جست‌وجوی استاد و پژوهشگر

Yaoping Huمشاهده پروفایل

استاد

• Brain-Machine Interfaces
• Multi-sensory User Interaction in XR
• Immersive Analytics
• +۵ مورد دیگر

Yaoping Hu is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary, and an Associate Member of the Hotchkiss Brain Institute. He holds a Ph.D. in Robotics and Neuroscience from the University of Western Ontario. His research focuses on Human-Computer Interaction within Virtual Environments, particularly in Brain-Machine Interfaces, multisensory user interaction in XR, and immersive analytics. He teaches courses such as ENSF 619.36 (Brain-Machine Interface Systems), ENSF 519.51 (Brain-Machine Interfaces), ENEL 602 (Virtual Environments), and ENSF 545 (Introduction to VR). Research interests include modeling neural mechanisms for visuomotor processes, cognitive-ergonomic interaction design, and applications in surgical planning, data visualization, and human-machine collaboration. He leads the Visualization and Interaction Laboratory, collaborating with institutions like the neuroArm Project, Fluid Dynamics Group, and international partners in France. Past and current students include Stanley Tarng, Lida Ghaemi Dizaji, and Aida Erfanian. His work spans projects like neuroSim (a surgical simulator) and BioVista (biofilm visualization tools). Collaborations with industry partners such as Nova Chemicals and Calgary Scientific Inc. support applied research in complex data analysis and virtual reality systems.

Pierre Nyquistمشاهده پروفایل

دانشیار

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +۸ مورد دیگر

Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.

Kevin Zollmanمشاهده پروفایل

استاد

• Game Theory
• Agent-Based Modeling
• Philosophy of Science
• +۵ مورد دیگر

Kevin Zollman is the Herbert A. Simon Professor of Philosophy and Social and Decision Sciences at Carnegie Mellon University, with affiliations as an associate fellow at the University of Pittsburgh's Center for Philosophy of Science and a visiting professor at the Munich Center for Mathematical Philosophy. His work bridges game theory, agent-based modeling, and the philosophy of science. Education: Ph.D. and M.A. in Philosophy, University of California, Irvine B.S. in Philosophy, Kansas State University His research focuses on the intersection of strategic reasoning and social epistemology, including evolutionary dynamics in scientific communities, signaling theory in biological and academic contexts, and computational methods in philosophy. Recent publications examine hybrid equilibria in signaling games, incentives in peer review, and the epistemic consequences of stochasticity. Kevin actively engages in public outreach, co-authoring the popular science book The Game Theorist's Guide to Parenting and contributing to discussions about applying game theory to real-world challenges. Leadership: Director, Institute for Complex Social Dynamics

Baktash Behmaneshمشاهده پروفایل

استادیار

• Analog Integrated Circuit Design
• RFIC Design
• Filter Design
• +۵ مورد دیگر

Baktash Behmanesh is an Assistant Professor and Associate Senior Lecturer at Lund University's Faculty of Engineering, Department of Integrated Electronic Systems. He is affiliated with LTH Profile Areas: Engineering Health, AI and Digitalization, and Nanoscience and Semiconductor Technology, as well as the Sentio: Integrated Sensors and Adaptive Technology initiative. His research focuses on analog integrated circuit design, RFIC design, filter design, power amplifier design, and low-noise analog/mixed-signal systems. Behmanesh has contributed to 12 peer-reviewed publications and leads or collaborates on seven research projects, including the NEUROMORF innovation platform and projects on 5G/6G front-end technologies. He advises students, such as I. Ghotbi, on advanced telecommunication systems. His work emphasizes reconfigurable RF front-ends, fractional bandwidth optimization, and sensor integration for next-generation communication and health technologies. Education: Not explicitly stated in provided text. Labs/Teams: Sentio: Integrated Sensors and Adaptive Technology, NEUROMORF, and collaborations within LTH's profile areas. Behmanesh's research spans 5G/6G receiver design, reconfigurable filters, and energy-efficient millimeter-wave amplifiers. His recent work includes a 2024 paper on 5G direct-sampling receivers and a 2023 study on 6G Q-enhanced filters. Projects like 'NIPS' and 'Sentio' highlight his focus on sustainable, adaptive sensor technologies and neuromorphic innovations. While no specific awards are listed, his contributions to advanced RF circuit design are evident through his publications and funded projects.

