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Tim J. NyeView profile

Associate Professor

• Advanced Manufacturing
• Operations Research
• Process Modelling
• +4 more

Tim J. Nye is an Associate Professor in the Department of Mechanical Engineering at McMaster University's Faculty of Engineering. He holds a Ph.D. in Mechanical Engineering (1997) from the University of Waterloo, following an M.Sc. (1989) at Ohio State and B.A.Sc. (1987) at Waterloo. His research focuses on applying operations research techniques to manufacturing systems, with specific expertise in optimization algorithms for sheet metal processes, hydroforming reliability, and adaptive control in forging. Education: Ph.D. Mechanical Engineering, University of Waterloo (1997) M.Sc. Mechanical Engineering, Ohio State (1989) B.A.Sc. Mechanical Engineering, University of Waterloo (1987) Research interests span multiple dimensions of advanced manufacturing: developing decision models for production investment, creating novel lot-sizing algorithms incorporating work-in-process costs, exact solutions for 2D nesting problems, and agent-based systems for reliability prediction using warranty data. His work bridges theoretical operations research with practical metal forming applications. Recent publications demonstrate consistent contributions to manufacturing optimization, with particular focus on stamping processes, sheet metal design, and hydroforming reliability. These align with McMaster's research clusters in Advanced Materials & Manufacturing and Infrastructure. Scientific awards include the 2002 CSME Best Student Paper competition win for machine vision research with S. Dworkin. He maintains active collaborations with industry partners, as evidenced by his research on industry-university R&D ventures. Current projects explore intelligent open die forging as a solid freeform fabrication method, demonstrating his commitment to both traditional manufacturing improvement and emerging rapid prototyping technologies.

Tyler HollettView profile

Associate Professor

• Interest-driven learning
• Learning in informal settings
• Digital media and learning
• +5 more

Dr. Tyler Hollett serves as an Associate Professor of Learning Sciences in the Department of Learning and Performance Systems at The Pennsylvania State University, contributing to the Learning, Design, and Technology program from 315 Keller Building, University Park, PA. His research champions interest-driven learning often devalued by formal institutions and neoliberal markets, investigating contexts like skateboarding, video gaming, and nature hobbies (fishing, birding). Hollett examines how passion-fueled activities generate embodied, affective learning that transcends goal-oriented education, advocating for equitable alternatives to capitalist learning trajectories. Current projects focus on intergenerational play through the FIGMENT Lab, exploring family gaming dynamics, climate engagement via outdoor hobbies, and evolving children's play across settings. Analysis of his recent publications reveals a methodological commitment to qualitative inquiry, with recurring themes of spatial-temporal rhythms, affective atmospheres, and embodied cognition. His work bridges learning sciences, critical pedagogy, and digital media studies, increasingly emphasizing intergenerational and ecological dimensions of informal learning. No scientific awards are listed in his current profile. However, Hollett actively mentors graduate students, inviting collaboration on FIGMENT Lab initiatives. His advising philosophy emphasizes rigorous scholarship within supportive, interest-driven research teams, with the FIGMENT Lab representing his current focus on family-centered learning in gaming, nature, and play contexts.

Ahmed F AbdelghanyView profile

Professor

• Commercial Airlines
• Airports
• Big Data Cloud Computing
• +7 more

Dr. Ahmed F. Abdelghany is the Associate Dean for Research and Professor of Operations Management at the David O'Maley College of Business, Embry-Riddle Aeronautical University, since January 2006. He specializes in commercial airlines, airports, big data cloud computing, business analytics, and operations research models. Prior to his academic career, Dr. Abdelghany worked in enterprise optimization at United Airlines, Chicago. Education: Ph.D. in Civil Engineering (Transportation Systems) from the University of Texas at Austin (2001) Dr. Abdelghany’s research focuses on airline network planning, flight scheduling, simulation of complex transportation systems, and NextGen air traffic management. He has authored two influential books: Modeling Applications in the Airline Industry (Routledge 2010) and Airline Network Planning and Scheduling (Wiley 2018). His publications analyze airline operations, competitive dynamics, and crowd management in transportation facilities. He teaches courses like Airline Management (BA 315) and Airline Operations & Mgmnt (BA 609), and participates in industry short courses. Dr. Abdelghany contributes to research projects such as NextGen air traffic implementation, integrated airport initiatives, and benefit-cost analysis of arrival management systems. His work bridges academic theory with real-world airline and transportation challenges.

