Jonathan Fan is an Associate Professor at Stanford University in the Department of Electrical Engineering. His teaching portfolio includes graduate and undergraduate courses in electromagnetics, integrated circuit fabrication, and specialized studies across all quarters. EE 242: Electromagnetic Waves (Autumn) EE 312: Integrated Circuit Fabrication Laboratory (Winter) ENGR 42/EE 42: Electromagnetics and Applications (Spring) 11 independent studies and thesis courses (EE 190, EE 191, EE 300, etc.) His research focuses on nanophotonics and metasurface engineering , with particular emphasis on inverse design methodologies, machine learning -driven photonic optimization, and machine learning in electromagnetic simulation. His recent publications demonstrate a strong trend toward deep learning-enabled photonic design and high-speed optimization of complex optical systems. His work spans metamaterial fabrication , nonlocal effects in metasurfaces, and multi-functional optical devices such as spaceplates for aberration correction. Key technical contributions include physics-augmented neural networks , reparameterization techniques for design constraints, and topology-optimized metasurfaces .
Wenzel Jakob is an Associate Professor and leader of the Realistic Graphics Lab at EPFL's School of Computer and Communication Sciences , currently on sabbatical at the University of Tokyo until Fall 2025. His work bridges inverse graphics , physically based rendering , and compiler/systems research , with a focus on developing robust differentiable rendering frameworks. Key research themes include: Backpropagation through rendering algorithms for inverse problems Material appearance modeling and optical measurement systems Compiler design for differentiable rendering pipelines Manifold sampling techniques and light transport derivatives His group created Mitsuba renderer , Dr.Jit , and Instant Meshes (recipient of the SGP Software Award). Recent publications (2021–2024) explore volumetric rendering, SDF-based differentiable systems, and efficient Monte Carlo estimators. Awards include the ACM SIGGRAPH Significant Researcher Award , Eurographics Young Researcher Award , and ERC Starting Grant . Teaching roles (2016–2024) span Advanced Computer Graphics and Numerical Methods for Visual Computing courses at EPFL.
Jon Schuller is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), within the College of Engineering. His research focuses on nanophotonics, metamaterials, plasmonics, and their applications in energy-efficient technologies such as photovoltaics, thermal management systems, and advanced optical devices. He leads the Schuller Lab, which explores engineered metasurfaces and naturally occurring materials to control light-matter interactions at subwavelength scales. His work bridges fundamental science (e.g., quantum phenomena in hybrid perovskites) and engineering (e.g., reconfigurable semiconductor meta-optics). He is affiliated with the California NanoSystems Institute (CNSI) and actively contributes to interdisciplinary research initiatives. Contact: jonschuller@ece.ucsb.edu, Office 3221C Engineering Science Building. Research interests include directional light emission control via metasurfaces, thermal radiation tuning using phase-change materials, and the development of high-efficiency photonic devices. His lab emphasizes fabrication and characterization of nanophotonic structures, with applications ranging from space technology to exoplanet imaging systems. Recent efforts focus on electrically tunable metasurfaces and multipolar optical phenomena in layered materials. Key technical contributions involve designing reconfigurable optical antennas, optimizing metasurface-based LEDs, and exploring magnetic dipole emission in 2D perovskites. His team collaborates across disciplines to address challenges in energy, aerospace, and quantum technologies. Current opportunities include postdoctoral positions in nonlinear optics and photonics.
Yu He is an Assistant Professor of Applied Physics and Physics at Yale University, affiliated with the Department of Physics. His research focuses on condensed matter physics and experimental techniques such as angle-resolved photoemission spectroscopy (ARPES) and x-ray scattering to study correlated electronic systems and quantum materials. Prior to Yale, he completed a Miller Research Fellowship at UC Berkeley (2019) after earning his Ph.D. in Applied Physics from Stanford University. Key research areas include metal-to-insulator transitions, superconductivity, 2D magnetism, and solid-state quantum simulation. He has contributed to advancements in material characterization techniques, including high-resolution ARPES using tabletop lasers. His work integrates crystal synthesis, electric transport measurements, and surface decoration to explore material properties. Education: B.S. in Physics from University of Science and Technology of China (USTC); M.S. in Electrical Engineering and Ph.D. in Applied Physics from Stanford University. Research Interests: Experimental condensed matter physics, quantum materials, superconductivity, and light-matter interaction studies. His current projects aim to dissect microscopic degrees of freedom (electronic, lattice, spin) in novel materials using cutting-edge spectroscopic methods. The lab employs complementary techniques like electric transport measurements and crystal growth to characterize material properties comprehensively. Awards: Miller Research Fellow, UC Berkeley (2019) Advising & Grants: No student advisees listed. Research supported by Yale University and prior fellowships. Labs & Teams: Leads a research group at Yale focused on experimental condensed matter physics, collaborating on projects involving advanced material characterization and quantum material discovery.
Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.
Asst. Prof. OU Pengfei is an Assistant Professor and NUS Presidential Young Professor in the Department of Chemistry at the National University of Singapore, Faculty of Science. He leads the AI for Chemistry (AI4Chem) research group, focusing on computational catalysis, machine learning, and materials science. Previously, he was a Research Associate at Northwestern University and a Postdoctoral Fellow at the University of Toronto under Prof. Edward H. Sargent, and earned his Ph.D. from McGill University. Education: Ph.D., McGill University, 2020 M.Eng., Central South University, 2015 B.Eng., Central South University, 2012 Research interests include catalyst design for electrochemical reactions using ab initio DFT, molecular dynamics simulations, and AI-driven methods. He develops dynamic simulations of chemical processes under reaction conditions and machine learning tools for accelerated catalyst discovery. His work addresses challenges in energy and environmental applications such as CO2 reduction and hydrogen evolution. Notable awards include the NUS Presidential Young Professorship (2024), Climate Positive Energy Postdoctoral Fellowship (2021), and Chinese Government Award for Outstanding Self-Financed Students Abroad (2020). Labs/Teams: The AI4Chem group integrates theory-guided and data-driven approaches to advance computational catalysis, with three core research directions: (1) reaction mechanism exploration and catalyst optimization, (2) dynamic structure-performance relationships under reaction conditions, and (3) machine learning algorithms for high-throughput screening.
Henry Liang, Ph.D., is a Professor in the Department of Cell Physiology and Molecular Biophysics at Texas Tech University Health Sciences Center (TTUHSC), with adjunct appointments in Chemical Engineering and Chemistry at Texas Tech University. His lab focuses on bridging biology with synthetic systems through membrane biophysics and bioengineering. Research Interests: Dr. Liang's work spans membrane protein reconstitution, nanodisc technology, antimicrobial nanoparticles, blood-brain barrier targeting, and immunotherapy platforms. Key areas include: Design of synthetic proteomembranes for protein function studies Development of environmentally responsive nanoantibiotics Nanoparticle-based theranostic systems for cancer Light-driven energy transduction in biohybrid materials Publication Trends: His 15 most recent articles (2011-2023) demonstrate consistent focus on nanotechnology solutions for biomedical challenges, with evolving emphasis on antimicrobial nanostructures (35%), membrane protein platforms (30%), cancer nanomedicine (20%), and sustainable nanomaterials (15%). Methodological strengths include polymer synthesis, X-ray scattering, and biomimetic system design. Training: The Liang Lab actively recruits graduate students and postdoctoral researchers for projects in membrane biophysics and bioengineering. Current research infrastructure includes capabilities for synchrotron small-angle X-ray scattering, molecular dynamics simulations, and nanomaterial characterization.
