جستجوی استاد راهنمای دانشگاهی

Mahsa Baktashmotlaghمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Domain Adaptation
• +۳ مورد دیگر

Associate Professor Mahsa Baktashmotlagh is an ARC Future Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. Her research focuses on machine learning techniques applied to visual data analysis, biomedical data (e.g., antibacterial activity prediction), and cybersecurity. She holds a PhD from the University of Queensland (2014) and has contributed to over 50 peer-reviewed publications. Her research interests include domain adaptation, deep learning, and robust generalization across domains. Notable contributions include the development of DI-NIDS (a domain-invariant network intrusion detection system) and advancements in open-set domain adaptation. Her work bridges theoretical machine learning with practical applications in healthcare and computer vision. Education: PhD in Machine Learning, The University of Queensland (2014) Awards: ARC Future Fellowship (202X) Research Themes: Domain Adaptation, Cybersecurity, Biomedical AI Her recent work explores challenges in cross-domain generalization, adversarial machine learning, and scalable 3D object detection. She is actively involved in supervising graduate students and collaborates on interdisciplinary projects involving robotics and medical imaging.

David Jacobsمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). He holds a Ph.D. from MIT (1992) and has expertise in computer vision, machine learning, and deep learning. His research focuses on object recognition, lighting analysis, and applications like electronic field guides (e.g., Leafsnap and Birdsnap). He has been recognized with awards including the Honda Initiation Grant (2007) and the Edward O. Wilson Biodiversity Technology Pioneer Award (2011). Jacobs has taught courses such as CMSC 422 (Machine Learning) and CMSC 828L (Deep Learning), and advised multiple Ph.D. students. His work spans theoretical advancements and practical applications, including collaborations with institutions like the Smithsonian and Columbia University. Education: B.A. Yale, M.S./Ph.D. MIT Positions: Interim Director of UMIACS (2018), Program Co-Chair CVPR Key Projects: Leafsnap (1M+ downloads), Birdsnap Awards: CVPR Best Paper Honorable Mention (2000), Eurographics Best Paper (2016)

Nathan van de Wouwمشاهده پروفایل

استاد

• Dynamics and Control
• Mechatronics and Robotics
• Smart Manufacturing
• +۶ مورد دیگر

Nathan van de Wouw is a Full Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with ICMS, EAISI Mobility, EAISI High Tech Systems, EAISI Foundational, and EIRES. He also holds an adjunct Full Professor position at the University of Minnesota and a part-time Full Professorship at Delft University of Technology. His research focuses on dynamics and control of mechanical systems, including mechatronics, robotics, smart manufacturing, energy systems, and networked control. He has supervised over 150 students and led numerous projects funded by industry partners like ASML, Philips, and Shell. Education: M.Sc. (with Honors) in Mechanical Engineering, TU/e (1994) Ph.D. in Mechanical Engineering, TU/e (1999) Research Interests: Nonlinear systems and control Model reduction and complexity analysis Data-driven and networked control strategies Applications in high-tech systems, autonomous vehicles, and energy systems Awards: IEEE Control Systems Technology Award (2015) for variable-gain control in motion systems Grants & Projects: Lead projects on mechatronic design, lithography systems, and thermodynamic optimization Collaborations with TNO, ASML, and industrial partners Labs & Teams: Member of TU/e’s Dynamics and Control group Affiliated with EAISI (Eindhoven AI Systems Institute)

Janet Walkoeمشاهده پروفایل

دانشیار

• Mathematics Education
• Algebra Teaching & Learning
• Teacher Noticing
• +۳ مورد دیگر

