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.
John Capobianco, PhD, is a Professor in the Department of Chemistry and Biochemistry at Concordia University and holds the Honorary Concordia University Research Chair in Nanoscience. His research focuses on lanthanide-doped nanoparticles, upconversion luminescence, and biomedical applications. PhD, University of Geneva Key research areas include: Nanomaterials synthesis and spectroscopy Upconversion for biomedical imaging Drug delivery systems Photodynamic therapy for cancer treatment Optical thermometry and sensing Recent publications highlight advancements in: X-ray detection via photochromic nanoparticles Lipid-coated nanoparticles for lung permeation Cooperative energy transfer in Yb3+/Eu3+ complexes Biocompatible nanomaterials for secure information storage Pr3+-doped radiosensitizers for glioblastoma therapy Scientific recognitions: Honorary Concordia University Research Chair in Nanoscience Teaching includes undergraduate and graduate courses in inorganic chemistry and spectroscopy. His work bridges fundamental material science with applied biomedical engineering, emphasizing optical properties and therapeutic applications of lanthanide-based nanomaterials.
Susie Dai is a Professor in the Department of Chemical and Biomedical Engineering at the University of Missouri, with a laboratory located at the Bond Life Sciences Center. Her research bridges chemistry, biology, and engineering to address critical environmental and sustainability challenges. Education: PhD in Chemistry from Duke University; Certificate in Biomedical Engineering from Duke University; Certificate in Regulatory Science from Texas A&M University; BS in Chemistry from Fudan University Dr. Dai specializes in biological and material engineering, carbon waste conversion, contaminant remediation, and synthetic biology. She is developing RAPIMER, a lignin-based fungal scaffold for PFAS removal, and pioneering electro-microbial systems to convert CO2 into bioplastics and biofuels. Her work focuses on scalable, sustainable solutions for environmental pollutants and carbon utilization. Recent research trends include creating biomimetic materials for sustainable packaging, optimizing lignocellulosic biorefineries, and designing lignin-derived photocatalysts. She leads projects funded by Tito's Handmade Vodka's philanthropic arm for PFAS remediation and collaborates with the NSF Engineering Research Center CURB at Washington University in St. Louis. At Mizzou, Dai integrates engineering and life sciences, leveraging both Mizzou Engineering and Bond Life Sciences Center's resources. Her interdisciplinary approach combines electrochemistry, microbial engineering, and social impact analysis to advance circular bioeconomy solutions.
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.
Michael D. Smith is a Professor of Information Technology and Public Policy at Carnegie Mellon University, with joint appointments at Heinz College and Tepper School of Business. His research employs economic and statistical methods to analyze digital markets, focusing on firm and consumer behavior in online environments. Education: B.Sc. in Electrical Engineering (Summa Cum Laude), University of Maryland M.Sc. in Telecommunications Science, University of Maryland Ph.D. in Management Science and Information Technology, MIT Research Interests: Professor Smith investigates the economics of digital information markets, consumer behavior in online platforms, and policy implications of technological disruption. His work spans copyright enforcement, digital advertising, and the impact of piracy on legal media consumption. Publications: His recent studies examine AI’s role in copyright policy, effectiveness of anti-piracy measures, and market dynamics in digital streaming. Articles often bridge economics, computer science, and public policy. Awards: National Science Foundation CAREER Award Multiple Best Teacher Awards at CMU Recognized as a Top 100 Emerging Engineering Leader (NAE, 2020) Best Paper Runner-Up (Information Systems Research, 2006) Editorial and Industry Roles: Served as Senior Editor at Information Systems Research and Associate Editor at Management Science . Prior to academia, he worked in telecommunications at GTE and Booz Allen Hamilton, earning a patent for AI applications in network design. Contact: mds@cmu.edu | Office: 4800 Forbes Avenue, Hamburg Hall 2204, Pittsburgh PA 15213
Hamza Salih Erden is an Associate Professor (Docent) at the Informatics Institute of Istanbul Technical University in Turkey. His research focuses on energy optimization in data centers, thermal management systems, and computational fluid dynamics applications. With over 34 research outputs and an h-index of 12, he leads projects in energy-grid integration and carbon-aware load management. Research Focus Dr. Erden's work centers on improving energy efficiency in technological infrastructure through: Advanced cooling techniques for data centers Integration of thermal energy storage systems Computational fluid dynamics modeling Demand-response optimization for smart grids AI-driven monitoring of energy systems Publication Trends Recent works (2022-2025) demonstrate strong focus on sustainable energy technologies, particularly optimization of data center operations through thermal management innovations, integration of renewable energy solutions, and AI applications for system monitoring. Economic assessments of energy-saving techniques feature prominently. Awards and Recognition Technical Paper Award (2016) Multiple International Scientific Publication Incentive Awards (2017-2021) Poster Award (2012) Graduate Student Grant (2007) Projects and Funding Leads multiple energy research projects including: Carbon-aware load management in data centers (2025) Grid-integrated energy system modeling for data centers (2021-2023) CFD analysis of CRAH bypass methods (2018-2020) Economizer applications in Turkish data centers (2016-2017)
