جست‌وجوی استاد و پژوهشگر

• Social Networks
• Platform Economy
• Online Word-of-Mouth Marketing
• +۵ مورد دیگر

Tao Lu serves as an Assistant Professor and Doctoral Supervisor at the Business School of Southern University of Science and Technology (SUSTech) since September 2020. His academic appointments focus on bridging information systems, finance, and artificial intelligence research within a business context. Education: Ph.D. in Information Systems, The Chinese University of Hong Kong (2020) M.Phil. in Information Systems, Hong Kong University of Science and Technology (2017) B.S. in Physics/Economics, Tsinghua University (2015) Dr. Lu's research spans three interconnected domains: (1) Social network dynamics and online word-of-mouth marketing using analytic modeling and experimental design; (2) Quantitative finance focusing on distributional uncertainty in asset pricing and neural network applications; (3) Algorithmic bias mitigation in machine learning systems. His work combines rigorous theoretical modeling with empirical validation, often translating to practical applications in quantitative finance. Scientific Recognition: Innovation Research Award of China Information Economics Society (2022) Dr. Lu actively supervises research through his team's recruitment of research assistants and postdoctoral fellows, with projects applying to real-world trading systems in quantitative hedge funds. His research group focuses on developing computational models for financial markets and social platforms, emphasizing both theoretical contributions and practical implementation.

Dmitri Bershadskyyمشاهده پروفایل

استادیار

• Behavioral and Experimental Economics
• Artificial Intelligence
• Lie Detection
• +۵ مورد دیگر

Dr. Dmitri Bershadskyy serves as Assistant Professor at the Vienna University of Economics and Business within the Institute for Digital Ecosystems, while maintaining an active research role as Senior Research Associate at the Chair of Economics (Faculty of Business and Economics) at Otto von Guericke University Magdeburg. His interdisciplinary work bridges economic theory with artificial intelligence and software engineering methodologies to investigate human behavior in digital environments. His research spans Behavioral and Experimental Economics, Artificial Intelligence, Lie Detection, Software Engineering, Algorithm Aversion, and Institutional Design. Employing laboratory and online experiments, surveys, and software engineering techniques, he examines deception detection mechanisms, human responses to algorithmic advice, and incentive structures in collaborative settings. His methodological approach emphasizes the integration of economic principles with computational tools. Recent publications reveal a strong interdisciplinary trajectory, with increasing collaboration between economics and computer science. Key thematic clusters include AI-driven lie detection systems, human-AI interaction dynamics in decision-making contexts, and the development of experimental frameworks for software engineering research. This work demonstrates consistent methodological innovation through the creation of specialized experimental tools like the Magdeburg Tool for Video Experiments (MTVE). Dr. Bershadskyy actively supervises undergraduate and graduate thesis work, offering dedicated seminars for Bachelor's and Master's candidates. He leads a major DFG-funded project on AI-based lie detection that was recently renewed for three additional years, involving collaboration with software engineers and psychologists across multiple institutions. As a core member of the MaXLab research group at OVGU Magdeburg, he contributes to the development of experimental infrastructure and participates in establishing industry standards, including contributions to official guidelines for software engineering experimentation protocols published by the ACM SIGSOFT Empirical Standards initiative.

• Vehicle Safety
• Crash Analysis
• Advanced Driver Assistance Systems (ADAS)
• +۳ مورد دیگر

Ernst Tomasch is a researcher at the Institute for Vehicle Safety (VSI) within Graz University of Technology . His work focuses on vehicle safety systems , crash analysis , and advanced driver assistance systems (ADAS) , with particular emphasis on preventing accidents involving trucks, cyclists, and pedestrians. Recent publications highlight his expertise in Crash cushion and roadside barrier design Cornering ABS for motorcycles ADAS effectiveness for vulnerable road users Child pedestrian safety modeling Heavy goods vehicle safety Simulation-based accident prevention . His research contributes to harmonized pre-crash scenarios and safety protocols for automated driving systems. He collaborates on projects like KISIMO (child pedestrian modeling) and PREVENT (guardrail transitions). Contact: ernst.tomasch@tugraz.at

• Artificial Intelligence
• Human-AI Collaboration
• Reinforcement Learning
• +۳ مورد دیگر

