جست‌وجوی استاد و پژوهشگر

J. Edward Colgateمشاهده پروفایل

استاد

• Human-Robot Interaction
• Haptic Display
• Teleoperation
• +۴ مورد دیگر

J. Edward Colgate is the Walter P. Murphy Professor of Mechanical Engineering and Director of the Human Augmentation via Dexterity (HAND) Engineering Research Center at Northwestern University's McCormick School of Engineering. He also holds the title of Breed Senior Professor of Design. His academic career includes leadership roles as founding co-Director of the Segal Design Institute and director of the Master of Science in Engineering Design and Innovation program. Colgate earned his Ph.D. (1988), S.M. (1986), and S.B. in Physics (1983) from the Massachusetts Institute of Technology. Colgate's research focuses on physical human-robot interaction with specialization in surface haptic interactive design and electroadhesion technology development. His work spans three interconnected domains: haptic interfaces (including wearable haptic arrays and Touchbot systems), robot dexterity through Shape-Based Remote Manipulation (SBRM) for overcoming communication delays, and high-speed electroadhesive actuators. The Northwestern Haptics Lab under his direction aims to create realistic virtual environments by merging these research vectors. His publications demonstrate consistent focus on tactile perception mechanisms, electroadhesion applications, and haptic rendering algorithms. Recent work explores texture playback fidelity, wearable electroadhesive arrays, robotic manipulation, and human-swarm control systems, reflecting interdisciplinary integration of mechanical engineering, materials science, and neuroscience principles. Awards: Elected to National Academy of Engineering (2021) for contributions to haptics, human-robot systems, and design education Inducted into National Academy of Inventors (2015) Educational initiatives include developing Northwestern's Design Thinking and Communication curriculum, establishing the Certificate in Engineering Design, and creating the Master of Science in Engineering Design and Innovation. He teaches ME 390: Introduction to System Dynamics using a flipped classroom model. Colgate directs the Northwestern Haptics Lab within the Center for Robotics and Biosystems, focusing on fundamental haptics research with applications in virtual reality, prosthetics, and human-assistive devices. The lab maintains active industry partnerships for technology transfer of haptic innovations.

• Econometric Theory
• Machine Learning/Nonparametrics
• Robust Inference for Clustered & Network Data
• +۶ مورد دیگر

Harold D. Chiang is an Assistant Professor in the Department of Economics at the University of Wisconsin-Madison. His research focuses on econometric theory and methods, particularly robust inference techniques for clustered and network data, machine learning applications, and causal inference frameworks like regression discontinuity/kink designs. He employs computational statistics and asymptotic theory to address methodological challenges in high-dimensional and complex datasets.

• Machine Learning
• Deep Learning
• Signal Processing
• +۹ مورد دیگر

Prof. Dr.-Ing. Ahmad Osman is a Professor at the Saarland University of Applied Sciences (htw saar), specializing in Test Technologies and Test Methods within the Faculty of Engineering. He also holds an Adjunct Professor position at Laval University in Quebec, Canada, in the Department of Electrical Engineering and Computer Science. His research focuses on Artificial Intelligence applications in Signal and Image Processing for Non-destructive Testing (NDT) , with extensive work on Deep Learning , 3D Ultrasound Tomography , and Sensor Data Fusion in industrial contexts. Engineering Artificial Intelligence Signal Processing Image Processing Non-destructive Testing Quality Control Augmented Reality Osman leads the AutomaTiQ research group and serves as Head of the Algorithms/Signal and Data Processing Department at Fraunhofer IZFP . His recent publications (2017–2022) emphasize Deep Learning for defect detection in CFRP , Terahertz Imaging for artwork diagnostics, and Acoustic Sensors for agricultural quality control. He has organized international conferences on Structural Health Monitoring and contributed to Springer books on NDT technologies. His projects include ComforTex-AI (2024) and development of 3D positioners for ultrasound measurements. Collaborations span institutions in Germany, Canada, Italy, and Brazil, with advisory roles in the German Society for NDT and technical committees for conferences in Montreal and Egypt.

