Shad Roundy is an Associate Professor at the University of Utah , holding dual appointments in the Department of Mechanical Engineering and Department of Electrical and Computer Engineering within the College of Engineering . He directs the Integrated Self-Powered Sensing Lab , focusing on energy harvesting, wireless power transfer, and ubiquitous sensing systems. PhD in Mechanical Engineering, University of California, Berkeley (2003) Senior Lecturer, Australian National University (postdoctoral) His research bridges mechanical design , smart materials , and sensor networks , with significant contributions to: Self-powered biomedical implants Wearable health-monitoring devices Underground agricultural sensor networks MEMS sensor development His 15 most recent publications demonstrate interdisciplinary work spanning wireless power transfer for soil sensors, flexoelectric materials, and disease severity assessment via wearables. Articles emphasize energy efficiency , novel transducer designs , and environmental sensor applications . Scientific recognition includes: NSF CAREER Award for magnetoelectric implant powering Associate Editor, Smart Materials and Structures and International Journal of Precision Engineering and Manufacturing-Green Technology Research funded by National Science Foundation , NASA , and industry partners through the ASSIST Engineering Research Center . Publications highlight collaborations across biomedical, agricultural, and wearable technology domains.
Loic Binan is an Assistant Professor in the Department of Human Genetics at McGill University, with additional affiliations as an Associate Member in the Department of Biomedical Engineering and the Integrated Program in Neuroscience. His research focuses on developing cutting-edge technologies to investigate how gene networks control the self-organization of cells into complex 3D tissues during development and in disease conditions. Dr. Binan's research interests span multiple interdisciplinary fields, with particular emphasis on cancer metastasis , where he investigates the genetic mechanisms allowing cells to reversibly transition between epithelial and mesenchymal phenotypes. His work also explores isoforms and non-coding regions , developing technologies to understand alternative splicing in neurodegenerative diseases, and examining how past cell-cell interactions shape present transcriptional activity during development. His laboratory employs a diverse array of techniques including CRISPR gene editing, spatial transcriptomics, single-cell RNA sequencing, advanced microscopy, and computational methods for image analysis. The recent publications reveal a strong trend toward integrating high-throughput genetic screening with spatial transcriptomics to map gene regulatory networks across both cancer biology and neuroscience contexts. Dr. Binan leads the Binan Lab at the Lady Davis Institute for Medical Research, where his team develops precision gene editing tools such as Cas9 and Cas12 for high-throughput screens, creates novel imaging tools to collect spatial context data, and builds computational tools to analyze these complex new data types. His research primarily focuses on cancer and neurodegenerative diseases, with particular attention to brain development and tumor microenvironments.
Luigi Bruno is an Associate Professor of Machine Design at the Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria. He has held this position since 2014, following 12 years as an Assistant Professor at the same institution and Visiting Professorships at IIT Gandhinagar (2012), University of Alabama at Birmingham (2013-2017), and Free University of Bozen-Bolzano (2021). 1999 : Master's in Mechanical Engineering, University of Calabria (110/110 cum laude) 2003 : PhD in Mechanical Engineering, University of Pisa His research interests span: Experimental Mechanics : Pioneering speckle interferometry for micro-displacement measurement and residual stress analysis. Materials Science : Elastic characterization of anisotropic materials, biomedical applications of soft substrates, and 3D-printed composites. Biomedical Engineering : Mechanical behavior of biological tissues, ocular biomechanics, and dental implant material testing. Recent research trends focus on: Integrating artificial muscles into rehabilitation devices Advancing full-field optical measurement via microCT/DVC Optimizing 3D printed polymer adhesion for industrial components Exploring neuronal biomechanics on soft surfaces Scientific contributions include: CS2007A00010 patent for dual-focus speckle interferometers Deputy Editor of Optics and Lasers in Engineering (2019-present) Guest Editor for special issues on optical methods in experimental mechanics and nanobiotechnology Academic leadership extends to coordinating Mechanical Engineering committees (2021-present), serving on editorial boards, and organizing international conferences like AIAS National Conference (2018). He has secured multiple MIUR research grants and industry collaborations with Alfagomma, 3DNA, and Ferrovie della Calabria. His laboratory, Mechanics of Materials and Structures , supports both research and teaching activities with advanced optical measurement systems and computational tools for mechanical design.
