Liudmila Alekseeva is an Assistant Professor of Entrepreneurship at the Department of Management, Strategy and Innovation, Faculty of Economics and Business (FEB), KU Leuven. She holds a PhD from IESE Business School, a Master's in Finance from Bocconi University, and a Bachelor's in Management from St. Petersburg State University. Her research focuses on Entrepreneurial Finance, Empirical Corporate Finance, Human Capital, and Technological Transformation. She has professional experience in auditing and strategy roles in Russian financial institutions. Education: PhD in Financial Management, IESE Business School Master's in Finance, Bocconi University Bachelor's in Management, St. Petersburg State University Her recent work examines digital transformation in venture capital, AI's impact on labor markets, and gender dynamics in investment decisions. Publications span topics from VC industry evolution to ownership structures in Russian firms. No scientific awards listed, but active in teaching entrepreneurship and corporate finance at the university level. Currently affiliated with KU Leuven's Department of Management, Strategy and Innovation with no listed grants or advising roles documented here.
Wade A Fagen-Ulmschneider is a Teaching Professor in the Department of Statistics at the University of Illinois Urbana-Champaign, with additional affiliations in the Siebel School of Computing and Data Science and Biomedical and Translational Sciences. He holds a Ph.D. in Computer Science from UIUC and focuses on educational technology, curriculum design, and student engagement. His work emphasizes tools for collaborative learning, including the development of platforms like 'junto' for team formation and studies on online queue systems to enhance academic support. His research spans educational technology applications such as curriculum visualization, active learning strategies, and the integration of tablet PCs and pen-based systems in classrooms. He has contributed to reengineering introductory computing courses and exploring quantitative metrics linking student engagement (e.g., office hours utilization) to academic performance. Fagen-Ulmschneider’s earlier work includes foundational research on network testing frameworks like Goliath, reflecting a dual focus on both educational innovation and cybersecurity infrastructure. His advising and grants are not explicitly listed, but his roles highlight a commitment to pedagogical excellence. He is involved in interdisciplinary initiatives through the College of Liberal Arts & Sciences and the Siebel School, contributing to both statistical education and computational science advancements.
Maire O'Neill is a Professor at the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, affiliated with the Secure Digital Systems (SDS) group and the Institute of Electronics, Communications & Information Technology (ECIT). Her research focuses on hardware security, cryptography, and secure embedded systems. She has held significant leadership roles and pioneered work in FPGA security, approximate computing, and post-quantum cryptography. Dr. O'Neill's academic journey includes over 20 years of contributions to secure digital systems, with a particular emphasis on cryptographic hardware, side-channel analysis, and IoT security. She has led major research projects such as the EU-funded 'TruDetect' initiative for hardware Trojan detection and the 'Secure IoT Processor Platform' project. Her work bridges theoretical research with practical applications, emphasizing real-world security challenges in electronics and computing. Education: Background in electrical engineering and computer science (details not explicitly stated in the text) Research Interests: Her work spans hardware security primitives, FPGA-based cryptographic solutions, and energy-efficient computing. She explores vulnerabilities like Rowhammer attacks and side-channel leaks while developing defenses through approximate computing and machine learning techniques. Her research addresses emerging threats in IoT, 5G networks, and post-quantum cryptography. Publications Trends: Recent work emphasizes machine learning applications in security (e.g., ML-KEM accelerator designs), hardware Trojan detection, and energy-efficient approximate computing. She frequently publishes in top-tier venues like IEEE Transactions and ACM conferences, with a focus on practical implementations and FPGA demonstrators. Awards: 2007: BFIIN ITEC Platinum Award, British Female Inventor of the Year, European Union Women Innovators 2015: Fellow of the Irish Academy of Engineering 2015: INVENT Award for collaborative innovation Advising & Grants: Supervised 8 PhD students (explicitly stated). Active in securing research grants (18 projects listed), including EU and industry collaborations. Engages in academic service roles like IEEE Distinguished Lecturer and international conference organization. Labs/Teams: Leads the Secure Digital Systems (SDS) group, collaborating with global partners on hardware security and IoT initiatives. Maintains strong ties with industry through projects like NIO New Deal Cyber Bid and TruDetect.
Daniel Brazier is a part-time Research Affiliate at George Mason University, focusing on advanced computing systems research. His work intersects autonomic computing, cybersecurity, and distributed systems optimization. He specializes in performance modeling for cloud/fog environments, dynamic reconfiguration strategies, and resilience engineering. Key research areas include: Autonomic resource management in edge and cloud systems Stochastic optimization algorithms Security mechanisms like moving target defense Performance-security tradeoff analysis Decentralized runtime system modeling His recent work emphasizes practical frameworks for distributed system trustworthiness and adaptive elasticity control under variable workloads. Outputs include novel meta-heuristic algorithms for virtual network optimization and analytic models for parallel server architectures. Publications highlight contributions to fog/cloud computing, manufacturing process optimization, and security-aware system design. Current efforts focus on applying decision analytics to smart manufacturing and autonomic emergency department frameworks.
