based settlement systems, and educational approaches through serious games. He also maintains research interests in power electronics, including inverter technologies and semiconductor device characterization. His research demonstrates strong interdisciplinary connections across electrical engineering, computer science, and energy economics. An analysis of Dr. Nykyri's publication history reveals a clear evolution from fundamental power electronics research toward increasingly complex system-level applications. His early work focused on specific technical challenges in power conversion and semiconductor devices, while his recent publications address holistic energy system challenges including community-scale economics, AI-driven optimization, and educational frameworks. This progression reflects the broader energy transition from component-level improvements to integrated system solutions, with particular emphasis on the role of digitalization in enabling new energy paradigms. Dr. Nykyri has established a robust collaborative network, frequently working with researchers including Tommi J. Kärkkäinen, Saku Levikari, and Pertti Silventoinen. His peer review activity for Applied Energy demonstrates his recognized expertise in the field. His research maintains a practical orientation, with multiple studies involving real-world industrial applications and educational demonstrators that bridge academic research with industry implementation needs.

• Control Theory
• Quantum Systems
• Partial Differential Equations
• +4 more

Thomas Chambrion is a Professor of Applied Mathematics at the University of Burgundy, affiliated with the engineering school ESIREM and the Institute of Mathematics of Burgundy (IMB). He leads the Statistics, Probability, Optimization and Control (SPOC) research team at IMB, focusing on theoretical and applied aspects of control systems. His research encompasses: Quantum control : Infinite-dimensional bilinear systems, Schrödinger/Gross-Pitaevskii equations, molecular rotation Optimization : Real-time constraints, switching systems, signal denoising algorithms Cross-disciplinary applications : Fluid dynamics (swimmer locomotion), MRI pulse design, industrial process modeling Recent publications (2020-2024) show a strong emphasis on controllability analysis for quantum systems and development of efficient algorithms for signal processing. His work frequently employs geometric methods, perturbation theory, and statistical modeling. No grants or awards are detailed in the available information. His laboratory, SPOC, operates within the Institute of Mathematics of Burgundy, collaborating extensively with researchers in quantum physics and engineering.

Christoph TreudeView profile

Associate Professor

• Software Engineering
• Empirical Studies
• Natural Language Processing in Software Development
• +5 more

Christoph Treude is an Associate Professor of Computer Science at Singapore Management University. His academic journey includes senior lecturer positions at the University of Melbourne and the University of Adelaide, and postdoctoral research at McGill University, the University of São Paulo, and the Federal University of Rio Grande do Norte. Treude's research focuses on improving software quality and developer efficiency through better access to relevant information. His methodology combines empirical studies with tool development that considers natural language artifacts in software repositories. His work spans several key areas of software engineering: Empirical studies of developer behavior and practices Integration of large language models in software development Human-AI collaboration frameworks Software documentation quality and maintenance Reproducibility in scientific software Security practices in code reviews His recent publications demonstrate a strong focus on the intersection of artificial intelligence and software engineering, examining how AI technologies can enhance developer productivity while maintaining code quality and security. His research often involves large-scale empirical studies across various software ecosystems and developer communities. Treude has received significant recognition for his contributions: ARC Discovery Early Career Research Award (2018-2020) Four best paper awards, including two ACM SIGSOFT Distinguished Paper Awards He serves on the Editorial Boards of IEEE Transactions on Software Engineering and Springer's Empirical Software Engineering journal, and holds the role of Open Science Editor for Elsevier's Journal of Systems and Software. Treude has chaired major conferences including ICSME 2020, ICPC 2023, and TechDebt 2023, and regularly participates in software engineering conference program committees. His research has been funded by industry leaders including Google, Facebook, and DST, and he has authored over 150 scientific articles with more than 250 co-authors.