Nicholas Ouelletteمشاهده پروفایل

استاد

• Complex Systems
• Fluid Dynamics
• Environmental Engineering
• +۴ مورد دیگر

Nicholas Ouellette is a Professor of Civil and Environmental Engineering at Stanford University, specializing in complex systems far from equilibrium. His research emphasizes dynamical self-organization across disciplines like fluid dynamics, granular media, and collective animal behavior. He holds a Ph.D. in Physics from Cornell University (2006), an M.S. in Physics (2005), and a B.A. in Physics and Computer Science from Swarthmore College (2002). Prior to Stanford, he was on the faculty at Yale University’s Mechanical Engineering and Materials Science department from 2008-2015. He has received teaching awards at both institutions. His work spans experimental and theoretical studies of turbulence, granular erosion, insect swarms, and urban dynamics. Current research includes sediment transport in turbulent flows, collective behavior in bird flocks, and urban street network analysis. He leads the Environmental Complexity Lab and collaborates on projects involving ocean mixing instrumentation and computational modeling of complex systems. Research interests include turbulent flows in fluids, particle transport dynamics, granular bed erosion mechanics, and quantitative analysis of animal collective motion. Techniques employed range from particle tracking velocimetry to machine learning for free-surface reconstruction. Ouellette’s recent work explores interdisciplinary connections between physics and urban systems, such as pedestrian access optimization in suburban layouts and the interplay of multiple environmental stressors in policy modeling. Notable contributions include studies on midge swarm mechanics, jackdaw flock kinematics, and the application of spectral analysis to energy transfer in turbulence. His lab develops advanced measurement systems for oceanographic studies and applies network theory to environmental and urban systems analysis. Ouellette maintains active collaborations across engineering, biology, and policy domains.

• Signal Processing
• Antenna Engineering
• Radar Imaging
• +۵ مورد دیگر

Amir Masoud Molaei is a Research Fellow at the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast. His research focuses on advanced radar imaging techniques, antenna engineering, and signal processing, with particular emphasis on near-field localization, MIMO systems, and computational imaging using metasurface antennas. He has collaborated extensively on projects involving millimeter-wave systems, sparse array configurations, and security applications. His work integrates theoretical models like Kirchhoff migration with experimental validation, emphasizing hardware-efficient designs and algorithmic improvements. Key themes include overcoming challenges in mutual coupling effects, optimizing dynamic antenna configurations, and enhancing 3D imaging resolution. He has explored applications ranging from medical imaging to security systems, with a strong focus on real-time processing and experimental validation. Notable contributions include pioneering studies on reconfigurable metasurface antennas for radar and computational imaging, as well as algorithmic advancements in direction-of-arrival estimation and mixed-field source localization. His research bridges electromagnetic theory, signal processing, and practical hardware implementations, often addressing trade-offs between computational efficiency and accuracy. Molaei has published over 40 papers since 2021, with a focus on IEEE journals and conferences. His work frequently addresses challenges in signal reconstruction, sparse sampling techniques, and the integration of machine learning with traditional signal processing methodologies.

Giancarlo Manziمشاهده پروفایل

دانشیار

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +۵ مورد دیگر

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

Beau Schelbleمشاهده پروفایل

استادیار

• Human-AI Teaming
• Responsible AI
• Trust in Human-AI Systems
• +۱۲ مورد دیگر

Beau Schelble is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He is the founding director of the AI and Robotics for Collaborative Systems (ARCS) Lab, where he leads interdisciplinary research on human-AI teaming, responsible AI, and team cognition. His work bridges human factors, systems engineering, and artificial intelligence, with applications in advanced manufacturing, healthcare, emergency response, and defense. Education: PhD in Human-Centered Computing, Clemson University, 2023 BS in Psychology, Clemson University, 2018 Dr. Schelble's research focuses on optimizing collaborative systems involving humans and AI. Key interests include trust development, situational awareness, shared knowledge, and ethical AI integration. He employs mixed-methods approaches—qualitative, quantitative, and computational—to study human-AI interaction in complex environments. His work has resulted in over 30 peer-reviewed publications in top-tier venues across human factors, HCI, and systems engineering. The 15 most recent articles reflect a strong trend toward ethical AI, explainability, autonomy adaptation, and team cognition in human-AI systems. Topics span healthcare applications, collaborative technologies, training methods, and emotional expression in AI teammates, demonstrating a broad yet cohesive research agenda centered on responsible and effective human-AI collaboration. Scientific Awards and Recognitions: NSF Research Traineeship (THINKER) Scholarship, August 2019 Multiple best paper awards and nominations (5 total) in human factors and systems engineering conferences Dr. Schelble actively mentors students and leads a dynamic research lab. He has secured research support through academic collaborations and professional engagement with IEEE, HFES, ACM, and IISE. His lab, ARCS, fosters innovation in AI-driven collaborative systems. Future work includes expanding frameworks for outpatient care, adaptive autonomy in healthcare, and deeper integration of human teaming principles into AI design. He continues to publish extensively and contribute to shaping the future of human-AI teaming.