Silvia Tulli serves as an Assistant Professor at Sorbonne University within the Faculty of Science and Engineering, affiliated with the Institute of Intelligent Systems and Robotics (ISIR) and the ACID research team. Her work bridges theoretical AI development with practical human-centered applications, focusing on creating transparent and collaborative intelligent systems. Her research program centers on developing algorithms for human-AI symbiosis, with core expertise in: Reverse reinforcement learning from contrastive examples Human modeling for AI alignment Explainability through social cues in embodied agents Learning from explanations and demonstrations Human-robot interaction frameworks AI value alignment mechanisms Analysis of her recent publications reveals a cohesive trajectory toward making AI systems interpretable and cooperative. The 2021 review establishes foundational principles for explainable embodied agents using social signaling, while the 2020 pilot study pioneers hybrid learning approaches combining explanations with demonstrations—both advancing the field of transparent AI-human collaboration. No scientific awards were documented in the source materials. Current advising activities and grant funding details remain unspecified in available records, though her active research output indicates ongoing project leadership within the ACID team. She operates within ISIR's ACID research team at the Pyramide building (4 Place Jussieu, Paris), where her group develops algorithmic frameworks for socially aware AI systems through interdisciplinary collaboration in robotics and machine learning.

Mathieu Blondelمشاهده پروفایل

پژوهشگر

• Differentiable Programming
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Mathieu Blondel is a Researcher at Google DeepMind in Paris, France. He obtained his PhD in Machine Learning from Kobe University in 2013. From 2013 to 2019, he worked at NTT's Communication Science Laboratories in Kyoto, Japan. Research Interests: Differentiable programming Language models and deep learning Loss functions for structured prediction Optimization algorithms Optimal transport theory Polynomial networks Machine learning software Scientific Awards: Open-science honorable mention at ECML PKDD 2013 Software Contributions: Mathieu is a core contributor to scikit-learn , a foundational Python library for machine learning. He also maintains open-source projects on differentiable sorting, optimal transport, and Fenchel-Young losses.

Mohamed Irfan Refaiمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Biomechanics
• +۶ مورد دیگر

Mohamed Irfan Refai is an Assistant Professor specializing in Neuromuscular Robotics . His research bridges robotics and biomechanics, focusing on exoskeletons , machine learning , and human movement analysis . Education : Master's at University of Twente (2015-2017) and Bachelor's at Sri Sivasubramaniya Nadar College of Engineering (2008-2014). Refai's research explores: Neuro-mechanical control of soft exosuits and active exoskeletons Personalized electromyography-driven musculoskeletal models Adaptive assistance strategies for spinal load reduction Real-time biomechanical risk assessment using NIOSH guidelines Integration of wearable sensors and digital human models His publications (2024-2025) span soft robotics , biomechanical modeling , and industrial ergonomics , emphasizing applications in spinal compression analysis and gait kinematics . Scientific Awards ISB 3D Analysis of Human Movement Student Award (2018) Refai actively contributes to academic discourse through invited talks , conference organization , and peer review activities. His work aligns with the SOPHIA research project and involves collaborations across Europe in rehabilitation robotics and industrial assistive technologies .

Andres Krieteمشاهده پروفایل

استاد آموزشی

• Complex systems biology
• Robustness
• Aging
• +۴ مورد دیگر

Andres Kriete serves as Teaching Professor and Associate Dean for Academic Affairs at Drexel University, leading graduate program innovation while advancing research in complex systems biology. His work bridges theoretical frameworks of self-organization and robustness with experimental aging models, notably the ERiQ (Energy Restriction in Quiescence) system for post-mitotic cells. His educational foundation includes: Habilitation (Venia legendi) in Medical Informatics, University of Giessen Medical School (1997) PhD in Physics, University of Bremen, Germany (1985) Diploma in Physics, University of Bremen, Germany (1981) Kriete's research integrates thermodynamics, control theory, and semantic information processing to decode aging mechanisms, developmental scaling, and mind-body connections. He posits aging as a robustness tradeoff where acute stress survival pathways conflict with longevity assurance, validated through computational models simulating Akt/mTOR/NF-kappaB/p53 signaling deregulation. His experimental ERiQ model replicates age-related metabolic stress in quiescent human fibroblasts. Recent publications (2022-2025) reveal expanding focus on cognitive control in biological systems and dissipative scaling laws governing multicellular aging. His work consistently applies multiscale modeling—from molecular pathways to organismal phenotypes—with increasing integration of consciousness studies and top-down causation principles. Administratively, Kriete chairs accreditation committees and professional development conferences while securing significant funding like the Bristol Myers Squibb Grant for the Drexel Biomed STEM Pathways Fellowship Program. He co-founded the 'Focus on Microscopy' conference series and initiated the Santa Fe Institute's Systems Biology of Aging workshop series in 2007. His research group develops executable computational models using control theory motifs and rule-based aging descriptors, maintaining longstanding collaborations in biomedical imaging and pharmaceutical R&D training initiatives.

