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Prof. Dr. Marc Schneider holds a professorship in Biopharmaceutics and Pharmaceutical Technology at Saarland University's College of Pharmacy . His research focuses on colloidal drug delivery systems, particularly nanostructured and non-spherical particle engineering for overcoming biological barriers in pulmonary and transdermal applications. He leads an internationally recognized lab in Saarbrücken, collaborating with Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) and trinational institutions. Research Highlights: Development of inhalable nano/microparticle systems Surface modification of gelatin nanoparticles Characterization of mucus-penetrating particles 3D printing for microneedle fabrication Atomic Force Microscopy (AFM) for nanoparticle analysis Selected Scientific Awards: European Journal of Pharmaceutics and Biopharmaceutics Best Paper Award (2018) for mucus-penetrating nanoparticles Recognized in 'Ausgezeichnete Orte im Land der Ideen' competition (2018) for 'Nano-Mais' drug delivery system Collaborative Networks: Co-editor for Advanced Drug Delivery Reviews special issue on biological barriers Key participant in trinational Master's program in Biomedicine with Strasbourg, Mainz, and Luxembourg Active in Controlled Release Society (CRS) conferences and local chapters

• Colloidal drug delivery systems
• Nanoparticle and microparticle engineering
• Nanotechnology in pharmacy
• +3 more

Inseok Hwang is the Paul Stanley Professor of Aeronautics and Astronautics at Purdue University's School of Aeronautics and Astronautics. He earned his Ph.D. from Stanford University, specializing in multiple-vehicle control systems. His research focuses on hybrid systems, air traffic control, unmanned systems, and cybersecurity of cyber-physical systems. He leads the Flight Dynamics and Control/Hybrid Systems Laboratory and has received numerous awards, including the NSF CAREER Award and AIAA Associate Fellow designation. His work spans theoretical advancements in control theory and practical applications in aerospace systems. He has over 150 peer-reviewed publications and actively collaborates with industry and government agencies like NASA and the FAA. Education: B.S. (Seoul National University, 1992), M.S. (KAIST, 1994), Ph.D. (Stanford, 2004). Professional memberships include AIAA and IEEE. Research Interests: Hybrid systems analysis, air traffic surveillance and control, fault detection and isolation, spacecraft control, and cybersecurity for autonomous systems. His lab develops algorithms for safe and efficient operation of networked systems, including UAS traffic management and resilient control protocols against cyberattacks. Awards: NSF CAREER (2008), AIAA Associate Fellow (2012), University Faculty Scholar (2017), C.T. Sun Award (2019), multiple Seed for Success Awards (2020–2024), and Paul Stanley Professorship (2024). Grants and Collaborations: Active projects funded by NSF, NASA, FAA, and industry partners. Focus areas include resilient navigation, anomaly detection in air traffic systems, and cyberattack mitigation for autonomous vehicles.

• Hybrid Systems
• Nonlinear Systems
• Unmanned Aircraft/Spacecraft Control
• +5 more

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.

• Machine Learning & Artificial Intelligence
• Probabilistic Modelling
• Neuro-Symbolic Modelling
• +7 more

Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).

• Human-Centered Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +5 more

Kai-Yu Wang is a Professor of Marketing at the Goodman School of Business, Brock University. His research spans a wide range of topics in marketing, consumer behavior, and digital technologies, with a particular focus on service recovery, augmented reality, social media marketing, and brand management. Research Interests: Consumer behavior in digital environments Service recovery strategies in social media and chatbot contexts Augmented reality applications in marketing and consumer engagement Brand value co-creation and community building Cross-cultural consumer psychology His recent work delves into the nuances of consumer interactions with emerging technologies like chatbots and AR, examining how these tools influence customer relationships and decision-making processes. Contact Information: Email: kwang@brocku.ca Phone: 905 688 5550 x5597

