Dr. Cosmin Ioan Roman is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with the Chair in Micro and Nanosystems since 2006. His research focuses on solid-state micro and nanotransducers, spanning from traditional Silicon micromachining to carbon nanotube-based (CNT) devices for bio-sensing applications, with an emphasis on energy-efficient transducer concepts. Doctoral Degree: National Polytechnic Institute of Grenoble (INPG) Roman's expertise lies in multi-physics and compact modeling of transducers. His work bridges materials science, semiconductor device physics, and biomedical sensing, utilizing advanced fabrication techniques for scalable sensor arrays on flexible substrates. The selected publications highlight his contributions to tactile sensing and cell rheology. His co-supervised doctoral thesis on carbon nanotube resonators demonstrates his interdisciplinary approach to nanoscale and biomedical systems.
Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .
Judith Roitman is a full Professor of Mathematics at the University of Kansas, where she has been since 1976, rising to full professorship after earlier roles at Wellesley College and the Institute for Advanced Study. Her academic journey began with an English Literature degree from Sarah Lawrence College (1966) before transitioning to mathematics at UC Berkeley, earning a Ph.D. in 1974 under Robert Solovay, with research in set-theoretic topology and Boolean algebra. She has held leadership roles in the Association for Women in Mathematics (AWM), serving as its President (1979–1981) and shaping its early structure. Her research spans over 40 papers and a textbook, Introduction to Modern Set Theory (1990), while her work in mathematics education includes co-authoring the NCTM Principles and Standards 2000 . She received the 1990 Louise Hay Award for Mathematics Education and was inducted into the AWM Fellows Program in 2018. Roitman’s career combines research with advocacy: she mentored students, advised on national education policy, and ran teacher workshops for elementary and high school educators. Her non-academic pursuits include poetry (e.g., Slippage , 1999) and Zen Buddhism, co-founding the Kansas Zen Center in 1978.
Natasa Sesum is a Distinguished Professor in the Department of Mathematics at Rutgers University. Her research specializes in geometric flows and partial differential equations, with particular focus on Ricci flow, mean curvature flow, and their applications in geometric analysis. She maintains an active research program investigating singularity formation, ancient solutions, and asymptotic behavior in these flows. Her research explores fundamental aspects of geometric evolution equations, including: Classification of ancient solutions and singularity models Asymptotic behavior of flows on noncompact and singular surfaces Blow-up rates and curvature behavior at singular times Analytical aspects of Ricci and mean curvature flows Professor Sesum teaches across the mathematics curriculum, including undergraduate courses in Multivariable Calculus (Math 251), Linear Algebra (Math 350), Real Analysis (Math 311), and graduate courses on specialized topics in geometric analysis and PDEs (Math 510, Math 519). She has taught honors sections and maintains office hours by appointment.
Kaushik Nayak is an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad . His research spans semiconductor device physics, mesoscopic electronics, and electro-thermal effects in nanoscale transistors, with recent work on diamond MOSFETs, 2D material contacts, and thermal resistance in nano-sheet FETs. Ph.D., Indian Institute of Technology Bombay M. Tech., Microelectronics, IIT Bombay B.E., Electronics & Telecommunication, Utkal University He teaches advanced courses on semiconductor device modeling, mesoscopic electronics, and electromagnetic wave propagation. His publications focus on nanoelectronics, device variability, and high-temperature operations. Contact: knayak@ee.iith.ac.in .
Xieyuanli Chen is an Associate Professor at the National University of Defense Technology (NUDT), China. He holds a Dr.-Ing. (summa cum laude) from the University of Bonn (2022), a Master's in Robotics from NUDT (2017), and a Bachelor's in Electrical Engineering from Hunan University (2015). His research focuses on robot learning, perception, and navigation, with an emphasis on LiDAR-based SLAM, autonomous systems, and semantic perception. Education: PhD: University of Bonn, 2018-2022 (supervised by Prof. Cyrill Stachniss) Master's: NUDT, 2015-2017 Bachelor's: Hunan University, 2011-2015 Research interests include robotics, autonomous systems, computer vision, and LiDAR perception. He has authored over 90 papers in top venues like TRO, RSS, ICRA, and CVPR. He serves as an Associate Editor for IEEE RA-L, ICRA, and IROS, and is a member of the RoboCup Rescue Robot League Technical Committee. Awards include the RSS Pioneer Award (2021), Best-in-Class RoboCup awards, and recognition as a World’s Top 2% Scientist (2024). His work spans LiDAR localization, moving object segmentation, and efficient semantic mapping. He advises students in robotics and autonomous systems. Labs/Teams: Active in the PRBonn group (University of Bonn) and leads research at NUDT on LiDAR-based perception systems.