Salar Fattahiمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Robust Matrix Recovery
• +۴ مورد دیگر

Salar Fattahi is an Assistant Professor at the University of Michigan, affiliated with the College of Engineering’s Department of Industrial and Operations Engineering. He holds additional appointments with the Michigan Institute for Computational Discovery and Engineering (MICDE), Michigan Institute for Data Science (MIDAS), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). PhD in Industrial Engineering and Operations Research from UC Berkeley M.Sc. in Electrical Engineering from Columbia University B.Sc. in Electrical Engineering from Sharif University of Technology Research Focus: Developing scalable computational methods for structured optimization and machine learning problems by exploiting sparsity, low-rankness, and benign landscape properties. Applications span gene regulatory networks, power systems, and brain connectivity modeling. 2025: Parametric algorithms for MIQPs over trees 2024: Triple Component Matrix Factorization for global/local/noise separation 2023: Robust subspace recovery and dictionary learning Scientific Recognition: NSF CAREER Award (2023) INFORMS Best Paper Awards (2023, 2024) Dean’s MLK Spirit Award (2024) MICDE Catalyst Grant (2021) Academic Service: Associate Editor for INFORMS Journal on Data Science; Area Chair for NeurIPS, ICML, and ICLR. Mentored students including Jianhao Ma (now Tsinghua University), Geyu Liang (Amazon), and Aaresh Bhathena. Research supported by NSF, ONR, MICDE, MIDAS, START, and DEI Faculty grants.

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +۶ مورد دیگر

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).

Giampaolo Liuzziمشاهده پروفایل

دانشیار

• Nonlinear Optimization
• Global Optimization
• Mixed Integer Nonlinear Programming
• +۳ مورد دیگر

Giampaolo Liuzzi is an Associate Professor at the Department of Computer, Automation, and Management Engineering 'Antonio Ruberti' (DIAG) at Sapienza University of Rome since July 2023. Previously, he was a fixed-term researcher at the same department (July 2020-June 2023) and a Senior Researcher at the Institute of Systems Analysis and Computer Science 'A. Ruberti' of the CNR until July 2020. He teaches Mathematical Programming, Complements of Mathematics, and Mathematical Analysis 2 for various engineering programs at Sapienza University. Dr. Liuzzi's research focuses on Nonlinear Optimization , particularly derivative-free methods for constrained and unconstrained optimization, global optimization, mixed integer nonlinear programming, and applications in operations research and machine learning. His work spans theoretical developments in optimization algorithms as well as practical applications in engineering design, simulation-based optimization, and biomedical systems. He has made significant contributions to derivative-free optimization techniques that don't require gradient information, which is particularly valuable for black-box optimization problems where derivatives are unavailable or expensive to compute. His recent publications (2022-2025) demonstrate a strong focus on advancing derivative-free optimization methods, with particular attention to complexity analysis, convergence properties, and practical implementations for challenging problem classes including nonsmooth, constrained, multi-objective, and mixed-integer optimization problems. His work bridges theoretical computer science with practical engineering applications, with publications appearing in top optimization journals like Optimization Methods & Software, Computational Optimization and Applications, and Journal of Optimization Theory and Applications. Dr. Liuzzi has received significant professional recognition through National Scientific Habilitations for both Associate Professor (2014) and Full Professor (2018) positions in Italy. He is actively involved in the academic community as an administrator of the Derivative-Free Library (DFL), a collection of algorithms and methods for derivative-free optimization developed through collaboration among several prestigious Italian research institutions. As an educator, Dr. Liuzzi has developed comprehensive teaching materials for courses in Mathematical Programming, Complements of Mathematics, and Mathematical Analysis. He is also engaged in academic entrepreneurship as a co-founder of DEIX s.r.l., a Sapienza startup focused on algorithms and industrial software for planning and control of complex systems. Additionally, he organized the 2nd Derivative-Free Optimization Symposium (DFOS'24) in June 2024 in Padua, highlighting his leadership role in this specialized optimization community.

A. Lee Swindlehurstمشاهده پروفایل

استاد

• Signal Processing
• Wireless Communications
• Biomedical Applications
• +۳ مورد دیگر

A. Lee Swindlehurst is a Professor and Associate Dean for Research and Graduate Studies at the Henry Samueli School of Engineering of the University of California, Irvine . He is also a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study , where he collaborates on next-generation wireless communications research. Ph.D., Stanford University (Electrical Engineering, 1991) M.S., Brigham Young University (Electrical Engineering, 1986) B.S., Brigham Young University (Electrical Engineering, 1985) Swindlehurst's research focuses on sensor array signal processing for radar, wireless communications, and biomedical applications. His work addresses detection and estimation theory , system identification, and channel modeling, with recent emphasis on massive MIMO systems , low-resolution hardware, and physical layer security . His 2018-2017 publications demonstrate technical advances in precoding algorithms , channel estimation , and ADC/DAC optimization for emerging wireless architectures. IEEE Fellow (2004) IEEE Signal Processing Society Best Paper Awards (2010, 2006) Stephen O. Rice Prize in Communications Theory (2006) IEEE W.R.G. Baker Prize Paper Award (2000) Karl G. Maeser Research Award (2004) His research has produced foundational work on space-time block codes , vector-perturbation techniques , and blind equalization methods that continue influencing modern wireless system design.

