جست‌وجوی استاد و پژوهشگر

Aldeida Aletiمشاهده پروفایل

استاد

• Automated Software Engineering
• Artificial Intelligence
• Optimization
• +۲ مورد دیگر

Aldeida Aleti is a Professor in the Department of Software Systems & Cybersecurity at Monash University. Her research focuses on Automated Software Engineering, leveraging AI and optimization techniques for tasks like software design, testing, and repair. She has held roles including Chief Examiner for units like FIT4002 and FIT5136, and has contributed to teaching FIT3077 and FIT1008. Education: PhD in Software Engineering (Swinburne University of Technology, 2012), Master of Computer Engineering (Polytechnic University of Tirana, 2008), and Bachelor Honours in Computer Engineering (Yildiz Technical University, 2005). Research Interests: Automated software engineering, fitness landscape analysis, optimization, and search-based techniques. She leads projects like RAISE (Responsible AI Software Engineering) and collaborates on quantum computing and healthcare AI initiatives. Awards include the FIT Dean's Award (2016), Best Paper Awards (2015, 2011), and the Heidelberg Laureate Forum invitation (2014). She has been a grant assessor for the Australian Research Council since 2015. Advising: Accepting PhD students in AI-driven software engineering, optimization, and automated testing. Active in committees like the Faculty Research Committee and Early Career Researcher committee.

Paul Cohenمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Analysis
• Education Informatics
• +۴ مورد دیگر

Paul Cohen is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information (SCI), where he also directs the Modeling and Managing Complicated Systems Institute (MOMACS). Previously, he served as the founding Dean of SCI from 2017 to 2020. Before joining Pitt, he was a Program Manager at DARPA (2013–2017), leading initiatives like Big Mechanism and Communicating with Computers. Earlier roles include founding director of the University of Arizona’s School of Information: Science, Technology and Arts (SISTA), and professor at the University of Southern California’s Information Sciences Institute and the University of Massachusetts. Education: PhD in Computer Science and Psychology (Stanford University), MS in Psychology (UCLA), BS in Psychology (UC San Diego). Research Interests: Focuses on artificial intelligence, machine learning, natural language processing, and modeling complex systems like cell signaling pathways and socio-environmental interactions. His work emphasizes explainable AI, human-computer communication, and interdisciplinary problem-solving. Key Contributions: Authored Empirical Methods for Artificial Intelligence and over 200 peer-reviewed articles. His research spans robotics, education technology (e.g., the AnimalWatch tutoring system), and collaborative analysis tools like COLAB. He has won a Telly Award for his video on systemic challenges and a Best Paper award for spatial language learning frameworks. Awards & Recognition: Elected Fellow of the AAAI, recipient of the Telly Award, and winner of the Best Paper Award at the IEEE Conference on Development and Learning. Leadership & Outreach: Advocates for polymathy in education to address global challenges. His work includes developing curricula for complex systems thinking and promoting diversity in STEM through initiatives like AnimalWatch.

