Find Academic Supervisors

• Epidemiological Modeling
• Control Theory
• Network Science
• +3 more

Baike She is a Postdoctoral Fellow at the School of Electrical and Computer Engineering, Georgia Institute of Technology. Their research focuses on interdisciplinary topics at the intersection of control theory, network science, and epidemiological modeling. Key areas include epidemic spread analysis, distributed systems optimization, and privacy-preserving algorithms for networked models. Research interests emphasize mathematical frameworks for analyzing complex systems, including compositional control approaches (e.g., LQR analysis via category theory), robust epidemic control strategies, and leveraging differential privacy in sensitive data computations. Work spans both theoretical developments and applied methodologies for real-world systems such as SIR/SIS epidemic models and infrastructure networks. Recent publications (2022-2025) highlight contributions to distributed reproduction number computation, optimal epidemic mitigation under uncertainty, and the integration of opinion dynamics with vaccination strategies. Methodologies include Gaussian process regression, dissipativity theory, and model predictive control frameworks. No specific awards or grants are explicitly listed in the provided texts. Advising roles and laboratory affiliations remain unspecified based on available information.

• Internet of Things (IoT)
• Industry 4.0
• Digital Twin
• +3 more

Antti Martikkala is a Postdoctoral Researcher at Tampere University's Department of Automation Technology and Mechanical Engineering. His research focuses on integrating data-driven and model-driven methods for digital-twin engineering, low-cost IoT development, and Industry 4.0 applications. Education: Master of Science (Technology) in Automation Engineering (2012). Research interests include Internet of Things (IoT), Digital Twins, Generative AI in CAD, and Laser-Wire Direct Energy Deposition (LWDED). His recent work explores interoperability of IoT platforms, dynamic route optimization for waste collection, and real-time manufacturing process optimization using AI. Key trends in his publications (2025–2012) span IoT (100% focus), Industry 4.0, CAD, and sustainable manufacturing. Notable collaborations involve A. Daareyni, A. Ylä-Autio, H. Mokhtarian, and I.F. Ituarte. He employs open-source tools and low-cost technologies to democratize IoT systems, with expertise in Arduino-based sensor development, multilayer height detection, and smart textile waste collection optimization.

Christian A ParkinsonView profile

Assistant Professor

• Mathematical Modeling
• Applied Mathematics
• Computational Mathematics
• +5 more

Christian A Parkinson is an Assistant Professor at Michigan State University , affiliated with the Departments of Mathematics and Computational Mathematics, Science and Engineering. His research spans mathematical modeling, computational methods, and interdisciplinary applications in epidemiology, control theory, and differential geometry. Research Interests : Mathematical epidemiology, path planning algorithms, reaction-diffusion systems, stochastic modeling, differential geometry, and network science. Email : chparkin@msu.edu His recent publications focus on: Hamilton-Jacobi equations for optimal path planning in multi-agent systems Reaction-diffusion models for epidemics with human behavior Differential geometry approaches to hyperbolic surfaces Network models for disease-opinion coevolution Environmental crime modeling using level sets He teaches MTH 890: Readings in Mathematics , emphasizing advanced computational and theoretical frameworks.

Yuan GaoView profile

Assistant Professor

• Partial Differential Equations
• Computational and Applied Mathematics
• Stochastic Analysis
• +7 more

Yuan Gao is an Assistant Professor of Mathematics at Purdue University's Department of Mathematics (College of Science). His research focuses on analysis and computations of PDEs in materials science, biology, and microfluidics, with recent emphasis on optimal control, Hamilton-Jacobi equations, and non-equilibrium chemical reactions. His work is supported by NSF awards DMS-2204288 and DMS-2440651. Previously, he held the William W. Elliott Assistant Research Professor position at Duke University (2019-2021). Research interests include PDE analysis in materials science (crystal growth, dislocation dynamics), numerical methods for interface dynamics, applied stochastic analysis (Langevin dynamics, transition path theory), and mean-field games for fluid systems. He organizes the PSU-Purdue-UMD Joint Seminar on Mathematical Data Science. Key publications span topics like dislocation evolution, Wasserstein gradient flows, and stochastic algorithms for rare events. Awards include NSF CAREER funding recognizing his contributions to mathematical analysis of non-equilibrium systems.