Pekka Marttinenمشاهده پروفایل

دانشیار

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +۸ مورد دیگر

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

Hedvig Kjellströmمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Human-Computer Interaction
• +۶ مورد دیگر

Hedvig Kjellström is a Professor at the Division of Robotics, Perception and Learning within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. She holds significant affiliations with the Swedish e-Science Research Centre and the Max Planck Institute for Intelligent Systems in Germany. Her work spans multiple interdisciplinary domains and she serves as Editor-in-Chief for CVIU and was Program Chair for CVPR 2025. Her research centers on Computer Vision as a sub-field of AI, with three interconnected themes: Computational Aesthetics (exploring aesthetic aspects of human communicative behavior), Communicative Behavior (developing models of how humans and animals perceive and produce non-verbal communication), and Embodied Artificial Intelligence (creating methodologies for robots and autonomous agents to perceive the world through sensors, primarily vision). Her work has significant applications in medical diagnostics, animal welfare, human-robot interaction, and creative arts. Analysis of her recent publications reveals a strong trend toward multimodal AI systems that integrate vision, language, and action understanding. Her research increasingly focuses on animal-centered applications, particularly equine pain detection and behavior analysis, while maintaining strong foundations in human communication modeling, gesture recognition, and 3D reconstruction techniques. The interdisciplinary nature of her work bridges computer science with veterinary medicine, neuroscience, and performing arts. Hedvig Kjellström actively supervises numerous PhD and Master's students across various projects and maintains extensive collaborations with institutions including Karolinska Institutet, Swedish University of Agricultural Sciences, and international partners. Her research is supported by major funding bodies including WASP, VR, and SeRC. She leads or participates in several notable projects including OrchestrAI (communication between conductor and orchestra), ANITA (Animal Translator), MARTHA (3D horse motion analysis), and STING (synthesis and analysis with transducers and invertible neural generators). Her work with ACAI (Animal Centered Artificial Intelligence), which she co-founded and directs, demonstrates her commitment to applying AI for animal welfare.

• Artificial Intelligence
• Marketing Analytics
• Digital Marketing
• +۴ مورد دیگر

Natasha Zhang Foutz is a Research Associate Professor of Commerce at the McIntire School of Commerce, University of Virginia . Her work bridges Artificial Intelligence , Marketing Analytics , and Consumer Behavior , with a focus on Digital Content and Location-Based Services . She teaches Marketing Analytics , Entertainment Marketing , and Marketing Models across undergraduate to PhD programs. Ph.D. in Marketing, Cornell University M.S. in Statistics & Marketing, Cornell University B.S. in Economics, Fudan University Her research investigates: AI-Powered Entertainment Marketing : Using machine learning to analyze consumer behavior in digital content. Privacy and Ethical Data Use : Studying consumer trade-offs between privacy and public good, especially during crises like the COVID-19 pandemic . Location Analytics : Leveraging mobile data for urban economics, real estate, and emergency response modeling (e.g., MobiRescue for disaster logistics). Prosocial Consumer Behavior : Exploring how social capital and diversity drive innovation and policy compliance. Recent publications highlight trends in Reinforcement Learning for crisis management, Privacy-Preserving AI , and Freemium Pricing Models in digital markets. Her awards include the 2025 UVA Outstanding Researcher Award and the 2018 Mallen Award for motion picture studies. Natasha serves as an Area Editor for multiple journals, emphasizing Data Science in marketing. She has advised numerous PhD students and collaborated on projects analyzing Big Data in consumer mobility and platform economics.

Alicia Orea Ginerمشاهده پروفایل

استاد

• Sustainable Tourism
• Food Tourism
• Tourism and Social Justice
• +۵ مورد دیگر

Alicia Orea Giner is a Professor at the Faculty of Economics and Business Sciences of Rey Juan Carlos University, specializing in sustainable tourism and social justice. Her research focuses on Generation Z's sustainable consumption practices in food tourism, AI integration in hospitality, and ethical dimensions of tourism. She co-authored the award-winning article The way we live, the way we travel: generation Z and sustainable consumption in food tourism experiences , recognized with the 2024 Emerald Literati Outstanding Paper Award. Her work bridges sustainability, technology, and cultural studies. Key themes include feminist methodologies in tourism research, transformative travel through ecofeminism, and post-pandemic food tourism trends. She explores AI applications in service robots, wellness tourism's mental health connections, and marginalized communities' roles in culinary tourism. Recent studies address LGBTQ+ tourism challenges, stakeholder participation in World Heritage sites, and ghost tours' cultural narratives. Awards highlight her contributions to sustainability and social justice in tourism. Awards: 2024 Emerald Literati Outstanding Paper Award Research Foci: Food Tourism, AI Ethics, Feminist Tourism Studies, Cultural Heritage Management Labs/Teams: No specific lab mentioned; collaborates across interdisciplinary tourism research networks.

