جست‌وجوی استاد و پژوهشگر

Farbod Hassaniمشاهده پروفایل

پژوهشگر

• Cosmology
• Modified Gravity
• Dark Energy
• +۳ مورد دیگر

Farbod Hassani is a Researcher in Cosmology at the University of Oslo, Norway, with a focus on dark energy, modified gravity, and large-scale structure. He holds a PhD from the University of Geneva (2020) and MSc/BSc degrees from Sharif University in Physics and Mathematics. His research explores nonlinear cosmological dynamics using N-body simulations and effective field theory. Key areas include probing dark energy models, gravitational wave signatures from dark sector phenomena, and machine learning applications in cosmological datasets. Education: PhD in Cosmology - University of Geneva (2016-2020) MSc in Physics/Cosmology - Sharif University (2014-2016) BSc in Physics & Pure Mathematics - Sharif University (2009-2014) Recent work analyzes cosmic velocity fields, dark matter halo properties, and gravitational wave emission from domain walls. He contributes to the Euclid mission by simulating non-standard cosmologies. Hassani's studies bridge theoretical frameworks (e.g., k-essence, effective field theory) with observational probes like lensing and large-scale structure. Lab affiliations include the Cosmology and Extragalactic Astronomy group at UiO. Current projects focus on instability mechanisms in dark energy models and relativistic N-body simulation techniques.

Martin Weißmannمشاهده پروفایل

استاد

• Numerical weather prediction
• Atmospheric data assimilation
• Ensemble forecasting
• +۴ مورد دیگر

Martin Weißmann is a Professor for Theoretical Meteorology at the University of Vienna's Faculty of Earth Sciences, Geography and Astronomy, where he has served since 2020. Previously, he headed the Department of Meteorology and Geophysics (2020-2022) and the Data Assimilation Branch at the Hans-Ertel Centre for Weather Research. His research focuses on numerical weather prediction, atmospheric data assimilation, ensemble forecasting, error modeling, and applications of wind lidar and satellite data. Education includes an MSc in Meteorology from the University of Innsbruck (1996-2003) and a PhD from DLR Oberpfaffenhofen in cooperation with University of Innsbruck (2003-2006). His scientific commitments include membership in the ESA Aeolus Science and data quality Advisory Group and coordination of the project Experimental Validation and Assimilation of Aeolus Observations. Research interests center on: Advanced data assimilation techniques for numerical weather prediction Ensemble methods for probabilistic forecasting and error quantification Integration of novel observation systems like Doppler wind lidars Validation and application of Aeolus satellite wind data Convective-scale modeling and high-resolution forecasting His publication record demonstrates consistent focus on improving weather prediction systems through innovative assimilation methods, with recent work emphasizing machine learning applications, convective-scale data assimilation, and satellite data validation. Teaching responsibilities include courses in dynamic meteorology, numerical weather prediction, and scientific communication.

• Deep Learning
• Sound Synthesis
• Generative Modelling
• +۵ مورد دیگر

Cyrus Vahidi is a researcher affiliated with the School of Electronic Engineering and Computer Science at Queen Mary University of London. His PhD project focuses on perceptual end-to-end learning for music understanding, exploring synergies between sparse audio signal representation, psychoacoustics, and deep learning. He investigates novel neural network architectures to emulate human auditory tasks like event detection and source separation, with applications in music production and audio transformation. Teaching responsibilities include contributing to undergraduate and postgraduate modules such as Digital Signal Processing and Fundamentals of DSP , covering topics like sampling theory, filter design, and practical DSP applications. His research interests span generative modeling, timbre analysis, and computational auditory perception. Current work emphasizes adaptive perceptual audio representations and end-to-end learning techniques, leveraging psychoacoustic principles for industry-relevant audio processing tasks.

