جست‌وجوی استاد و پژوهشگر

Justin Rombergمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Optimization
• +۳ مورد دیگر

Justin Romberg is the Schlumberger Professor and Associate Chair for Research in the School of Electrical and Computer Engineering at Georgia Tech, and serves as Associate Director for the Center for Machine Learning. He holds a B.S.E.E., M.S., and Ph.D. from Rice University, followed by postdoctoral work at Caltech and visiting roles at UCLA and the Laboratoire Jacques-Louis Lions. His research bridges signal processing, machine learning, optimization, and applied probability, with notable contributions to imaging inverse problems, data compression, and broadband beamforming. Dr. Romberg has received prestigious awards including the ONR Young Investigator Award (2008), PECASE (2009), Packard Fellowship (2009), and IEEE Fellowship. His recent work emphasizes real-time RF emulation, neural adjoint methods for sound speed learning, and decentralized optimization frameworks. Current projects include radar signal processing, multi-agent reinforcement learning, and hardware-friendly sparse neural networks. His lab focuses on translating theoretical insights into practical systems, such as low-bit beamforming accelerators and medical monitoring devices using MEMS sensors.

Mahdi Soltanolkotabiمشاهده پروفایل

استاد

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

Mahdi Soltanolkotabi is a Professor at the University of Southern California (USC) in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering. He serves as the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Previously, he was a postdoctoral researcher at UC Berkeley, advised by Ben Recht and Martin Wainwright, after completing his Ph.D. in Electrical Engineering at Stanford under Emmanuel Candès. His research bridges theoretical foundations of data science with applied AI, focusing on generative AI, machine learning, signal processing, and computational imaging. Education: Ph.D. in Electrical Engineering (2014, Stanford), Postdoc (2014-2015, UC Berkeley). Research interests include developing mathematical frameworks for generative AI, non-convex optimization, high-dimensional probability, and reliable AI systems for healthcare and scientific discovery. His applied work emphasizes trustworthy AI, medical imaging, and interdisciplinary collaborations with domain scientists. Publications highlight contributions to phase retrieval, subspace clustering, federated learning, and neural network optimization. Recent work emphasizes AI reliability (e.g., MediConfusion, Serpent) and scalable image restoration techniques. Awards: Packard Fellowship, NIH Innovator Award, Sloan Fellowship, NSF Career Award, AFOSR-YIP. Grants/Outreach: NIH-funded projects, collaborations with K-12 STEM education programs (Nava College Preparatory Academy, USC Viterbi Adopt-a-School). Labs/Teams: Leads USC’s AIF4S, focusing on foundational AI for science.

Mahdi Imaniمشاهده پروفایل

استادیار

• Machine Learning
• Control Theory
• Learning Theory
• +۳ مورد دیگر

Mahdi Imani is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. He holds a PhD in Electrical Engineering from Texas A&M University (2019), and MSc and BSc degrees in Electrical and Mechanical Engineering from the University of Tehran (2014 and 2012, respectively). His research focuses on machine learning, control theory, Bayesian statistics, and signal processing, with applications in gene regulatory networks, network security, and human-AI collaboration. Dr. Imani has received prestigious awards, including the NIH NIBIB Trailblazer Award (2022), the NSF CISE Career Award (2020), and the Outstanding Associate Editor Award from IEEE Transactions on Neural Networks and Learning Systems (2023 and 2024). He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Vehicular Technology, and is a Senior Member of IEEE. His research projects include DARPA-funded work on verified probabilistic reasoning in mixed reality systems, NSF-funded statistical inference methods, and ONR-funded studies on human-AI team synergy. He leads a lab focused on developing scalable Bayesian methods and reinforcement learning techniques for complex systems.

