Scot T. Martin is the Gordon McKay Professor of Environmental Science and Engineering and Professor of Earth and Planetary Sciences at Harvard University, with affiliations in the School of Engineering and Applied Sciences and the Department of Earth and Planetary Sciences. His research focuses on engineering solutions to global environmental challenges, particularly air/water pollution and their connections to regional/global change, with significant work in the Amazon basin. Key research interests include aerosol dynamics, climate impacts of pollution, and the interplay between human activities and natural ecosystems. His work integrates field observations, laboratory experiments, and computational modeling to address complex environmental systems. Recent studies emphasize Amazonian deforestation patterns, aerosol-cloud interactions in urban and pristine environments, and the development of novel sensing technologies for atmospheric chemistry. Notable methodologies include unmanned aerial vehicle (UAV) measurements, advanced spectroscopic techniques, and machine learning-driven models. Scientific contributions span over 15 years, with a focus on air quality, climate feedback mechanisms, and environmental policy implications. His interdisciplinary approach bridges engineering, earth sciences, and public health.
Douglas Weber is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), where he leads research at the intersection of neuroscience and engineering. He is a founding member of DARPA’s Biological Technologies Office and has led major neurotechnology programs, including HAPTIX, ElectRx, and TNT. His research is centered at the NeuroMechatronics Lab, with collaborations at the University of Pittsburgh and industry partners like Meta. He is actively engaged in developing neuroprosthetics, spinal cord stimulation, and wearable devices for sensory and motor restoration. Ph.D., Bioengineering, Arizona State University (2001) MS, Bioengineering, Arizona State University (2000) BS, Biomedical Engineering, Milwaukee School of Engineering (1994) Post-doctoral training, Centre for Neuroscience, University of Alberta Douglas Weber's research focuses on understanding sensory feedback in motor control, neuroplasticity, and the development of neurotechnologies for individuals with stroke, spinal cord injury, or limb loss. His work spans neural engineering , biomechanics , neuromodulation , and bioelectronic medicine . He aims to translate academic research into real-world applications, such as brain-computer interfaces and sensory-restoring prosthetics. His lab explores high-density electromyography (HD-sEMG), spinal cord stimulation, and closed-loop systems to enhance human-machine interaction. The recent articles reflect a strong emphasis on spinal cord and peripheral nerve stimulation , brain-computer interfaces , and sensory feedback restoration . Many studies investigate neuromodulation for motor recovery , endovascular BCIs , and non-invasive stimulation techniques . There is a recurring theme of clinical translation , with multiple early feasibility trials and human studies. The work integrates electrophysiology , signal processing , and rehabilitation engineering to improve outcomes for patients with neurological impairments. Douglas Weber has mentored over 100 undergraduate, graduate, and medical students, as well as several postdoctoral fellows. He holds eight issued U.S. patents and has secured significant research funding, including awards from ARPA-H and collaborations with the Pitt CTSI. His work has led to practical technologies aimed at improving independence for individuals with paralysis or limb loss. He is actively involved in several research teams and labs, most notably the NeuroMechatronics Lab at CMU. His team collaborates with institutions like the University of Pittsburgh and industry partners such as Meta to develop wearable interfaces and implantable devices. Projects include wristband-based sEMG control systems, spinal cord stimulation for stroke recovery, and bioelectronic implants for chronic disease management.
Yu-Lin Wei is a Teaching Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds a Ph.D. from UIUC (2024), and M.S. and B.S. degrees in Computer Science and Information Engineering from National Taiwan University (2016 and 2014, respectively). His research focuses on audio signal processing, IoT systems, wireless/visible light communication, and indoor positioning. He has been honored with the ECE Rambus Fellowship (2020-2021) and was a Young Researcher at the Heidelberg Laureate Forum (2019). He teaches CS/ECE 438: Communication Networks and CS/ECE 434: Real-World Algorithms for IoT and Data Science . His work spans innovations in acoustic augmented reality (Ear-AR), teeth activity sensing via earphones (EarSense), and positioning systems using polarized light (CELLI). His research group, SiNRG, explores signal processing and IoT applications under the advisement of Romit Roy Choudhury. Key contributions include MobiSys Best Demo Runner-up (2017) for CELLI and advancements in sample-constrained optimization for audio personalization. He has contributed to IEEE standards (e.g., 802.15.7r1) and served as a tutorial speaker at MobiCom and conference co-chair roles.
