جست‌وجوی استاد و پژوهشگر

Theo Hofmanمشاهده پروفایل

دانشیار

• Powertrain Systems
• Automotive Engineering
• Hybrid Electric Vehicles
• +۵ مورد دیگر

Theo Hofman is an Associate Professor and Program Director in the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He specializes in integrated design methods for complex engineering systems, focusing on powertrain systems for automotive, maritime, and aerospace applications. His work emphasizes computational design synthesis, machine learning, and model-based optimization. Education: Hofman holds an MSc (1999) and PhD (2007) in Mechanical Engineering from TU/e. He has held roles at Thales Cryogenics and Drivetrain Innovations before joining TU/e. He also served as an Invited Professor at ETH Zurich and Université Polytechnique Hauts-de-France. Research Interests: His research spans hybrid electric vehicles, powertrain design, energy management systems, and sustainable transportation. Key areas include automated design tools, thermal management, and co-design of plant and control systems. Applications include electric trucks, ships, and aircraft. Articles Trends: His recent publications (2021–2025) emphasize electric vehicle infrastructure optimization, battery systems, and control strategies. Key themes include energy efficiency, thermal management, and co-design methodologies for automotive and mobility systems. Scientific Awards: IEEE VPPC 2024 Best Paper Award. Advising & Grants: He has supervised over 104 MSc, 14 PDEng, and 10 PhD students. Active projects include the 'Green Transport Delta' initiative (2021–2024) and Bosch Transmission collaborations. His courses include 'Electric and Hybrid Vehicle Powertrain Design' and 'Automotive Systems Engineering Project.' Labs/Teams: He leads the Group Hofman and collaborates with the MEGEVH (France) and TU/e’s EAISI Mobility initiative. His work contributes to UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and climate action.

Mykola Pechenizkiyمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Predictive Analytics
• +۵ مورد دیگر

Mykola Pechenizkiy is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), holding the Data Mining Chair. He also serves as an Adjunct Professor in Data Mining for Industrial Applications at the University of Jyväskylä. His research focuses on predictive analytics, data mining, and responsible AI, addressing real-world challenges in industry, healthcare, and education. He leads the Customer Journey research program at the Data Science Center Eindhoven, emphasizing ethical and transparent analytics. Academically, he holds a PhD from the University of Jyväskylä (2005) and has held visiting researcher positions at institutions like Columbia University and NYU. He has co-authored over 300 peer-reviewed publications and serves on editorial boards and committees for leading conferences (e.g., AAAI, IJCAI). He is the President of the International Educational Data Mining Society (IEDMS). His research interests include concept drift adaptation, sparsity techniques in neural networks, and fairness-aware AI. He has led projects such as the TKI PPS KPN Smart Two initiative and collaborates with industries like ASML, Philips, and Rabobank. His work contributes to UN SDGs, particularly in sustainable development through AI-driven solutions. Awards: Best Demo Paper Award (IEEE ICDE 2023), Best Paper Awards (ALA 2022, LoG 2022), and SensorKDD 2009 recognition. Grants/Projects: Active projects include TKI PPS KPN Smart Two (2019–2025) and Smart One W&I TKI KPN Flagship (2018–2022). Labs/Teams: Affiliated with EAISI Health, SIKS Scientific Board, and the University of Waikato’s AI Institute.

Tom Dhaeneمشاهده پروفایل

استاد

• Data-Efficient Machine Learning
• Surrogate Modeling
• Gaussian Processes
• +۳ مورد دیگر

Tom Dhaene is a Full Professor at Ghent University, affiliated with the Department of Information Technology (INTEC-IDLab) within the Faculty of Engineering and Architecture (FEA). He also holds a position at imec, a research and innovation hub in nanoelectronics and digital technologies. Research Unit: Internet Technology and Data Science Lab (IDLab) Academic Rank: Full Professor Affiliations: Ghent University, imec His research focuses on data-efficient machine learning, surrogate modeling, Gaussian processes, Bayesian optimization, and system identification. He has developed widely used software tools such as the SUMO toolbox and ooDACE, and holds 5 U.S. patents. His work bridges theoretical advancements with practical applications in engineering and biomedical domains. Recent publications highlight his contributions to physics-informed machine learning, antenna design, microwave optimization, and healthcare applications. Notably, he explores Bayesian active learning, multi-objective optimization under uncertainty, and efficient modeling techniques for complex systems. Prof. Dhaene's research has been recognized through over 500 peer-reviewed publications and collaborations across academia, industry, and government sectors globally.