Dr. Zaheer Nasar is a Reader in Atmospheric Aerosols at Cranfield University's School of Aerospace, Transport and Manufacturing. His work focuses on real-time bioaerosol characterization, indoor/outdoor air quality dynamics, and environmental health impacts of particulate matter. He leads the NERC-funded Light-Induced Fluorescence sensor project and contributes to the BioAirNet network (NE/V002171/1) as Co-I. Research Interests Physico-chemical and biological characterization of aerosols Spatio-temporal dynamics of particulate matter (PM) and bioaerosols Quantitative microbial risk assessment (QMRA) methodologies Low-cost air quality sensor networks and machine learning calibration Urban green infrastructure effects on air pollution Policy development in Hindu Kush Himalayan air quality Recent publications emphasize machine learning-enhanced sensor calibration (2024 IEEE paper), wastewater plant bioaerosol risks (2024 Water Research), and urban air quality interventions across the UK and Lahore. He has secured over £1.6M in grants from NERC, STFC, and UKRI GCRF, with significant work on BTEX exposure in Nigeria and SARS-CoV-2 risks in wastewater facilities. Scientific Recognition Fellow of the Higher Education Academy (FHEA) Co-investigator in multiple NERC/UKRI projects Active participant in BSI bioaerosol standards committee As an advisor, he mentors five postgraduate researchers including Reece Dillon and Hathaikarn Tathong, with a strong publication record in journals like Environmental Science: Atmospheres , Risk Analysis , and BJPsych Open . His work bridges environmental science, public health, and policy implementation through interdisciplinary research.
Dr. Majid Pahlevani is an Assistant Professor at the Department of Electrical and Computer Engineering, Queen's University, affiliated with the Smith School of Engineering. He holds a Ph.D. from Queen's University (2012) and has prior roles as an Assistant Professor at the University of Calgary (2016–2019) and Chief R&D Engineer/VP of Technology at SPARQ Systems, Inc. (2011–2016). His research focuses on power electronics, renewable energy systems, smart grids, and energy storage, with a lab environment emphasizing interdisciplinary collaboration. He has authored over 130 publications, holds 50 U.S. patents, and serves as an Associate Editor for the IEEE Journal of Emerging and Selected Topics in Power Electronics. Education: Ph.D. (2012) – Queen's University; B.Sc./M.Sc. (2002) – Isfahan University of Technology. Research Interests: Power Electronics Technology, Renewable Energy Systems, Micro-Grids, Smart-Grids, Electric Vehicles, Energy Storage Systems, Solar Technology, LED Technology. His lab, ePOWER Lab, engages in industrial projects across these domains, fostering teamwork and cross-disciplinary innovation. Scientific Awards: Includes the Early Research Excellence Award (Alberta), Research Achievement Award (University of Calgary), Teaching Achievement Award, and IEEE Canada's Research Excellence Award. Current Supervision: Postdoctoral Fellows Laleh Saleh Ghadimi, Sergey Dayneko, and Pavel Linkov (2022). He leads the ePOWER Lab, collaborating with industry partners like Freescale Semiconductor and SPARQ Systems. Affiliations: Member of the IEEE Power Electronics Society and the Queen's Centre for Energy and Power Electronics Research.
Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.
Dr. Louise Willingale is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. Specializing in high-intensity laser-plasma interactions, she leads experimental research at facilities including the ZEUS laser system and OMEGA EP. Her work combines experimental diagnostics with numerical modeling to advance understanding of relativistic plasma physics and ion acceleration mechanisms. Education: PhD in Physics from Imperial College London (2007) Research Focus: Investigates relativistic laser-plasma interactions through ion acceleration, magnetic field generation, and direct laser acceleration of electrons. Her work spans underdense/near-critical density plasmas, shock formation physics, and extreme electromagnetic field generation in laboratory astrophysics contexts. Recent Publication Trends: 2024-2025 studies emphasize ZEUS laser facility development, optimization of acceleration mechanisms (direct laser acceleration, wakefield acceleration), and magnetic field dynamics in multi-PW laser-solid interactions. Common subfields include collisionless shocks, radiation-driven plasma instabilities, and advanced diagnostics for relativistic charge particles. Labs & Collaborations: Affiliated with the Center for Ultrafast Optical Science (CUOS) and the Center for High-Energy-Density Laboratory Astrophysics Research (CHEDAR), working closely with the ZEUS laser facility team.
Özüm Asirim is a Researcher at the Technical University of Munich (TUM) under the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek. Her work focuses on computational photonics , quantum optics , and nonlinear optical phenomena , particularly in micro-resonators and semiconductor devices. Education: Ph.D. in Electrical Engineering from Middle East Technical University (Ankara, Turkey). Research spans optical parametric amplification , Fourier domain mode-locked lasers , self-phase modulation , and machine learning applications in photonics . Her studies include optimizing gain factors, enhancing harmonic generation, and modeling supercontinuum sources via carrier injection. Recent publications (2019–2023) highlight interdisciplinary approaches, merging photonics with computational finance and nonlinear dynamics . She contributes to EU Project QOMBS and teaches courses like Python for Engineering Data Analysis and Quantum Engineering and Machine Learning seminars. Collaborations include Prof. Christian Jirauschek (TUM), Prof. Mustafa Kuzuoğlu (Middle East Technical University), and teams in computational photonics and quantum optics. Her work impacts semiconductor physics , laser technology , and adaptive optical systems .