• Marketing
• Consumer Behavior
• Digital Marketing
• +6 more

João F. Mano is a Full Professor at the Department of Chemistry, University of Aveiro, and Director of the Doctoral Program on Biotechnology. He leads the COMPASS Research Group and serves as Vice-Director at CICECO - Aveiro Institute of Materials. His academic appointments include Invited Professor at University of Lorraine (France), Visiting Professor at KAIST (South Korea), and Adjunct Professor at Ajou University (South Korea). Education: PhD in Chemistry (1996, Technical University of Lisbon); D.Sc. in Tissue Engineering, Regenerative Medicine and Stem Cells (2012, University of Minho) Research Interests focus on Biomaterials for Regenerative Medicine , integrating Nanotechnology , Microtechnology , and Biofabrication . His group develops Bioinspired Materials using polymer chemistry, Decellularized Extracellular Matrix , and 3D Bioprinting to engineer Cell Microenvironments for therapeutic applications. Recent Publications highlight advancements in Human-Derived Hydrogels , Photopolymerizable Scaffolds , Magneto-Responsive Biomaterials , and Programmable Bioinks . Trends show emphasis on Organ-on-a-Chip integration, Smart Living Materials , and Green Bioprinting methodologies. Scientific Awards include: European Research Council Advanced Grants (2015, 2020) Fellow at IUPAC, European Academy of Sciences, and American Institute of Medical and Biological Engineering ERC Proof of Concept Grants Doctor Honoris Causa from University of Lorraine and Utrecht UNESCO Chair on Biomaterials George Winter Award (European Society for Biomaterials) Supervisions & Collaborations encompass 74+ MSc, 26+ PhD students, and 40+ postdocs. He co-founded METATISSUE and CELLULARIS Biomodels , and serves as Editor-in-Chief of Materials Today Bio .

• Biomaterials
• Tissue Engineering
• Regenerative Medicine
• +5 more

Helmut H. Strey is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University. His research focuses on micro- and nanotechnologies for quantitative biology , including single-cell analysis, cancer metabolism modeling, and functional MRI data analysis. He holds academic appointments since 2008 and has pioneered technologies like tumor-on-a-chip and optical decoders for translation stages. Education: PhD in Biophysics (Technical University München, 1993), postdoctoral training at NIH (1994-1998). Awards include the NSF CAREER Award (2000-2005), Dillon Medal (2003), and Weston Visiting Professorship (2020). Research interests span cell-to-cell variability , Warburg effect in cancer , and Bayesian analysis of time-series data . His lab develops tools for 3D tumor microenvironments, MRI-compatible drug delivery systems, and biomimetic neural circuit models. Teaching includes advanced numerical methods in biomedical engineering, quantitative biology, and biomolecular analysis. Active in open hardware projects, including microfluidics controllers and IoT devices for health monitoring.

• Biomedical Engineering
• Biophysics
• Microfluidics
• +6 more

Leon Shpanin is a Senior Lecturer in Electronic and Electrical Engineering at Sheffield Hallam University, where he serves as Course Leader for MSc Automation Control and Robotics. He holds an MSc in Radio Frequency Engineering and a PhD in Electrical Engineering from the University of Liverpool. His research focuses on electrical engineering applications including renewable energy systems, HVDC circuit interruptions, and electromagnetic techniques for current interruption. He received the RAEng Award (2020) for developing next-generation circuit breakers for rail networks. Key projects: Development of Novel Energy Efficient Magnetic Scroll Air Motor (EPSRC) Smart control of multirotor drone propellers for vibration energy harvesting

• Electrical Engineering
• Plasma Science
• Current Interruption
• +2 more

Dr. Danial Chitnis is a Chancellor's Fellow and Lecturer in Electronics at the School of Engineering, University of Edinburgh. He holds a DPhil in Engineering Science from the University of Oxford (2013) and has expertise in microelectronics, biomedical engineering, and quantum imaging. His research focuses on SPAD arrays, time-of-flight sensors, and wearable optical systems for biomedical applications. Education: BSc in Electronics Engineering, Chamran University of Ahvaz (2002–2007) MSc in Advanced Microelectronics Systems Engineering, University of Bristol (2007–2008) DPhil in Engineering Science, University of Oxford (2009–2013) Research Interests: Single-Photon Avalanche Diode (SPAD) arrays for optical communications and biomedical imaging Quantum-enhanced imaging via QuantIC Hub Wearable sensors for near-infrared spectroscopy (NIRS) AI-driven automation in test and measurement systems Articles Trends: Recent work emphasizes AI integration in electronics design, photon-counting receivers for 6G networks, and portable biomedical devices. Notable contributions include SYCL-based acceleration of circuit simulations and FPGA-driven time-to-digital converters. Grants & Collaborations: Principal Investigator of multiple grants, including EPSRC-funded projects on AI-enhanced human-machine interfaces and quantum technology applications. Collaborates with UCL, QuantIC, and industry partners like Keysight Technologies. Labs/Teams: Co-investigator at QuantIC, the UK Quantum Technology Hub in Quantum Enhanced Imaging. Leads interdisciplinary research on detector arrays and systems for quantum physics and consumer cameras.