• Geometric Quantization
• Complex Geometry
• Mathematical Physics
• +۳ مورد دیگر

Tatyana Barron is a Professor in the Department of Mathematics at the University of Western Ontario since July 2022. She holds a Ph.D. from Pennsylvania State University (1998) and has held academic positions at institutions like the University of Michigan and the University of Arizona. Her research focuses on geometric quantization, complex geometry, and mathematical physics. She has supervised five Ph.D. students and has an extensive publication record spanning topics from Toeplitz operators to entropy analysis in quantum systems. Education: Ph.D. in Mathematics, Pennsylvania State University (1998) Bachelor's Degree, Moscow Institute of Physics and Technology (1994) Research Interests: Tatyana Barron explores geometric quantization techniques applied to complex manifolds, with particular emphasis on Kähler and multisymplectic structures. Her work bridges differential geometry, algebraic geometry, and quantum mechanics, addressing topics like automorphic forms, entropy in quantum systems, and the asymptotic properties of geometric operators. She has contributed to understanding Berezin-Toeplitz operators, entanglement in mixed states, and geometric models for therapeutic approaches involving electromagnetic fields. Her recent publications (2023-2025) highlight advancements in geometric signal theory, entanglement quantification, and semiclassical asymptotics. She has also explored mathematical models for medical applications using electromagnetic principles. Advising: Supervised five Ph.D. students focusing on topics ranging from holomorphic differentials to singular Lagrangian submanifolds. She co-supervised three of these students with colleagues in geometric analysis and differential geometry.

Lesley Howellمشاهده پروفایل

استاد

• Medicinal Chemistry
• Pharmacology
• Chemical Education
• +۵ مورد دیگر

Lesley Howell is a Professor of Pharma and Medicinal Chemistry and Director of Education at Queen Mary University of London's School of Physical and Chemical Sciences. She holds a Queen Mary Academy Fellowship (2021-2024) focused on advancing pedagogical strategies like Peer Led Team Learning (PLTL), which enhances student engagement and reduces attainment gaps. Her research spans two core areas: medicinal chemistry targeting protein-protein interactions (PPIs) in GPCRs and chemical education initiatives emphasizing outreach, diversity, and decolonizing STEM curricula. She collaborates with the McCormick Group on GPCR heteromer projects and Dr. Tippu Sheriff on BAME scientist contributions to chemistry. Her work also explores mixed-reality laboratory environments to improve educational outcomes. In teaching, she leads modules such as Foundations of Practical Chemistry and Advanced Pharmaceutical Chemistry. Her supervisory roles include mentoring PhD students Zara Farooq and Christos Matsingos. Research highlights include developing stapled peptides to disrupt GPCR oligomers and identifying cannabinoid receptor modulators. She advocates for inclusive education through projects like 'Does chemistry outreach influence university choices?' and 'Effective outreach evaluation.'

• Pharmacology
• Chemotherapy
• Cytofluorimetry
• +۳ مورد دیگر

Sabrina Pacor is an Associate Professor in Applied Biology (BIO/13) at the University of Trieste , where she teaches Pharmacology in the Pharmacy LM and STB BSc programs. With over 30 years of research experience in experimental oncology and host defense peptides, she has made significant contributions to studying antimicrobial peptides (AMPs) and their interactions with bacterial membranes. Her research focuses on: Direct antimicrobial activity of AMPs against Gram-positive and Gram-negative bacteria Indirect immunomodulatory effects of host defense peptides Development of drug delivery systems using nanomaterials (carbon nanotubes, gold nanoparticles) Mechanistic studies of ruthenium-based antimetastatic drugs She leads extensive cytofluorimetry research using flow cytometry platforms, particularly for evaluating: Cytotoxicity (necrosis/apoptosis, proliferation index) Modulation of host biological responses (chemotaxis, phagocytosis, ROS production) Peptide-bacterial membrane interactions through fluorescent labeling Her recent work demonstrates trends in: Proline-rich antimicrobial peptides against ESKAPE pathogens Hybrid antibiotic design (peptide-aminoglycoside conjugates) Structure-activity relationships in membranolytic peptides Evolutionary insights into defensin and cathelicidin families Prof. Pacor has co-authored over 100 peer-reviewed publications and actively mentors students, having supervised: 70 experimental/thesis reviews for Pharmacy/CTF Master's students 20 Bachelor's degree theses