Yanlei Diao is a Professor of Computer Science at Ecole Polytechnique in France and also holds a professorship at the University of Massachusetts Amherst. She joined Ecole Polytechnique in September 2015 and leads the CEDAR research team focusing on Rich Data Exploration at Cloud Scale. Her work bridges theoretical computer science with practical big data systems that address real-world challenges in data analytics. Professor Diao's research spans big data analytics, scalable intelligent information systems, and cloud data processing infrastructure. Her work emphasizes practical solutions for explainable anomaly detection, interactive data exploration, and uncertain data management. She has pioneered systems like UDAO (a next-generation optimizer for cloud analytics), EXAD (explainable anomaly detection), AIDEme (interactive data exploration), and GESALL (genomic scalable analysis) that have influenced both academia and industry. Her recent publications reveal a strong focus on making big data analytics more explainable, efficient, and accessible. She has developed frameworks for unsupervised anomaly detection across heterogeneous domains, created benchmarks like Exathlon for evaluating explainable anomaly detection systems, and advanced human-in-the-loop approaches for interactive database exploration. Her work consistently bridges theoretical foundations with practical implementations in distributed systems. Selected Awards: ERC Consolidator Award (2017-2023) for "Charting a New Horizon of Big and Fast Data Analysis through Integrated Algorithm Design" CRA-W Borg Early Career Award (2013) NSF CAREER Award (2008) IBM Innovation Award on Scalable Data Analytics (2010) Professor Diao actively mentors PhD and Master's students, with former students now holding positions at top technology companies including Google, Facebook, Amazon, Netflix, and Huawei. Her research is supported by diverse funding sources including the European Research Council, National Science Foundation, ANR, and industry partners like Google, IBM, and Alibaba. She serves as PC Co-Chair of PVLDB 2025-2026 and has delivered keynotes at major industry events including Amazon Machine Learning Workshop (2024), SWIFT AI Forum (2023), and Berlin Institute for the Foundations of Learning and Data (2022). Her CEDAR research team at Inria/LIX develops cutting-edge technologies for big data analytics, with current projects focusing on foundation models for big data, explainable AI for anomaly detection, and genomic data analysis at scale. The team maintains strong collaborations with industry partners including Alibaba Cloud, where joint work has led to significant publications at top database conferences.
Hatice Köse is a Professor at Istanbul Technical University in the Department of Artificial Intelligence and Data Engineering within the College of Computer and Informatics. She holds a PhD in Computer Engineering from Boğaziçi University and has been a key figure in advancing AI applications for human-centered technologies, particularly in assistive robotics and human-computer interaction. PhD in Computer Engineering, Boğaziçi University (2000–2007) MSc in Computer Engineering, Boğaziçi University (1997–2000) BSc in Computer Engineering, Boğaziçi University (1993–1997) Her research focuses on Artificial Intelligence , Human-Robot Interaction , and Assistive Technologies , with a strong emphasis on applications for children with autism and hearing impairments. She explores affective computing, emotion recognition using facial and physiological signals, and multimodal interaction frameworks. Her work integrates deep learning, signal processing, and robotics to develop inclusive technologies. Recent publications highlight trends in lightweight AI models for edge devices , gaze and emotion detection in children , and explainable machine learning in medical diagnosis . Her articles span top venues in AI, robotics, and multimodal interaction, reflecting a consistent focus on socially beneficial AI systems. EELISA Project Diversity Reward (2023) Best Paper Award, INISTA 2020 İTÜARI Teknokent Graduation Design Project Competition (2016) Necdet Eraslan Project Competition Honorable Mention (2011) Professor Köse has led numerous research projects funded by TÜBİTAK, EU, and university programs, including initiatives on AI-enabled manufacturing, teaching technologies to children with neurodevelopmental disorders, and sensor-based digital biomarkers for early detection. She has supervised multiple graduate students and research assistants, contributing to the development of next-generation AI researchers. She has also held administrative leadership roles such as Department Head and Education Coordinator. She is actively involved in research labs and teams focusing on socially assistive robotics , affective computing , and AI for accessibility , fostering interdisciplinary collaboration across computer science, psychology, and education.