Dimitrios E. Anagnostou is Associate Professor in the Institute of Sensors, Signals & Systems within Heriot-Watt University’s School of Engineering & Physical Sciences, Edinburgh. He directs an anechoic-chamber & microwave characterisation facility, leads a thriving research group, and is currently recruiting PhD candidates. Education & Career: BSEE, Democritus University of Thrace, Greece (2000) MSEE, University of New Mexico, USA (2002) PhD, University of New Mexico, USA (2005) Post-doc, Georgia Tech (2005-2006) Assistant → tenured Associate Professor, South Dakota School of Mines & Technology (2007-2016) Associate Professor, Heriot-Watt University (2016-present) Research Focus: His work spans compact & reconfigurable antennas, 5G Massive-MIMO arrays, metasurfaces, metamaterials, functional materials (VO 2 ), RF-MEMS, microwave packaging, radar sensing for assisted-living, and AI/deep-learning applications in electromagnetics. He pursues “green” RF electronics printed on paper/organic substrates and hybrid integration of antennas on solar cells. Recent Publication Trends (2022-2025): Output is dominated by metasurface-enabled beam-steering antennas, VO 2 -based reconfigurable devices, radar absorbers/rasorbers, biomedical radar for vital-sign monitoring, and AI-assisted signal processing, with strong emphasis on experimental validation and open-access dissemination. Scientific Awards & Fellowships: IEEE John D. Kraus Antenna Award DARPA Young Faculty Award Marie Skłodowska-Curie Individual Fellowship (H2020) ASEE Campus Star Award Young Alumni Award, University of New Mexico Honored Faculty Award, SDSMT (×4) Distinguished Scientist Living Abroad, Hellenic Ministry of Defense Advising & Grants: He has mentored numerous PhD and MSc researchers; his students have won Best PhD Thesis and faculty-wide Engineering Prizes. He is supported by EU and UK research grants and continuously seeks motivated doctoral applicants. Facilities & Teams: He manages the Microwave Facility (anechoic chamber, mm-wave measurement systems) and collaborates with international academic/industry partners across Europe, North America and beyond.
Volker J Schmid is a Professor of Bayesian Imaging and Spatial Statistics at the Department of Statistics, Ludwig Maximilian University of Munich. He leads the Bayesian Imaging and Spatial Statistics group and contributes to interdisciplinary initiatives like the Munich Center of Machine Learning. His work bridges statistical theory with applications in medical imaging and biology. PhD in Statistics (2004), LMU Munich Diploma in Statistics (2000), LMU Munich Abitur, Joseph-von-Fraunhofer-Gymnasium Cham (1993) His research focuses on Bayesian computational methods for high-dimensional data, particularly in medical imaging (MRI, DCE-MRI) and biological microscopy (e.g., 3D nuclear architecture analysis via super-resolution microscopy). Key applications include disease mapping , image segmentation , and spatio-temporal modeling . His software tools (e.g., nucim , bioimagetools , BAMP ) enable quantitative analysis in nuclear imaging and age-period-cohort modeling. His 15 most recent publications span Bayesian modeling for medical imaging , spatio-temporal epidemiology , and computational biology . Topics include co-localization metrics in fluorescence microscopy, nuclear architecture analysis, and dynamic Bayesian frameworks for MRI data. Collaborations extend to neuroimaging, oncology, and nuclear biology.
Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.
Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.
Professor Robert Eason is a leading academic at the University of Southampton, specializing in photonics and laser technology. His research spans interdisciplinary areas combining Machine Learning , Medical Diagnostics , and Microfluidics . Research Interests : Eason focuses on AI-driven laser applications, including deep learning for phototherapy , autonomous laser machining , and low-cost paper-based diagnostic devices . His work bridges photonics with biomedicine and advanced manufacturing. Recent Publications : His 2025 article in Scientific Reports explores AI simulations for psoriasis treatment, while 2024-2022 works address laser-controlled microfluidics, deep learning in microscopy, and reinforcement learning for laser machining. Supervision : He supervises PhD student Georgia Mourkioti in laser-based research projects. External Roles : Eason has served as a speaker at international conferences including the International Symposium on Laser Precision Microfabrication (2018), LAISER (2019), and Deep Learning for Control of Light-Matter Interactions (2022).
Lukas Seitner is a researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Electrical Engineering. He operates within the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek, focusing on advanced modeling of quantum cascade devices and terahertz photonics systems. His research spans quantum cascade lasers (QCLs), terahertz frequency combs, optical solitons, and computational photonics. Seitner has developed sophisticated simulation frameworks including Maxwell-Bloch and density matrix approaches to study nonlinear dynamics in optoelectronic devices. Key contributions involve passive mode-locking mechanisms in THz QCLs, graphene-integrated saturable absorbers for pulse generation, and backscattering effects in ring-cavity soliton formation. His work bridges theoretical modeling with practical device engineering for next-generation terahertz sources. As an educator, Seitner serves as assistant lecturer for multiple courses including Computational Photonics Laboratory (5 PR), Partial Differential Equations for Electrical Engineering (4 VI), and Simulation of Quantum Devices (4 VI). He actively participates in doctoral candidate seminars and specialized courses on quantum engineering, demonstrating strong commitment to academic training in photonics and quantum device physics. His teaching integrates cutting-edge research concepts into practical computational exercises. Seitner maintains active collaboration within the EU Project QOMBS and contributes to TUM's Computational Photonics group research infrastructure. His technical expertise encompasses numerical methods for partial differential equations, semiconductor device simulation, and nonlinear optical modeling. Current projects focus on optimizing THz comb sources for spectroscopic applications and extending quantum walk models for novel frequency comb generation mechanisms.