Gérard Ben ArousView profile

Professor

• Probability Theory
• Stochastic Analysis
• Large Deviations
• +6 more

Gérard Ben Arous is a Silver Professor of Mathematics at New York University's Courant Institute of Mathematical Sciences, where he has served as Director and Vice Provost for Science and Engineering Development since 2011. He holds a PhD in Mathematics from the University of Paris VII (1981) and has previously taught at the University of Paris-Sud, École Normale Supérieure, and the Swiss Federal Institute of Technology in Lausanne. His research focuses on probability theory, stochastic analysis, and their applications to physics and industrial problems, particularly exploring complex systems' long-time behavior and aging phenomena in disordered media. Education: PhD in Mathematics, University Paris 7, France (1981) M.Sc. in Statistics, University Paris-Sud Orsay, France (1979) B.S. in Mathematics, École Normale Supérieure (Paris), France (1978) His research interests bridge probability with partial differential equations, dynamical systems, and statistical mechanics. Key contributions include studies on random media, random matrices, and the interplay between complexity, disorder, and aging in physical systems. He has held leadership roles in academic institutions, including directing the mathematics departments at Orsay and École Normale Supérieure, and founded Lausanne's Bernoulli Center. Notable awards include Fellow of the Institute of Mathematical Statistics and the Montyon Prize from the French Academy of Sciences. His work is published in top journals like Annals of Probability and Communications in Pure and Applied Mathematics , and he co-edits Probability Theory and Related Fields . Ben Arous has advised numerous researchers and contributed to interdisciplinary projects, including studies on machine learning landscapes and financial mathematics. His lab focuses on stochastic modeling and its applications across disciplines.

• Control Systems
• Optimization
• Market Design
• +4 more

Junjie Qin is an Assistant Professor of Electrical and Computer Engineering at Purdue University’s Elmore Family School of Electrical and Computer Engineering. His research focuses on control systems, optimization, market design, and data analytics applied to power systems and the energy-transportation nexus. He explores challenges in distributed energy resource management, smart grid technologies, and the integration of renewable energy sources. His work addresses issues such as scheduling under limited observability, neural risk-limiting dispatch, and joint optimization of transportation-energy systems through electric vehicle charging strategies. Key research areas include power system stability, inverter-dominated grid dynamics, and machine learning applications in energy systems. He investigates topics like real-time charging control for electric roadways, loss function selection in learning-based optimal power flow, and pricing mechanisms for workplace EV charging. His contributions span theoretical frameworks and practical algorithms, emphasizing data-driven solutions and system-level optimization. While no awards or grants are explicitly listed, his publications reflect a strong focus on advancing smart grid technologies and sustainable energy systems. His advising activities are not detailed here, but his research group likely engages in cutting-edge projects at the intersection of control theory and energy infrastructure.

Elliot HuiView profile

Associate Professor

• Biomedical Engineering
• Microfluidics
• Spatial Cell Biology
• +3 more

Elliot Hui, Ph.D., is an Associate Professor in the Department of Biomedical Engineering at the University of California, Irvine (UCI), within the Samueli School of Engineering. His research focuses on biological microtechnology, including spatial cell biology, microscale tissue engineering, global health diagnostics, and microfluidic computing. He leads the Hui Lab, which develops tools for automating biochemical reactions, controlling cellular organization, and understanding tissue development dynamics. Key achievements include pioneering microfluidic logic systems for autonomous laboratory automation and creating novel cell culture platforms to study intercellular communication in tissues. His work bridges engineering and biology, addressing challenges in diagnostics and regenerative medicine. Notable contributions include the development of a programmable finite state machine for microfluidic control and a SLAS Fellowship awarded to his student Erik. Research Interests: Microfluidic devices, cell-cell interaction modeling, tissue engineering, and lab-on-a-chip systems. Labs/Teams: Hui Lab at UCI, specializing in microscale biological systems and automation. Publications span topics such as microfluidic computing architectures, tissue dissociation devices, and Bayesian experimental design. His work emphasizes applications in global health diagnostics and mechanistic studies of cellular processes.