Ian Stewart is an Assistant Professor in the Music Production and Engineering (MP&E) department at Berklee College of Music, where he teaches audio mastering and music technology. He also operates Flotown Mastering, a professional audio mastering studio he has run since 2011, and has collaborated with artists such as KRS-One and Mr. Lif. He co-developed the Basslane Pro plugin with Tone Projects in 2022 and is a recognized thought leader in hybrid learning technologies for audio education. B.S. in Music Engineering Technology, University of Miami (2007) Stewart's research and teaching focus on audio mastering, digital audio fundamentals, loudness standards (LUFS), and the integration of technology in music production. He is a passionate advocate for audio quality and has authored numerous educational articles on topics such as multiband compression, dynamic EQ, AI in mastering, and headphone-based mastering workflows. His work bridges academic instruction with real-world engineering practice, emphasizing accessibility and adaptability in non-ideal acoustic environments. His recent publications reflect a strong trend toward practical, accessible mastering techniques for modern platforms like Spotify and Apple Music, with emphasis on loudness normalization, tonal balance, and workflow efficiency. He explores both technical and aesthetic aspects of mastering, including the use of tools like Ozone, dithering, true peak limiting, and AI-assisted processing. His articles are widely used in audio education and industry training. Commercial Integrator's 40 Under 40 (2021) Stewart has contributed to the development of online mastering academies for the Audio Engineering Society (AES) and has helped pioneer hybrid learning models during the pandemic. While no formal grants are mentioned, his work in educational technology and plugin development indicates active engagement in research and innovation. He has advised on best practices for remote mastering, home studio setups, and collaborative audio workflows. He runs Flotown Mastering, offering services in music mastering, audio restoration, and forensic audio analysis. The studio emphasizes collaboration, customer service, and technical excellence. Stewart also contributes to iZotope’s educational content as an author and is active in promoting audio literacy among producers and engineers.
Angelika Peer is a Full Professor in the Faculty of Engineering at the Free University of Bozen-Bolzano, Italy, a position she has held since November 2017. Previously, she served as a Full Professor at the Bristol Robotics Laboratory, University of the West of England, UK (2014–2017), and as a senior researcher and lecturer at the Technical University of Munich, Germany, where she was also a TUM-IAS Junior Fellow. Her academic base is firmly within engineering, particularly in intelligent and human-centered systems. Diploma in Electrical Engineering and Information Technology, Technical University of Munich, 2004 Doctor of Engineering, Technical University of Munich, 2008 Her research interests are centered on intelligent systems that bridge humans and machines. Key areas include robotics, control systems, human-robot interaction (HRI), emotion recognition through physiological signals, shared control, and intelligent manufacturing. She investigates how dynamic models and machine learning can improve safety, adaptability, and efficiency in human-machine collaboration. Her work spans both theoretical control design and practical applications in Industry 5.0, sustainable production, and assistive technologies. Her recent publications (2023–2025) reflect a strong trend toward data-driven and model-based intelligent systems. Topics include energy-based safety control in HRI, neural-network-based trajectory adaptation in powder compaction, emotion intensity estimation using NARX models and appraisal theory, and task prediction in digitalized shop floors using support vector regression. These works demonstrate expertise in integrating dynamic modeling, machine learning, and real-time control with applications in manufacturing and human-centered computing. Angelika Peer has advised PhD students including Ehtisham Ul Hasan and Isabel Francisca Sota Machado Barradas. She is involved in research initiatives such as the Human-centered Intelligent Systems (HCIS) macro area at her university, promoting interdisciplinary work at the intersection of computer science, engineering, and human factors. Her research is supported by collaborations and publications in high-impact journals and conferences, with a strong emphasis on open access and peer-reviewed dissemination.
