جستجوی استاد راهنمای دانشگاهی

• Wireless Communications
• Information Theory
• Communication Networks
• +۱۸ مورد دیگر

Cenk Gursoy is a Professor in the Department of Electrical Engineering and Computer Science at the College of Engineering and Computer Science, Syracuse University. He previously served as a faculty member at the University of Nebraska-Lincoln from 2004 to 2011. He holds a Ph.D. in Electrical Engineering from Princeton University and a B.S. from Bogazici University, Turkey. His research spans wireless communications, information theory, signal processing, and networking. Key areas include 5G/6G technologies, millimeter-wave communications, UAV-assisted networks, energy efficiency, intelligent reflecting surfaces, and machine learning for networking. He has made significant contributions to finite blocklength communications, QoS provisioning, and secure wireless transmissions. The recent publications highlight a strong trend in integrating machine learning—particularly deep reinforcement learning—into wireless resource allocation, network slicing, UAV trajectory planning, and anomaly detection. His work increasingly bridges theoretical information-theoretic models with practical implementations in emerging wireless systems such as RIS-aided networks, NOMA, and edge computing. NSF CAREER Award, 2006 2020 IEEE Region 1 Technological Innovation (Academic) Award IEEE Green Communications & Computing Technical Committee Best Journal Paper Award EURASIP Journal of Wireless Communications and Networking Best Paper Award IEEE PIMRC Best Paper Award Maude Hammond Fling Faculty Research Fellowship All-University Doctoral Prize (awarded twice to his students) Dr. Gursoy has advised over 20 Ph.D. and M.S. students, many of whom now hold positions at leading universities and tech companies. His research has been funded by multiple National Science Foundation grants as Principal or Co-Principal Investigator, focusing on fundamental limits of wireless systems, millimeter-wave networking, and green cloud platforms. He serves as an Area Editor for IEEE Transactions on Vehicular Technology and as an Editor for several other IEEE Transactions journals. He leads the Wireless Communications & Networking Lab and the Smart Vision Systems Lab at Syracuse University, fostering interdisciplinary research in smart networks, IoT, and intelligent sensing systems.

Tingting Liمشاهده پروفایل

مدرس

• AI for Cyber Security
• Automated Cyber Defence
• Diversification and Deception Strategies
• +۸ مورد دیگر

Dr. Tingting Li is a Lecturer (Assistant Professor) in Cyber Security at Cardiff University's School of Computing and Informatics and a member of the Centre for Cyber Security Research. She also holds an Honorary Research Fellow position at Imperial College London, reflecting her ongoing research collaboration. Her work bridges artificial intelligence and cybersecurity, with a focus on protecting critical systems such as cyber-physical systems (CPS), industrial control systems (ICS/SCADA), and autonomous vehicles. BEng (Hons) in Information Security, Xidian University, China MSc in Computing, Imperial College London PhD in Artificial Intelligence, University of Bath Dr. Li’s research interests lie at the intersection of AI and cybersecurity, particularly in automated cyber defense , diversification/deception strategies , and symbolic AI for knowledge representation . She investigates how AI can enhance the resilience of critical infrastructures through intelligent, adaptive defense mechanisms. Her recent work explores quantum-inspired reinforcement learning and machine learning models for proactive threat mitigation in complex systems. Her recent publications demonstrate a strong trend in applying advanced AI techniques—especially deep reinforcement learning, LSTM networks, and Bayesian models—to cybersecurity challenges in industrial and autonomous systems. These works span domains like IoT malware suppression, network diversity for ICS resilience, and automated compliance checking, reflecting a multidisciplinary approach combining security, AI, and systems engineering. Dr. Li has secured significant research funding, including a grant from the Alan Turing Institute (2024–2025) on AI safety in autonomous cyber defense, a RITICS/NCSC-funded project on diversity-based cybersecurity (2021–2022), and an EPSRC-funded project on metric-driven cybersecurity frameworks for critical national infrastructure (2021–2023). She actively supervises PhD students in areas including cybersecurity for autonomous vehicles, moving target defense, and cyber-physical system security. Her team includes Iryna Bernyk, Sanyam Vyas, Victoria Marcinkiewicz, Ellis Doran, Stephen Morris, and Sam Braithwaite. She also leads teaching modules on databases (CM6125/CM6625) and cybersecurity (CM6224/CM6724). Dr. Li’s research is conducted within the Centre for Cyber Security Research at Cardiff University, where she collaborates with experts in AI, security, and critical infrastructure protection. Her work is highly interdisciplinary, involving partnerships with institutions like Imperial College London and funding bodies such as EPSRC, NCSC, and the Alan Turing Institute.