Dr. Anas Iftikhar is an International Lecturer (Assistant Professor) in Logistics and Supply Chain Management at Lancaster University's Management School, United Kingdom. He is affiliated with the Centre for Productivity and Efficiency and contributes to the Supply Chain Management department with research focusing on contemporary supply chain challenges. Dr. Iftikhar completed his Ph.D. in Economics, Management, and Quantitative Methods from the University of Salento, Italy in May 2021. During his doctoral studies, he held visiting researcher positions at Ghent University (Belgium) and Cardiff Business School at Cardiff University (UK), gaining valuable international research experience. Prior to his academic career, he accumulated industry experience in inventory planning and operations management. Dr. Iftikhar's research primarily investigates how supply chain capabilities enhance resilience in disruptive and complex environments. His work explores the intersection of supply chain complexity, digital technologies, and resilience strategies. He employs methodological approaches including systematic literature reviews, meta-analysis, and structural equation modeling. His research has significant implications for organizations navigating geopolitical uncertainties, digital transformation, and supply chain disruptions. His publication portfolio demonstrates a clear evolution from foundational work on network trust and ripple effects to more sophisticated examinations of digital transformation's role in supply chain resilience. Recent publications increasingly focus on the synergistic effects of multiple capabilities (network capability, innovation ambidexterity) in addressing complex challenges like geopolitical turmoil. The research trajectory shows a progression from single-factor analyses to more complex, integrated models that better reflect real-world supply chain dynamics. Dr. Iftikhar actively participates in the academic community through presentations at major conferences including the European Operations Management Association (EUROMA) annual conference, IFAC Conference on Manufacturing Modelling, Management & Control, and Industrial Engineering Operations Management (IEOM) society events. He has also delivered invited talks such as 'Contemporary Issues in Logistics Management' at Bukhara State University. As an educator, Dr. Iftikhar is willing to supervise PhD students in supply chain resilience, disruption management, supply chain complexity, and innovative technologies in supply chain management. His industry experience enriches his teaching approach, bridging theoretical concepts with practical applications. His research outputs have been published in leading Operations and Supply Chain Management journals including International Journal of Production Research, Production Planning and Control, Annals of Operations Research, and Journal of Business Research.
Mohammad Hamdaqa is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads the Laboratory of Software and Emerging Technologies. His academic journey includes a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (2016), a Master's in Electrical and Computer Engineering from Concordia University, an MBA from the New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of software engineering and emerging technologies, particularly examining how software engineering approaches can be adapted for complex new platforms like cloud computing and blockchain. His work spans model-driven software engineering, cloud application architecture, smart contract development, and infrastructure as code. He investigates both how traditional software engineering practices can evolve to address the challenges of modern distributed systems and how emerging technologies can transform software development processes themselves. Analysis of his recent publications reveals a strong emphasis on blockchain technologies (particularly smart contracts), cloud-native applications, and the application of AI to software engineering tasks. His work shows a consistent thread of empirical research combined with practical tool development, with increasing focus on sustainability aspects of software systems in recent years. Much of his research bridges theoretical foundations with practical implementation concerns. Professor Hamdaqa serves as a thesis supervisor for multiple graduate students, with recent completed Master's theses focusing on smart contract auditing, prompt engineering for OCL generation, model-driven epidemiology, and security practices in infrastructure as code. He actively recruits students for research projects in his laboratory. He is a member of both the IEEE Computer Society and the Association for Computing Machinery (ACM), has served on program committees for major software engineering conferences, and is on the editorial board of Service Transaction on Internet of Thing. His laboratory, the Laboratory of Software and Emerging Technologies, serves as the hub for his research activities in blockchain, cloud computing, and model-driven engineering.