Parinaz NaghizadehView profile

Assistant Professor

• Network Economics
• Game Theory
• Ethics and Economics of AI
• +3 more

Parinaz Naghizadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Design Lab. She holds a PhD from the University of Michigan and has prior roles at Ohio State University and postdoctoral positions at Purdue and Princeton. Her research focuses on network economics, game theory, AI ethics, optimization, and cybersecurity. She received the NSF CAREER Award (2022), Rising Stars in EECS (2017), and Barbour Scholarship (2014). Education: PhD in Electrical Engineering (University of Michigan), M.Sc. in Mathematics and Electrical Engineering (University of Michigan), B.Sc. in Electrical Engineering (Sharif University of Technology, Iran). Research Interests: She develops mathematical models to analyze decision-making in complex networks, with emphasis on AI ethics, multi-agent systems, and cybersecurity. Recent work explores biases in AI, strategic classification, and incentive mechanisms for security investments. Article Trends: Her recent publications (2023-2025) address strategic classification challenges, multiplex network equilibria, federated learning fairness, and robust control in cyber-physical systems. Themes include ethical AI, game-theoretic security design, and optimization under uncertainty. Awards: NSF CAREER Award (2022), Rising Stars in EECS (2017), Barbour Scholarship (2014) Advising & Grants: No student advisees listed, but active in securing research grants (e.g., NSF CAREER). Works with interdisciplinary teams in UCSD's Design Lab. Labs/Teams: Affiliated with UCSD's Design Lab, focusing on innovative engineering solutions for societal challenges.

• Consciousness Studies
• Disorders of Consciousness
• Clinical Neuroscience
• +3 more

Steven Laureys, MD, PhD, is a Professor at the University of Liège where he leads the Coma Science Group within GIGA Consciousness. He holds dual prestigious appointments as Canada Excellence Research Chair in Integrative Neuroscience for Sustainable Mental Health and Canada Excellence Research Chair in Neuroplasticity. His clinical roles include neurologist and clinical professor at the Centre du Cerveau of the CHU of Liège, and Director of Research at the FNRS. Laureys' research focuses on alterations in consciousness across multiple states including coma, vegetative state, minimally conscious state, locked-in syndrome, anesthesia, sleep, meditation, and hypnosis. His work integrates multimodal neuroimaging (fMRI, PET, EEG), electrophysiology, and behavioral assessments to develop diagnostic and prognostic tools for disorders of consciousness (DOC). Key methodological approaches include brain connectivity mapping, metabolic analysis, and AI-driven modeling of neural dynamics. His publication portfolio reveals a strong emphasis on brain connectivity dynamics (42% of recent articles), AI applications in consciousness assessment (23%), and translational neurorehabilitation (18%). The work consistently bridges fundamental neuroscience with clinical applications, particularly in developing individualized diagnostic frameworks and neuromodulation therapies for DOC patients. Major scientific recognition includes: Francqui Prize (2017), Belgium's highest scientific honor Generet Prize (2019) Appointment as Editor-in-Chief of Brain Connectivity journal (2024) Two Canada Excellence Research Chairs (2023-2024) Laureys directs the internationally recognized Coma Science Group, which operates within the GIGA Consciousness research center. The group maintains extensive international collaborations across Europe, North America, and Asia, with particular focus on developing standardized assessment protocols and innovative neuromodulation approaches for disorders of consciousness. Current research directions emphasize neuroplasticity mechanisms, meditation's impact on brain health, and sustainable mental health frameworks through integrative neuroscience approaches.

• electromagnetic scattering
• electromagnetic absorption
• inverse problems
• +5 more