Sam Silvaمشاهده پروفایل

استادیار

• Atmospheric Chemistry
• Computational Modeling
• Air Quality
• +۳ مورد دیگر

Sam Silva is Assistant Professor in Earth Sciences, Civil and Environmental Engineering, and Population and Public Health Sciences at the University of Southern California. He leads the Atmospheric Composition and Earth Data Science group, investigating atmospheric chemistry's role in air quality and climate change through computational modeling and machine learning. Silva holds a PhD in Environmental Engineering and Computation from MIT and a BSc in Physics and MSc in Atmospheric Science from the University of Arizona. Previously, he was a Linus Pauling Distinguished Postdoctoral Fellow and Research Data Scientist at Pacific Northwest National Laboratory. Research focuses on atmospheric chemical composition using computer models, data science, and machine learning to address global environmental challenges. Work emphasizes aerosol-cloud interactions, chemical cycling dynamics, climate-health relationships, and equitable modeling approaches. Publications demonstrate strong trends in developing machine learning frameworks for atmospheric and climate science, with applications in chemical transport modeling, health impact assessments, and biogeochemical cycling. Recent work advances interpretable AI methods while addressing urban air quality-climate interactions.

Taewoon Kimمشاهده پروفایل

پژوهشگر

• Memory Systems
• Multimodal Interaction
• Cognitive Science
• +۳ مورد دیگر

Taewoon Kim is an External PhD Candidate in the Faculty of Science at Vrije Universiteit Amsterdam, specializing in Artificial Intelligence. His research focuses on creating computational models that emulate human memory systems, including short-term, episodic, and semantic memory architectures. Key research areas include: Human-like artificial memory modeling Emotion recognition in conversational agents Multimodal interaction encoding Cognitive computing frameworks Ontological references for situated AI His publications demonstrate a strong focus on artificial intelligence applications in cognitive science, with a particular emphasis on memory system integration. Kim's work often combines theoretical neuroscience concepts with practical AI implementations. Current research output includes: 4 publications across AAAI and arXiv platforms 2024 arXiv preprint on unified memory systems 2023 AAAI conference contribution on synthetic memory architectures 2021 MMSR paper on multimodal interaction encoding 2021 arXiv study on emotion recognition

Xiaogan Liangمشاهده پروفایل

استاد

• Advanced Nanofabrication
• Nanomanufacturing
• Microsystem Technologies
• +۷ مورد دیگر

Professor Xiaogan Liang is a faculty member in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor, and serves as Associate Chair for Undergraduate Education. His research focuses on advanced nanofabrication, nanomanufacturing, and microsystem technologies, with applications in nanoelectronics, nanophotonics, and biosensing systems. He leads the Nanoengineering and Nanodevice Laboratory and holds a tenured position. Liang has been recognized with awards such as the Monroe-Brown Education Excellence Award (2018-19) and the NSF CAREER Award (2015). His work bridges nanotechnology and biomedical engineering, emphasizing scalable fabrication techniques and neuromorphic computing hardware. Education and Career: Liang joined the University of Michigan in 2012 as an Assistant Professor, advancing to his current rank. His academic background includes expertise in mechanical engineering and nanotechnology. Key research contributions include novel 2D material-based biosensors, memristive devices, and plasmonic systems for real-world applications. Research Interests: His lab develops nanoscale fabrication methods (e.g., nanoimprint lithography) for creating high-performance devices like MoS₂ photodetectors and Bi₂Se₃ memristors. Applications span environmental monitoring, medical diagnostics, and energy-efficient robotics. Recent innovations include brain-inspired computing hardware for robotics and bioinspired sensors for viral detection. Publications: Over 150 peer-reviewed articles highlight his work on neuromorphic systems, plasmonic colorimetric sensing, and scalable nanoassembly. Notable 2020-2025 publications address reservoir computing hardware, site-selective 2D material growth, and airborne particle monitoring systems. Awards: Monroe-Brown Award (2018), NSF CAREER (2015), CoE Awards (2016) Grants: Supported by NSF, U-M, and industry partnerships Advising: Mentors graduate students (e.g., Hongsuk Nam, Sungjin Wi) in nanofabrication and biosensor development. Collaborates with Professors Katsuo Kurabayashi and Allen Liu on interdisciplinary projects. Labs/Teams: Directs the Nanoengineering and Nanodevice Laboratory, fostering cross-disciplinary research in nanotechnology and biomedical engineering. Engages in collaborative efforts with materials science and electrical engineering groups.