Matthijs van Leeuwen is an Associate Professor and Director of Education at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. He leads the Explanatory Data Analysis group and is affiliated with the university-wide SAILS AI research program. Academic rank: Associate Professor Institution: Leiden University Research group: Explanatory Data Analysis University affiliation: SAILS AI research program His research focuses on exploratory data mining with emphasis on explainability for domain experts. Key areas include pattern discovery, anomaly detection, and human-in-the-loop systems. He applies information-theoretic concepts like Minimum Description Length (MDL) and develops interactive methods for real-world applications in life sciences, social sciences, manufacturing, and healthcare. Recent publications demonstrate his work in explainable AI through diverse subgroup discovery, graph anomaly detection, and medical data analysis. Notable contributions include synthetic health record generation, wearable sensor data interpretation, and hemoglobin deferral prediction models. Scientific Awards : Senior Teaching Qualification (SKO) certificate (2022) As Director of Education for LIACS' Master's programs, he plays a leadership role in academic training while maintaining active research partnerships across multiple domains including manufacturing, aviation, and medical informatics.
Andreas Jedlitschka is a researcher at Fraunhofer IESE in Kaiserslautern, Germany, affiliated with the University of Kaiserslautern. He has made significant contributions to empirical software engineering, software process improvement, and experience management. His research focuses on evidence-based decision support, technology transfer, and quality in agile and AI-driven software development. PhD, University of Kaiserslautern (2009) His research interests include empirical software engineering, requirements engineering, software quality, experience management, and AI in software development. He investigates how to improve software processes through data-driven methods, empirical validation, and knowledge reuse. His work often bridges industry and academia, emphasizing practical applicability. The recent articles highlight a growing focus on AI-enabled systems, sustainability in software engineering, and data-driven technical debt management. His work spans both theoretical frameworks and industrial case studies, particularly in agile environments and safety-critical systems. He has co-organized workshops and edited conference proceedings, indicating active participation in the research community. He mentors researchers through collaborative projects and has contributed to doctoral research frameworks. His work is supported by collaborations across Europe, particularly in EU-funded projects related to software quality and AI. He is involved in initiatives like the Q-Rapids project, which supports decision-makers in managing quality in rapid software development, and contributes to building AI innovation labs with industry partners.
Professor Ian Mitchell is a faculty member in the Department of Computer Science at the University of British Columbia's Faculty of Science. He received his B.A.Sc. and M.Sc. from UBC and PhD in Scientific Computing and Computational Mathematics from Stanford University. His research focuses on algorithms for hybrid systems, level set methods for Hamilton-Jacobi PDEs, control systems for cyber-physical applications, assistive technology development, and reproducible research practices. Key research contributions include the Toolbox of Level Set Methods Development of Dijkstra-like ordered upwind methods Advancements in safety-preserving control algorithms Applications in smart wheelchair navigation and user interface design His recent publications demonstrate expertise in Hamilton-Jacobi equation solving, robotics applications of level set methods, and assistive technology design. While no explicit awards are listed, his work spans multiple disciplines including computer science, control theory, and biomedical engineering. He teaches both graduate (CPSC 5xx) and undergraduate (CPSC 4xx and below) courses in computational robotics and parallel computing.