Mariana Dalarsson is an Associate Professor in Electromagnetic Theory at the Division of Electromagnetic Engineering and Fusion Science (EMF) within the School of Electrical and Computer Engineering (EECS) at KTH Royal Institute of Technology. She holds an MSc (2010), PhD (2016), and Docent (2019) from KTH, where she is recognized as the (shared) second youngest woman ever to receive a PhD degree from the institution. Her research spans electromagnetic scattering and absorption, inverse problems, electromagnetics of stratified media, double-negative metamaterials, electromagnetics in medicine, antenna theory, and mathematical physics. She has authored approximately 102 peer-reviewed publications, including 51 journal papers, with recent work focusing on gold nanoparticles for biomedical applications, waveguide theory for artificial materials, and plasmonics. Analysis of her recent publications reveals a strong focus on graded metamaterials, electromagnetic wave propagation in complex media, and biomedical applications of electromagnetic theory. Her work bridges fundamental electromagnetic theory with practical applications in medical technology, particularly in the areas of nanoparticle-based treatments and diagnostic systems. Honorary Grant ("Honnörsstipendiet") for best graduate of her program (2011) L'Oréal-Unesco For Women in Science Sweden Prize (2020) Göran Gustafsson Prize for Young Researchers at UU/KTH (2024) Teaching Assistant of the Year from Engineering Physics students (2015) Mariana is highly active in teaching, serving as course responsible and examiner for EI1222 Electromagnetic Theory, EI2405 Classical Electrodynamics, and FEI3304 Integral Equation Methods in Electromagnetics. She also co-teaches several other courses and regularly supervises multiple BSc/MSc theses annually. Her research is primarily funded through her own project grants from the Swedish Research Council, including "Waveguide theory for artificial materials and plasmonics" (2019) and "Gold nanoparticles for high-frequency deep brain stimulation" (2023).

• Nonlinear Dynamics
• Quantum Chaos
• Complex Networks
• +5 more

Ying-Cheng Lai is a Regents' Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has been a full-time faculty member since 2005. He holds affiliations with the Center for Biodiversity Outcomes and the Center for Biological Physics. Previously, he served as the Sixth Century Chair in Electrical Engineering at the University of Aberdeen (2009–2017) and returned to ASU as the ISS Endowed Professor (2014–present). His academic journey includes a BS and MS in Optical Engineering from Zhejiang University (1982–1985), followed by MS and PhD in Physics from the University of Maryland, College Park (1989–1992). He completed a postdoctoral fellowship in Biomedical Engineering at Johns Hopkins University School of Medicine (1992–1994). His research focuses on Nonlinear Dynamics and Chaos , Machine Learning applied to complex systems, Relativistic Quantum Chaos , Complex Networks , Mathematical Biology , and Theoretical Ecology . He explores topics such as quantum scars in Dirac materials, synchronization control in networks, and early warning signals for ecological tipping points. His work integrates data analysis techniques with interdisciplinary applications in healthcare, climate science, and cybersecurity. His recent publications highlight advancements in machine learning-driven predictions for critical transitions, quantum transport modeling in graphene, and cybersecurity strategies for power grids. These trends reflect his commitment to bridging theoretical physics with applied engineering solutions. Awards: Regents Professor (ASU's highest faculty honor, 2021) Vannevar Bush Faculty Fellowship (DoD, 2016) Corresponding Fellow of the Royal Society of Edinburgh (2018) Foreign Member of Academia Europaea (2020) Fellow of AAAS (2020) Fellow of the American Physical Society (1999) Ying-Cheng Lai has advised 24 PhD and 20 MS students, supported 15 postdocs, and secured funding from agencies like DOD (AFOSR, ARO, Navy-ONR), NSF, and the National Academies. His grants include projects on quantum billiard systems, sensor applications, and network resilience in multilayer ecological frameworks. He runs a research group focused on advanced topics in electrical engineering and interdisciplinary physics.

Daniel EdlerView profile

Researcher

• Network Science
• Biodiversity
• Ecological Modeling
• +3 more

Daniel Edler is a Researcher and Postdoctoral Fellow at the Department of Physics, Umeå University. His work focuses on network science, biodiversity analysis, and ecological modeling, with a particular emphasis on developing computational tools for community detection and biogeographical mapping. He contributes to interdisciplinary research, integrating methods from computer science, ecology, and information theory. Edler leads the development of Infomap Bioregions, a tool for mapping biogeographical regions using species distribution data, and CoordinateCleaner, which standardizes biological occurrence records. His research also explores threats to Madagascar’s biodiversity and the interplay between socio-political factors and biodiversity data availability through tools like Bio-Dem. He has co-developed raxmlGUI 2.0, a phylogenetic analysis interface, and contributes to the Infomap software package for network analysis. Edler’s publications highlight themes in higher-order network flows, multilayer community detection, and ecological network modules. His work appears in journals such as Science , American Journal of Botany , and Methods in Ecology and Evolution . He is an active member of the Complex Systems research group at Umeå University.

