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 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 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.
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 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 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 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 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.
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. 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.
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.
Eleni Stai is an Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), affiliated with the Division of Communication, Electronic and Information Engineering. She holds advanced degrees in Electrical Engineering, Mathematics, and Applied Mathematical Sciences from NTUA and the National and Kapodistrian University of Athens. Her academic credentials include: Diploma in Electrical and Computer Engineering, NTUA (2009) B.Sc. in Mathematics, National and Kapodistrian University of Athens (2013) M.Sc. in Applied Mathematical Sciences, NTUA (2014) Ph.D. in Electrical Engineering, NTUA (2015) Dr. Stai's research integrates advanced optimization techniques with communications networks and energy systems. She develops stochastic and deterministic optimization frameworks for network resource allocation, data analytics on complex topologies, and smart-grid control applications. Her work bridges theoretical foundations with practical implementations in energy-harvesting networks, network slicing, and reinforcement learning for distributed systems. Analysis of her recent publications reveals dominant research thrusts in AI-driven network management (particularly O-RAN and network slicing), energy-integrated communications, and optimization of energy communities. A significant portion of her work addresses the convergence of 5G/6G networking with power systems, emphasizing real-time control and sustainability. Her scientific contributions have been recognized through prestigious awards: Chorafas Foundation Best Ph.D. Thesis award Thomaidis Foundation Best M.Sc. Thesis award Best Paper Award at ICT 2016 Best Presenter Award at IEEE ENERGYCON 2022 Dr. Stai serves on technical program committees for major international conferences and has co-authored the book "Evolutionary Dynamics of Complex Communications Networks". She teaches undergraduate courses in Queuing Systems, Computer Networks, and Social Network Analysis, reflecting her expertise in network theory and applications. Her research trajectory demonstrates continuous evolution from fundamental network optimization to AI-enhanced solutions for next-generation communication-energy systems. Her work builds upon her postdoctoral experience at EPFL (2016-2020) and ETH Zurich (2020-2023), where she developed advanced frameworks for communications networks and energy systems.
Ozgur S. Oguz is an Assistant Professor at Bilkent University , Faculty of Computer Engineering, and the lead of the Learning for Intelligent Robotic Agents (LiRA) Lab . His research focuses on enhancing autonomous agents' capabilities in learning, reasoning, and planning, particularly for robotics applications. Education : PhD in Computer Science from TU Munich , studies at University of British Columbia (UBC) and Koç University , postdoctoral work at University of Stuttgart and Max Planck Institute for Intelligent Systems . His research explores algorithms for autonomous decision-making, with emphasis on deep learning , reinforcement learning , and robotics . Recent work includes diffusion-based reinforcement learning , hindsight experience prioritization , and hybrid manipulation planning , often addressing challenges in sequential task execution and tactile-based control. Key trends in his publications revolve around robotic manipulation , motion planning , and human-robot interaction . He has contributed to conferences like NeurIPS , ICRA , IROS , and journals such as IEEE TRO and Scientific Reports .
Valerio Pascucci is a Professor at the University of Utah's School of Computing and a DOE Laboratory Fellow at Pacific Northwest National Laboratory. He directs the Center for Extreme Data Management Analysis and Visualization (CEDMAV) and previously led projects at Lawrence Livermore National Laboratory and University of Texas at Austin. PhD in Computer Science (Purdue University, 2000) MSc in Electrical Engineering (University 'La Sapienza', Rome, 1993) As a pioneer in Big Data Management , Scientific Visualization , and Computational Topology , his work connects topological methods with progressive algorithms to enable interactive exploration of petascale datasets. His research spans climate modeling , neuroscience , materials science , and precision agriculture , focusing on multi-resolution techniques and geometric compression . Recent publications show specialization in web-based visualization and AI-driven analytics for climate data, with emphasis on equity in data access and FAIR data principles . His ViSUS project enables real-time data streaming from supercomputers to desktops, while NAPA explores GPU-based architectures for streaming algorithms. Scientific Awards : Best Paper Award, IEEE Pacific Visualization 2011 Best Application Paper Award, IEEE VIS 2006 DOE Laboratory Fellow He advises numerous graduate students and leads collaborations across national laboratories , universities , and industry . Funded by NSF Grant #2127548 , he develops technologies for exascale computing and geospatial intelligence .
Maria Leonilde Rocha Varela is an Associate Professor with Habilitation at the School of Engineering, University of Minho, Portugal, where she also serves as a Senior Researcher at the Algoritmi Research Centre. She has been an integrated member of the Algoritmi Research Centre since 2012 and works in the Department of Production and Systems. Dr. Varela earned her degree in Production Engineering from the University of Minho in 1994, completed a Master's in Computer Integrated Production at DPS-UMinho in 1999, and received her Ph.D. in Production and Systems from the University of Minho in 2007. Her primary research focuses on Manufacturing Management, particularly Production Planning, Control and Optimization, and Collaborative Paradigms, Networks and Decision Making Models. She maintains extensive international collaborations with institutions worldwide including the National Institute of Industrial Engineering, VSB-Technick Univerzita Ostrava, University of Belgrade, and others. Her research spans Web Applications and Services for supporting Engineering and Production Management, with increasing emphasis on Artificial Intelligence, Robotic Process Automation, and Industry 4.0/5.0 applications. She has made significant contributions to scheduling algorithms, optimization techniques, and decision support systems for manufacturing environments. Analysis of her recent publications reveals a strong trend toward integrating Artificial Intelligence with traditional manufacturing processes, particularly in Robotic Process Automation applications. Her research increasingly focuses on sustainable manufacturing practices, with numerous publications addressing energy efficiency, environmental sustainability, and resource optimization. There is a clear emphasis on multi-objective optimization approaches to solve complex manufacturing problems, particularly in distributed job shop scheduling. Her work demonstrates an evolution from traditional production planning methods to more advanced AI-driven approaches for Industry 4.0 and 5.0 environments. Dr. Varela has held significant academic leadership roles, currently serving as the director of the master's course in Engineering and Quality Management at DPS-UMinho. She previously coordinated the industrial management and systems subgroup from 2012 to 2021 and was part of the steering committee for the master's course in systems engineering between 2016 and 2019. She has successfully supervised more than 70 MSc projects, with over 15 currently ongoing, focusing on Production and Systems Engineering. Her supervision encompasses collaborative management models, traditional decision approaches, and web-based platforms incorporating AI techniques. She coordinates research projects including 2 concluded Ph.D. projects and 6 ongoing ones. She collaborates as a research member in several R&D projects with national and international industrial enterprises and institutions, and in international Erasmus projects. Dr. Varela is an active participant in the academic community, serving on editorial boards of several international journals and as a member of organizing and scientific committees for numerous international conferences. She is a member of several prestigious research networks including the Euro Working Group of Decision Support Systems (EWG-DSS), Institute of Electrical and Electronics Engineers (IEEE), Industrial Engineering Network, and the Institute of Industrial and Systems Engineers (IISE).