Veronica Santosمشاهده پروفایل

استاد

• Robotics
• Haptics
• Biomechatronics
• +۷ مورد دیگر

Veronica Santos is a Professor and Inclusive Excellence Officer in the Department of Bioengineering at the University of California, Los Angeles (UCLA) Samueli School of Engineering. She serves as Associate Dean for Inclusive Excellence and Faculty Affairs, and directs the UCLA Biomechatronics Laboratory. Santos holds a Ph.D. in Mechanical and Aerospace Engineering from Cornell University and a B.S. in Mechanical Engineering from UC Berkeley, with minors in Biometry and Music. Ph.D., Mechanical and Aerospace Engineering (Biometry minor), Cornell University (2007) B.S., Mechanical Engineering (Music minor), University of California, Berkeley (1999) Her research spans robotics, haptics, and human-machine systems, focusing on grasp and manipulation, tactile sensors, prosthetics, and neural control of movement. She integrates machine learning and stochastic modeling with biomechanical studies of hand function, bridging robotics and biomedical applications. The 15 most recent publications highlight trends in tactile sensing for robotics, granular media interaction, and human-robot collaboration. A significant portion explores neural network applications for tactile perception, tendon-driven actuation systems, and biomimetic sensor design. Many papers address practical challenges in robotic manipulation, including shear force measurement, edge orientation detection, and autonomous learning through reinforcement strategies. 2018: Robohub.org '25 women in robotics you need to know about' 2018-2019: Defense Science Study Group Selectee 2017: UCLA Mechanical and Aerospace Engineering Teaching Award 2010: NAE Frontiers of Engineering Education Symposium Selectee 2010: NSF CAREER Award As director of the UCLA Biomechatronics Lab, she leads multidisciplinary teams developing advanced robotic systems for applications ranging from mine defusal to prosthetics restoration of touch sensation. Her work has received media coverage from ASME, National Geographic, and Nature, and she was featured in a $100M gift to UCLA that underscores private donors' growing role in public universities.

Iñaki Esnaolaمشاهده پروفایل

مدرس ارشد

• Information Theory
• Machine Learning
• Cybersecurity
• +۶ مورد دیگر

Dr. Iñaki Esnaola is a Senior Lecturer at the Department of Automatic Control and Systems Engineering, University of Sheffield, and a Visiting Research Collaborator at Princeton University. He holds a MSc from the University of Navarra (2006) and a PhD from the University of Delaware (2011). His research focuses on information theory, machine learning, and cybersecurity, particularly in cyberphysical systems like smart grids. His work addresses data integrity, privacy, robust estimation, and optimal sensor placement. Research interests include: Information theory and data science, machine learning and high-dimensional statistics, cybersecurity (especially data injection attacks), privacy, robust estimation, and sensor placement optimization. Recent projects involve empirical risk minimization with regularization, stealth attacks on control systems, and compressive sensing for environmental monitoring. Key publications include studies on relative entropy in machine learning, sensor placement for sewer networks, and stealth attacks in smart grids. He leads a research group with ongoing projects in resilient cyberphysical systems and received a UKRI grant for advanced manufacturing. His work bridges theoretical foundations with real-world applications in energy systems and environmental monitoring.

Taylor Johnsonمشاهده پروفایل

دانشیار

• Formal Verification
• Cyber-Physical Systems (CPS)
• Artificial Intelligence
• +۷ مورد دیگر

Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.

Sarah Masud Preumمشاهده پروفایل

استادیار

• Human-AI Interaction
• Natural Language Processing
• Applied Machine Learning
• +۴ مورد دیگر

Sarah Masud Preum is an Assistant Professor of Computer Science at Dartmouth College, with adjunct roles in the Department of Biomedical Data Science at Geisel School of Medicine and as Faculty Affiliate at the Center for Technology and Behavioral Health (CTBH). She also serves as Technical Associate Director of the Dartmouth Center for Precision Health and Artificial Intelligence. Her work focuses on machine learning for computational health, including natural language processing, temporal modeling, and human-AI interaction to develop personalized decision support systems in healthcare. Education includes a B.Sc. from Bangladesh University of Engineering and Technology, followed by M.Sc. and Ph.D. degrees from the University of Virginia. Previously, she was a postdoctoral research scholar at Carnegie Mellon University's School of Computer Science, recognized as a Rising Stars in EECS (2020) for her academic excellence and contributions to equity in STEM. Her research interests span Human-AI Interaction, Natural Language Processing, Mobile Health, and Cyber-Physical Systems. Over 2020–2023, her publications emphasize AI-driven solutions for healthcare challenges like conflict detection in health information and cognitive assistants for emergency response. Earlier work includes behavioral prediction models (MAPer) and spatial database optimizations (Maximum Visibility Queries). Awards: Rising Stars in EECS (2020) In teaching, she offers courses like Transforming Healthcare through Machine Learning and Machine Learning and Statistical Data Analysis. Her affiliations with multidisciplinary centers reflect her commitment to bridging technology and healthcare.