Anne-Marie Georgeمشاهده پروفایل

دانشیار

• Preference Learning
• Preference Modeling
• Computational Social Choice
• +۴ مورد دیگر

Anne-Marie George is an Associate Professor at the University of Oslo, affiliated with the Scientific Computing and Machine Learning department. Her research focuses on handling user preferences through modeling, elicitation, inference, and fairness in group decision-making systems. Academic Rank: Associate Professor Department: Scientific Computing and Machine Learning Email: annemage@ifi.uio.no Her work spans computational social choice, preference learning, and algorithmic fairness, with recent publications addressing dynamic resource allocation, robust recourse in binary problems, and fair voting procedures. Collaborations with researchers like Christos Dimitrakakis highlight interdisciplinary efforts in AI ethics and decision theory. Recent trends in her publications include: Dynamic allocation fairness Preference-based decision systems Explainable AI for resource distribution Ontology engineering applications Robustness in allocation algorithms Multiwinner voting optimization

Darrell Worthyمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Decision-Making
• Categorization
• +۱۱ مورد دیگر

Dr. Darrell Worthy is an Associate Professor in the Department of Psychology at Texas A&M University, affiliated with the Neuroscience & Personality Processes research cluster. His work integrates behavioral, computational, and neuroscience methods to study learning and decision-making. Key research areas include reinforcement learning, decision-making under uncertainty, and the impact of clinical issues like substance abuse and depression on cognitive processes. He explores both basic cognitive mechanisms and individual differences, bridging sub-disciplines through neuroscientific approaches. His research employs formal mathematical models and fMRI to test hypotheses about cognitive processes. Notable contributions include studies on frequency-based decision-making, the inverse base-rate effect, and the effects of acute stress on decision strategies. He is actively involved in mentoring students, with current openings for 2024-2025 academic year. Scientific achievements include the 2016 Best Article award in Cognitive, Affective, and Behavioral Neuroscience. His lab (Worthylab) focuses on strategic decision-making models, with recent work on reward segmentation and category learning. Collaborative projects address topics like zoonotic disease communication and dopamine's role in decision-making.

• Structural Health Monitoring
• Wind Energy
• Graph Neural Networks
• +۳ مورد دیگر

Dr. Gregory Duthé is a researcher at the ETH Zürich in the Structural Mechanics and Monitoring department. His work bridges computational modeling and structural diagnostics with applications in renewable energy systems. Research contributions include: Development of Graph Neural Networks for aerodynamic flow reconstruction Advancements in wake-induced load estimation for wind farms Innovations in unsupervised fault detection for offshore turbine systems Key publication trends show focus on physics-informed machine learning (2025), multi-agent infrastructure decision support (2025), and leading edge erosion modeling (2021). He combines graph-based architectures with structural health monitoring techniques across wind energy applications. Contact: duthe@ibk.baug.ethz.ch

Zhigen Zhaoمشاهده پروفایل

دانشیار

• Bayesian/empirical Bayesian statistics
• High-dimensional data analysis
• Multiple comparison
• +۵ مورد دیگر

Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.

Edward Lobarinasمشاهده پروفایل

استاد

• Hearing Loss
• Auditory Perception
• Tinnitus
• +۷ مورد دیگر

Edward Lobarinas is a Professor and Department Head of Speech, Language, and Hearing Sciences at the University of Texas at Dallas (UTD), concurrently serving as Assistant Dean of Graduate Studies. His primary affiliation is with the School of Behavioral and Brain Sciences. Dr. Lobarinas leads the Translational Auditory Perception Lab, focusing on auditory neuroscience and clinical audiology. His research integrates animal models (chinchillas, rats) with human studies to understand hearing loss mechanisms, particularly noise-induced cochlear damage, tinnitus, and hyperacusis. Key innovations include Bayesian machine learning approaches for personalized hearing aid amplification and smartphone-integrated clinical tools. His work bridges basic science and translational medicine, with emphasis on otoprotective strategies and audiological rehabilitation. Education details are not explicitly provided in the text, though his academic roles suggest advanced training in audiology or neuroscience. His lab specializes in studying inner hair cell dysfunction, cochlear synaptopathy, and the middle ear reflex's role in hearing assessment. He has pioneered methodologies for evaluating auditory perception deficits in animal models and translating findings to human clinical applications. Notable projects include studies on carboplatin-induced hearing loss, impulse noise effects, and the efficacy of antioxidants (e.g., ebselen) in preventing noise-induced hearing loss. Dr. Lobarinas' translational research emphasizes practical solutions like adaptive dynamic range optimization (ADRO) for hearing aids and real-time smartphone apps for DSLv5 prescription fitting. His work frequently addresses gaps between audiometric thresholds and functional hearing deficits, particularly in pediatric hyperacusis and noise-exposed populations. Collaborations involve investigating blast wave effects on hearing and developing low-cost hearing conservation tools for shooters. Laboratory facilities include specialized equipment for behavioral audiometry in animals and advanced metabolic imaging techniques for tinnitus neural correlates. His publication trends since 2020 highlight machine learning applications in audiology, personalization of hearing devices, and refining animal models to better predict human auditory outcomes. While no scientific awards are listed, his prolific output and leadership roles indicate significant contributions to the field. His research also extends to dietary influences on hearing (e.g., magnesium/vitamin effects) and interdisciplinary projects integrating pharmacology, bioengineering, and computational modeling.