• Network Science
• Applied Dynamical Systems
• Mathematical Biology
• +۴ مورد دیگر

Joel Nishimura is an Associate Professor at Arizona State University's School of Mathematical and Natural Sciences. His research spans several interconnected fields including network science, dynamical systems, and mathematical biology. He focuses on how simple rules can create complex behaviors and structures, with applications in biological systems, network algorithms, and engineered protocols. Dr. Nishimura received his Ph.D. from Cornell University in 2013. His dissertation explored decentralized coordination and how actors with limited information can coordinate their timing. His research interests include Network Science, Applied Dynamical Systems, Mathematical Biology, Mathematical Modeling, and Game Theory. A unifying feature of his work is examining how simple rules can create complex behaviors and structures. In biological settings, these simple models capture key qualitative features of observed behaviors, suggesting generalizable principles. In network settings, simple rules form the basis for predictive algorithms, while in engineered settings, they serve as backbones for robust decentralized protocols. Analysis of Dr. Nishimura's recent publications reveals a strong focus on network science and its applications across multiple domains. His work spans theoretical foundations of graph theory and network models, applications to social and biological systems, and connections to decision-making processes and game theory. A notable trend is his consistent exploration of how simple rules or interactions can produce complex emergent phenomena, whether in network structures, biological behaviors, or decision-making processes. Dr. Nishimura has supervised several undergraduate and graduate students, as indicated by the asterisks and other symbols in his publication record. His teaching includes courses such as Applied Linear Algebra (MAT 343), Individualized Instruction (MAT 499), Honors Directed Study (MAT 492), and Honors Thesis (MAT 493). His research has been supported by various grants, though specific details are not provided in the available information. His GitHub repository for the "Double Edge Swap MCMC Graph Sampler" suggests involvement in computational tools development for network science research.

Nelly Bencomoمشاهده پروفایل

دانشیار

• Software Engineering
• Requirements Engineering
• Decision-making under Uncertainty
• +۷ مورد دیگر

Nelly Bencomo is an Associate Professor in the Department of Computer Science at Durham University in the UK, where she joined in September 2021. Previously, she was a Senior Lecturer at Aston University and has held research positions at Lancaster University and INRIA Paris. She is a leading researcher in software engineering for autonomous and self-adaptive systems, with a focus on uncertainty quantification and decision-making under uncertainty. Her research interests span Decision-making under Uncertainty, Software Engineering for Autonomous and Self-adaptive Systems, Bayesian Inference and Machine Learning, Requirements Engineering, Model-Driven Engineering, and runtime models. She has pioneered concepts like models@run.time and requirements@run.time, exploring how systems can maintain awareness of their requirements and models during operation. Her work increasingly addresses AI ethics, human values in software, and responsible AI. Her recent publications (2021-2025) reveal a strong focus on uncertainty representation and management in adaptive systems, with growing attention to explainability, digital twins, and LLM applications in software engineering. Her work bridges theoretical foundations with practical applications in healthcare, education, and sustainable systems. Best Paper Award at International Conference on Engineering Digital Twins 2024 (EDTconf'24) Best Software Artefact at Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'24) Listed in Women for Innovation in Health Science in the North of England 10-year Most Influential Paper Awards 2019 for seminal work in self-adaptive systems Best Paper Award REFSQ 2013 Nelly has successfully supervised multiple PhD students to completion and currently mentors several postgraduate researchers. She leads the EPSRC-funded Twenty20Insight project and the Leverhulme Fellowship QuantUn, focusing on uncertainty quantification using Bayesian surprises. Her research portfolio includes significant contributions to European projects and UK research councils in the area of self-adaptive and autonomous systems. She is also involved in the Leverhulme Centre for Algorithmic Life as Project Lead in the Department of Computer Science and leads the Durham Strategic Research Fund project JusTN0W.