• Quantum Machine Learning
• Reinforcement Learning
• Medical Informatics
• +۱۲ مورد دیگر

Jose David Martin Guerrero is a Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His research is conducted within the Intelligent Data Analysis Laboratory (IDAL), focusing on the intersection of quantum computing, machine learning, and biomedical applications. His research interests span Quantum Machine Learning , Reinforcement Learning for Quantum Control , Deep Learning for Medical Image Analysis , and Long-COVID Symptom Trajectory Modeling . He applies advanced AI techniques to solve complex problems in physics, healthcare, and engineering, particularly in modeling prostate cancer, hemodialysis, and post-COVID neurological and functional impairments. The recent publications highlight a strong trend toward integrating quantum computing with classical machine learning, especially in developing quantum neural networks, quantum autoencoders, and reinforcement learning for quantum gate optimization. Simultaneously, a significant body of work uses clustering, deep learning, and Bayesian modeling to analyze longitudinal clinical data from multicenter studies like LONG-COVID-EXP, aiming to understand symptom persistence and recovery patterns in post-COVID patients. Dr. Martin Guerrero earned his PhD from the Universitat de València in 2004 with a thesis on unsupervised techniques for web trend detection and collaborative filtering-based recommendation systems, supervised by Dr. Emilio Soria Olivas and Dr. Paulo Jorge Gomes Lisboa. His work demonstrates active collaboration across disciplines, particularly in clinical research involving multicenter studies, and in quantum computing applications in physics and finance. While no specific grants are mentioned, his involvement in large-scale clinical studies and quantum computing research suggests significant project leadership and funding acquisition. He leads research within the Intelligent Data Analysis Laboratory (IDAL) , which focuses on self-organizing maps, clustering, learning vector quantization, and their applications in security, healthcare, and quantum systems.

• Artificial Intelligence
• Generative AI
• Reinforcement Learning
• +۷ مورد دیگر

Peter Beling is a Professor at the University of Virginia School of Data Science and serves as Director of the National Security Data and Policy Institute. His research integrates systems theory and artificial intelligence, focusing on resilient, mission-aligned AI systems with applications in national security, including mission engineering, test and evaluation, and predictive maintenance. His educational background includes: Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley (1991) M.S. in Operations Research from The George Washington University (1987) B.A. in Mathematics from the University of Virginia (1984) Beling's research interests center on the co-design of algorithms and systems, with emphasis on generative AI, reinforcement learning, and large language models. His work promotes cyber resilience and trustworthiness in AI systems, particularly in high-stakes environments. He has led major research centers, including the Adaptive Decision Systems Laboratory and the UVA site of the NSF-funded Center for Visual and Decision Informatics. His recent scholarly output reflects a strong trajectory in trustworthy and resilient AI, with focus on national security applications and system-level integration of intelligent algorithms. He has held significant leadership roles, including Director of the Intelligent Systems Division at Virginia Tech’s National Security Institute (2021–2025) and active participation in the Systems Engineering Research Center (SERC), a DoD UARC. He has not received any explicitly mentioned scientific awards in the provided text. Beling advises students and leads research teams, though specific advisees are not listed. He has secured research funding through federally supported centers and institutes. His lab, the Adaptive Decision Systems Laboratory, has been a hub for interdisciplinary research in decision support and intelligent systems. He is affiliated with professional platforms including Google Scholar, ORCID, and LinkedIn, and maintains an active research profile.