Yan HuangView profile

Associate Professor

• Machine Learning
• Cybersecurity Education
• Extended Reality (XR)
• +5 more

Yan Huang is an Associate Professor in the Department of Software Engineering and Game Development at Kennesaw State University (KSU). His work bridges Federated Learning (FL) and Cybersecurity Education , with a focus on personalization and privacy in distributed systems. Research spans Machine Learning , Extended Reality (XR) , and Data Privacy . He has served as Editor of WCMC and Program Co-Chair for CyberSciTech 2020-2024. Research Trends: Recent publications emphasize Federated Learning for non-IID data, VR-based Cybersecurity Education , and Privacy-Preserving Algorithms in IoT and social media analytics. Key subfields include personalized learning architectures, graph learning, and game-theoretic privacy frameworks. Scientific Awards: Excellent Paper Award (Tsinghua Science and Technology, 2021) Best Paper Award (Future Generation Computer Systems, 2019) Best Paper Awards at IEEE SmartWorld 2021, COCOA 2019, and WASA 2019 Grants: Led over $600,000 in NSF and NSA-funded projects, including VR cybersecurity education for K-12 and XR engineering curricula. His lab recruits VR/AR Research Assistants via industry partnerships.

Derek T. RobinsonView profile

Associate Professor

• Land-use and land-cover change
• Agent-based modelling
• Geographical Information Systems (GIS)
• +4 more

Derek T. Robinson is an Associate Professor at the University of Waterloo's Department of Geography and Environmental Management , specializing in land-use science, agent-based modeling, and geospatial analysis. His work integrates GIS, ecological models, and human decision-making to assess impacts of land policies on ecosystem services and human well-being. Research Interests : Land-use/cover change and carbon cycle dynamics Agent-based modeling of socio-ecological systems Exurban land management and fragmentation Ecosystem service quantification Land policy scenario analysis Teaching : Courses in spatial analysis, advanced GIS, and land-use-carbon interactions. His lab utilizes cutting-edge tools like ArcGIS, NetLogo, and UAV systems (e.g., Aeryon SkyRanger) for fieldwork and modeling.

Yan ChenView profile

Assistant Professor

• Human-Computer Interaction
• Computer Science Education
• Real-Time Data Analysis
• +3 more

Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.

Jeeseop KimView profile

Assistant Professor

• Robotics
• Autonomy
• Control Theory
• +3 more

Jeeseop Kim is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at The University of Texas at El Paso (UTEP), College of Engineering, specializing in robotics, autonomy, and control theory. His research focuses on safety-critical planning and control, with emphasis on bipedal/quadrupedal locomotion, hybrid dynamical system control, and whole-body planning and control. Education: B.S. in Mechanical and Aerospace Engineering, Seoul National University (2014) M.S. in Intelligence and Information (Robotics), Seoul National University (2017) Ph.D. in Mechanical Engineering, Virginia Tech (2022) Postdoctoral Scholar, Mechanical and Civil Engineering, Caltech (2022–2025) His research spans safety-critical control systems for legged robots, including obstacle-aware nonlinear model predictive control (MPC), control barrier functions, and distributed coordination algorithms. Recent work explores adaptive delay estimation, tactile sensing for robotic grasping, and hardware-software co-design for humanoid robots. Key article trends highlight advancements in autonomous inspection robotics, hybrid control architectures, and real-time planning for quadrupedal systems. His work integrates control theory with practical applications in industrial and healthcare domains. Awards: ASME DSCD Rudolf Kalman Best Paper Award (2022) IEEE ICRA Outstanding Paper Award (2023) Jeeseop teaches MECH 4332: Mechanical Computational Applications in Vision and Robotics (Fall 2025). He actively recruits Ph.D. students for Spring/Fall 2026 and seeks motivated undergraduates/MS students with skills in robotics kinematics, programming (C/C++, Python, MATLAB), and CAD design. The AIGIS Lab welcomes applicants with interests in robotics, controls, and autonomous systems.