Femke van Beek is an Assistant Professor in the Robotics section of the Department of Mechanical Engineering at Eindhoven University of Technology. Her research focuses on integrating soft robotics with haptic perception to develop autonomous robots with tactile sensing capabilities, particularly for agricultural applications within the 4TU Green Sensors project . She employs VR/AR experiments to study human haptic perception and design bio-inspired sensors. Academic Background : PhD in Psychonomics and Cognitive Psychology (Vrije Universiteit Amsterdam), MSc in Sensory Biology and Biomechanics (Wageningen University), and BSc in Biology (Wageningen University). Her research spans soft robotics , haptic feedback , and human-in-the-loop engineering , emphasizing practical sensor design and robotic movement optimization. Recent work includes open-source platforms for real-time control of soft robots and studies on vibrotactile vs. auditory feedback in tele-operation. Scientific Awards : Eurohaptics Society PhD Award (2017)
Andreas Eriksen serves as an Associate Professor at Oslo Metropolitan University's Centre for the Study of Professions, focusing on the ethical and philosophical dimensions of professional practice in complex institutional settings. His work bridges political theory, public administration, and applied ethics with concrete implications for healthcare, education, and governance systems. Research interests center on Political Philosophy , Professional Ethics , and Research Literacy , examining how professionals navigate normative challenges in bureaucratic environments. Key themes include the role of trust in high-stakes decision-making, legitimacy in administrative reasoning, and the integration of research evidence into professional practice—particularly in teacher education and healthcare contexts. His scholarship consistently addresses tensions between technical expertise and democratic accountability. Recent publications reveal a pronounced interdisciplinary trajectory merging bioethics with AI governance, educational philosophy with public administration, and Scandinavian welfare models with EU policy frameworks. Dominant trends include ethical implications of machine learning in professional judgment ( American Journal of Bioethics ), constitutional dimensions of health policy ( EU Regulatory Responses to Crises ), and foundational work on research literacy for educators ( Educational Philosophy and Theory ). His Norwegian-language contributions critically examine NAV (Labour and Welfare Administration) practices and school leadership ethics. Eriksen leads the REPOSE project developing methodological tools for research implementation in education, while prior work includes studies on academic freedom ( Et ytringsklima under press? ), trust in knowledge societies ( Integrasjon og integritet ), and school leadership ethics ( Å tenke som en rektor ). His grant portfolio emphasizes practical applications of philosophical frameworks to institutional challenges. Based at the Centre for the Study of Professions, Eriksen contributes to this interdisciplinary hub investigating professional roles across healthcare, education, and public administration through normative analysis and empirical research on ethical reasoning in complex systems.
Marinelli Marina serves as Assistant Professor at the National Technical University of Athens (NTUA), School of Civil Engineering, with her office located in the Building of Strength of Materials at Zografou Campus. Her active contact details and current appointment date (February 2024) confirm ongoing faculty status within Greece's premier technical institution. Her research focuses on digital transformation in construction and infrastructure management, with particular expertise in Construction 4.0 technologies, big data analytics for facilities management, and human-robot collaboration systems. Current work explores lean-agile hybrid methodologies, sustainable construction practices, and value optimization in public infrastructure procurement across European and emerging markets. Analysis of her recent publication record (2020-2024) reveals strong methodological diversity including structural equation modeling, best-worst method, and interpretive structural modeling applied to construction technology adoption. Key research trends include the integration of Industry 4.0 concepts into Construction 5.0 frameworks, with growing emphasis on circular economy principles in metal disassembly and green cement production. Professional activities span UK facilities management sectors, Indian infrastructure contexts, and EU public procurement systems, demonstrating cross-cultural research applicability. Her work consistently addresses implementation barriers for emerging technologies while developing practical frameworks for value creation in civil engineering projects.