Steven M. LaValle is a Professor at the University of Oulu's Faculty of Information Technology and Electrical Engineering since 2018. Previously, he held tenured positions at the University of Illinois Urbana-Champaign (UIUC) and was a Principal Scientist at Oculus VR. His research spans robotics, motion planning (notably pioneering RRT algorithms), virtual reality, and sensor fusion. He has authored influential textbooks like Planning Algorithms and Virtual Reality . Education: PhD (1995), MS (1993), and BS (1990) in Electrical Engineering from UIUC. Research Interests: Focuses on minimal information requirements for robots, perception engineering, and foundational VR/AR systems. His work integrates control theory, computational geometry, and human perception. Achievements: Recipient of the IEEE ICRA Milestone Award (2019), University Scholar (UIUC, 2012), and XTIC Award 2024 for Innovation. Leads the Perception Engineering Group at Oulu, advancing VR/AR and telepresence technologies. Grants & Industry: ERC Advanced Grant (2021–2026), former VP of Huawei's VR/AR division, and collaborator with institutions like IIT Madras. Advises startups in robotics and virtual reality.
Trey Porto is an Adjunct Professor at the University of Maryland, affiliated with the Joint Quantum Institute (JQI) and NIST. His research focuses on ultra-cold atoms, quantum optics, and quantum information science. He leads projects on Rydberg atoms, optical lattices, and quantum networking, leveraging cold atom systems to explore novel quantum phenomena and control strategies. Research areas include ultra-cold Rb/Yb mixtures for studying Bose-Einstein condensates and engineered dissipation, as well as photon-photon interactions using Rydberg-dressed polaritons. His work bridges quantum simulation, quantum computing, and precision measurement, with applications in quantum networking and many-body physics. Key achievements include the 2023 UMD Quantum Invention of the Year Award for developing photon-counting methods that preserve quantum states. Porto collaborates with groups such as RQS and JQI, contributing to advancements in subwavelength optical potentials and Floquet-engineered systems. He mentors graduate students in experimental and theoretical aspects of cold atoms and quantum technologies. Publications highlight breakthroughs in Rydberg blockade enhancement, prethermal Bose-Einstein condensation, and compact auto-alignment systems for experimental setups. His lab is based in the Physical Sciences Complex on the UMD campus, with ongoing projects exploring quantum dissipation and photon-atom hybrid systems.
Lande Liu is a Senior Lecturer in Chemical Engineering at the University of Huddersfield's School of Applied Sciences. Previously, he held a Lectureship at the University of Manchester (2010-2014), and earlier worked as an industrial consultant and research fellow at Leeds and Sheffield Universities. His academic journey began with a MEng in Chemical Engineering and a PhD in kinetic theory of aggregation from Sheffield (2004), preceded by a visiting PhD at Twente University (2002). Education: PhD in Chemical Engineering (University of Sheffield, 2004) Visiting PhD (Twente University, 2002) MEng in Chemical Engineering (Tsinghua University, 1999) BSc in Applied Mathematics (Tsinghua University, 1996) Liu's research focuses on multi-scale particle interactions (molecular to granular) using kinetic theory of aggregation, with applications spanning nanotechnology, pharmaceutical engineering, and sustainable chemical processes. His work aligns with UN Sustainable Development Goals for environmental protection and industrial innovation. Recent publications examine particle deposition in turbulent flows, enhanced heat exchanger designs, and nanofluid stabilization techniques. He teaches core chemical engineering topics including transport phenomena, unit operations, and process design. Active in collaborative research, Liu has partnered with institutions across Europe on projects involving spectroscopy, ultrasonics, and dynamic modeling. His technical expertise includes particle size analysis, tomography, and computational simulation of complex systems.