Dr. Janet Walkoe is an Associate Professor in the Department of Teaching and Learning, Policy and Leadership at the University of Maryland. Her work focuses on mathematics education, particularly algebra teaching and learning, and the role of teacher noticing in responsive instruction. She holds a Ph.D. in Learning Sciences from Northwestern University (2013), an MS in Mathematics from the University of Illinois at Chicago, and a BA in Mathematics from the University of Chicago. Before academia, she taught high school mathematics for a decade, earning National Board Certification. Her research emphasizes how teachers attend to student algebraic thinking during instruction and how pre-service teachers can develop noticing skills through video analysis. Key areas include teacher responsiveness, multimodal algebraic thinking, and computational thinking integration in mathematics education. She has led the NSF-funded CAREER grant (2020) exploring teacher noticing of student thinking, and co-authored influential works like Lenses for examining students’ mathematical thinking (2014), which earned an NCTM award. Notable Grants: NSF CAREER DRL 17-537 ($736,914), INFACT Subaward ($250k), Spencer Foundation Grant ($44k) Teaching: Courses include Interdisciplinary Teaching in Middle Grades (TLPL 423/424), Elementary Mathematics Education (EDCI 652), and Foundations of Mathematics Education (EDCI 751) Professional Development: Develops video-based tools for teacher reflection and computational thinking integration Walkoe advocates for culturally sustaining pedagogy and equity in teacher education. Her recent work bridges computational thinking with mathematics instruction, emphasizing interdisciplinary approaches to foster student engagement.

Kylie Pepplerمشاهده پروفایل

استاد

• STEM Education
• Maker Culture
• Computational Thinking
• +۳ مورد دیگر

Professor Kylie Peppler is a dual Professor of Informatics and Education at the University of California, Irvine, leading the Creativity Labs and the Connected Learning Lab. Her research focuses on leveraging hands-on creativity—such as e-textiles, robotics, and traditional fiber crafts—to enhance STEM education, particularly for marginalized populations. She emphasizes the role of materiality in fostering systems thinking and equity in learning environments. Education: PhD in Education (UCLA), Postdoctoral training at UC Irvine, and prior roles at Indiana University. Her academic journey bridges psychology, art, and technology. Research Interests: Maker culture, computational thinking, STEAM integration, workforce development, and the impact of arts in education. She explores how tools like e-textiles and looms democratize access to STEM while addressing gender disparities. Key Projects: NSF-funded work on computational construction kits, Re-Crafting STEM initiatives, and Future of Work research using AR/VR for manufacturing training. Collaborations include Boeing, Inner-City Arts, and NYSCI. Awards: NSF Early CAREER Award, Mira Tech Educator of the Year, and Indiana Governor's Award. Her work is supported by NSF, Wallace Foundation, and industry partners. Grants & Labs: Over $10M in grants; directs labs advancing connected learning and equity through technology. Recent studies include virtual reality welding simulators and culturally sustaining arts practices. Labs/Teams: Creativity Labs (designing maker tools), Connected Learning Lab (digital equity), and partnerships with museums and industry to scale inclusive learning.

Carolyn Sparreyمشاهده پروفایل

دانشیار

• Biomechanics
• Injury Biomechanics
• Computational models of injury
• +۴ مورد دیگر

Carolyn Sparrey is an Associate Professor and Graduate Student Supervisor in the School of Mechatronic Systems Engineering at Simon Fraser University (SFU). She leads the Neurospine Biomechanics Laboratory, focusing on injury prevention and management through biomechanical research. Her work bridges engineering principles with clinical applications, collaborating with Vancouver General Hospital and accident reconstruction experts. Dr. Sparrey holds a Ph.D. in Mechanical Engineering (UC Berkeley, 2008), M.A.Sc. (UBC, 2004), and B.A.Sc. (University of Waterloo, 2001). Her research emphasizes spinal cord injury mechanics, tissue material characterization, and patient-specific modeling. She teaches courses like MSE 420 (Biomechanical Engineering) and MSE 222 (Kinematics and Dynamics). Her lab’s current projects include mechanical characterization of neurological tissues, thermodynamic modeling of spinal cord injury treatments, and injury prevention device design. Funding opportunities are available for exceptional graduate students through NSERC and CIHR awards. Undergraduate and co-op students can engage in projects aimed at publishing or presenting results. Dr. Sparrey actively promotes interdisciplinary collaboration, with a focus on translating engineering solutions into clinical practice. Her teaching philosophy prioritizes preparing students for industry, research, and societal contributions through innovative curricula.