Dr Shu-Ling Lu is an Associate Professor at the University of Reading , serving as Director of the MSc Project Management Programme and a Member of Senate . Her research spans Innovation Management , Quality Control , and Net Zero Transitions in construction, alongside Heritage Building Integration and Gender Dynamics in built environment sectors. Her academic journey includes a PhD , MSc in Construction Engineering , and a Diploma in Architectural Engineering , all from institutions in Taiwan and the UK, complemented by a Postgraduate Certificate in Higher Education Practice from the University of Salford. Research Interests : Innovation in construction, quality management, heritage conservation, net-zero transitions, gender equity, and system dynamics applications. Article Trends : Focus on defects analysis , heritage integration , gender dynamics , system dynamics , and net-zero strategies across 15 recent publications. Scientific Awards : Fellow of the Chartered Institute of Building (FCIOB) Fellow of Higher Education Academy (FHEA) Full member, Association for Project Management (MAPM) BSI Committee Participation (Quality Management Standards) Supervision & Grants : Mentored 7 PhD students and led/co-led projects funded by Natural Environment Research Council (NERC) , EPSRC , and COST , with total grants exceeding £500,000.
Maria Elena Valcher is a Professor at the Department of Information Engineering, University of Padova, Italy. She is an IEEE Fellow (since 2012), IFAC Fellow (since 2023), Socio Effettivo of Istituto Veneto di Scienze, Lettere ed Arti (since 2017, previously Socio Corrispondente 2008-2017), and Socio Effettivo of Accademia Galieliana di Scienze, Lettere ed Arti in Padova (since 2022, previously Socio Corrispondente 2017-2022). She currently serves as Administrator of the Istituto Veneto and holds leadership positions including EUCA President (2024-2025) and IEEE Control Systems Society Past President. Her research focuses on control systems, systems theory, optimization, Boolean control networks, multi-agent systems, and consensus problems. She has made significant contributions in distributed control, data-driven methods, and network optimization, with recent work exploring applications in opinion dynamics and social networks. Recent publications demonstrate a strong emphasis on data-driven approaches to control systems, particularly in distributed state estimation, unknown-input observer design, and multi-agent coordination. Her work shows consistent development in theoretical frameworks for networked systems with practical applications. Awards and Honors: IEEE Fellow (2012) IFAC Fellow (2023) Socio Effettivo, Istituto Veneto di Scienze, Lettere ed Arti (2017-present) Socio Effettivo, Accademia Galieliana di Scienze, Lettere ed Arti in Padova (2022-present) She teaches 'Controlli Automatici' (Bachelor in Information Engineering) and 'Systems Theory' (Master in Control Systems Engineering) during the 2024/2025 academic year. She has chaired major conferences including the 61st IEEE Conference on Decision and Control (CDC 2022) and serves as Program Chair for ICSTCC 2025.
Handan Kulan serves as Assistant Professor at Yeditepe University's Faculty of Computer and Information Sciences, Department of Information Systems and Technologies since 2024. Previously, she held faculty positions at Istinye University (2023), Uskudar University (2022), and Beykoz University (2020) across computer engineering and software engineering departments. Education: Ph.D. in Computer Engineering, Kadir Has University (2016-2020): Thesis on critical proteins in Down syndrome learning processes M.S. in Computer Science and Engineering, Sabanci University (2013-2014): Thesis analyzing protein residue networks B.S. in Genetics and Bioengineering, Yeditepe University (2007-2013) Second Major in Computer Engineering, Yeditepe University (2009-2013) Her research integrates machine learning with biomedical challenges, specializing in Down syndrome proteomics, neural network analysis of brain aging, and immune system disorders. She develops computational models for protein identification and applies gradient boosting algorithms to biological datasets, bridging AI with healthcare decision systems as demonstrated in her 2023 Springer book. Publications reveal consistent focus on computational approaches to Down syndrome, with recent conference presentations expanding into statistical clustering of biological data and gene ontology analysis for drug discovery. Her work demonstrates methodological evolution from protein network analysis to advanced predictive analytics. Awards: No specific scientific awards documented in source materials. Dr. Kulan actively supervises graduate theses while teaching core computer science courses including Deep Learning, Artificial Intelligence, and Data Structures at both undergraduate and graduate levels across multiple institutions. Her teaching portfolio reflects direct alignment with her research in AI-driven biomedical analysis.