• Machine Learning
• Bioinformatics
• Computational Biology
• +۵ مورد دیگر

Jian Peng is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on computational biology, machine learning, and their applications to protein structure prediction, drug design, and molecular modeling. He has contributed to advancements in antibody engineering, protein-ligand docking, and generative models for biological systems. Key research areas include: Machine Learning for Molecular Modeling Protein Structure Prediction Antibody and Peptide Design Genomics and Single-Cell Analysis Structure-Based Drug Discovery His work emphasizes integrating deep learning techniques with biological datasets to address challenges in precision medicine, drug development, and systems biology. Notable achievements include developing the FastFold system to accelerate AlphaFold training and pioneering flow-based methods for antibody design. Awards include the Overton Prize (2020), recognizing contributions to computational biology. His research has been published in top journals and conferences, spanning topics from protein mutation prediction to geodesic-based immune complex modeling.

Fethiye Irmak Doganمشاهده پروفایل

پژوهشگر

• Human-Robot Interaction
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.

Jakoah Brgochمشاهده پروفایل

استادیار

• Machine Learning in Materials Science
• Solid-State Lighting
• Superhard Materials
• +۳ مورد دیگر

Jakoah Brgoch is an Assistant Professor in the Department of Chemistry at the University of Houston. His research focuses on leveraging machine learning to design inorganic compounds for applications in LED-based lighting and superhard materials. Key areas include phosphor development, sparse data handling, and predicting material formation. He leads the Brgoch Group, which emphasizes interdisciplinary approaches combining computational modeling and experimental synthesis. Research interests span luminescent materials, crystal chemistry, and defect engineering, with a particular emphasis on optimizing phosphors for solid-state lighting and high-performance materials under extreme conditions. His work bridges data science and traditional materials discovery to accelerate innovation in optoelectronics and mechanical materials. Recent publications highlight advancements in cyan-emitting nitridation processes, machine learning-guided phosphor discovery, and understanding oxidation resistance in silicides. His team has developed novel phosphors like Na2CaZr2Ge3O12:Cr³⁺ for NIR bioimaging and explored luminescent properties of Sr-based solid solutions. Active in translational research, Dr. Brgoch collaborates on applications like smartphone-readable diagnostic platforms using nanophosphors and point-of-care testing. His lab emphasizes open science practices and has pioneered methods like Single-crystal automated refinement (SCAR) for structural determination.

Hasan Davulcuمشاهده پروفایل

استاد

• Sociocultural Modeling
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.

• Marine Propulsion Systems
• Hydrodynamics of High-Speed Vehicles
• Ship Performance Analysis
• +۶ مورد دیگر

Sverre Steen is a Professor and Head of the Department of Marine Technology at the Norwegian University of Science and Technology (NTNU). He leads the Kongsberg Maritime University Technology Centre focused on 'Ship Performance and Cyber-physical Systems' and is a member of the standing committee for the Symposium of Marine Propulsors. His research emphasizes ship propulsion, hydrodynamics, and big data analysis of in-service vessel performance. Key interests include seakeeping, high-speed marine vehicles, and model testing techniques. Steen teaches TMR 4217 Hydrodynamics of High-Speed Marine Vehicles , covering cavitation, experimental hydrodynamics, and propulsion systems. He collaborates internationally on projects like the Norwegian Ocean Technology Centre. His recent work explores wave-energy extraction via hydrofoil vessels, resistance modeling for fast ferries, and propulsion efficiency in real sea states. He has contributed to global shipping emission models (MariTEAM) and reliability analysis of structural components under vibration. Steen's publications span propulsion in waves, engine-propeller dynamics, and data-driven methods for ship performance monitoring. His applied research bridges experimental testing and computational modeling to address challenges in sustainable maritime transport and operational safety.

• Fluid Dynamics
• Turbulence
• Machine Learning
• +۶ مورد دیگر

Adrián Lozano-Durán is an Associate Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Guggenheim Laboratory for Aeronautics (GALCIT). He holds a B.S., M.S., and Ph.D. from the Polytechnic University of Madrid (2010–2015) and joined Caltech as a Visiting Associate in 2024 before becoming a faculty member in the same year. His research focuses on fluid dynamics, turbulence, and machine learning applications in computational fluid dynamics (CFD), particularly for aerospace systems. He leads the Aerofluids, Learning & Discovery (ALD) Lab, collaborating with MIT’s AeroAstro department. Key research areas include causal inference in fluid systems, reduced-order modeling, and machine-learning-based closure models for large-eddy simulation (LES). His work addresses challenges in low-speed aerodynamics, supersonic, and hypersonic flows. Notable recent contributions include advancements in LES wall models and information-theoretic approaches to turbulence control. He frequently presents at international conferences and has co-authored high-impact papers in Nature Communications , Journal of Fluid Mechanics , and Physical Review Research . Education: B.S., Polytechnic University of Madrid (2010) M.S., Polytechnic University of Madrid (2012) Ph.D., Polytechnic University of Madrid (2015) Affiliations: GALCIT, Caltech AeroAstro, MIT (collaboration) Advising focuses on students like Álvaro Martínez-Sánchez and Tristan, whose work spans causality in turbulence and flow control. He actively engages in interdisciplinary research, bridging fluid mechanics with machine learning and information theory to advance aerospace engineering solutions.