Barbara Grochowicz serves as a Lecturer in the Department of Drive Automation and Robotics at Opole University of Technology. She maintains active teaching duties during the 2024/2025 academic year, conducting consultations on Tuesdays (10:10-10:55 and 12:30-13:15) in building P1, room 111/120. Her research centers on robotics and automation systems, with specialized focus on drive mechanisms and control engineering. These fields are critical for advancing industrial automation, robotic mobility systems, and precision motion control applications across manufacturing and engineering sectors. Her work bridges theoretical control principles with practical robotic implementations. Contact details include phone: 77 449 8028 and email: b.grochowicz@po.edu.pl. She participates in standard university teaching activities through the Employee's schedule and PO Knowledge Base profile.
Marta Halina is a University Associate Professor in the Philosophy of Cognitive Science at the University of Cambridge, affiliated with the Department of History and Philosophy of Science. She serves as a Senior Research Fellow at the Leverhulme Centre for the Future of Intelligence and is a Fellow of Selwyn College. Her academic journey began with a PhD in Philosophy and Science Studies from the University of California, San Diego in 2013, followed by a McDonnell Postdoctoral Fellowship in the Philosophy-Neuroscience-Psychology Program at Washington University in St. Louis before joining Cambridge in 2014. Halina's educational background includes a PhD from UC San Diego (2013) and postdoctoral training at Washington University in St. Louis. Her academic trajectory reflects a strong interdisciplinary foundation bridging philosophy, cognitive science, and neuroscience. Her research focuses on nonhuman animal cognition, mechanistic explanation, and artificial intelligence, with particular emphasis on comparative cognition and the philosophical foundations of cognitive science. Halina investigates how researchers design studies to address complex questions about animal minds, arguing that current methods in comparative cognition often face challenges with hypothesis underdetermination by empirical evidence. She advocates for additional behavioral constraints on theorizing, known as 'signature testing,' while emphasizing the need to incorporate neuroscience and biology more substantially into animal cognition research. Her work on major transitions in cognitive evolution proposes treating the evolution of cognition as a series of major evolutionary transitions to better comprehend cognitive complexity across species. Analysis of Halina's recent publications reveals a clear trajectory toward computational comparative cognition. Her work increasingly integrates AI and machine learning techniques with traditional comparative cognition approaches, exemplified by her development of the Animal-AI Testbed. This platform allows for direct comparison between AI systems, humans, and animals on cognitive tasks, revealing that while AI and children perform similarly on basic navigational tasks, children outperform AI on more complex cognitive tests requiring object permanence. Her research demonstrates how computational modeling can generate novel hypotheses about animal behavior that generate precise, testable predictions beyond what traditional experimental methods alone can achieve. McDonnell Postdoctoral Fellowship Halina directs research initiatives at the Leverhulme Centre for the Future of Intelligence, particularly focusing on the intersection of AI and animal cognition. Her work on the Animal-AI Environment has received significant funding and collaborative support, enabling interdisciplinary research that bridges computer science, cognitive science, and biology. She actively collaborates with researchers across multiple institutions to develop computational frameworks for understanding nonhuman animal cognition. Halina leads significant research initiatives through the Leverhulme Centre for the Future of Intelligence, where she develops the Animal-AI Environment—a research platform for conducting cognitive experiments with artificial agents, humans, and nonhuman animals in directly comparable, ecologically valid contexts. This environment facilitates interdisciplinary collaboration between computer scientists, engineers, biologists, and cognitive scientists, reducing the 'language barrier' between these fields and enabling cross-pollination of ideas and methodologies.