Anna Monreale is an Associate Professor of Computer Science at the University of Pisa and serves as the Delegate for Masters and Continuing Education. She concurrently holds an Adjunct Professor position at Dalhousie University's Faculty of Computer Science. Her institutional leadership includes vice-coordinating the National Doctorate in Artificial Intelligence for Society and chairing the Internships Committee for the Master's in Data Science & Business Informatics. She earned her Bachelor's and Master's degrees with honors in Computer Science from the University of Pisa in 2007 and completed her PhD there in 2011. Prior to her faculty appointment, she conducted research on European projects from 2011-2014 and has been affiliated with the CNR's Institute of Information Science and Technologies since 2008. Professor Monreale's research centers on ethical AI development, with core expertise in data mining, explainable AI, and privacy-by-design methodologies. Her work bridges technical innovation with societal impact, particularly in healthcare applications, mobility data analysis, and social media systems. She emphasizes transparency and legal compliance in algorithmic decision-making. Her recent publications (2023-2025) reveal a concentrated focus on trustworthy AI frameworks, with significant contributions to federated learning privacy, trajectory data protection, and real-time explainability systems. She actively addresses emerging challenges in content moderation regulation and healthcare AI deployment. As an educator, she teaches Data Mining and Data Science Lab courses while directing major academic initiatives. She leads a PNRR-funded national project and a European research project, with over 90 publications and 3,200+ citations reflecting her scholarly impact. She is a key member of the University of Pisa's KDD Lab and maintains strong ties with CNR research units.
Dr. Wendy Lee is an Associate Professor and the MS Bioinformatics Program Coordinator in the Department of Computer Science at San José State University's College of Science. With over a decade of industry experience developing bioinformatics analytical pipelines and enterprise software, she brings practical expertise to her academic role. She serves as faculty advisor for the SJSU Girls Who Code College Loop and the SJSU Bioinformatics Club, demonstrating her commitment to broadening participation in computing. Ph.D. Bioinformatics, University of California Santa Cruz, CA M.S. Molecular Biology and Microbiology, San Jose State University, CA B. Math Computer Science (Co-op), University of Waterloo, ON, Canada Dr. Lee's research focuses on developing and applying computational methods to analyze and integrate large-scale 'omics' datasets including genomic, epigenomic, transcriptomic, proteomic, metabolomic, and metagenomic data. Her work aims to understand how genes work together to comprise functioning cells and organisms. She is particularly interested in next-generation sequencing data analysis for genomic, transcriptomic, and metagenomic studies. Dr. Lee also serves as a faculty member of the Applied Programming Experiences (APEX) program that embeds Python-based computing modules into introductory Statistics and Biology courses to expose diverse students to computer programming. Analysis of Dr. Lee's recent publications (2018-2024) reveals a strong focus on bioinformatics applications in genomics, toxicology, and microbiome research, particularly using Drosophila melanogaster as a model system. Her work spans computational methods development, including machine learning approaches for sequencing data analysis, and interdisciplinary educational initiatives aimed at broadening participation in computing. The publications demonstrate her dual commitment to advancing bioinformatics research and improving STEM education. Journal Reviewer for Investigative Ophthalmology & Visual Science (2020-present) Journal Reviewer for Network Modeling Analysis in Health Informatics and Bioinformatics (2021-present) CSU Cal-Bridge CS Program Liaison and Mentor (2020-present) Dr. Lee actively mentors students through thesis projects (CS 297/298) and has advised numerous students who appear as co-authors on her publications. She serves on multiple departmental committees including the TA Training Committee, Computer Science Scholarship Committee, and previously the College of Science Research Committee. Her educational initiatives, particularly the APEX program, demonstrate her commitment to integrating computational thinking across disciplines and fostering diversity in computing fields. Dr. Lee directs The Lee Research Lab, which focuses on computational methods for 'omics' data analysis. She is also involved with the Applied Programming Experiences (APEX) program that embeds Python-based computing modules into introductory Statistics and Biology courses. Her lab work combines bioinformatics method development with biological applications, particularly in genomics and toxicology research using model organisms.