Ferenc Huszárمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning Theory
• Optimization
• +۶ مورد دیگر

Ferenc Huszár is an Associate Professor of Machine Learning at the University of Cambridge, affiliated with the Department of Computer Science and Technology. His research focuses on foundational aspects of deep learning, including optimization, generalization, representation learning, and causal reasoning. He co-founded Magic Pony Technology, where he contributed to super-resolution and compression techniques, later acquired by Twitter. Education: PhD in Bayesian Machine Learning from the University of Cambridge (supervised by Carl Rasmussen, Máté Lengyel, and Zoubin Ghahramani), followed by roles in tech/startups. Research Interests: Theoretical underpinnings of deep learning, neural network behavior analysis, LLM theory, causal inference, and AI safety. His lab explores algorithmic reasoning in neural networks and implicit Bayesian inference in LLMs. Selected Contributions: Co-authored influential papers on super-resolution (CVPR 2016) and GAN-based image enhancement (CVPR 2017). Active in advising 9 PhD students and mentoring research assistants. Grants & Collaborations: Collaborates with institutions like the Max Planck Institute and ELLIS. Supervises projects on causal representation learning, geometric deep learning, and federated learning.

• Robotics
• Control Systems
• Rehabilitation Robotics
• +۴ مورد دیگر

Timothy Bretl is a Professor of Aerospace Engineering at the University of Illinois at Urbana-Champaign, holding the Severns Faculty Scholar position since 2021. He also serves as Associate Head of the Aerospace Engineering department. His research focuses on robotics, control systems, rehabilitation robotics, and engineering education. Bretl earned his Ph.D. from Stanford University (2005), with prior degrees from Swarthmore College. He holds affiliate roles across multiple departments, including Neuroscience, Coordinated Science Laboratory, and Computer Science. Education: Ph.D. in Aeronautics and Astronautics, Stanford University (2005) B.A. in Mathematics and B.S. in Engineering, Swarthmore College (1999) His research spans engineering education innovations, robotic manipulation, and brain-machine interfaces. Notable awards include the NSF CAREER Award (2010), Best Manipulation Paper (2012), and multiple teaching honors like the Rose Award for Teaching Excellence (2016). Bretl’s work integrates theoretical foundations with practical applications in prosthetics, autonomous systems, and educational technology. He has advised numerous projects on robotics, control systems, and human-robot interaction. His lab explores advanced topics like elastic rod manipulation, magnetic positioning, and curriculum reform in STEM education. Collaborative projects include partnerships with industry and interdisciplinary teams at the Beckman Institute.

Dennis Herhausenمشاهده پروفایل

استاد

• Digital Communication
• Customer Journeys and Experience
• Multichannel Management
• +۷ مورد دیگر

Prof. Dr. Dennis Herhausen is a Full Professor of Marketing and Head of the Marketing Department at Vrije Universiteit Amsterdam’s School of Business and Economics. Previously, he held positions as Associate Professor at KEDGE Business School, Visiting Professor at the University of St. Gallen, and Assistant Professor at the University of St. Gallen. His research focuses on digital communication, customer journeys, multichannel management, and social media strategies, with publications in top-tier journals like the Journal of Marketing and Journal of Marketing Research. His work addresses critical issues such as online firestorm mitigation, complaint de-escalation, and gig economy communication. He has been awarded multiple prestigious prizes, including the 2021 CBSIG Consumer Research Award and the 2019 William R. Davidson Award. Education: PhD in Marketing from University of St. Gallen (2011) Teaching: Courses on Customer Experience Management, Survey Research Methods, and Thesis Guidance Research Interests: His work explores digital marketing strategies, customer-centric innovations, and the dynamics of online platforms. Recent studies address topics like business-to-investor marketing signals, privacy orientation measurement, and machine learning biases in marketing. He actively contributes to editorial boards of journals including Journal of Marketing and Journal of Interactive Marketing. Awards: 2021 CBSIG Consumer Research in Practice Award 2021 Retail & Pricing SIG Best Paper Award 2021 SERVSIG Best Services Article Award 2020 William R. Davidson Award Grants & Editorial Work: His research has been funded by national/international grants, and he serves on multiple editorial boards. Notable datasets include studies on online retailer strategies and virtual brand sabotage responses.

Una-May O'Reillyمشاهده پروفایل

استاد پژوهشی

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Una-May O'Reilly is a Principal Research Scientist at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), leading the ALFA group. She holds a PhD in Computer Science from Carleton University (1995), with prior roles including a postdoctoral appointment at MIT's Artificial Intelligence Laboratory. Her research focuses on cybersecurity, adversarial AI, software security, and disinformation dynamics, applying evolutionary algorithms and machine learning to address arms races in cyber defense and societal challenges like climate change communication. Education: B.Sc., University of Calgary M.C.S., Carleton University Ph.D., Carleton University (1995) Research Interests: Adversarial machine learning for secure systems Coevolutionary algorithms in cybersecurity and healthcare Program comprehension via neuroscience and AI Climate disinformation mitigation on social media Large language model applications in code synthesis and threat hunting Key Projects: Adversarial Cyber Security : Modeling cyber attack-defense arms races GIGABEATS : AI-driven medical sensor data analysis for critical care MOOC Learner Project : Data science for online education insights Awards: EvoStar Award (2013) for contributions to evolutionary computation Fellow of ACM Sig-EVO Leadership & Service: Co-founder and Vice-Chair of ACM Sig-EVO Former Chair of GECCO (2005), major evolutionary computation conference Editorial roles in Evolutionary Computation and Genetic Programming and Evolvable Machines Labs & Groups: Leads the AnyScale Learning for All (ALFA) group at CSAIL, focusing on scalable AI for cybersecurity, healthcare, and education.