Gabriel Pereiraمشاهده پروفایل

استادیار

• Critical Algorithm Studies
• Computer Vision
• Surveillance Studies
• +۵ مورد دیگر

Gabriel Pereira is Assistant Professor in AI & Digital Culture at the University of Amsterdam (UvA), based at the Media Studies department and the Institute for Logic, Language and Computation (ILLC). His primary affiliation is with the Natural Language Processing & Digital Humanities (NLP&DH) group. Pereira's research focuses on the critical study of data and algorithms, particularly as they intersect with vision, images, and surveillance systems. He brings an interdisciplinary approach that combines Science and Technology Studies (STS), critical algorithm studies, platform studies, and artistic interventions to examine how computational systems shape social relations and power structures. Pereira's research interests center on critical perspectives of computer vision and algorithmic governance, with particular attention to how these technologies operate in contexts of surveillance, platform labor, and digital culture. His work critically examines hegemonic computer vision systems that reinforce surveillance capitalism while exploring alternative ways of seeing and engaging with technology. He has conducted extensive research on Automated License Plate Recognition systems, WhatsApp disruptions in Brazil, and the political economy of smart surveillance projects like Curitiba's 'Digital Wall.' His methodology combines qualitative research with practice-based inquiry, including arts-based and interventionist approaches that challenge dominant technological narratives. His publication trends reveal a consistent focus on critical examinations of algorithmic systems, with growing attention to resistance practices, decolonial perspectives, and situated knowledge from the Global South. The articles show a trajectory from analyzing specific surveillance technologies toward broader theoretical frameworks for understanding algorithmic antagonisms and developing critical technical practices. His work consistently bridges theory and practice, often collaborating with artists and activists to create interventions that make visible the hidden operations of algorithmic systems. Independent Research Fund Denmark International Postdoc grant Deviant Practice Grant (Van Abbemuseum) Pereira actively organizes academic and artistic events, serving as Secretary of the Association of Internet Researchers and participating in the Tierra Común network. He co-organized the Con/Crit/Tec residency in São Paulo that brought together over 80 transdisciplinary researchers to explore alternative geographies of digital consciousness. His collaborative approach extends to numerous grant-funded projects examining platform labor, algorithmic surveillance, and critical data literacy. Pereira emphasizes organizing as an essential element of research work, facilitating knowledge exchange between academic, artistic, and activist communities. He is affiliated with multiple research collectives including the Center for Arts, Design and Social Research (CAD+SR), where he serves as a Researcher in Residence. His practice-based projects like 'Future Movement Future – REJECTED,' 'Algorithmic Sea,' and 'History of _rt' demonstrate his commitment to using artistic interventions as critical research methods. These projects often involve collaborations with artists, technologists, and activists to create alternative ways of engaging with and understanding algorithmic systems.

Mo-Yuen Chowمشاهده پروفایل

استاد

• Micro/Smart Grids Energy Management
• Collaborative Distributed Control
• Battery Modeling and State Estimation
• +۴ مورد دیگر

Mo-Yuen Chow is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NCSU), holding the position since July 1999. He also serves as a Qiushi Chair Professor at Zhejiang University, China, and a Chang Jiang Visiting Chair Professor (2010–2013). His research focuses on Micro/Smart Grids Energy Management, Collaborative Distributed Control, and Battery Modeling. He holds significant IEEE awards, including Fellow status (2007) and the Dr.-Ing. Eugene Mittelmann Achievement Award (2020). Chow earned his Ph.D. in Electrical Engineering from Cornell University (1987), following an M.Eng. (1983) and B.S. (1982) from the University of Wisconsin-Madison. His work spans battery health monitoring, distributed control systems, and cyber-physical security in smart grids. Notable contributions include innovations in battery state-of-charge estimation and resilient microgrid management frameworks. His publications emphasize cybersecurity in distributed energy systems, AI-driven energy management, and fault diagnosis in battery systems. He has pioneered frameworks like the DEED-ADMM algorithm for multi-energy systems and developed models for solid electrolyte interface growth in lithium-ion batteries. Awards: Over 10 major IEEE awards, including recognition for service and education. Research Themes: Smart grids, distributed control, battery systems, and cyber-physical resilience. Labs/Projects: DC microgrid testbeds and collaborative distributed energy management systems.