• Neuropharmacology
• Neuroendocrinology
• Sleep Research
• +3 more

Jessica A. Mong, PhD , is a Professor in the Department of Pharmacology & Physiology at the University of Maryland School of Medicine , where she also serves as Assistant Dean for Graduate & Post-Doctoral Studies and Director of Graduate Education for the Program in Neuroscience. Her research focuses on the neuroendocrine mechanisms underlying sex differences in sleep circuitry and the estrogenic modulation of sleep-wake cycles. Primary Appointment: Pharmacology & Physiology Administrative Title: Assistant Dean for Graduate & Post-Doctoral Studies Laboratory Director: Program in Neuroscience Research Interests: Dr. Mong's work investigates how ovarian steroids influence sleep-wake behavior through sexually differentiated neuroanatomical substrates. Key areas include: Mechanisms of estrogenic modulation in the median preoptic nucleus (MnPN) Developmental programming of sex differences in sleep sensitivity Translational studies using rodent and nonhuman primate models of menopause Functional significance of hormonal influences on sleep quality and recovery Scientific Trends: Her recent publications (2023-2025) emphasize: Role of KCNMA1 channelopathy in sleep regulation Adenosinergic signaling in MnPN Translational menopause models Estrogen's protective effects against noise-induced hearing loss Sex-dependent responses to kynurenine pathway challenges Awards & Appointments: NIH BIRCWH Scholar (Building Interdisciplinary Research Careers in Women's Health) Co-Chair, Society for Women’s Health Research Interdisciplinary Research Network on Sex-Differences in Sleep Health NIH/NHLBI R01 HL129138 grant recipient Education & Training: B.S., Biology, Gettysburg College (1987-1991) Ph.D., Neuropharmacology, University of Maryland Baltimore (1994-2000) NIH Postdoctoral Fellowship in Endocrinology, Rockefeller University (2000-2003)

Nathorn ChaiyakunaprukView profile

Professor

• Evidence Synthesis
• Health Technology Assessment
• Global Health
• +4 more

Nathorn Chaiyakunapruk is a Professor in the Department of Pharmacotherapy at the University of Utah College of Pharmacy . He holds an adjunct appointment in Population Health Sciences and serves on multiple institutional committees including the Global Health Steering Committee and Health Economics Core at CTSI . His academic leadership extends to international roles with the World Health Organization and founding initiatives like the ISPOR Asia Consortium . Education : PhD in Pharmaceutical Outcomes Research, University of Washington PharmD, University of Wisconsin-Madison BS in Pharmaceutical Science, Chulalongkorn University Research Interests span health technology assessment , global health economics , and evidence synthesis . His work applies methodologies like network meta-analysis and umbrella reviews to address health equity, infectious disease modeling, and pharmaceutical policy. Recent studies focus on social determinants of health and vaccine economic value . Article Trends highlight collaborations in AI-assisted systematic reviews , vaccine rollout optimization , and health disparities . His publications frequently address cost-effectiveness and global health burden across infectious and non-communicable diseases. Scientific Awards : Senior Class (P4) Distinguished Teacher (2022) NRCT Outstanding Research Award (2019, 2012) Monash University PVC Research Award (2015) Nagai Research Foundation Awards (2006-2010) ISPOR Task Force Leadership (CHEERS 2022) Teaching & Service : Courses include Systematic Review and Meta-analysis and Global Health Policy . He chairs the Asia Pacific Evidence-based Medicine Network and advises WHO on vaccine economics and Thailand’s National Health Security Office on pharmaceutical policy.

Panagiotis PapapetrouView profile

Professor

• Data Science
• Machine Learning
• Time Series Analysis
• +5 more

Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)

Ronald PeggView profile

Professor

• Functional foods
• Nutraceuticals
• Bioactive phytochemicals
• +6 more

Ronald Pegg is the Josiah Meigs Distinguished Teaching Professor in the Food Science & Technology department at the University of Georgia's College of Agricultural & Environmental Sciences. His research focuses on functional foods, nutraceuticals, and the bioactive properties of phytochemicals, with expertise in separation and identification of bioactives, nutrient analysis, and lipid oxidation studies. He teaches courses ranging from introductory food science to advanced functional foods and analytical methods. Specializes in antioxidant activity of tree nuts, fruits, and legumes Develops analytical assays for food composition Studies shelf-life extension strategies for food products His recent work emphasizes cellular antioxidant activity, phenolic profiling, and encapsulation technologies. Awards include the Josiah Meigs Distinguished Teaching Professor title. Publications span journals in food chemistry, nutrition, and analytical science.