• Computer Architecture
• Memory Systems
• Reconfigurable Computing
• +۶ مورد دیگر

Ronald Dreslinski is a Professor in the Department of Computer Science and Engineering at the University of Michigan, College of Engineering. His work focuses on computer architecture, memory systems, and reconfigurable computing, with applications in machine learning, wireless communication, and autonomous systems. Research spans hybrid memory systems, fault-tolerant networks, and low-power processors Key technologies include PIM (Processing-in-Memory), systolic arrays, and Galois field accelerators His publications highlight trends in adapting reconfigurable architectures for sparse data, optimizing wireless communication decoders, and integrating neural networks with traditional circuit design. He mentors PhD students through weekly 1-on-1 meetings, project-specific sessions, and group presentations. The department emphasizes broadening participation in computing and student support through community initiatives.

• Social Cognition
• Prejudice Formation
• Neural Mechanisms of Bias
• +۲ مورد دیگر

Prof. David Amodio is a faculty member at the University of Amsterdam, affiliated with the Faculty of Social and Behavioural Sciences and the Programme group Social Psychology. His research investigates the psychological and neural mechanisms of social cognition, prejudice, and bias. His research focuses on how social attitudes form through learning mechanisms, the role of AI in propagating societal biases, and neural correlates of prejudice regulation. Key areas include instrumental learning of stereotypes, computational modeling of social cognition, and interventions to reduce bias. Analysis of his publications reveals consistent themes in social neuroscience, including neural encoding of prejudice, cross-cultural bias transmission, and computational approaches to social behavior. His work integrates experimental psychology with neuroimaging techniques to study real-world social issues. Scientific Awards: 2024 SESP Trajectory Award for mid-career contributions to social psychology As head of the Amodio Lab, he leads collaborative projects on social learning mechanisms and maintains partnerships with New York University. The lab prioritizes diversity and equity in scientific communities.

Lulu Qianمشاهده پروفایل

استاد

• Biological Engineering
• Molecular Programming
• DNA Nanotechnology
• +۶ مورد دیگر

Lulu Qian is a Professor of Bioengineering at the California Institute of Technology (Caltech), leading the Qian Lab. She holds a B.E. from Southeast University (2002) and a Ph.D. from Shanghai Jiao Tong University (2007). Prior to her current role, she served as an Assistant Professor at Caltech (2013–2018). Her research focuses on designing molecular systems with programmable behaviors, including biochemical circuits, DNA neural networks, and molecular robots. These systems aim to mimic biological intelligence and perform tasks like pattern recognition, decision-making, and spatial organization at the molecular level. Her lab’s key projects include developing DNA strand displacement cascades for scalable computation, creating DNA-based neural networks capable of associative memory, and engineering molecular robots that perform collective tasks like cargo sorting. Notable achievements include the world’s smallest DNA-based Tic-Tac-Toe game, fractal assembly of DNA origami arrays, and a DNA robot capable of sorting molecules. Qian’s work bridges molecular engineering, computer science, and synthetic biology to advance nanotechnology and programmable matter. Her lab actively collaborates with Caltech’s graduate programs in Bioengineering, Computer Science, and Chemical Engineering. Current and past students have contributed to projects ranging from programmable DNA circuits to bio-inspired robotics. The lab emphasizes interdisciplinary training, preparing students for careers in academic research and industry innovation.

• Multimedia Communication
• Signal Processing
• Image and Video Communication
• +۳ مورد دیگر

Dr. Shadan Khan Khattak is a Senior Lecturer at the Cardiff School of Technologies, Cardiff Metropolitan University since 2020. His expertise lies in Multimedia Communication and Signal Processing. Prior roles include Assistant Professorships at King Faisal University (Saudi Arabia) and COMSATS University Islamabad (Pakistan), and an R&D Engineer at Panasonic R&D Centre Singapore (2017–2018). His research focuses on image/video communication, cybersecurity in multimedia, and hardware design for signal processing. He has over 10 peer-reviewed publications in journals/conferences like IEEE Transactions on Circuits and Systems, IEEE Signal Processing Letters, and Picture Coding Symposium. He is an active IEEE member and serves as a reviewer for several journals. Recent work includes advancements in ensemble deep learning for medical imaging, privacy in metaverse environments, and FPGA-based TCAM emulation. His contributions span both theoretical research and applied engineering solutions, emphasizing interdisciplinary innovation in technology and healthcare domains.