Anahita Khojandiمشاهده پروفایل

دانشیار

• Decision Making Under Uncertainty
• Reinforcement Learning
• Markov Decision Processes
• +۹ مورد دیگر

Anahita Khojandi is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, where she also holds the Heath Endowed Faculty Fellow in Business & Engineering title. She is also a Joint Associate Professor at the Bredesen Center for Interdisciplinary Research and Graduate Education and served as the Director of the Reliability and Maintainability Engineering Program from 2021 to 2024. Her interdisciplinary research spans healthcare, transportation, environmental engineering, and advanced manufacturing. Education: PhD in Industrial Engineering, University of Pittsburgh MS in Industrial Engineering, University of Pittsburgh BS in Industrial Engineering, Sharif University of Technology, Tehran, Iran Her research focuses on decision-making under uncertainty, reinforcement learning, Markov decision processes, and predictive analytics. These methodologies are applied to critical domains such as sepsis prediction, Parkinson’s disease treatment using wearable sensors, intelligent transportation systems, nuclear safety, and green infrastructure resilience. She integrates machine learning with stochastic modeling to improve real-time decision-making in complex systems. The recent publications highlight a strong trend toward AI-driven solutions in healthcare and infrastructure, with emphasis on explainability, anomaly detection, and real-time adaptation. Her work frequently appears in high-impact journals such as Management Science , IEEE Transactions , and Nuclear Technology , reflecting both methodological rigor and practical relevance. Scientific Awards and Honors: AAAS Rapid Response AI Fellow (2024) NIH AIM-AHEAD Fellowship in Leadership (2023) First Place, Harvey J. Greenberg Research Award (INFORMS, 2022) Best Paper Award, OMEGA Journal (2020, 2021) INFORMS Senior Member (2024) Teaching Award, IISE DAIS Division (2024) Khojandi has advised numerous graduate students and postdoctoral researchers, many of whom are co-authors on her publications. Her research has been supported by the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and other federal and institutional grants. She actively contributes to professional service, including leadership roles in INFORMS, where she served as Chair of the Diversity, Equity, and Inclusion Committee and currently serves as Vice President of Membership and Professional Recognition. She leads research initiatives at the intersection of industrial engineering and data science, often in collaboration with the Health Innovation Technology & Simulation (HITS) Lab and the Center for Transportation Research. Her lab focuses on developing AI-enabled decision support tools for clinical and operational environments, with future work targeting autonomous systems, personalized medicine, and climate-resilient infrastructure.

Maris Ozolsمشاهده پروفایل

استادیار

• Quantum Algorithms
• Quantum Information Theory
• Quantum Cryptography
• +۳ مورد دیگر