Stefano Gogioso is a Departmental Lecturer at the University of Oxford , specializing in quantum theory and quantum software. He holds a DPhil in Computer Science from Oxford (2013–2017) and advanced degrees from Cambridge (MASt, BA) and the University of Genova (MSc, BSc). As a Fellow at Kellogg College and co-founder of Hashberg Ltd , he develops quantum programming tools and focuses on quantum causal structures, quantum field theory, and natural language processing applications. His research bridges foundational quantum theory with practical applications, including near-term quantum computing and educational outreach through visual methods like Quantum in Pictures . Research Interests: Quantum foundations, quantum software, categorical quantum mechanics, quantum field theory, and quantum causality. His work emphasizes pictorial formalisms and compositional methods, with contributions to indefinite causality, quantum cellular automata, and quantum natural language processing (QNLP). Key Contributions: Published over 25 papers, including works on causal polytopes, categorical Feynman diagrams, and QNLP pipelines. Co-developed Hashberg 's quantum programming tools and serves as a mentor for AI initiatives at CDL-Oxford. His thesis introduced dynamics in categorical quantum mechanics, addressing symmetry and quantum clocks. Teaching: Teaches quantum computing courses for MSc/MFoCS students, professionals, and continued education. Courses include Quantum Software , Quantum Computing for Software Engineers , and bespoke corporate training. Labs/Teams: Part of the Oxford Quantum Group and involved in collaborative projects with industry and academia. Advising: Supervised students like Nicola Pinzani (causal orders) and Maria Stasinou (quantum field theory). Grants/Awards: Not explicitly listed, but recognized for contributions to quantum foundations and education.
Guo Ping is an Associate Professor of Mechanical Engineering at Northwestern University, leading the Advanced Intelligent Manufacturing Laboratory (AIM). His research focuses on precision manufacturing, intelligent metrology via deep learning, and advanced manufacturing applications. He holds a Ph.D. from Northwestern University and a B.S. in Automotive Engineering from Tsinghua University. Education: Ph.D. in Mechanical Engineering, Northwestern University, Evanston, IL B.S. in Automotive Engineering, Tsinghua University, Beijing, China Research Interests: Dr. Guo’s work emphasizes innovations in precision engineering, including ductile-regime machining, smart metrology systems, and robotics-driven manufacturing. Key areas include structural coloration, additive manufacturing, and human-robot collaboration in industrial settings. His lab explores cutting-edge techniques like ultrasonic vibration machining and machine learning for defect detection and process optimization. Publications Trends: Recent work spans AI-driven quality control (e.g., photometric stereo networks), robotic swarm patterning, and wearable fatigue monitoring systems. His research bridges machine learning, robotics, and traditional manufacturing to address scalability and precision challenges. Awards: F.W. Taylor Medal (CIRP, 2023) ASME Kornel F. Ehman Manufacturing Medal (2021) SME Outstanding Young Manufacturing Engineer Award (2020) Professional Service: Associate Editor of the Journal of Manufacturing Processes (2017–present). Active in organizing conferences and reviewing for top journals. Labs & Teams: Directs the AIM Lab, which integrates robotics, AI, and advanced materials to solve problems in precision fabrication and smart manufacturing. Current projects include structural coloration for anti-counterfeiting and fatigue prediction in industrial workers.
Vivek Boominathan is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Rice University. He is affiliated with the GLEE lab (Geometry, Light, & Imaging lab). His research focuses on computational imaging, combining computer vision, machine learning, applied optics, and nanofabrication to develop innovative imaging systems for applications such as robotics, medical sensing, and virtual/augmented reality. He has contributed to projects like PhlatCam (a lensless camera) and NeuWS (neural wavefront shaping). His work bridges optics, algorithms, and materials science to overcome traditional limitations in imaging systems. Boominathan's research interests include lensless imaging, optical meta-devices, turbulence mitigation, and bio-inspired imaging systems. He has developed systems like Foveated thermal imaging prototypes and real-time lensless microscopes. His lab emphasizes interdisciplinary approaches, integrating hardware design with machine learning. Key projects include: NeuWS: Neural wavefront shaping for imaging through scattering media CoIR: Compressive implicit radar for sensing applications FlatCam and PhlatCam: Ultra-thin lensless imaging devices Bioluminescence imaging in marine species His work has been published in top venues like Science Advances, Optica, and IEEE TPAMI. He collaborates with institutions like NASA JPL and industry partners on applied imaging solutions. Current research trends emphasize sensor-algorithm co-design and high-speed imaging systems for AR/VR applications. Boominathan holds a PhD in Electrical Engineering and has extensive postdoctoral experience in computational imaging. He advises projects in the GLEE lab and mentors students in hardware-software co-design for imaging systems. His lab focuses on translating theoretical innovations into practical devices with commercial potential.