Franziska Mathis-Ullrichمشاهده پروفایل

استاد

• Surgical Robotics
• Medical Robotics
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Prof. Dr. Franziska Mathis-Ullrich is a Professor at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) leading the Surgical Planning and Robotic Cognition Lab (SPARC) in the Department of Artificial Intelligence in Biomedical Engineering. Previously, she was an Assistant Professor at Karlsruhe Institute of Technology (KIT) from 2019 to 2023. Her research focuses on minimally invasive robotic systems, soft robotics, and embedded machine learning for surgical applications. She holds a PhD in Microrobotics from ETH Zurich (2017), with earlier degrees from the same institution. Education: B.Sc. and M.Sc. in Mechanical Engineering and Robotics (ETH Zurich, 2009–2012) Ph.D. in Microrobotics (ETH Zurich, 2017) Research Interests: Minimally invasive medical robotics, soft robotic systems, AI-driven surgical assistance, microrobotics, and robot-assisted surgery. Her work emphasizes translating robotics innovations into clinical applications through interdisciplinary collaboration. Key Awards: IEEE ICRA Best Paper Award in Medical Robotics (2014) IEEE BioRob Best Student Paper Award (2016) ICRA Microassembly Challenge First Prize (2014 & 2015) Forbes 30 under 30 (2017) Grants & Projects: Leading a Bavarian State Ministry-funded project on endometriosis diagnostics (€3M). Active in multidisciplinary collaborations with Erlangen University Hospital. Serves as Vice-President of the German Society for Computer- and Robot-assisted Surgery (CURAC). Labs & Teams: Directs the SPARC Lab, which develops cognitive robotic systems for surgical planning and execution. Collaborates with institutions like Max Planck, Fraunhofer, and Helmholtz.

Emily Whitingمشاهده پروفایل

دانشیار

• Computer Graphics
• Computational Fabrication
• Architectural Geometry
• +۴ مورد دیگر

Emily Whiting is an Associate Professor of Computer Science at Boston University and Director of the Shape Design & Computation Lab. She also serves as Director of PhD Admissions and Co-Director of the BU Computer Graphics Lab. Her research focuses on computational fabrication, architectural geometry, and computer-aided design, bridging digital geometry processing, engineering mechanics, and rapid prototyping. She holds a PhD from MIT (2012), an SM in Design & Computation from MIT (2006), and a BASc in Engineering Science from the University of Toronto (2004). Previously, she was faculty at Dartmouth and a Marie Curie Postdoctoral Fellow at ETH Zurich. Her research interests include 3D printing optimization, structural design for fabrication, and tools for functionally-valid object creation. Notable projects include work on elastic garments, climbing experience replication, and print-wind instrument design. Her work has been featured on TEDx and PBS NOVA, and she has received awards such as the NSF CAREER Award and Sloan Research Fellowship. Education: PhD (MIT), SM (MIT), BASc (University of Toronto) Labs: Shape Design & Computation Lab, BU Computer Graphics Lab Key Projects: Knitting 4D garments, Environment-Scale Fabrication, Thermal-comfort casts Recent professional activities include program committee roles at SIGGRAPH 2025 and UIST 2024, and serving as Program Co-Chair for Pacific Graphics 2024. She advises a team of PhD and MS students, with alumni now in academia and tech industries.