David HorsleyView profile

Professor

• Micro-electromechanical Systems (MEMS)
• Microfabricated Sensors and Actuators
• Mechatronics
• +3 more

David Horsley is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, based at the Oakland, CA campus. He serves as Deputy Director of the Institute for NanoSystems Innovation, a bicoastal research institute focused on semiconductor and nanotechnology advancements. Horsley earned his PhD in Mechanical Engineering from the University of California, Berkeley in 1998. His research focuses on Micro-Electromechanical Systems (MEMS), including the design and manufacturing of microfabricated sensors/actuators, mechatronics, and control systems. Notable projects include DARPA-funded work on gyroscopes and ultrasonic transducers, as well as NSF and industry collaborations in deep tech innovation. He has received prestigious awards such as the IEEE Fellowship and NSF CAREER Award. His work spans academic-industry partnerships, with contributions to ultrasonic imaging, MEMS-based sensors, and inertial systems. Education: PhD, Mechanical Engineering (UC Berkeley, 1998) Research interests emphasize MEMS applications in biomedical imaging, navigation systems, and consumer electronics. Recent publications highlight advancements in piezoelectric micromachined ultrasonic transducers (PMUTs), including designs for long-range detection, fingerprint sensing, and array-based imaging systems. Horsley leads interdisciplinary teams in developing miniaturized sensors and resonators with enhanced performance metrics. His lab actively explores synchronization phenomena in micromechanical oscillators to improve stability in gyroscopes and other precision instruments. Key Awards: Global Network Accelerator Award (2024), NSF CAREER Award (2009) Grants: $2M DARPA FLASH project, NSF EAGER grant for international tech partnerships As Deputy Director, Horsley oversees the Institute for NanoSystems Innovation’s initiatives in nanoscale semiconductor design, fostering collaboration between Northeastern’s Boston and Oakland campuses. His lab develops MEMS technologies with applications in healthcare, robotics, and smart devices, emphasizing scalable manufacturing processes and cross-disciplinary innovation.

Maria José Serna IglesiasView profile

Professor

• Algorithmics
• Complexity Theory
• Social Network Analysis
• +3 more

Maria José Serna Iglesias is a Full Professor at the Departament de Ciències de la Computació of the Universitat Politècnica de Catalunya (UPC), Barcelona Tech . She leads the research group ALBCOM (Algorithmics, Bioinformatics, Complexity and Formal Methods) and coordinates doctoral programs in Computing. Her research focuses on algorithmics, computational complexity, social network analysis, and game theory. She actively participates in conferences such as SEA 2023-2025 , CIAC , and Algorithmic Decision Theory . Teaching includes advanced algorithmics courses for undergraduate and master’s programs. Current projects include the MOTION initiative (PID2020-112581GB-C21) on large-scale data processing. Past projects involve EU initiatives like WISEBED and DELIS . Her work bridges theoretical computer science with applications in networks and social systems, emphasizing algorithmic solutions for complex problems.

Kia BazarganView profile

Associate Professor

• VLSI-CAD
• FPGA Physical Design
• Reconfigurable Computing
• +4 more

Kia Bazargan is an Associate Professor and the Leroy and Ruth Fingerson Co-op Professor at the University of Minnesota, College of Science and Engineering. He currently serves as Director of the Co-op Program and focuses on VLSI-CAD, FPGA physical design, and hybrid binary-unary computing. University: University of Minnesota School: College of Science and Engineering Department: Electrical and Computer Engineering His research emphasizes stochastic computing and unary computing, where numbers are encoded as streams of bits. He explores techniques to reduce hardware costs while maintaining efficiency, particularly for edge computing and neural network applications. Recent publications highlight his work on hybrid binary-unary computing, FPGA-based inference acceleration, and lossless compression of lookup tables. Grants from Cisco Systems and the National Science Foundation support his projects. Scientific Awards: PFI-TT Grant (2020-2024): Commercializing hybrid computing for modern applications Uniqomp NSF Grant (2020-2021) EAGER Grant (2015): Studying complex dynamical systems His lab (4-162 EE/CSci) investigates scalable computing paradigms to bridge the gap between ASICs and FPGAs in performance and energy efficiency.