Nicolas Rouleauمشاهده پروفایل

استادیار

• Neural Tissue Engineering
• Consciousness Studies
• Bioelectromagnetism
• +۴ مورد دیگر

Dr. Nicolas Rouleau is an Assistant Professor in the Department of Health Sciences at Wilfrid Laurier University, serving as Undergraduate Advisor and Principal Investigator of the Self-Organizing Units Lab (SOUL). He leads an interdisciplinary team focused on neural tissue engineering, bioelectromagnetism, and consciousness studies. His lab develops 3D brain tissue models to investigate minimal cognition, neurodegenerative diseases (e.g., Alzheimer's), and brain-EMF interactions. Key projects include modeling traumatic brain injury, exploring photon emissions as neural activity markers, and examining consciousness in unconventional systems. Research interests span synthetic biology, neural tissue architecture, and the biophysical basis of cognition. Collaboratively directs the Center for Tissue Plasticity and Biophysics (TPAB) with Dr. Nirosha Murugan. Current lab members include M.Sc. candidates Cooper Kansala and Joselle Solarino, as well as undergraduate researchers. Active in teaching and advising, with a focus on undergraduate mentorship. Recent work emphasizes electromagnetic field interactions with neural tissues, biophotonic signaling, and embodied intelligence systems. While no specific awards are listed, his innovative approaches to neural engineering have positioned him at the forefront of in vitro cognitive research. Ongoing projects aim to bridge synthetic biology with consciousness studies through advanced tissue engineering platforms.

Garrett Ethan Katzمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Cognitive Modeling
• Machine Learning
• +۶ مورد دیگر

Garrett Ethan Katz is an Associate Professor in the Department of Electrical Engineering & Computer Science at Syracuse University. His research focuses on vertically integrated artificial intelligence, spanning low-level robotic control to high-level reasoning. He is actively involved in projects such as single-pass learning, automated algorithm discovery, and neuro-symbolic robotic control. His lab explores the intersection of cognitive modeling, machine learning, and robotics. Education: B.A. Philosophy, Cornell University, 2007 M.A. Mathematics, City College of New York, 2011 Ph.D. Computer Science, University of Maryland, College Park, 2017 Research Interests: Single-pass learning rules with high capacity and efficiency Robotic imitation learning using cause-effect reasoning frameworks (CERIL) Neuro-symbolic programming and neural virtual machines Numerical analysis of neural network dynamics Human-robot teaming environments and cognitive workload assessment Publications Overview: Recent work emphasizes theoretical impossibility results in single-pass learning, frameworks for robotic imitation learning, and neurocomputational models that bridge symbolic algorithms and neural networks. His research also explores applications in cryo-electron microscopy and human cognitive-motor performance analysis. Awards: Best Paper Award (Augmented Cognition at HCII, 2024) SAI Computing Conference Best Paper Award (2020) Larry S. Davis Doctoral Dissertation Award (2018) Best Student Paper (Artificial General Intelligence Conference, 2016) Advising & Teaching: Currently advising PhD students Naveed Tahir, Akshay, Xulin Chen, Borui He, and Ruipeng Liu. Former advisees include Khushboo Gupta (M.S. 2019) and Chad Thom Smith (Summer REU 2023). Teaches courses like CIS 700 (Advanced Topics in AI), CIS 467/667 (Intro to AI), and ECS102/CIS151 (Programming Fundamentals). Labs & Projects: Leads research on neurocognitive robotic systems using programmable neural architectures. Active in developing frameworks like CERIL and NeuroCERIL for humanoid robot learning. Collaborates on projects applying neural virtual machines for algorithmic tasks and motor control.