Eugene Vinitskyمشاهده پروفایل

استادیار

• Reinforcement Learning
• Multi-agent Learning
• Autonomous Vehicles
• +۲ مورد دیگر

Eugene Vinitsky is an Assistant Professor at NYU Tandon School of Engineering, holding joint appointments in Civil and Urban Engineering and Computer Science. His research develops multi-agent reinforcement learning systems for autonomous vehicles and traffic control, with applications in robotics and intelligent infrastructure. He directs the Computational Transportation Systems Lab and leads projects like CIRCLES on congestion reduction. Research Focus: Designs algorithms enabling complex behaviors through unsupervised agent interactions, human-AI compatibility, and environment synthesis for autonomous systems. Awards & Leadership: NSF Graduate Fellow (2016), Eisenhower Fellow (2018, 2020), and PI on multiple grants including Amazon Research Awards. Mentored 14+ graduate students and organized international RL conferences.

Hans Jonas Fossum Moenمشاهده پروفایل

دانشیار

• Autonomous Systems
• Sensor Technologies
• Robotics
• +۳ مورد دیگر

Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.

David Fouheyمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• AI for Science
• +۶ مورد دیگر

David Fouhey is an Assistant Professor at New York University, jointly appointed between the Courant Institute of Mathematical Sciences (Computer Science) and the Tandon School of Engineering (Electrical and Computer Engineering). He previously held positions at the University of Michigan and was a postdoctoral researcher at UC Berkeley. His research focuses on learning-based computer vision, particularly in 3D reconstruction, AI for science, and human-object interaction. Education: PhD in Robotics from Carnegie Mellon University (2013-2018) Bachelor of Arts in Computer Science from Middlebury College (2007-2011) Research Interests: His work spans 3D reconstruction from images , AI-driven scientific measurement (e.g., solar physics, evolutionary ecology), and human interaction modeling . Notable projects include Stereo4D for 3D motion analysis and SyntheticIA for solar magnetogram fusion. Recent Articles: Recent work emphasizes interdisciplinary applications of vision (e.g., bird morphology analysis) and robust 3D techniques like Perspective Fields for camera calibration. His 2025 Nature Scientific Data paper on bird skeletal traits highlights his AI-for-science focus. Grants & Collaborations: Secured a NASA grant for heliophysics tools and collaborates with institutions like NASA’s SDO mission and the Astrophysical Journal. Labs & Teams: Leads a NYU research group focused on vision and robotics, with active collaborations in astrophysics and ecology.

Christian Jacobمشاهده پروفایل

استاد

• Algorithms gleaned from nature
• Biocomputing
• Bioinformatics
• +۶ مورد دیگر

Christian Jacob is a Professor in the Department of Computer Science within the Faculty of Science at the University of Calgary . He holds a B.S. in Computer Science and a Doctor of Engineering Science from Erlangen University . His research focuses on nature-inspired algorithms, biocomputing, and agent-based simulations applied to biological systems and education. Key initiatives include the LINDSAY Virtual Human Project , which uses immersive virtual reality to explore human anatomy and physiology. He contributes to the university's strategic priorities in Digital Worlds and Health and Life initiatives. His work integrates evolutionary algorithms, cellular automata, and swarm intelligence into creative and medical applications. Notable achievements include the ASTech Award (2015) from Alberta Science and Technology. His projects emphasize interactive education through tools like LeukemiaSIM , Eukaryo , and the Giant Walkthrough Gut . Jacob also explores visualization techniques, such as evoVision3D and LifeBrush , to enhance scientific understanding. His research bridges computational methods with real-world applications in healthcare, architecture, and game design. Collaborative efforts include developing agent-based models for immune systems, nervous responses, and crowd behavior. Jacob's work spans interdisciplinary fields, blending computer science with biology, engineering, and the arts.