Maris Ozols is an Assistant Professor at the University of Amsterdam and a researcher at QuSoft, affiliated with the Algorithms and Complexity department at Centrum Wiskunde & Informatica (CWI). His primary research focuses on quantum algorithms and quantum information theory, with significant contributions to quantum complexity, quantum cryptography, and quantum state discrimination. His research interests span quantum algorithms, quantum information theory, quantum cryptography, quantum complexity, and theoretical computer science. Ozols has developed fundamental techniques in quantum query complexity, quantum state discrimination, and quantum cryptographic security models. His work often bridges theoretical computer science with quantum information physics, demonstrating practical implications for quantum computing architectures. His publication record shows consistent output in top venues including Communications in Mathematical Physics, Leibniz International Proceedings in Informatics, and Quantum journal. Recent work (2022-2025) focuses on quantum state discrimination, quantum circuit optimization, quantum machine learning, and cryptographic applications of quantum algorithms. His research demonstrates strong theoretical foundations with practical implications for quantum computing development. Leverhulme Early Career Fellow (University of Cambridge) Ozols has secured research funding through multiple Netherlands Organisation for Scientific Research (NWO) grants including the Quantum Software Consortium (QSC) and Quantum Computation with Bounded Space projects. His collaborative work spans international institutions including the University of Waterloo (where he earned his PhD), University of Cambridge, IBM Research, and various European quantum computing groups. He maintains active research groups in quantum algorithms at both QuSoft and CWI, with recent focus on quantum machine learning applications and quantum cryptographic protocols.

Steven Wuمشاهده پروفایل

دانشیار

• Machine Learning
• Algorithms
• Privacy
• +۵ مورد دیگر

Steven Wu is an Associate Professor in the School of Computer Science at Carnegie Mellon University, with primary appointments in the Software and Societal Systems Department (S3D) and affiliated roles in the Machine Learning Department, Human-Computer Interaction Institute, CyLab, and Theory Group. Previously, he held positions at the University of Minnesota (Assistant Professor) and Microsoft Research-New York City (post-doctoral researcher). Ph.D. in Computer Science, University of Pennsylvania (co-advised by Michael Kearns and Aaron Roth) His research spans Machine Learning , Algorithms , Privacy , and Fairness , focusing on responsible AI foundations, interactive learning, causal inference, and economic applications. Recent work explores uncertainty quantification and privacy risks in synthetic data. He has received prestigious awards including the NSF CAREER Award and Penn's Rubinoff Award for his dissertation. His group mentors students across Ph.D. , postdoc, and visiting programs, with alumni now at institutions like UC Berkeley, Stanford, and Amazon. Key grants: NSF, Okawa Foundation, Open Philanthropy, Amazon, Google, J.P. Morgan, Meta, Mozilla, Apple, Cisco

NECİP ALTAY ERENمشاهده پروفایل

استادیار

• Civil Engineering
• Construction Engineering
• Concrete Technology
• +۶ مورد دیگر

Necip Altay Eren serves as an Assistant Professor in the Construction Technology Department at Gaziantep University's Islahiye Vocational School, where he has held academic positions since 2014 (Lecturer until 2022, promoted to Assistant Professor in 2022). His institutional affiliation spans multiple units within Gaziantep University, including the Technical Sciences Vocational School (2014-2022) and current appointment at Islahiye Vocational School, with administrative duties as Head of Department since 2024. His educational background includes: Doctorate (2021): Gaziantep University, Institute of Science, Civil Engineering Master's-equivalent (2009): University of Connecticut, USA Bachelor's (2002): Gaziantep University, Faculty of Engineering, Civil Engineering (English Program) Dr. Eren's research centers on advanced concrete technologies, with emphasis on geopolymer concrete systems, fiber reinforcement mechanisms, and nano-material applications. His work investigates critical performance aspects including mechanical properties under extreme conditions (acid/sulfate attack, thermal exposure), impact resistance, and structural behavior of innovative concrete composites. His methodology integrates experimental testing with computational approaches like artificial neural networks for strength prediction. Analysis of his 11 journal publications (2021-2024) reveals consistent focus on sustainable construction materials, particularly alkali-activated systems using industrial byproducts (slag), with increasing sophistication in hybrid reinforcement strategies combining steel fibers, nano-silica, and recycled materials like tire rubber. His work bridges fundamental material science with practical structural applications, predominantly targeting flat slab systems and shear-critical elements. Dr. Eren actively supervises graduate research, having completed two master's theses in 2023 on geopolymer concrete fire resistance and fiber-reinforced concrete shear strength. He secured national research funding for the project 'Investigation of the Impact Behavior of Geopolymer Concretes' (2017-2020), demonstrating capacity for independent research leadership. His teaching portfolio spans structural mechanics, concrete technology, and building materials across 57 course instances since 2013, maintaining continuous instructional engagement through 2024-2025. While no formal laboratory affiliation is specified, his project management and publication record indicate active involvement in concrete materials testing facilities. His research trajectory shows progression toward sustainable material systems with enhanced multifunctional properties, particularly for infrastructure resilience under dynamic loading conditions.