Professor Kristopher Kilian is Director of the Laboratory for Advanced Biomaterials & Matrix Engineering (LAB&ME) with a joint position across the School of Chemistry and the School of Materials Science & Engineering in the Faculty of Science at UNSW Sydney. He serves as co-Director of the Australian Centre for NanoMedicine (ACN) and is a member of the Adult Cancer Program in the Prince of Wales Clinical School. His interdisciplinary research focuses on unraveling 'matrix structure-cell function' relationships through innovative biomaterial design. After completing his PhD at the University of New South Wales, Kilian pursued NIH postdoctoral training at the University of Chicago before faculty positions at the University of Illinois at Urbana-Champaign (2011-2018). He returned to UNSW in 2018 as a Scientia Fellow, establishing his current leadership roles. Research Focus: Design of model extracellular matrices and dynamic hydrogels for cell and tissue engineering Fundamental studies in cell plasticity and matrix-directed cell fate Development of synthetic tumor microenvironments for drug testing iPSC-derived organoid bioengineering 4D biofabrication techniques Tissue engineering approaches for lab-grown meat applications His extensive publication record demonstrates consistent focus on hydrogel mechanics, dynamic biomaterials, and the role of physical cues in directing cell behavior. Recent work emphasizes mechanochemistry, spatial control of cell differentiation, and the development of sophisticated tumor models that replicate the complexity of cancer microenvironments. Scientific Recognition: Cornforth Medal (2008) NIH Ruth L. Kirchstein Award (2008) Kavli Fellow (2014) NSF CAREER Award (2015) Australian Research Council Future Fellowship (2018) Eureka Prize finalist (2023) Kilian's research program bridges fundamental cell biology with translational applications, particularly in cancer modeling and regenerative medicine. His laboratory develops innovative biomaterial platforms that enable precise control over cellular microenvironments, facilitating discoveries in cell plasticity and tissue engineering. The group's work on dynamic hydrogels and mechanochemical systems represents a significant contribution to the field of biomaterials science. As Director of LAB&ME, Kilian leads a multidisciplinary team that integrates nano- and micro-fabrication techniques with synthetic chemistry to create biomimetic materials. The laboratory's approach centers on the concept that cell state and fate are governed by inherent cell plasticity within specific multivariate signaling contexts.
The Atomic Quantum Optics Group at ICFO, Barcelona , led by Morgan W. Mitchell , investigates quantum phenomena at the interface of light and matter. The group develops advanced sensing technologies with applications in biomedicine, space science, and fundamental physics. Research focuses on ultra-cold atoms, high-coherence photons, and entanglement, aiming to understand and utilize atomic coherence for quantum technologies. Their work includes pushing sensitivity limits in magnetic field detection, quantum thermometry, and miniaturized quantum devices. Recent publications highlight advancements in cavity-enhanced spin detection , anomalous noise in SERF magnetometry , and spread-spectrum magnetic sensing . These studies span quantum optics, atomic physics, and applied quantum technologies. Scientific awards include mentoring students like Joanna Zielinska and Carlos Abellan , who won the UPC Thesis Prize. The group actively trains PhD students, postdocs, and visiting researchers in quantum technologies.
Amanda Watson is an Assistant Professor in Electrical and Computer Engineering at the University of Virginia, with joint appointments in Computer Science. She leads the Watson Research Lab within the UVA Link Lab, a multidisciplinary center for Cyber-Physical Systems (CPS) and Internet of Medical Things (IoMT) research. Her work bridges wearable technology with healthcare and athletic performance applications, focusing on noninvasive monitoring, physiological signal analysis, and safety-critical medical devices. She is also the cofounder and CEO of Luminosity Wearables, commercializing a noninvasive continuous glucose monitor. Education: PhD in Computer Science (2020) - College of William & Mary MSc in Computer Science (2016) - College of William & Mary Bachelors in Computer Science and Mathematics (2014) - Drury University Her research spans multiple domains including: Wearable spectroscopy for nutrition and skin health Machine learning for drug overdose and fall risk detection Biomechanical monitoring in sports medicine Wearable support for visual and neurological impairments IoMT device integration and analytics Recent publications (2024-2025) show strong emphasis on calibration-free physiological monitoring systems, with technical contributions in spectral analysis , multi-wavelength sensing , and rapid prototyping for healthcare wearables. Applications range from maternal health to gerontological social isolation detection. Lab and Team: The Watson Research Lab at UVA develops wearable solutions for clinical and athletic contexts, with ongoing collaborations in the PRECISE Center at University of Pennsylvania and LENS lab at William & Mary alumni network. She works with multidisciplinary teams including engineers, clinicians, and data scientists.