Taskin Padirمشاهده پروفایل

استاد

• Experiential Robotics
• Human-Robot Teaming
• Embodied Artificial Intelligence
• +۴ مورد دیگر

Taskin Padir is a Professor in the Department of Electrical and Computer Engineering at Northeastern University and concurrently serves as an Amazon Scholar. He holds a PhD and MS from Purdue University and a BS from Middle East Technical University. His research focuses on experiential robotics, human-robot teaming, and embodied AI, with leadership roles in the Robotics and Intelligent Vehicles Research Laboratory (RIVeR Lab) and the Institute for Experiential Robotics. Padir has led projects for DARPA, NASA, and industry partners, advancing autonomous systems for extreme environments and human-robot collaboration. Education: PhD, Electrical and Computer Engineering, Purdue University (2004) MS, Electrical and Computer Engineering, Purdue University (1997) BS, Electrical and Electronic Engineering, Middle East Technical University (1993) Research Interests: Shared autonomy and human-in-the-loop robotics Embodied artificial intelligence Human-robot teaming in extreme environments (e.g., space, disaster zones) Collaborative robotics for industrial applications His work bridges robotics, AI, and real-world challenges, with recent projects addressing seafood processing automation, robotic navigation in unstructured terrains, and spectroscopy-based environmental monitoring. Awards: Recipient of the 2024 Faculty Research Team Award, 2023 Impact Award, and 2022 Amazon Scholar distinction. His research has been funded by NSF, DARPA, NASA, and industry collaborators like Amazon Robotics and Intel. Labs: Director of the RIVeR Lab and Institute for Experiential Robotics, fostering interdisciplinary research in autonomous systems and intelligent vehicles. Current projects include CRISP (Co-worker Robots for Seafood Processing) and PROSPECT (robotic spectroscopy tools).

• Bio-inspired electronics
• Mechanically adaptive electronics
• Multimaterial additive fabrication
• +۹ مورد دیگر

Alex Chortos is an Assistant Professor of Mechanical Engineering at Purdue University's School of Mechanical Engineering. His research focuses on bio-inspired electronics, mechanically adaptive materials, and advanced manufacturing techniques. He leads the Chortos Lab, which explores innovations in soft actuators, wearable haptics, and polymer design. Chortos holds a B.A.Sc. from the University of Waterloo (2011), a Ph.D. from Stanford University (2017), and completed a postdoctoral fellowship at Harvard University (2020). His academic work bridges fundamental material science with practical applications in robotics, biomedical devices, and human-machine interfaces. Key research areas include: Multimaterial additive fabrication for soft robotics Stretchable sensors and transistors for e-skin applications Design of durable and adaptive polymer systems His publications emphasize advancements in 3D printing techniques, bioinspired sensor systems, and the development of mechanically robust electronic components. Recent work explores photodynamic polymers and machine learning-driven optimization of soft actuators.

Ruixiang Tangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Ruixiang Tang is an Assistant Professor at Rutgers, The State University of New Jersey. His research focuses on artificial intelligence, machine learning, and natural language processing, with an emphasis on multimodal learning, model security, and ethical AI. He explores topics such as adversarial robustness, bias mitigation, and applications in healthcare and robotics. Key research interests include developing robust algorithms for vision-language models, analyzing model vulnerabilities like backdoors and hallucinations, and designing trustworthy AI systems. His work bridges theoretical advancements and practical applications, addressing challenges in healthcare data augmentation, copyright infringement detection, and cognitive reasoning. His recent publications highlight contributions to multimodal in-context learning, counterfactual reasoning benchmarks, and secure model optimization. Tang's research also intersects with fairness in AI, such as mitigating bias in NLP models and ensuring equitable outcomes in medical applications.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +۳ مورد دیگر