Charlotte Bay Hasagerمشاهده پروفایل

استاد

• Offshore Wind Energy Meteorology
• Remote Sensing
• Boundary-layer Meteorology
• +۴ مورد دیگر

Charlotte Bay Hasager serves as Professor in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU), specializing in offshore wind energy meteorology with expertise in remote sensing, boundary-layer processes, and environmental load impacts on wind turbines. Her research directly supports UN Sustainable Development Goals through renewable energy innovation and climate action initiatives. Her primary research domains include: Offshore wind resource assessment using satellite and radar technologies Boundary-layer meteorology and surface flux dynamics Leading edge erosion mechanisms from rain-wind interactions AI-driven operational strategies for wind farm durability Synthetic Aperture Radar (SAR) applications for wind field mapping Recent publications (2024-2025) demonstrate converging trends in erosion risk modeling, reinforcement learning for turbine operation, and advanced remote sensing techniques. These works span renewable energy engineering, meteorological science, and artificial intelligence applications, with strong emphasis on practical solutions for offshore wind farm challenges under extreme weather conditions. Professor Hasager actively secures major research funding through projects including: AIRE Horizon Europe project (2023-2026): Atmospheric flow integration for wind farm design Thunderbolt project (2023-2025): Wind power optimization initiatives ESA WAII project (2023-2025): Explainable AI for SAR wind assessment CCA LEE project (2022): Leading Edge Erosion cross-cutting research INOWER project (2022-2023): International offshore wind energy networks She maintains significant scientific leadership through conference organization (6th International Symposium on Leading Edge Erosion, 2025), workshop participation (Sandia Blade Workshop, 2024), and media contributions on wind energy topics including North Sea wind farm impacts and satellite Earth observation.