• Microelectronics
• Biomedical Engineering
• Quantum Technology
• +3 more

Maithilee Kunda is an Associate Professor in the Department of Computer Science and Computer Engineering at Vanderbilt University's School of Engineering. She leads the Laboratory for Artificial Intelligence and Visual Analogical Systems and co-leads the Vanderbilt Initiative for Autism, Innovation, and the Workforce. Her research focuses on AI-driven cognitive systems, particularly visual reasoning and technology applications for neurodiverse populations, including autism spectrum disorder. Dr. Kunda holds a B.S. in Mathematics with Computer Science from MIT (2007) and a Ph.D. in Computer Science from Georgia Tech (2013). She was honored as an MIT Technology Review Innovator Under 35 in 2016 for her groundbreaking work. Her research bridges computational models of visual thinking and real-world impact, emphasizing neurodiverse cognition. Notable projects include developing AI systems for autism-related social skills interventions, designing visual analogy problem-solving frameworks, and advancing nonverbal communication understanding in AI. Her work addresses challenges in abstract reasoning tasks (e.g., Raven's Matrices), visual imagery-based learning, and ethical AI integration. She advocates for inclusive technology design, leveraging both generative AI and cognitive principles to create human-centered solutions. Key awards: MIT Technology Review Innovator Under 35 (2016) Her labs focus on practical AI applications for education, healthcare, and workforce development, emphasizing interdisciplinary collaboration between engineering, psychology, and neuroscience.

• Artificial Intelligence
• Computational Cognitive Systems
• Visual Reasoning
• +3 more

Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.

• Cybersecurity
• Program Analysis
• Virtualization Security
• +5 more

Andrew Zahrt is an Assistant Professor of Chemistry at the University of Pennsylvania's School of Arts & Sciences. His research focuses on integrating computational methods, automation, and machine learning to address challenges in organic chemistry. The Zahrt Group develops data-efficient workflows, automated experimentation systems, and machine learning tools to accelerate reaction optimization, catalyst design, and reaction discovery. Education: BS in Chemistry and Biology, Aquinas College (2014) PhD in Chemistry, University of Illinois at Urbana-Champaign (2020) Postdoctoral Fellow, Massachusetts Institute of Technology (2020–2023) Research emphasizes minimizing experimental burdens through automation and accessible computational pipelines. The group’s work spans reaction analysis, discovery, and mechanistic inquiry using interdisciplinary approaches. Their tools aim to be adaptable for labs without specialized equipment.

• Organic Chemistry
• Data-Driven Reaction Development
• Automated Experimentation
• +3 more

Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +6 more

Abdullah Muzahid is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University (since August 2024), previously serving as an Assistant Professor there since August 2018. Prior, he held an Assistant Professor role at the University of Texas at San Antonio (2012-2018). He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2012), focusing on architectural support for debugging concurrency bugs under Prof. Josep Torrellas. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, University of Illinois at Urbana-Champaign (2009) B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (2005) Research Interests: His work spans Computer Architecture , Systems , and Artificial Intelligence , with focus on multiprocessor architecture, parallel programming, debugging, and applying machine learning to system optimization. Recent projects include cache indexing via entropy estimation, DNN training acceleration, and hardware-software co-design for security. Awards: NSF CAREER Award (2017) Excellence in Research Award (UTSA, 2015 & 2017) W. J. Poppelbaum Award (UIUC, 2012) Intel Ph.D. Fellowship (2011) Grants & Advising: He leads NSF-funded projects on robust deep learning and stream processing systems. Advised 5 PhD graduates and currently mentors 4 PhD students. Served on program committees for ISCA, HPCA, MICRO, and as NSF panelist. Labs/Teams: Active in Texas A&M’s Computer Architecture group, collaborating on machine programming, hardware security, and AI-driven systems optimization.

• Computer Architecture
• Systems
• Artificial Intelligence
• +5 more

Linda Li is an LSE Fellow (Research Fellow) in the Department of Methodology at the London School of Economics and Political Science (LSE), specializing in advanced quantitative methods and computational social science research. Education PhD in Social Data Science, Oxford Internet Institute, University of Oxford Research Interests Impact of automated agents on human behavior and network dynamics in online activism AI's societal impact Political communications Civic politics participation Social network analysis Computational social science methods Science of science Social data science methodologies Teaching & Expertise Linda teaches diverse courses in research design, statistics, programming, and political sociology, emphasizing advanced quantitative methods for students from varied backgrounds. Her technical proficiency includes causal inference and digital ethnography, with fluency in English and Mandarin supporting cross-cultural research collaboration.

• AI's societal impact
• political communications
• computational social science methods
• +4 more