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

Mark D. Grossمشاهده پروفایل

استاد

• Design Methods
• Modular Robotics
• Computational Design
• +۵ مورد دیگر

Mark D. Gross is a Professor of Computer Science and Director of the ATLAS Institute at the University of Colorado Boulder, where he leads an interdisciplinary hub for creativity and invention. His academic journey began at MIT with BS and PhD degrees, followed by faculty roles at Carnegie Mellon University (2004-2013), University of Washington Seattle (1999-2004), and CU-Boulder (1990-1999, 2014-present). As co-founder of Modular Robotics Incorporated and Blank Slate Systems LLC, he bridges academia and entrepreneurship. Education: BS and PhD in Computer Science from MIT Gross’ research spans design methods, modular robotics, computational design tools, and tangible interaction. He pioneered sketch recognition software like 'The Electronic Cocktail Napkin' and explores physical computing through projects such as shape-changing interfaces, interactive construction kits, and augmented reality systems. His work integrates IoT, digital fabrication, and educational technology. Recent publications highlight innovations in AR/VR collaboration, shape-changing robotics, and interactive fabrication. Key themes include climate communication through data physicalization, AI-driven creative systems, and soft robotics for dynamic interfaces. Despite no explicit awards listed, his career demonstrates sustained impact through ACM conference leadership (Creativity and Cognition 2009, TEI 2011) and industry partnerships. Gross’ prior industry experience includes positions at Atari Cambridge Research and Logo Computer Systems. His lab at ATLAS fosters radical creativity through projects like PaperMech, DynaBlock, and WearAir, emphasizing hands-on learning and cross-disciplinary experimentation.

• Data Science
• Computational Social Science
• Misinformation
• +۳ مورد دیگر

Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.

Olivier Sigaudمشاهده پروفایل

استاد

• Reinforcement Learning
• Robotics
• Decision Modeling
• +۳ مورد دیگر

Olivier Sigaud is a Full Professor at Sorbonne University, affiliated with the ISIR (Intelligent Systems and Robotics Institute) and the Machine Learning and Intelligent Autonomous Systems (MLIA) team. He holds an engineering degree from ISEN and dual PhDs in Computer Science (University of Paris XI, 1996) and Philosophy (University of Paris I, 2004). Previously employed at Dassault Aviation (1995–2001), he transitioned to academia as a Lecturer and later a Professor at LIP6 and ISIR. His research focuses on reinforcement learning, robotics, computational neuroscience of decision-making in animals, and human-robot interaction. Key contributions include advances in goal-conditioned reinforcement learning, intrinsically motivated agents, and human-in-the-loop systems. He has co-authored over 100 publications in top-tier conferences (NeurIPS, ICML) and journals, with recent work exploring large language model grounding, open-ended learning frameworks, and motor skill acquisition through interactive curricula. Notable projects include the CURIOUS framework for modular multi-goal RL and the DREAM architecture for open-ended robotic learning. His work bridges theoretical AI with practical robotics applications, emphasizing interdisciplinary collaboration between computer science and neuroscience.

Tom Mitchellمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Neuroimaging
• +۴ مورد دیگر

Tom Mitchell is the Fredkin Professor of AI and Learning and Director of the Center for Automated Learning and Discovery (CALD) at Carnegie Mellon University's School of Computer Science. His research focuses on machine learning, computational neuroscience, and their applications in neuroimaging and natural language processing. He is renowned for pioneering work in developing algorithms to decode brain activity and for contributions to foundational machine learning theory, including co-training and explanation-based learning. Mitchell authored the seminal textbook *Machine Learning* (McGraw Hill, 1997) and has led projects like Never-Ending Learning (NELL), an AI system that autonomously learns from web content. His work bridges computer science and cognitive science, exploring how machines can learn from data and human interaction. Notable research interests include brain-computer interfaces, automated knowledge extraction, and ethical AI. Mitchell's publications span influential journals like *Science* and *Nature*, and he has been recognized for advancing interdisciplinary research in AI and neuroscience. He has advised numerous students and contributed to initiatives like the AAAI Presidential Address on AI and brain sciences. Mitchell's current projects include studying the neural basis of language and developing AI tools for education and healthcare.

Ting Huمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Bioinformatics
• Data Mining
• +۷ مورد دیگر

Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods