جست‌وجوی استاد و پژوهشگر

• Digital Sociology
• Sociological Theory
• Ethnomethodology
• +۷ مورد دیگر

Professor William Housley is a Chair in Sociology at Cardiff University’s School of Social Sciences. He holds a PhD and DSc.Econ. (2012), and is a Fellow of both the Academy of Social Sciences (FAcSS) and Learned Society of Wales (FLSW). His expertise spans qualitative research methods, sociological theory, ethnomethodology, and digital sociology. He co-founded the COSMOS social media observatory and leads the EMTEDS research group, focusing on disruptive technologies and socio-digital systems. Education: PhD; DSc.Econ. (Cardiff University, 2012) Research interests include algorithmic accountability, automation, interaction studies, and the sociological implications of digital technologies. He has held prestigious roles such as the Vincent Wright Chair at Sciences Po, Paris (2017), and serves on international panels for the Volkswagen Foundation and UK REF 2021. His work emphasizes digital sociology’s role in understanding new socio-technical systems, with recent focus on AI ethics, social media governance, and interdisciplinary collaboration. Awards: DSc.Econ. (2012), Vincent Wright Chair (2017) Professor Housley supervises research in digital societies, ethnomethodology, and collaborative interdisciplinary practice. He has led projects like the Cardiff University Research Fellowship on disruptive digital technologies and contributed to policy reviews for ESRC, British Academy, and others. Active in editorial roles (e.g., Qualitative Research co-editor, 2012–2018), his lab (EMTEDS) explores emerging tech’s societal impacts. Current projects address algorithmic transparency, digital citizenship, and governance of social media.

Joseph Mooreمشاهده پروفایل

استادیار

• Aerial Robotics
• Computational Control
• Learning-based Control
• +۳ مورد دیگر

Dr. Joseph Moore is an Assistant Professor in the Department of Mechanical Engineering at Johns Hopkins University (JHU), serving as Director of the Agile and Intelligent Robotics (AIRO) Laboratory. He is affiliated with the Laboratory for Computational Sensing and Robotics (LCSR), the Institute for Assured Autonomy (IAA), and holds a Bridging Faculty appointment in the Research and Exploratory Development Department (REDD) at JHU/APL. His research focuses on computational control, machine learning, and robotics to enable agile systems operating in complex environments. Dr. Moore previously served as Robotics Group Chief Scientist at JHU/APL, leading projects on hybrid unmanned aerial-aquatic vehicles and aerobatic fixed-wing systems. He has secured funding as Principal Investigator (PI) for ONR, DARPA, and ARL programs, particularly in post-stall maneuvering control and multi-robot coordination. His work emphasizes robust control strategies for autonomous systems in constrained environments. Research interests include aerial robotics, optimization, and learning-based control. Notable contributions involve NMPC-based systems, UAV navigation, and adaptive control for uncertain environments. His recent articles highlight advancements in swarm coordination, morphing-wing UAVs, and PAC-NMPC frameworks. Dr. Moore advises students such as Mark Gonzales and Adam Polevoy. Key grants include ONR/DARPA-funded projects on post-stall flight control and Army-funded multi-robot coordination efforts. His lab (AIRO) and collaborations (LCSR, IAA) drive applied and theoretical robotics research.

Jay Pujaraمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Jay Pujara is a Research Associate Professor in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering and the Information Sciences Institute (ISI). He directs the Center on Knowledge Graphs and leads research in artificial intelligence, specializing in knowledge graph construction, scalable machine learning, and probabilistic models. Education : PhD in Computer Science (University of Maryland, 2016), MS and BS in Computer Science from Carnegie Mellon University (2005 and 2004), with minors in Robotics, Mathematical Sciences, and Logic & Computation. Research Interests : His work focuses on probabilistic models for dynamic data, knowledge graph construction, entity resolution, and applications in NLP and social network analysis. He emphasizes scalable algorithms and real-world impact in domains like finance, climate science, and healthcare. Awards : Includes the SWSA Ten-Year Award (2023), Best Paper Awards at IUI 2019 and ISWC 2013, and grants totaling over $9M from DARPA, NSF, and industry partners. Grants & Mentorship : Principal Investigator on projects like "Artificial Domain-Understanding and Collaborative Agency" (DARPA) and "Explainable and Robust AI Agents" (NSF). Mentored over 50 students in PhD, MS, and undergraduate programs, focusing on knowledge graphs, NLP, and machine learning. Labs & Teams : Leads ISI’s Knowledge Graph and Neurosymbolic AI teams, coordinating the Open Knowledge Network (OKN) and tools like KGTK. Active in academic service, including roles on PhD admissions committees and ISI’s Space Management Committee.

Mario Romeroمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Data Visualization
• Ubiquitous Computing
• +۶ مورد دیگر

Mario Romero is an Associate Professor in Visualization at the Department of Computational Science and Technology (CST), KTH Royal Institute of Technology. He leads the InfraVis national research infrastructure for data visualization and is a Digital Futures Faculty member. His roles include national technical manager of InfraVis, member of the Executive Committee of Digital Futures, and Associate Director for Seminars & Workshops. Education: PhD in Computer Science (Georgia Tech, 2009), MSc in Computer Science (UIUC, 2001), and dual BSc degrees in Industrial Engineering and Construction Engineering (Universidad San Francisco de Quito, 1996). He is a Fulbright Scholar from Ecuador and holds postdoctoral experience at Uppsala University. Research focuses on Human-Computer Interaction, Visualization, and Ubiquitous Computing. Key projects include: TENT: Technology-Enhanced Neurosurgical Training VisBac: Visualizing airborne bacteria in ORs PSP: Platform for Smart People (autism support) SMART: Predictive maintenance in pharmaceuticals Homo Colossus: Energy footprint visualization Awards: Selected for IVA's 100 research2business projects (2021). Co-founded BrailleTouch (blind-friendly keyboard) and Anymaker (3D sketching app). Supervised students in C-Awards-winning projects (e.g., Yue Liu's thesis defense in 2024). Teaching: Responsible for courses like Information Visualization (DH2321) and Advanced Graphics & Interaction (DH2413). Active in organizing conferences (e.g., Eurographics 2020 Education Track Chair).

Prashanth Krishnamurthyمشاهده پروفایل

پژوهشگر

• Robotics
• Control Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Prashanth Krishnamurthy is a Research Scientist in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on robotics, control systems, and cybersecurity, particularly in cyber-physical systems such as power grids and embedded devices. He holds a Ph.D. in Electrical Engineering from NYU. Key research areas include hardware security (e.g., detecting Trojans in chips), anomaly detection in critical infrastructure, and resilient control strategies for robotic systems. He has led or contributed to projects funded by the U.S. Department of Energy (DOE), Office of Naval Research (ONR), and others, including the Tracking Real-time Anomalies in Power Systems (TRAPS) initiative and hardware Trojan detection using short-term aging phenomena. Education: Ph.D., Electrical Engineering, NYU His work bridges theoretical advancements and practical implementations, such as developing FPGA-based testbeds for hardware security validation and creating AI-driven cybersecurity tools like the CRAKEN LLM agent. Collaborators include institutions like SRI International, Karlsruhe Institute of Technology, and the NYU Center for Cybersecurity. Grants include a $1.94M DOE grant for TRAPS and a $359K DURIP grant for hardware Trojan detection. His technical contributions span control systems, anomaly detection algorithms, and cybersecurity frameworks for embedded systems. He is actively involved in advancing secure cyber-physical systems through innovations in real-time monitoring, robust control mechanisms, and AI-augmented security solutions.

João Paulo Costeiraمشاهده پروفایل

دانشیار

• Computer Vision
• 3D Reconstruction
• Structure from Motion
• +۳ مورد دیگر

João Paulo Costeira is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (1995) and was a Visiting Scientist at Carnegie Mellon University's Robotics Institute (1991–1995). His research focuses on Computer Vision, 3D Reconstruction, and Structure from Motion, with contributions to object recognition, robotics, and multimedia analysis. Education: PhD in Electrical and Computer Engineering, IST (1995); Visiting Scientist, CMU Robotics Institute (1991–1995). Roles: Coordinator of the Signal and Image Processing Group (SIPg), Co-director of the Carnegie Mellon|Portugal Dual PhD Program in ECE and Robotics (2007–2018), and Scientific Director of Carnegie Mellon|Portugal (2014–2018). Research Interests: João's work emphasizes 3D reconstruction from video, rigid and non-rigid motion analysis, and applications in robotics and urban surveillance. He has pioneered methods for motion segmentation and robust correspondence problems in computer vision. Publications: His recent work includes advancements in apple counting systems, rotation averaging for robotics, and domain adaptation for traffic density estimation. These contributions highlight his expertise in real-world computer vision challenges. Awards: None explicitly listed. However, his extensive publication record and academic leadership reflect significant scholarly impact. Advising & Grants: Supervised 13 PhD students, many co-advised with CMU faculty. Active in projects like CityCam (vehicle counting) and MultiDrone (robotics collaboration). Funded by FCT, EU, and industry partnerships. Labs/Teams: Leader of the Signal and Image Processing Group (SIPg) at ISR. Involved in NETSyS program for networked systems and robotics.

Xiang Limingمشاهده پروفایل

دانشیار

• Survival Analysis
• Longitudinal/Clustered Data Analysis
• Mixture Modelling
• +۳ مورد دیگر

Assoc Prof Xiang Liming is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (Students). She holds editorial roles at *Computational Statistics & Data Analysis* and *Statistics in Medicine*. With a PhD in Statistics (City University of Hong Kong, 2002), her research focuses on survival analysis, longitudinal data analysis, and biostatistical methods. Notable contributions include methodologies for semi-competing risks, interval-censored data, and mixture models. Her work bridges statistical theory with biomedical applications, addressing challenges in clinical trials and public health. Awards include the 2009 IIE Transactions Best Paper Award and the Outstanding Research Thesis Award (2002–2003, CityU). Education: PhD in Statistics, City University of Hong Kong (2002) Postdoctoral Research: Hong Kong University of Science and Technology (2002–2003) and CityU (2003–2006) Research Interests: Survival analysis methodologies, including frailty models, cure models, and quantile regression for censored data. She develops robust statistical approaches for clustered/longitudinal data, addressing missingness and overdispersion. Applications span biomedical research, epidemiology, and quality management. Grants & Collaborations: Her grants include work on robotic-assisted stroke rehabilitation (2021) and LNG cold energy utilization systems (2017–2019). She collaborates with clinical teams on trials involving upper limb neurorehabilitation technologies. Labs & Teams: Leads statistical method development for multi-center clinical trials, particularly in biostatistics and survival analysis frameworks. Active in NTU’s School of Physical & Mathematical Sciences research initiatives.

Karl-Erik Årzénمشاهده پروفایل

استاد

• Control Engineering
• Cyber-Physical Systems
• Real-Time Systems
• +۹ مورد دیگر

Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.

Emanuele Truccoمشاهده پروفایل

استاد

• Computer Vision
• Medical Image Analysis
• Retinal Image Analysis
• +۷ مورد دیگر

Professor Emanuele (Manuel) Trucco is the NRP Chair of Computational Vision in the Department of Computing, School of Science and Engineering, at the University of Dundee. He holds additional roles as an Honorary Clinical Researcher at NHS Tayside and formerly served as an Adjunct Professor at the Chinese Academy of Sciences from 2018 to 2021. He earned his MSc and PhD in Electronic Engineering from the University of Genoa, Italy, in 1984 and 1990, respectively. His research focuses on medical image and data analysis, particularly in retinal imaging, using machine and deep learning techniques. He is co-director of VAMPIRE, a major international initiative in retinal image analysis, which supports biomarker studies in cardiovascular disease, diabetes, dementia, and neurodegenerative disorders. His recent work includes AI-driven tools for predicting dementia from brain scans and cardiovascular risk from retinal images. The latest publications reflect strong trends in applying deep learning to retinal and brain imaging for early disease detection, with themes centered on AI in healthcare, precision medicine, and non-invasive diagnostics. FRSA (Fellow of the Royal Society of Arts) FIAPR (Fellow of the International Association for Pattern Recognition) Professor Trucco has led and co-led significant research projects funded by EPSRC, NIHR, and EU programs. He has supervised PhD students through industry-sponsored studentships (e.g., OPTOS, NIDEK, Toshiba) and collaborated with institutions such as the Universities of Edinburgh and Liverpool. His research is supported by extensive industrial partnerships including Canon Medical, Epipole plc, and NIDEK. He is a key member of the UK Biobank Eye and Vision Consortium and co-director of the VAMPIRE initiative, a collaborative effort between the Universities of Dundee and Edinburgh focused on retinal image analysis. His work also involves participation in major research networks such as the Academic Health Science Partnership in Tayside.

Susan Hopkinsمشاهده پروفایل

مدرس ارشد

• Curriculum and Pedagogy
• Gender Studies
• Media Studies
• +۵ مورد دیگر

Susan Hopkins is a Senior Lecturer in Education (Curriculum and Pedagogy) and Associate Dean (Learning and Teaching) at the School of Education and Tertiary Access , University of the Sunshine Coast , Queensland, Australia. She earned her PhD from Queensland University of Technology and has also served as a Lecturer in Communication at the University of Southern Queensland. Institution: University of the Sunshine Coast School: School of Education and Tertiary Access Rank: Senior Lecturer Leadership Role: Associate Dean (Learning and Teaching) Her research centers on curriculum and pedagogy , with strong interdisciplinary interests in gender studies, media, cultural studies, and feminist theory . She critically examines representations of women, motherhood, sexuality, and power in film and television, often linking popular narratives to broader social and ethical issues such as animal welfare, digital abuse, and environmentalism. Her work frequently appears in public-facing platforms like The Conversation , where she bridges academic insight with accessible commentary. The trends in her recent articles reflect a consistent focus on popular culture as a site of ideological struggle , particularly how gender, power, and identity are constructed and challenged in media. She analyzes genres ranging from horror and documentary to romantic comedy and animated features, often highlighting feminist and postfeminist themes. Her writing reveals a deep engagement with cinematic history, celebrity culture, and the emotional dimensions of storytelling. Susan Hopkins has not been publicly recognized with scientific awards based on available information. She is actively involved in research supervision, consulting, speaking engagements, and media collaborations . Her institutional roles suggest leadership in academic program development and pedagogical innovation. Though no formal lab or research team is mentioned, her public scholarship indicates an influential voice in education and cultural critique.

• Microfluidics
• Materials Science
• Biomedical Engineering
• +۷ مورد دیگر

Klas Hjort is a Professor of Materials Science at Uppsala University's Ångström Laboratory , specializing in Microsystems Technology . He leads the microsystems technology program and has pioneered research in heterogeneous microsystems on stainless steel, flexible foils, and elastic substrates for biomedical applications and wireless sensor/actuator systems . Key projects: SSF robotic textiles , PERSIMMON smart patches Research themes: Microfluidic actuation , Liquid metal patterning , Stretchable electronics His recent publications focus on soft robotics , smart patches , and high-pressure microfluidic systems , with keywords spanning Microfluidics , Biomedical Engineering , and Stretchable Electronics . He collaborates extensively in robotic textiles , microvalve design , and liquid metal composites . Contact: klas.hjort@angstrom.uu.se

Alex John Londonمشاهده پروفایل

استاد

• Ethics of Artificial Intelligence
• Bioethics
• Research Ethics
• +۵ مورد دیگر

Alex John London is the K&L Gates Professor of Ethics and Computational Technologies at Carnegie Mellon University, where he also serves as co-lead of the K&L Gates Initiative in Ethics and Computational Technologies, Director of the Center for Ethics and Policy, and Chief Ethicist at the Block Center for Technology and Society. His work spans multiple institutions, including affiliations with the Center for Bioethics and Health Law at the University of Pittsburgh. Dr. London earned his Ph.D. in Philosophy from the University of Virginia, followed by a post-doctoral fellowship at the University of Minnesota's Center for Bioethics. He joined Carnegie Mellon University in 2000 and has established himself as a leading scholar in ethics at the intersection of technology, medicine, and policy. His academic journey includes being a Visiting Scholar at Harvard University's Program in Ethics and Health. Professor London's research spans ethical and policy issues surrounding novel technologies in medicine, biotechnology and artificial intelligence, methodological issues in theoretical and practical ethics, and cross-national issues of justice and fairness. His work in AI ethics critically examines structural obstacles to safe and effective technologies, challenges conventional notions of algorithmic bias, and questions requirements for explainability in medical contexts. His foundational work on clinical equipoise and the 'integrative approach' to risk assessment has shaped research ethics guidelines globally. He has made significant contributions to international research ethics, particularly regarding justice, responsiveness to host community health needs, and post-trial access. Professor London's scholarly output demonstrates a trajectory toward addressing the ethical challenges of emerging technologies, with increasing focus on justice-led approaches to AI innovation, accountability frameworks, and the sociotechnical dimensions of healthcare AI systems. His work increasingly addresses how AI can be designed to respect human dignity while navigating complex ethical terrain in healthcare settings. New Directions Fellowship from the Andrew W. Mellon Foundation (2005, 2010) Hastings Center Fellow (2011) Elliott Dunlap Smith Award for Distinguished Teaching (2016) Distinguished Service Award from the American Society of Bioethics and Humanities (2017) As an educator, Professor London teaches courses on ethical theory, bioethics, ethics and AI, and research ethics. His influential textbook 'Ethical Issues in Modern Medicine' (8th edition) is one of the most widely used resources in medical ethics education. His book 'For the Common Good: Philosophical Foundations of Research Ethics' (2022) provides a comprehensive framework for understanding research ethics as serving the common good. Professor London has advised numerous students and mentored early-career researchers in bioethics and technology ethics. His policy work extends to multiple national and international organizations including the World Health Organization Expert Group on Ethics and Governance of AI, the National Academy of Medicine Action Collaborative, and the U.S. National Science Advisory Board for Biosecurity. Professor London leads several significant research initiatives, including serving as co-leader of the ethics core for the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING). His Center for Ethics and Policy at CMU serves as a hub for interdisciplinary research addressing pressing ethical challenges in technology and healthcare. He is actively involved in shaping policy through his membership on the steering committee of the AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES) and his co-chair role in the U.S. National Academies planning committee on computational modeling of biological agents.

Jeff Zhangمشاهده پروفایل

استادیار

• Deep learning
• Computer architecture
• Embedded systems
• +۵ مورد دیگر

Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.

• Artificial Intelligence
• Embodied AI
• Robotics
• +۴ مورد دیگر

Roozbeh Mottaghi is a Senior AI Research Scientist Manager at Meta's Fundamental AI Research (FAIR) division and an Affiliate Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His career spans roles as Research Manager at the Allen Institute for AI and Postdoctoral Researcher at Stanford University. He holds a Ph.D. in Computer Science from UCLA, advised by Alan Yuille, and advanced degrees from Simon Fraser University, Georgia Tech, and a B.Sc. from Sharif University of Technology. Ph.D., Computer Science, UCLA (2016) M.Sc., Computer Science, Simon Fraser University M.Sc., Electrical Engineering, Georgia Institute of Technology B.Sc., Computer Engineering, Sharif University of Technology His research focuses on Embodied AI, Robotics, Computer Vision, and Multimodal Learning, with key contributions to human-robot collaboration benchmarks (PARTNR), 3D scene understanding, and adaptive navigation systems. Recent work explores zero-shot manipulation via video tracking (Track2Act) and lifelong navigation benchmarks (GOAT-Bench). Publications span 3D vision, reinforcement learning, and visual reasoning, including NeurIPS 2022 Outstanding Paper Award for "Ask4Help" and leadership in organizing challenges at CVPR and ICCV workshops. He advises students and interns who have transitioned to leading institutions like AI2, Stanford, and Brown University.

Laura Alvarezمشاهده پروفایل

استادیار

• Active matter
• Chemical Communication
• Bio-inspired microsystems
• +۳ مورد دیگر

Laura Alvarez is a Junior Chair (Tenure Track) at the University of Bordeaux since 2022, conducting research at the Paul Pascal Research Center (CRPP), a joint CNRS-University of Bordeaux unit. Her office is located at B-224, 115 Avenue du Dr Albert Schweitzer, 33600 Pessac, France, with contact via (+33) 05 56 84 30 27 or laura.alvarez-frances@u-bordeaux.fr. She leads the BIO 2.0 team's research on active matter and colloidal systems. Her educational trajectory includes: PhD at University of Bordeaux and KU Leuven (2013-2016) under Prof. MP Lettinga and Dr. Eric Grelet Postdoctoral research at ETH Zurich (2017-2021) with Prof. Lucio Isa SNSF Spark postdoctoral fellowship (2020-2021) Her research centers on Active Matter , Chemical Communication , and Bio-inspired microsystems , investigating active liposome design, colloidal-lipid membrane interactions, and collective colloidal behavior. Key methodologies include optical tweezers and microfluidics for studying enzymatic particle navigation and colloidal lattice assembly. Her publication record (2017-2023) reveals a strong focus on programmable active colloids and microfluidic applications , with significant contributions to artificial microswimmers and reconfigurable systems appearing in Nature Communications, PNAS, and Physical Review Letters. Emerging trends show increasing emphasis on biomedical applications of active matter. Key recognitions include: Spark postdoctoral grant (SNSF, 2020) IdEx PhD fellowship (2013) She secured major grants including ANR JCJ Project MYMESYS (2023) and France-Berkeley Fund (2023), while her mentoring role involves graduate student supervision through University of Bordeaux's tenure-track framework. As BIO 2.0 team lead at CRPP, she directs experimental work on active colloidal particles using microfluidic platforms and optical manipulation, maintaining active collaborations with ETH Zurich, University of Bordeaux, and international partners across Europe.