Daniel Rakitaمشاهده پروفایل

استادیار

• Robotics
• Motion Planning
• Human-Robot Interaction
• +۷ مورد دیگر

Dr. Daniel Rakita is an Assistant Professor of Computer Science at Yale University, specializing in robotics and human-robot interaction. His research focuses on developing real-time motion planning algorithms and intuitive shared-control interfaces to enable robots to safely assist humans in critical tasks such as healthcare, disaster relief, and manufacturing. He holds a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison. His work integrates robotics, machine learning, and human factors to create end-to-end solutions for robot manipulation. Key contributions include RelaxedIK (real-time collision-free motion synthesis) and CollisionIK (per-instant pose optimization). His research also explores teleoperation systems and adaptive viewpoints for remote control in complex environments. Education: Ph.D./M.S., University of Wisconsin-Madison (Computer Science) Affiliations: School of Engineering & Applied Science, Yale University Labs/Teams: Robotics and Human-Computer Interaction research groups at Yale His publications span top venues like Robotics: Science and Systems (RSS), ICRA, and HRI. Notable awards include the Microsoft PhD Fellowship (2019-2021) and Best Paper Awards at HRI 2018. Current projects emphasize diffusion policies for manipulation learning and optimization-based motion generation frameworks.

• Science Education
• Informal Science Learning
• Education Policy
• +۳ مورد دیگر

Shane Bergin is an Associate Professor in Science Education at University College Dublin (UCD), School of Education. He holds adjunct roles, including Adjunct Assistant Professor at Trinity College Dublin. His work focuses on informal science learning, education policy, and public engagement, with a strong emphasis on fostering trust in science. Bergin leads research groups funded by EU Horizon Europe, Marie Curie Actions, and Irish government bodies. He has created initiatives like Quavers to Quadratics and hosts media programs such as The Trust Race podcast. His teaching includes a cross-disciplinary module, 'Why Trust Science?' Educations : BA (Mod) in Physics & Chemistry of Advanced Materials, Trinity College Dublin PhD in Physics, Trinity College Dublin Professional Certificate in University Teaching & Learning, UCD Universal Design for Teaching & Learning certification (2023) Research Interests : Bergin investigates informal learning strategies, science communication, and curriculum design. His work bridges education policy and community outreach, addressing gender representation in STEM and ethical dimensions of academic collaboration. He advocates for inclusive pedagogical frameworks, such as Universal Design for Learning, to adapt to diverse student needs. Grants & Advising : He directs UCD's MSc in Mathematics & Science Education and oversees Teaching & Learning initiatives. His grants fund interdisciplinary research on science trust and public engagement. He mentors PhD students and postdocs in education and nanomaterials. Labs & Teams : Bergin leads a dynamic research group exploring science education and nanomaterials. Collaborations include projects on graphene surface energy and carbon nanotube dispersion, reflecting his dual expertise in education and physics.