• Food Technology
• Food Processing Engineering
• Electrotechnologies in Food Preservation
• +۴ مورد دیگر

Henry Jäger is a Professor in Food Technology at the University of Natural Resources and Life Sciences, Vienna (BOKU) since 2014. Previously, he worked as a Project Manager at Nestlé (2012-2014) and Lecturer at TU Berlin (2012-2017), following his PhD/Postdoc at TU Berlin (2006-2012) and Diploma in Food Technology (2000-2006). His research focuses on electrotechnologies in food processing , particularly pulsed electric fields (PEF) and ohmic heating , for microbial inactivation, food preservation, and quality optimization. He explores applications in plant material processing , gluten-free baking , and novel food preservation methods . His work also addresses edible insect processing for allergenicity reduction and protein recovery. Recent publications (2025-2024) analyze synergies between PEF and ohmic heating, biofilm imitation systems for hygiene validation, and computational models for sterilization processes. These studies span food safety , sustainable processing , and functional food design . Henry Jäger actively contributes to scientific communities, serving on the EFFoST managing board , as scientific advisor for food conferences, and as reviewer for journals like Food Chemistry and Trends in Food Science & Technology . He has organized workshops on PEF applications and contributed to EU food technology initiatives.

Yanchao Liuمشاهده پروفایل

دانشیار

• Industrial Engineering
• Operations Research
• Transportation Systems Optimization
• +۴ مورد دیگر

Dr. Yanchao Liu is an Associate Professor at Wayne State University's College of Engineering, Department of Industrial and Systems Engineering. He has received research funding from the National Science Foundation and the State of Michigan, including the NSF Career Award. His academic career spans prior industry roles as a Data Scientist and Manager of Advanced Analytics at Sears Holdings Corporation (2016-2017) and Director of Brand Marketing Analytics at Catalina Marketing Corporation (2017). He teaches courses in data science, IoT, and stochastic processes. B.S. Industrial Engineering, Huazhong University of Science and Technology (2006) M.S. Industrial Engineering, University of Arkansas (2008) Ph.D. Industrial and Systems Engineering, University of Wisconsin-Madison (2014) Dr. Liu's research focuses on mathematical modeling for transportation systems, industrial AI, and data analytics. His work addresses drone traffic management, battery-constrained delivery routing, and optimization algorithms for urban mobility. He has developed novel methods for UAV safety diagnostics, random forest implementations, and fairness-aware path planning in urban air mobility. His publications span journals like Journal of Guidance, Control and Dynamics , Transportation Research Part C , and IEEE Transactions on Intelligent Transportation Systems , with conference contributions at IISE and FAIM. His research combines theoretical advancements with practical applications in smart cities and logistics. NSF Career Award (2020) Faculty Research Excellence Award (2021) IEEE PES Best Conference Paper (2015) IEEE Transactions on Smart Grid Best Reviewer (2015) Hubei Province Distinguished Bachelor’s Thesis Award (2006) Dr. Liu advises PhD students like Zhenyu Zhou and J. Chen. He has contributed to energy market modeling (with M.C. Ferris) and published extensively on drone operations, machine learning algorithms, and stochastic processes. His work includes U.S. patent pending applications for UAV safety systems.

Anant Sahaiمشاهده پروفایل

استاد

• Machine Learning
• Wireless Communication
• Information Theory
• +۴ مورد دیگر

Anant Sahai is the Qualcomm Chair Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He holds affiliations with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Laboratory for Information and System Sciences (BLISS), and the Berkeley Wireless Research Center (BWRC). His academic journey includes a BS from UC Berkeley (1994), and MS (1996) and PhD (2001) degrees from MIT. He previously worked at Enuvis, Inc., focusing on adaptive software radio techniques for low-SNR GPS environments. Research interests span machine learning, wireless communication, information theory, signal processing, and decentralized control, with a focus on intersections between these fields. Key areas include spectrum sharing, ultra-reliable low-latency wireless protocols, and the foundations of overparameterized machine learning. Recent work explores in-context learning in modern AI models. He has received awards such as the IEEE ComSoc Leonard G. Abraham Prize (2012) and teaching/mentorship accolades at Berkeley. He advises UC Berkeley’s Eta Kappa Nu chapter and coordinates machine learning efforts for NSF’s SpectrumX. Current teaching includes CS 182/282A on deep neural networks. His lab focuses on theoretical and applied challenges in communication systems, AI, and control theory. Awards: IEEE ComSoc Leonard G. Abraham Prize (2012), Teaching Excellence Awards (2015–2017) Grants: NSF Center for Spectrum Innovation (SpectrumX), multiple collaborative projects in wireless and AI Labs/Teams: BLISS, BAIR, BWRC