Zhenyu YangView profile

Lecturer

• Urban Transportation Network Modeling
• Travel Demand Management
• Transport Economics
• +5 more

Zhenyu Yang is a Lecturer and Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering through the Department of Civil Engineering and the Urban Transport Systems Laboratory (LUTS) . He holds a PhD in Industrial System Engineering from the National University of Singapore (2022), an M.Eng from Beijing Jiaotong University, and a Diploma in Transportation Engineering from Huazhong University of Science and Technology. PhD, Industrial System Engineering, National University of Singapore (2022) M.Eng, Beijing Jiaotong University Diploma, Transportation Engineering, Huazhong University of Science and Technology His research focuses on urban transportation network modeling , travel demand management , and traffic information provision , with a strong emphasis on handling uncertainty and optimizing shared mobility systems. Recent work explores reinforcement learning applications, vehicle-drone cooperative delivery , and dynamic incident-responsive traffic systems . His publications highlight advancements in ridesourcing algorithms , congestion pricing , and multi-modal transport regulation . As a lecturer, he teaches Transportation Economics , covering demand-supply dynamics, welfare analysis, and environmental policy in transport systems. He is affiliated with EPFL's Urban Transport Systems Laboratory (LUTS) and contributes to the SGC-ENS teaching unit.

• Demography
• Sociology
• Family Dynamics
• +7 more

Jan Van Bavel is a full professor at the Faculty of Social Sciences, KU Leuven, with a focus on demography, family dynamics, and climate change impacts. He is a member of LC&Y - KU Leuven Institute for Child and Youth and participates in councils like POC Sociology and POC Master Psychology. Research spans fertility trends, migration, poverty analysis, and adolescent decision-making Active promoter/co-promoter in 8 projects (2021–2028) on climate-family links, migrant deservingness, and wealth inequality Teaches courses on population studies, climate change, and societal transitions His recent work examines climate change's effect on fertility desires, adolescent loyalties, and immigrant integration frameworks, with articles in journals like Demographic Research and Socio-Economic Review . No scientific awards were explicitly mentioned in the provided data.

David R. LiuView profile

Professor

• Organic Chemistry
• Chemical Biology
• Biochemistry
• +7 more

David R. Liu is the Thomas Dudley Cabot Professor of the Natural Sciences at Harvard University and the Richard Merkin Professor at the Broad Institute. He serves as a principal investigator at the Howard Hughes Medical Institute and director of the Merkin Institute of Transformative Technologies in Healthcare. His laboratory pioneers revolutionary genome editing technologies with profound implications for basic research and therapeutic development. Liu's research spans multiple disciplines at the intersection of chemistry and biology: Organic Chemistry Chemical Biology Biochemistry Genetic Engineering Genome Editing Technologies He pioneered base editing and prime editing techniques that enable precise DNA modifications without creating double-stranded breaks, overcoming key limitations of traditional CRISPR-Cas9 systems. His work on DNA-templated synthesis and phage-assisted continuous evolution (PACE) has transformed protein engineering and drug discovery approaches. Liu's contributions have been recognized with numerous prestigious awards: ACS Award in Pure Chemistry (2006) Election to National Academy of Medicine (2020) Election to National Academy of Sciences (2021) King Faisal Prize (2022) Breakthrough Prize in Life Sciences (2025) As an entrepreneur, Liu has co-founded multiple biotechnology companies including Editas Medicine, Beam Therapeutics, and Prime Medicine to translate his discoveries into therapeutic applications. His laboratory continues to advance genome editing technologies while developing novel approaches to treat genetic diseases through precise DNA modifications.

• Model-Based Testing
• Automata Theory
• Machine Learning
• +5 more

Bernhard Aichernig is an Associate Professor at the Institute of Software Engineering and Artificial Intelligence. His work bridges formal methods, model-based testing, and artificial intelligence, with a focus on automata learning, digital twins, and AI-assisted programming. Institution: Institute of Software Engineering and Artificial Intelligence Key Research Areas: Model-Based Testing, Automata Learning, AI-Driven Verification His research explores the integration of machine learning into formal verification, enabling scalable testing of complex systems like IoT devices and reinforcement learning agents. Recent projects include AI-Augmented DevOps frameworks (AIDOaRT) and digital twin validation (LearnTwins). Notable scientific awards include multiple best paper recognitions at SEFM (2020, 2021) and the TAYSIR Competition first place (2023). His publications emphasize hybrid approaches combining genetic programming, SMT solving, and neural networks for system modeling. 2025 : AI-assisted programming, timed automata via domain knowledge 2024 : Stochastic environment modeling, Git system learning 2023 : Reinforcement learning under partial observability, digital twins for VPN servers He actively contributes to testing frameworks like AALpy and investigates explainable AI for fault diagnosis in cyber-physical systems.