• Robot Learning under Uncertainty
• Reward Inference from Human Input
• AI Safety
• +۵ مورد دیگر

Daniel S. Brown is an Assistant Professor at the University of Utah’s Robotics Center and School of Computing. His research focuses on robot learning under uncertainty, reward inference from human input, and AI safety. He completed his PhD in Computer Science at UT Austin (2020) under Prof. Scott Niekum and was a postdoc at UC Berkeley working with Anca Dragan and Ken Goldberg. He leads the Aligned, Robust, and Interactive Autonomy (ARIA) Lab, which explores human-robot collaboration, safe AI systems, and emergent swarm behaviors. His work emphasizes practical methods for ensuring robustness and safety in autonomous systems. Education: PhD in CS (UT Austin, 2020) Research interests include: Bayesian methods for reward learning, interactive imitation learning, and scalable human-swarm interaction. His NIH-funded projects integrate AI with assistive technologies like powered neck exoskeletons. Key publications focus on Bayesian inverse reinforcement learning, demonstration sufficiency, and robust policy optimization. His NIH Trailblazer Award (2023) supports adaptive control systems for medical exoskeletons. Awards: RSS Pioneer (2021), NeurIPS Workshop Best Paper (2020) Teaching includes courses like 'Human-AI Alignment' and collaborative work on interdisciplinary projects combining robotics with game design and healthcare.

• Media Aesthetics
• Media Culture
• Game Studies
• +۵ مورد دیگر

Dr. Rolf F. Nohr serves as Professor of Media Aesthetics/Media Culture at Braunschweig University of Art (HBK Braunschweig) and holds an External Affiliate position at the University of Arizona's College of Humanities. His research centers on media evidence methodologies, game studies, and instant images, with particular emphasis on simulation games as cultural techniques that shape rationality and decision-making processes. Nohr's scholarly work investigates the interplay between media theory, cultural techniques, and ludic systems. He examines how games function as evidentiary media through historical analysis of business simulations (1955-1975), military war games, and contemporary digital environments. His research reveals how strategic play constructs rational subject positions while interrogating the materiality of game interfaces and the epistemological status of media images. Recent publications demonstrate a cohesive trajectory analyzing games as media artifacts that produce knowledge through procedural logic. His work bridges historical media archaeology with contemporary game design, focusing on genre theory, player agency, and the pedagogical dimensions of serious games. Key themes include the transformation of work logic into play systems, feedback mechanisms in teaching machines, and the cultural techniques embedded in simulation interfaces. Scientific recognition includes: LehrLeo TU Braunschweig Award for Best Lecture in BA Studies (2013) German Study Prize, 3rd Place by Körber Foundation (1998) Nohr has secured major research funding from the German Research Foundation (DFG), Federal Ministry of Education and Research (BMBF), and NORD/LB Foundation for projects including 'Strategie Spielen' (2006-2011), 'Kulturtechnik Unternehmensplanspiel' (2012-2019), and 'PlanA – Planspiel Arbeitswelten' (2018). As Regional Director of the Learning Games Initiative Research Archive since 2015, he leads game preservation efforts while collaborating across engineering, humanities, and social science disciplines. He co-founded the Games Research Group within the German Society for Media Studies (GfM) and serves as editor for the 'Medien'Welten' and 'Braunschweiger Schriften zur Medienkultur' book series. His interdisciplinary collaborations span the TU Braunschweig's engineering departments, University of Arizona humanities scholars, and international game researchers through the Learning Games Initiative network.

Alessio Benavoliمشاهده پروفایل

دانشیار

• Bayesian Probability Theory
• Bayesian Nonparametric Methods
• Skew Gaussian Processes
• +۸ مورد دیگر

Alessio Benavoli serves as Associate Professor in Statistics within the School of Computer Science and Statistics at Trinity College Dublin, Ireland. His interdisciplinary research bridges theoretical statistics with practical applications in smart manufacturing, renewable energy forecasting, finance, and social sciences through advanced computational methodologies. Benavoli's core research program centers on Bayesian probability theory and its extensions, with significant contributions to Bayesian nonparametric methods, Skew Gaussian processes for regression and classification, and Bayesian optimization frameworks. He simultaneously explores foundational aspects of imprecise probability theory and develops novel connections between Bayesian principles and quantum information theory, particularly investigating quantum paradoxes through probabilistic lenses. His work demonstrates exceptional breadth across theoretical statistics, machine learning, and quantum computation. Analysis of his recent publications reveals a cohesive research trajectory focused on creating unified Bayesian frameworks for complex data problems. His 2021 Skew Gaussian Processes paper establishes a comprehensive methodology for diverse machine learning tasks, while his quantum mechanics research pioneers Bayesian interpretations of quantum phenomena. The 2017 classifier comparison tutorial exemplifies his commitment to making advanced statistical techniques accessible for practical machine learning evaluation. Benavoli currently leads the HLOOP project (Humans-in-the-Loop towards a more effective AI in manufacturing), which integrates human expertise with AI systems for industrial optimization, reflecting his emphasis on human-centered AI solutions that address real-world manufacturing challenges.