Brad Hayesمشاهده پروفایل

دانشیار

• Collaborative Robotics
• Human-Robot Interaction
• Artificial Intelligence
• +۵ مورد دیگر

Brad Hayes is an Associate Professor of Computer Science at the University of Colorado Boulder within the College of Engineering and Applied Science, where he directs the Collaborative AI and Robotics (CAIRO) Laboratory. He also serves as Chief Technology Officer at Circadence, leading efforts in developing AI-enabled products for cybersecurity training and assessment. Undergraduate degree from Boston College PhD in Computer Science from Yale University Postdoctoral Associate at MIT Professor Hayes' research focuses on developing techniques that enable autonomous agents and robots to learn from and collaborate with humans safely, reliably, and productively. His work occurs at the intersection of pervasive and personalized artificial intelligence, human-robot teaming, and decision support. He has made significant contributions to collaborative robotics, dependable explainable AI, and imitation learning, with applications spanning manufacturing, healthcare, disaster response, autonomous vehicles, and space exploration. His recent publications reveal a strong emphasis on human-robot interaction, with particular focus on improving predictability in collaborative tasks, developing explainable AI systems that build trust, leveraging augmented and virtual reality for enhanced collaboration, and creating more efficient learning algorithms from human demonstrations. His work increasingly integrates large language models and advanced neural network architectures while maintaining a strong human-centered design approach. Sustainability Recognition (2025) for computational efficiency in motion planning Best Student Paper Runner-up at AAMAS 2022 Nominated for Best Technical Paper at HRI 2024 Best Technical Paper Runner-up at HRI 2019 Hayes has successfully mentored numerous graduate students through the CAIRO Lab, including multiple PhD graduates in 2024 alone. His lab receives funding from various organizations supporting research in human-robot interaction and collaborative AI. He frequently collaborates with industry partners and has established connections with major technology companies through his research and speaking engagements. The CAIRO Lab, under Hayes' direction, is a vibrant research environment focused on turning theoretical concepts into practical applications through hands-on work with real robots and human participants. The lab's research spans multiple domains including manufacturing, disaster response, autonomous vehicles, and space exploration, with a consistent emphasis on safe and effective human-machine teaming.

Changyou Chenمشاهده پروفایل

دانشیار

• Multi-Modal Learning
• Vision and Language
• Foundation Models
• +۷ مورد دیگر

Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.

Veysel Murat İstemihan Gençمشاهده پروفایل

استاد

• Power Systems
• Transient Stability Assessment
• Machine Learning in Power Systems
• +۷ مورد دیگر

Veysel Murat İstemihan Genç is a Professor in the Department of Electrical Engineering at Istanbul Technical University (ITU), College of Engineering. His research is centered on modern power systems, with a focus on transient stability, cybersecurity, and integration of renewable energy sources. He actively leads multiple research projects and supervises graduate students in advanced power system technologies. Research Interests: His work spans key areas including transient stability assessment, machine learning applications in power systems, cyber-attack detection in AGC systems, and dynamic security evaluation under high renewable penetration. He employs cutting-edge techniques such as ensemble learning, deep neural networks, and hybrid optimization algorithms. Publication Trends: Recent publications (2023–2025) highlight a strong trend toward integrating AI and machine learning for real-time transient stability prediction, cybersecurity in distributed energy systems, and performance optimization of solar and wind-integrated grids. His work frequently addresses challenges in low-inertia systems and false data injection attacks. Scientific Projects: Strengthened Machine Learning-Based Dynamic Security Evaluation for Transient Stability under False Data Injection Attacks (BAP, 2025) Analysis and Control Methods for Stability of Large-Scale Low-Inertia Power Systems (BAP, 2023–2024) Dynamics Security Evaluation of Renewable-Rich and Cyber-Attacked Power Systems (BAP, 2022–2024) Risk-Based Stability Assessment and Corrective Control Methods in Power Systems (BAP, 2019–2022) Wide-Area Monitoring Protection and Control System Design Using Advanced Signal Processing and Machine Learning (TÜBİTAK, 2018–2020) Advising and Grants: He is the principal investigator (PI) on multiple funded research projects from BAP and TÜBİTAK, indicating strong grant acquisition and leadership. His supervision of 27 ongoing theses reflects an active role in mentoring graduate students in electrical engineering and power systems. Labs and Teams: While specific lab names are not mentioned, his projects suggest leadership in a research group focused on smart grid technologies, AI-enabled power system security, and renewable integration at Istanbul Technical University.