Zachary Tatlock is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he leads the Programming Languages & Software Engineering Group (PLSE) and the SAMPL Group. His research spans programming languages, formal verification, compilers, and computational fabrication. He is also an Amazon Scholar with AWS's Automated Reasoning Group and previously advised OctoML. Tatlock's work bridges theoretical foundations with practical systems, focusing on making it easier to write tricky code while ensuring correctness through rigorous proofs and measurements. PhD in Computer Science & Engineering, University of California, San Diego (2014) Thesis: Reducing the Costs of Proof Assistant Based Formal Verification Advisor: Sorin Lerner BS in Computer Science (Honors) and Mathematics, Purdue University (2007) Professor Tatlock's research focuses on the intersection of programming languages, formal methods, and systems. His work in compilers and formal verification aims to make it easier to write tricky code while ensuring correctness through rigorous proofs. He explores computational fabrication techniques that bridge digital design with physical manufacturing. His recent work on equality saturation (via the egg framework) has transformed program optimization and synthesis. Tatlock also investigates floating-point numerics, distributed systems verification, and hardware/software co-design, always seeking to balance theoretical rigor with practical implementation. Tatlock's recent publications demonstrate a strong focus on equality saturation techniques (egg framework), computational fabrication, and verified systems. His work increasingly integrates machine learning with program analysis and synthesis. There's a clear trajectory toward more practical applications of formal methods in real-world systems, particularly in numerical computing and fabrication. His research group has made significant contributions to e-graph technology, floating-point accuracy, and the verification of distributed systems. Distinguished Paper Award for Rewrite Rule Inference Using Equality Saturation (OOPSLA 2021) Spotlight Paper Award for Dynamic Tensor Rematerialization (ICLR 2021) Distinguished Paper Award for egg: Fast and Extensible Equality Saturation (POPL 2021) Faculty Appreciation for Career Education & Training (FACET) Award (2020) NSF CAREER Award: Verifying Distributed System Implementations (2017) Distinguished Paper Award for Automatically Improving Accuracy for Floating Point Expressions (PLDI 2015) Distinguished Teaching Award Nomination (2015) Professor Tatlock has advised numerous doctoral, master's, and undergraduate students who have gone on to prominent positions in academia and industry, including faculty positions at the University of Utah and Brown University, and leadership roles at companies like OctoML and Certora. His research is supported by significant funding from NSF, DARPA, DOE, and industry partners, totaling millions of dollars. Current grants include projects on computer-aided reasoning, formal verification, computational fabrication, and machine learning systems. He has served on numerous program committees and organized workshops including FPTalks, EGRAPHS, and PNW PLSE. As co-leader of the Programming Languages & Software Engineering (PLSE) research group and affiliate of the SAMPL Group at the University of Washington, Tatlock has developed influential tools including egg (an equality saturation toolkit), Carpentry Compiler, and Odyssey. His group actively collaborates with industry partners including Amazon Web Services, where he serves as an Amazon Scholar. The group has made significant contributions to equality saturation, floating-point accuracy, program synthesis, and computational fabrication, with applications ranging from compiler optimization to 3D printing.
Kevin Chetty is a Professor of Wireless Sensing at University College London (UCL), leading the Urban Wireless Sensing Lab within the Department of Security and Crime Science. His work bridges radar technology, machine learning, and healthcare applications, with a focus on passive sensing systems. Education: PhD in Medical Ultrasound Physics (Imperial College London, 2004-2007), MRes in Image and X-Ray Physics (King's College London, 2003), BSc in Physics (King's College London, 1999) Research spans radar micro-Doppler signature analysis for human behavior classification, software-defined radar development, and integrated communication-sensing systems, with applications in security, healthcare, and smart environments. Recent work emphasizes privacy-preserving technologies and edge processing for real-time operations. Scientific awards include the 2022 IET Radar Systems Best Paper Runner-Up, 2022 IEEE Radar Conference 2nd Place, and 2015 National Instruments Engineering Impact Award. He has received funding from government and industry sectors in telecommunications, IoT, security, and healthcare. Teaching roles: Programme Convener for MSc Crime Science and IEP Minor in Crime and Security Engineering; Module Convener for Security Technologies and Crime Mapping & Spatial Analysis Consultancy: Huawei Technologies (2020-2022), Metropolitan Police Service (2019)
William R. Cluett is a Professor at the University of Toronto's Department of Chemical Engineering & Applied Chemistry within the Faculty of Applied Science and Engineering. He holds a B.Sc. from Queen’s University and a Ph.D. from the University of Alberta, and is a licensed Professional Engineer (P.Eng). Currently serving as Dean’s Advisor on Innovations in Undergraduate Education, Cluett bridges engineering principles with systems biology in his research. B.Sc., Queen’s University Ph.D., University of Alberta Cluett's research spans traditional process control and design, extending into systems biology where he collaborates with Professor Krishna Mahadevan. His work focuses on integrating engineering methodologies with biological systems, including multiscale modeling, dynamic metabolic engineering, and computational toxicology. His publications highlight trends in applying control theory to metabolic networks, developing algorithms for genome-scale modeling, and designing bistable cell factories. These contributions reflect interdisciplinary efforts between chemical engineering and computational biology. Scientific Awards & Recognitions: Fellow of Engineers Canada (2021) Medal for Distinction in Engineering Education (2021) OCUFA Teaching Award (2020) President’s Teaching Award (2018) Sustained Excellence in Teaching Award (2016) Bill Burgess Teacher of the Year Award (2014) Fellow, AAAS (2009) Fellow, Chemical Institute of Canada (1998) Syncrude Canada Innovation Award (1997) Cluett has contributed to major grants and collaborative projects in systems biology and metabolic engineering. He actively advises on undergraduate education innovations and maintains strong affiliations with the Department of Chemical Engineering & Applied Chemistry.