• Machine Learning
• Deep Learning
• Neural Memory Networks
• +۳ مورد دیگر

Dr. Tharindu Fernando Warnakulasuriya is a Research Fellow at Queensland University of Technology (QUT), affiliated with the School of Electrical Engineering & Robotics. He holds a PhD from QUT and a BSc (Special Degree in Computer Science) from the University of Peradeniya, Sri Lanka. His primary research focuses on Machine Learning, Deep Learning, Neural Memory Networks, Computer Vision, and Biomedical Signal Processing. He has received several awards, including the QUT Outstanding Doctoral Thesis Award (2019), QUT Early Career Researcher Award (2022), and EER Rising Star Award (2023). His work bridges interdisciplinary fields such as healthcare, neuroscience, and autonomous systems, with notable contributions to medical anomaly detection, autonomous vehicle behavior prediction, and surveillance systems. Teaching roles include coordinating EGH404 and lecturing in EGB103. He supervises PhD students in areas like deep learning for biosignal analysis and multimodal perception. His research outputs span over 50 publications in top-tier journals like IEEE Transactions and ACM Computing Surveys. Key collaborations include projects on neural memory plasticity, autonomous steering, and stress detection using wearable devices. He actively reviews for journals like IEEE Transactions on Neural Networks and Learning Systems, and is part of professional networks like IEEE.

Amit K. Roy-Chowdhuryمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robot Autonomy
• +۳ مورد دیگر

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

Hadrien Courtecuisseمشاهده پروفایل

پژوهشگر

• Medical Robotics
• Real-Time Simulations
• Finite Element Methods
• +۳ مورد دیگر

Dr. Hadrien Courtecuisse is a Researcher (Chargé de Recherche) at CNRS, affiliated with the AVR/ICube team in Strasbourg since 2013. He holds a Ph.D. in Computer Science from Inria (2011), specializing in parallel architectures for medical simulations. His postdoctoral work at Cardiff University (2012) focused on biomechanical modeling of soft tissues. He later contributed to the Institut Hospitalo-Universitaire (IHU) as a research engineer. His expertise spans sparse linear algebra, real-time simulations, GPU computing, and medical robotics, particularly in surgical training systems and robotic control using inverse finite element models. Education: Ph.D., SHAMAN Team, Inria (2011) Postdoctoral Research, IMAM, Cardiff University (2012) Research Interests: Courtecuisse’s work emphasizes real-time computational methods for medical applications, including deformable object simulation, haptic feedback, and robotic assistance. He develops algorithms for contact response, topological changes, and parallel computing to enhance surgical training and robotic precision. His contributions address challenges in needle insertion, soft tissue interaction, and real-time error control in surgical simulations. Awards & Recognition: No specific scientific awards mentioned. Key Projects: MIMESIS Project (Inria): Focuses on medical robotics and real-time simulation for therapy planning. SOFA-FrameWork: Contributed to open-source simulation software for deformable objects and haptics. Calipso: Image editing via physics-based CAD models. Labs & Teams: Active in the AVR/ICube team at CNRS, collaborating with Inria and international institutions like Cardiff University.

Olov Anderssonمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

Florian T. Pokornyمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Florian T. Pokorny is an Associate Professor in Machine Learning at the Division of Robotics, Perception and Learning (RPL), Department of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He coordinates research projects such as the Horizon Europe-funded SoftEnable and the WASP-funded Intelligent Cloud Robotics for Real-Time Manipulation at Scale . His research focuses on data-driven methods for robotic manipulation, including transfer learning, cloud robotics, and caging-based manipulation of rigid and deformable objects. Education: PhD in Mathematics, University of Edinburgh (supervisor: Michael Singer) MSc in Advanced Study in Mathematics (Part III), University of Cambridge BSc in Mathematics, University of Edinburgh Research Interests: His work emphasizes scalable robotic manipulation, leveraging deep learning and geometric/topological methods. Key areas include training data requirements, transfer learning, and cloud robotics paradigms for manipulation at scale. Recent projects explore energy margin analysis, caging-guided morphology optimization, and robust policy learning. Publications: His recent work spans topics like caging-based manipulation, federated learning, and cloud robotics infrastructure. Notable contributions include CageCoOpt (manipulation robustness), CloudGripper (open-source testbed), and RealCraft (zero-shot video editing). Awards & Grants: Funded by WASP, Horizon Europe, and the Knut and Alice Wallenberg Foundation. His group hosts the CloudGripper platform and collaborates on benchmarks like DLO@Scale. Advising & Labs: Supervises multiple PhD students and research engineers. Alumni include Robert Gieselmann (Amazon Robotics), Yiannis Karayiannidis (Chalmers University), and Anastasiia Varava (Postdoc at KTH). Active in organizing workshops like RoDGE (IROS 2025) and ICRA 2025 robotics learning events.