Chantal Caraمشاهده پروفایل

استاد

• Humanistic Caring in Nursing
• Caring Science
• Phenomenology
• +۷ مورد دیگر

Chantal Cara is a Full Professor at the Faculty of Nursing (Faculté des sciences infirmières) at Université de Montréal, where she has established herself as a leading scholar in humanistic nursing and caring science. With a Ph.D. in Nursing from the University of Colorado and postdoctoral training from the Watson Caring Science Institute, she has dedicated her career to advancing humanistic approaches in nursing education and practice. Her work bridges theoretical frameworks with practical applications, particularly in the areas of caring science, phenomenology, and the humanization of healthcare. Dr. Cara's educational background includes: Ph.D. in Nursing (1997), School of Nursing, University of Colorado Postdoctorate in Nursing (2017), Watson Caring Science Institute M.Sc. in Nursing (1988), Université de Montréal B.Sc. in Nursing (1983), Université de Montréal Her primary research interests focus on humanistic caring in nursing, with particular emphasis on the philosophy of caring, phenomenological approaches to understanding nursing experiences, and the humanization of clinical care, education, and management. She has developed the "Modèle humaniste des soins infirmiers - UdeM" (Humanistic Nursing Care Model), which provides a framework for operationalizing caring science in practice. Her work explores how humanistic approaches can address dehumanization in healthcare settings, improve quality of life for both patients and healthcare providers, and transform nursing education through caring-based pedagogies. Dr. Cara's extensive publication record demonstrates a clear trajectory toward strengthening the theoretical and practical foundations of caring science in nursing. Her recent work has focused on developing and validating educational interventions to reinforce humanistic practices among nursing professionals, particularly in challenging clinical environments like hemodialysis units. She has pioneered research on the development of humanistic caring competencies among nursing students and the transformation of clinical practice through caring-based educational approaches. Her scholarship consistently bridges theory and practice, with a strong emphasis on phenomenological methodology to understand the lived experience of caring in healthcare contexts. Her significant contributions to the field have been recognized through numerous prestigious awards: 2021: 2nd place in the "Nursing Education" category of the American Journal of Nursing "Book of the Year Awards 2020" 2020: Fellow of the Canadian Academy of Nursing (founding member) 2018: Fellow of the American Academy of Nursing (first in Quebec) 2016: Distinguished Caring Science Scholar from the Watson Caring Science Institute 1997: Florence Nightingale Award nomination, Denver, Colorado Dr. Cara has been an exceptional mentor, supervising numerous doctoral and master's students whose research explores various dimensions of humanistic caring in nursing. Her students have investigated topics ranging from the meaning of humanistic caring practices for nursing faculty to the significance of caring relationships in clinical simulation. She has secured significant research funding, including a project examining "Effects on nurses' quality of working life and on patients' quality of life of an educational intervention to strengthen humanist practice among hemodialysis nurses in the Canton of Vaud, Switzerland." This international collaboration demonstrates her ability to translate caring science principles into practical interventions with measurable outcomes. As a member of the Centre de recherche interdisciplinaire en réadaptation du Montréal métropolitain (CRIR), Dr. Cara contributes to interdisciplinary rehabilitation research. Her work has been instrumental in developing the Canadian Caring Consortium Initiative, which brings together caring science scholars across Canada. She has presented her research internationally, including at the Global Human Caring Conference and the International Association for Human Caring Conference, demonstrating the global impact of her work on humanizing nursing practice and education.

Candan Tamerlerمشاهده پروفایل

استاد

• Molecular Biomimetics
• Bio-nanotechnology
• Bio-enabled Materials Science
• +۳ مورد دیگر

Dr. Candan Tamerler is a Professor in the Department of Mechanical Engineering at the University of Kansas and Track Director for the Biomaterials & Tissue Engineering graduate program. She also holds the Wesley G. Cramer Professorship and serves as the Associate Vice Chancellor for Research at KU. Current Affiliations Professor, Mechanical Engineering, University of Kansas (since 2013) Track Director, Biomaterials & Tissue Engineering, KU Graduate Program Associate Vice Chancellor for Research, University of Kansas Past Affiliations Research Professor, Materials Science & Engineering, University of Washington Assistant Director, Genetically-Engineered Materials Science & Engineering Center, University of Washington Full Professor, Molecular Biology & Genetics Department, Istanbul Technical University (2002-2010) Visiting Professor, Materials Science & Engineering, University of Washington Dr. Tamerler's research focuses on molecular biomimetics and bio-nanotechnology, developing bio-enabled materials for dental and biomedical applications. Her work bridges molecular biology with materials science to engineer peptide-based interfaces for tissue repair, antimicrobial surfaces, and enzyme immobilization systems. Recent publications highlight her innovations in dental biomaterials, collagen mineralization, and computational design of peptides. Key themes include autonomous strengthening adhesives, bio-nanoreactors, and machine learning approaches to antimicrobial peptide engineering. Scientific Awards Principal Member, Turkish Academy of Sciences Visiting Scientist, University of Westminster Visiting Professor, University of Nagoya Dr. Tamerler has organized international symposia for the American Chemical Society and TMS, and previously founded a multidisciplinary research center at Istanbul Technical University, securing funding for its 40,000 sq. ft. facility.

Karthik P. N.مشاهده پروفایل

استادیار

• Probability Theory
• Detection and Estimation Theory
• Markov Decision Processes
• +۶ مورد دیگر

Karthik P. N. is an Assistant Professor in the Department of Artificial Intelligence at the Indian Institute of Technology Hyderabad (IIT Hyderabad). He previously served as a Research Fellow at the Institute of Data Science, National University of Singapore (NUS), and completed his Ph.D. and Master of Science (Engineering) at the Department of Electrical Communication Engineering, Indian Institute of Science (IISc), Bengaluru, under the guidance of Prof. Rajesh Sundaresan. Earlier, he worked as a Project Assistant in Prof. Chandra R. Murthy's lab at IISc and earned a Bachelor of Engineering in Electronics and Communications from R V College of Engineering, Bengaluru. Ph.D., Electrical Communication Engineering, IISc Bengaluru M.Sc. (Engineering), IISc Bengaluru B.E., Electronics and Communications, R V College of Engineering His research focuses on Probability Theory, Detection and Estimation Theory, Markov Decision Processes , and Multi-armed Bandits , with applications in Federated Learning, Differential Privacy, Reinforcement Learning, Information Theory , and Statistics . Recent work explores optimal strategies for best arm identification in restless bandits and privacy-preserving machine learning. He has received high instructor ratings for courses like Stochastic Processes and Programming for AI , and was honored with the Faculty Teaching Excellence Award 2025 at IIT Hyderabad. His Google Scholar articles highlight advances in 3D point cloud security, railway accident prevention, and vision-language model robustness . Scientific awards include the Faculty Teaching Excellence Award 2025 and First place in the INAE Kanpur Chapter 100 Seconds Competition (2021) . He collaborates with researchers like Prof. Vincent Y. F. Tan and Prof. Yeow Meng Chee, and has served on technical program committees for conferences such as APWDSIT 2025 and ISIT 2025 . Teaching roles at IIT Hyderabad involve graduate-level courses on Probability and Stochastic Processes , cross-listed with Electrical Engineering.

Dimitrios Tsoumakosمشاهده پروفایل

دانشیار

• Big Data Management
• Distributed Systems
• Cloud Computing
• +۵ مورد دیگر

Dimitrios Tsoumakos serves as an Associate Professor of Big Data Management Systems at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) where he also directs DBLab, the Knowledge and Database Systems Laboratory. His academic career spans over two decades with significant contributions to large-scale data management and distributed systems. Diploma in Electrical and Computer Engineering from NTUA (1999) M.Sc. in Computer Sciences from University of Maryland (2002) Ph.D. in Computer Sciences from University of Maryland (2006) Professor Tsoumakos' research focuses on the intersection of big data management, cloud computing, and distributed systems. His work addresses fundamental challenges in large-scale data processing, including wavelet synopses for data summarization, vector embedding frameworks for analytics operators, multi-engine analytics systems, and cloud application deployment with failure recovery mechanisms. His research consistently bridges theoretical algorithms with practical implementations for real-world data challenges. His publication record demonstrates consistent innovation from early work on P2P data management systems through to current research on vector embeddings and deep reinforcement learning for cloud autoscaling. Recent work shows a clear progression toward content-based analytics, multi-dataset integration, and intelligent resource management in heterogeneous environments. Best Paper Runner Up Award at SSDBM 2019 Best Paper Award at CCGrid 2013 Professor Tsoumakos has secured substantial research funding through multiple European projects including RELAX (2023-2027), HiDALGO2 (2023-2026), DAPHNE (2021-2024), and previous initiatives like HiDALGO, TraMOOC, ASAP, CELAR, ARCOMEM, and GREDIA. These projects reflect his leadership in big data analytics, cloud computing, and distributed systems research. As director of DBLab, Professor Tsoumakos oversees research on the Knowledge and Database Systems Laboratory, which has produced significant work on analytics operators, multi-engine resource scheduling (IReS platform), cloud elasticity (TIRAMOLA), and RDF data management (H2RDF+). The lab maintains strong industry connections and has developed multiple open-source tools for big data analytics.