Dr. Leila Notash is a Professor in the Department of Mechanical and Materials Engineering at Queen's University, where she has been a faculty member since 1997. She is a Fellow of Engineers Canada (FEC) and a licensed Professional Engineer with Professional Engineers Ontario (PEO), with significant contributions to engineering education and professional service. Her educational background includes: Bachelor of Science in Mechanical Engineering, Middle East Technical University (Ankara, Turkey) - High Honor Student (2nd out of 166) Master of Applied Science in Mechanical Engineering, University of Toronto PhD in Mechanical Engineering, University of Victoria Dr. Notash's research centers on robotics and mechatronics, with specialized expertise in cable-driven parallel manipulators. Her work integrates kinematics, fault-tolerant design, and neural network applications to address challenges in robot calibration, workspace analysis, and motion control under real-world constraints like cable mass and elasticity. She investigates both theoretical frameworks and practical implementations for industrial and specialized robotic systems. Analysis of her recent publications (2020-2024) reveals a clear trajectory toward intelligent control systems, where machine learning techniques—particularly neural networks and reinforcement learning—are increasingly applied to solve complex problems in cable-driven robotics. This includes motion control optimization, path generation, and kineto-static analysis while accounting for physical limitations such as cable elasticity and mass effects, demonstrating a shift from traditional mechanical analysis to data-driven adaptive control methodologies. Her scientific recognition includes: Fellow of Engineers Canada (FEC) University of Toronto Open Fellowship University of Toronto International Differential Fee Waiver Charles S. Humphrey Graduate Student Award NSERC Doctoral Prize Nominee (1996) Dr. Notash has mentored 161 undergraduate students as Faculty Advisor for the Mechanical '06 cohort and pioneered international educational initiatives like the International Undergraduate Student Design project (IVDS), connecting Queen's University with Middle East Technical University and Union College. Her service extends to editorial leadership for Mechanism and Machine Theory and ASME journals, and governance roles including Faculty Senator at Queen's University (2009-2025) and PEO Council Councillor-at-Large (2019-2025). She has established collaborative research networks through initiatives like the Reading Week shop course 'Design Basics 1.0' and sustained leadership in the Canadian Committee for the Promotion of Mechanism and Machine Science (CCToMM) and the International Federation for the Promotion of Mechanism and Machine Science (IFToMM), where she chaired the Permanent Commission on Communications (2006-2011).
Kelsey Swingle is an Assistant Professor of Bioengineering at Rice University, where she leads the Swingle Lab at the intersection of biomaterials science, immune engineering, and reproductive biology. Her research focuses on engineering therapeutic and vaccine technologies with translational potential. Ph.D. in Bioengineering from the University of Pennsylvania B.S.E. in Biomedical Engineering from Case Western Reserve University Dr. Swingle’s research explores the design of lipid nanoparticles (LNPs) and nucleic acid therapeutics for women’s health applications, including pre-eclampsia, preterm birth, and gynecologic cancers. Her work integrates bioengineering principles with immune modulation strategies to develop targeted therapies. The trends in her publications highlight advancements in LNP elasticity optimization for placental mRNA delivery, targeted systemic RNA delivery to the brain, and in utero gene editing applications. Her lab prioritizes interdisciplinary approaches to overcome biological barriers in women’s health. 2025 Solomon R. Pollack Award for Excellence in Graduate Bioengineering Research 2024 Muriel Joan Drew Hege Award for Women in Cellular Immunotherapy Research 2024 Penn Engineering Outstanding Teaching Award 2023 Gordon Research Conference Travel Award 2022 Society for Biomaterials STAR Award 2020 NSF Graduate Research Fellowship The Swingle Lab collaborates with the Texas Medical Center to develop precision nanomedicines. Her team employs in vitro, ex vivo, and in vivo models to study biomaterial interactions with female-specific tissues, emphasizing translational research and inclusive scientific communication.
Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .
Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.