Mohammad Naraghiمشاهده پروفایل

استاد

• Nanomaterials Engineering
• Materials Science
• Polymer Engineering
• +۶ مورد دیگر

Dr. Mohammad Naraghi is a Professor and Associate Department Head for Academics in the Department of Aerospace Engineering at Texas A&M University. He leads the Nanostuctured Materials Lab , focusing on advanced nanomaterials for aerospace applications. His work integrates material science principles to develop lightweight, high-performance materials for structural, energy storage, and smart textile systems. Education: Ph.D., Aerospace Engineering (2009), University of Illinois at Urbana-Champaign M.S., Civil Engineering (2004), Sharif University of Technology B.S., Civil Engineering (2004), Sharif University of Technology Research Interests: Graphitic carbon nanomaterials, bio-inspired composites, experimental nanomechanics, and polymer nanofiber processing. His lab explores multifunctional materials for aerospace applications, including self-healing polymers, structural batteries, and sustainable carbon fiber recycling. Publications: Dr. Naraghi has authored over 150 peer-reviewed articles, with recent work focusing on carbon nanomaterial synthesis, self-healing vitrimers, and all-electric aircraft sustainability . His studies bridge nanoscale mechanics and macroscale applications, emphasizing scalability and industrial relevance. Awards: Best Paper Award (2009) for nano viscoelastic composites research Roger A. Strehlow Memorial Award (2009) for outstanding research First Place in Sandia MEMS Design Competition (2007) Advising & Grants: Leads NSF-funded projects on sustainable materials and structural energy storage. Advises graduate students in aerospace and materials engineering. Collaborates with Sandia National Labs and industry partners on advanced composite development. Labs & Facilities: Directs the Nanostuctured Materials Lab, equipped with advanced nanomechanical testing systems, electrospinning setups, and characterization tools for nanoscale materials analysis.

Ji Shuiwangمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence for Science and Engineering
• Language Models and Agents
• +۳ مورد دیگر

Shuiwang Ji is a Professor and Truchard Family Endowed Chair in the Department of Computer Science & Engineering at Texas A&M University, where he also holds Presidential Impact Fellow and Chancellor EDGES Fellow titles. He specializes in machine learning, AI for science/engineering, and language models/agents. His research bridges theoretical advances and practical applications in materials science, quantum chemistry, and biomedical engineering. Education: Ph.D. in Computer Science from Arizona State University (2010). Research focuses on equivaraint neural networks for symmetry-aware learning, graph-based molecular modeling, and generative AI for scientific discovery. He develops algorithms that integrate physics principles with deep learning, addressing challenges in materials design, PDE solving, and biomolecular structure prediction. Publications emphasize symmetry-aware architectures (e.g., equivariant Fourier neural operators), efficient interatomic potential computations, and diffusion models for protein/DNA design. Recent work explores trustworthiness in LLMs and causal reasoning in graph neural networks. Awards include NSF CAREER Award (2014), IEEE Fellow (2023), and Texas A&M teaching excellence awards. His work has been recognized in top venues like NeurIPS, ICML, and ICLR. His research group collaborates on projects funded by NSF, NIH, and industry partners, advancing AI applications in healthcare, robotics, and environmental science.

Francesco Bulloمشاهده پروفایل

استاد

• Control Theory
• Network Systems
• Distributed Control
• +۵ مورد دیگر

Francesco Bullo is a Distinguished Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. He holds joint appointments in Electrical and Computer Engineering, Computer Science, and the Center for Control, Dynamical Systems, and Computation. His research focuses on distributed control, network systems, and neural networks, with notable contributions to contraction theory and social dynamics analysis. Education: Laurea (1994, University of Padova), PhD (1998, Caltech). Leadership roles: Former IEEE CSS President, SIAG CST Chair. Research interests include biological/artificial neural networks, distributed control of robotic networks, and synchronization in power grids. He authored books like Lectures on Network Systems and Contraction Theory for Dynamical Systems . His work spans 300+ publications, including impactful articles on Hopfield networks, power grid stability, and optimization. Awards include IEEE Fellow, ASME Fellow, and SIAM Fellow. Advising and grants: Mentored over 30 PhD students and led major projects like the NSF MURI on team behavior modeling. Current research includes neural synchronization and AI-driven control. Labs/teams: Directs the UCSB Center for Control, Dynamical Systems, and Computation, and collaborates on interdisciplinary initiatives like the Network Science for Medicine white paper.