Samuel I. Gundersonمشاهده پروفایل

دانشیار

• Posttranscriptional gene expression
• pre-mRNA splicing
• polyadenylation
• +۶ مورد دیگر

Samuel I. Gunderson serves as an Associate Professor and Undergraduate Director in the Department of Molecular Biology and Biochemistry at Rutgers University's School of Arts and Sciences, Division of Life Sciences. His laboratory is housed in Nelson Laboratories where he conducts research on RNA processing mechanisms in human cells. Position: Associate Professor & Undergraduate Director Institution: Rutgers University, The State University of New Jersey Department: Molecular Biology and Biochemistry Facility: Nelson Laboratories, Room A320 Contact: gunderson@dls.rutgers.edu | (848) 445-1016 Dr. Gunderson's research focuses on posttranscriptional gene expression mechanisms in mammalian systems, with particular emphasis on pre-mRNA splicing and polyadenylation, RNA-protein interactions, and their implications for cancer biology and neurological diseases. His work bridges molecular biology, biochemistry, and bioinformatics approaches to understand how alternative RNA processing generates protein diversity from single transcription events. His laboratory employs biochemical, bioinformatic, and reconstitution analysis coupled with studies in tissue culture cells to identify both cis-acting RNA sequence elements and trans-acting protein factors involved in regulated pre-mRNA processing. Analysis of Dr. Gunderson's publication record reveals a consistent research trajectory focused on RNA processing mechanisms over two decades. His work spans fundamental biochemistry of RNA-protein interactions to translational applications in cancer therapeutics, particularly through the development of U1 adaptor technology for targeted gene silencing. The publications demonstrate expertise spanning computational modeling of polyadenylation sites, structural analysis of splicing factors, and therapeutic applications targeting oncogenic pathways. Dr. Gunderson leads research activities within the Division of Life Sciences at Rutgers University, where his laboratory in Nelson Laboratories focuses on understanding the basic machinery of pre-mRNA processing in the nucleus. His work combines biochemical analysis with bioinformatics approaches to investigate how RNA processing mechanisms can be harnessed for therapeutic applications, particularly in cancer treatment. The laboratory utilizes various research organisms including bacteria, cell lines, in vitro systems, and mice to investigate fundamental mechanisms of RNA processing and their dysregulation in disease.

Qi Liمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۷ مورد دیگر

Qi Li is an Assistant Professor at Iowa State University, specializing in Artificial Intelligence, Machine Learning, and Data Science. Their research focuses on advancing techniques in natural language processing, graph theory, federated learning, and knowledge graph construction. Key contributions include improving distant supervision for named entity recognition, enhancing fairness in graph-based systems, and developing adaptive methods for knowledge extraction and crowdsourcing. Research interests span a broad range of topics, including but not limited to: Named Entity Recognition (NER), fairness in machine learning, federated learning frameworks, knowledge graph completion, and optimization of crowdsourcing systems. Their work often combines theoretical advancements with practical applications, such as improving model robustness and scalability in real-world scenarios. Recent publications highlight innovations in entity linking re-ranking (GenDecider), personalized federated learning (pFedClub), and individual fairness in graph structures. These contributions reflect a commitment to addressing challenges in AI ethics, data efficiency, and system reliability. Qi Li’s research has been presented at top-tier conferences such as COLING, WWW, NeurIPS, and AIES, demonstrating a strong track record in impactful and interdisciplinary work.

Shuran Songمشاهده پروفایل

استادیار

• Robotics
• Embodied Artificial Intelligence
• Robot Learning
• +۵ مورد دیگر

Shuran Song is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. She leads the Robotics and Embodied Artificial Intelligence Lab (REAL@Stanford), focusing on algorithms enabling robots to learn through physical interaction with the world. Her research spans manipulation, perception, and embodied AI, emphasizing generalizable skills for assistive robotics. Affiliations: Stanford University School of Engineering Labs: REAL@Stanford Research highlights include work on dexterous manipulation, deformable object handling, cross-embodiment learning, and robotic systems like TidyBot and ToddlerBot. Notable awards include Best Paper at ICRA 2024 and Amazon Robotics Best Systems Paper Award (2018). Key publications (2023-2025) address topics such as diffusion policies, embodiment generalization, and robot teaching interfaces. She teaches courses like EE/CS227 (Robot Perception) and leads workshops at top conferences (RSS, ICRA, CoRL).