• Machine Learning and Deep Learning
• Big Data and Artificial Intelligence in Accounting
• Accounting Fraud
• +۳ مورد دیگر

Nurul Alam is a Lecturer in Accounting at the University of Sydney Business School. His research focuses on machine learning applications in finance and accounting, corporate distress prediction, and financial reporting. He holds a PhD from the University of Sydney, where he investigated machine learning models for US corporate bankruptcies. His teaching excellence has earned over 15 awards, including multiple Dean’s Citations and nominations for the Wayne Lonergan Award for Teaching Excellence. Education Master of Commerce (Finance & Accounting), University of Sydney Bachelor of Business Administration (Finance & Banking), University of Rajshahi, Bangladesh Research Interests Machine Learning and Deep Learning in accounting and finance Big Data analytics for corporate financial decision-making Accounting fraud detection mechanisms Financial reporting standards and regulations Current Projects Corporate bankruptcy prediction using survival models and AI Deep Learning applications in panel data analysis for firm distress Machine learning variable selection in corporate finance Accounting fraud patterns among lower/mid-level employees Teaching Contributions Financial Accounting B (ACCT3011) Quantitative Methods for Accounting (QBUS5002) Quantitative Business Analysis (BUSS1020)

• Database Systems
• Data Science
• Machine Learning
• +۳ مورد دیگر

Dr. H.V. Jagadish is a Professor in the Computer Science and Engineering department, focusing on database systems, data science, and ethical AI. He emphasizes mentorship and independence in research, guiding students to become colleagues through collaborative growth. Research Focus His work spans Data integration and fairness in machine learning Query processing and knowledge graph analysis Reproducibility and ethical data management Advising and Research Dr. Jagadish encourages open communication, frequent feedback, and proactive student-driven research. He supports internships and conference attendance, prioritizing SIGMOD and VLDB for presentations.

Zhengjie Miaoمشاهده پروفایل

استادیار

• Data Management
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Zhengjie Miao is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on data management and artificial intelligence, particularly developing algorithms and tools to enhance data science pipelines. He is part of the SFU Data Science Research Group and previously worked as a Research Scientist at Megagon Labs. Education: PhD in Computer Science from Duke University (advisors: Sudeepa Roy, Jun Yang), M.S. from Columbia University (advisor: Eugene Wu), B.S. from Peking University. Research interests span data management systems, machine learning integration in data science, and human-in-the-loop data preparation. His work emphasizes improving data usability through techniques like data lineage tracking, explainable query systems, and fairness-aware data processing. Recent publications highlight advancements in collaborative annotation with LLMs, automated data standardization, and contrastive learning frameworks for column annotation. Key contributions include the Rotom framework for meta-learned data augmentation, the Watchog system for column annotation, and CAPE for query explanation. His research bridges database theory with practical applications in education and industry.

Noman Mohammedمشاهده پروفایل

دانشیار

• Data Security
• Privacy-Preserving Techniques
• Healthcare Data Privacy
• +۷ مورد دیگر

Noman Mohammed is an Associate Professor of Computer Science at the University of Manitoba’s Faculty of Science, leading the Data Security & Privacy (DSP) laboratory. He specializes in privacy-preserving techniques for data sharing, addressing challenges in healthcare, genomic, and financial data. In 2020, he received the Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award for his contributions to bridging privacy and data utility gaps. His research focuses on balancing data accessibility and individual privacy through technical solutions like federated learning, differential privacy, and secure genomic data processing. He emphasizes integrating policy guidelines with advanced technologies to mitigate privacy risks from interconnected data sources. Notable achievements include developing toolkits for data anonymization and federated learning frameworks, as well as advancing methods to secure cloud-based data storage and analysis. His work aligns with societal needs for robust privacy mechanisms in an era of expanding personal data collection. Future objectives involve addressing privacy challenges in emerging technologies, such as heterogeneous data integration and scalable systems for personal data management. Despite his research focus, he notably avoids social media platforms.