Juan Solomon serves as an Associate Professor in the Department of Agriculture, Veterinary and Rangeland Sciences at the University of Nevada, Reno. His research lab operates from Building FA, Room 226d (Mail Stop 202), with direct contact via (775) 784-6888 or juansolomon@unr.edu. Education: B.S. in Agriculture, University of Guyana (2000) Graduate Diploma in Education-Science, University of Guyana (2005) M.S. in Agriculture, Mississippi State University (2010) Ph.D. in Agriculture, Mississippi State University (2013) Research Focus: Dr. Solomon's work centers on grassland ecology and sustainable pastoral systems for ruminant livestock, with emphasis on grazing management, forage quality evaluation, and drought-tolerant crop development. His research program investigates water use efficiency in semiarid forage systems, nutrient cycling dynamics, and climate-resilient crop screening—particularly for native and improved forages in arid western environments. Field studies often integrate ecosystem service valuation with practical livestock production metrics. Publication Trends: Recent work (2023-2025) demonstrates concentrated expertise in alternative forage systems, with 15 high-impact studies examining teff double-cropping, industrial hemp varietals, and cover crop nutrient cycling in Nevada's semiarid landscapes. Key methodological approaches include deficit irrigation analysis, nitrogen optimization trials, and biomass decomposition modeling—all targeting resource-efficient agricultural solutions for water-limited regions.
Dr. Jinxin Liu serves as Research Group Leader for the CVD subgroup at Dresden University of Technology's Center for Advancing Electronics Dresden (cfaed) within the Faculty of Chemistry and Food Chemistry since May 2023, following a postdoctoral position at the same institution under the Humboldt Research Fellowship (2020-2023). His academic foundation includes: Bachelor's in Chemistry Base Class, Wuhan University (2015) Doctorate in Physical Chemistry, Wuhan University (2020) Research centers on chemical vapor deposition synthesis of advanced 2D materials including conductive MOFs, COFs, polymers, and graphene nanoribbons. His work pioneers heterostructure engineering for next-generation optoelectronic and spintronic applications, with emphasis on precise material property control through novel fabrication techniques. Publication trends reveal consistent high-impact contributions in top-tier journals, evolving from fundamental 2D material synthesis (2019) toward functional device integration (2022), demonstrating increasing focus on application-oriented material design for electronics. Award highlights: Humboldt Research Fellowship (2022) Nature Materials publication (2020) Cell Press Paper of the Year China (2019) Multiple Wuhan University innovation prizes Leading the CVD research subgroup within Prof. Xinliang Feng's Chair, Dr. Liu directs experimental efforts in scalable 2D material production. His team operates within cfaed's interdisciplinary framework, bridging chemistry, materials science, and electronic engineering for advanced semiconductor development.
Dr. Ramkrishan Maheshwari is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark, specializing in power electronics and motor drive systems. His research focuses on advanced power converter topologies, wide bandgap semiconductors, and renewable energy integration. University: University of Southern Denmark Rank: Associate Professor Research: Power Converters, PWM Techniques, Wide Bandgap Devices Recent work involves small DC-link capacitors, machine learning-based component selection, and hydrogen production systems. His Google Scholar articles highlight innovations in converter design and control algorithms. Awards include the BHJ Foundation Teaching Prize (2023) and a Best Paper Award (ICPEE 2021). He supervises PhD students like M. A. Khan and R. K. Mahapatra and leads projects such as 'Efficient Cost Saving Grid Friendly PtX Converter' funded by Mads Clausens Fond.
Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.