Mark McKinneyمشاهده پروفایل

استاد

• Instrumentation and Measurements
• Circuit Protection Devices
• Electronics
• +۳ مورد دیگر

Dr. Mark McKinney is a full-time Professor and Department Head of Electrical and Computer Engineering at The Citadel, School of Engineering. He has been with the institution since 1997 and holds a Ph.D. in Electrical Engineering from the University of South Carolina. His research focuses on instrumentation, circuit protection, and renewable energy systems, with notable contributions to grid simulator design and testing facilities. Education: Ph.D., Electrical Engineering, University of South Carolina (1999) M.S., Electrical Engineering, University of South Carolina (Year not specified) B.S., Electrical Engineering, University of South Carolina (Year not specified) Research Interests: Dr. McKinney specializes in power systems protection, renewable energy testing, and interdisciplinary projects integrating LabVIEW with FPGA hardware. He has developed megawatt-scale testing facilities for evaluating solar inverters and grid stability. His work emphasizes practical applications, such as robotics education for underserved schools and collaborations with organizations like Clemson University on the Duke Energy eGRID lab. Articles and Trends: His recent publications (2014–2018) highlight advancements in grid simulation, fault ride-through evaluations, and high-power testing methodologies. Earlier work includes contributions to semiconductor device studies and thermal management systems. Awards and Grants: While no specific awards are listed, his sustained research collaborations and facility development projects demonstrate significant academic and industrial engagement. Labs and Teams: He leads the Electrical and Computer Engineering department and has contributed to initiatives like robotics summer camps and eWeek competitions, fostering engineering outreach and education.

Peter Steenkisteمشاهده پروفایل

استاد

• Distributed Systems
• Networking
• Security and Privacy
• +۲ مورد دیگر

Peter Steenkiste is a Professor of Computer Science and Electrical and Computer Engineering at Carnegie Mellon University, with appointments in the School of Computer Science and the Computer Science Department. His research focuses on distributed systems, networking, and wireless technologies, including future Internet architectures like XIA and applications in 5G networks. Steenkiste holds a Ph.D. in Electrical Engineering from Stanford University. His work integrates theoretical and applied approaches, often involving prototype development and real-world testing. Research interests include: Future Internet Architecture (XIA) Wireless network optimization Dynamic spectrum access Network telemetry His publications consistently address network efficiency and innovation, with recent works exploring low-latency video delivery, 5G virtualization, and programmable network infrastructure. Awards include IEEE and ACM Fellowships, Andrew Heiskell Honorable Mention, and University of Cantabria Research Award. He has advised students like Shawn Chen and secured grants for projects including REACH (Robust Content Delivery) and FLOYD (Vehicular Edge Computing). He leads research groups at CMU's Gates and Hillman Centers and collaborates with industry partners like Telefonica and ETH Zurich.

Moritz Münchmeyerمشاهده پروفایل

استادیار

• Cosmology
• Machine Learning
• Theoretical Physics
• +۳ مورد دیگر

Moritz Münchmeyer is an Assistant Professor in the Department of Physics at the University of Wisconsin-Madison. His research focuses on the intersection of cosmology and machine learning, specifically applying advanced computational techniques to analyze large-scale cosmological data and address fundamental questions in theoretical physics. He is affiliated with the College of Letters & Science. His research interests include cosmological field reconstruction, non-Gaussianity detection, CMB analysis, and developing novel neural network architectures for astrophysical data. Key projects involve collaborations with initiatives like the CHIME/FRB experiment, the Simons Observatory, and CMB-S4. Recent work emphasizes leveraging machine learning for tasks such as superresolution emulation of cosmological fields, Bayesian analysis of fast radio bursts, and forecasting cosmological parameters with Euclid and MegaMapper surveys. His methods often integrate deep learning with traditional astrophysical modeling to enhance data interpretation. Münchmeyer has contributed to studies of the kinetic Sunyaev-Zel’dovich effect, primordial non-Gaussianity, and the statistical analysis of large-scale structure. His work bridges theoretical predictions with observational data, addressing challenges in precision cosmology and astrophysical signal processing.