Andrew Kun is a Professor in the Department of Electrical and Computer Engineering at the University of New Hampshire, where he has served since 2000. His research bridges Human-Computer Interaction (HCI), autonomous vehicles, and pervasive computing, with a focus on driver interfaces and future work environments. Ph.D., Engineering, University of New Hampshire (1997) M.S., Electrical & Electronic Engineering Technology, University of New Hampshire (1994) B.S., Electrical & Electronic Engineering Technology, University of New Hampshire (1992) Kun's work explores how automation transforms mobility and remote work, emphasizing safety, inclusivity, and cognitive load management. He investigates interfaces for autonomous vehicles, hybrid conferencing, and AI-assisted collaboration, often leveraging wearable technology and multimodal interaction. His recent publications highlight trends in work-from-home practices post-pandemic, human-AI interaction in mobile environments, and adaptive interfaces for automated vehicles. Themes include balancing productivity with wellbeing, designing inclusive virtual spaces, and understanding cognitive shifts in hybrid settings.
Kim Wikström is a Professor at the Faculty of Science and Engineering, University of Åbo Akademi, affiliated with the Laboratory of Industrial Management and the Sea Technologies for a Sustainable Future initiative. Their research focuses on sustainable infrastructure, digital innovation in maritime transport, and project governance in business ecosystems. Key projects include NavisSpace (cruise ship sustainability) and GECKO (mobility governance). Wikström's work aligns with UN SDGs, particularly addressing sustainable infrastructure development and climate action. They have co-authored over 30 publications since 2003, with recent contributions on green transition strategies in shipping and infrastructure finance frameworks. Contact: kim.wikstrom@abo.fi Education background and career milestones are not explicitly detailed in the text. Research interests emphasize systemic approaches to project delivery, business model innovation, and digital transformation in port and maritime logistics. Supervised one student project, though specific advisees are unnamed. Active in EU and Finnish government-funded initiatives, including RAAS (autonomous systems research) and Healthy Travel (post-pandemic travel safety). Notable contributions include frameworks for discount rate setting in infrastructure investments and studies on pandemic-era passenger safety protocols. Current focus areas include collaborative innovation spaces for green shipping and digitalization's role in sustainable maritime systems. No scientific awards are mentioned in the provided text.
Prof. Matthias Harders is a Professor at the Department of Computer Science, University of Innsbruck. His work focuses on medical imaging, haptic systems, virtual reality, and data-driven simulation. He leads research in interactive visualization tools, medical device development, and machine learning applications in healthcare and environmental engineering. Research areas include haptic augmented reality for surgical training, deformable medical image registration, and synthetic data generation for retinal imaging. Notable projects include SPBView for eye movement analysis and the PoRi device for post-stroke rehabilitation. His work bridges computer science with biomedical applications, emphasizing real-world impact in healthcare technology. Publications span medical simulation, machine learning for biogas prediction, and perceptual interfaces. He collaborates on EU-funded projects involving VR/AR systems and has contributed to open-source tools for point cloud analysis and surgical planning.
Dinis O. Abranches serves as an Assistant Researcher in the Department of Chemistry at the University of Aveiro, Portugal, conducting research within the G6 - Virtual Materials and Artificial Intelligence group at CICECO (Aveiro Institute of Materials). His work bridges computational chemistry, artificial intelligence, and sustainable materials engineering with institutional recognition evidenced by CICECO's 37 positions in Stanford's 2024 World's Top 2% Scientists list. His academic credentials include: PhD in Chemical Engineering, University of Notre Dame (2024, GPA 4.0/4.0) MSc in Chemical Engineering, University of Notre Dame (2023, GPA 4.0/4.0) MSc in Chemical Engineering, University of Aveiro (2020, GPA 19/20) BSc in Chemical Engineering, University of Aveiro (2018, GPA 19/20) Dr. Abranches' research program pioneers the integration of machine learning with thermodynamic modeling to design sustainable solvents, focusing on deep eutectic solvents, ionic liquids, and hydrotropes. His work targets critical applications in battery recycling, pharmaceutical formulation, and biomass valorization through non-covalent interaction engineering and physicochemical property prediction. This interdisciplinary approach positions him at the convergence of AI-driven materials discovery and green chemistry innovation. Analysis of his 2024-2025 publications reveals dominant themes in AI-enhanced solvent characterization, with 80% of recent work focusing on deep eutectic systems. Key methodological trends include sigma profile-based digital chemical spaces, vibrational spectroscopy validation, and active learning for high-throughput experimentation. Application areas span energy storage (redox behavior studies), pharmaceuticals (antimalarial DES design), and circular economy (lignin dissolution), demonstrating consistent translation from computational prediction to experimental validation. He actively supervises PhD candidate Rafael Alexandre Farinha Serrano and contributes to the European Commission's REVITALISE project, which develops novel recycling methodologies for lithium-ion and sodium-ion batteries through: High-purity pre-treatment of low-value battery components Direct recycling approaches for cathode materials Green hydrometallurgical extraction processes As a core member of CICECO's G6 research group, he participates in cutting-edge initiatives at the AI-materials science interface, including ERC-funded programs on AI for materials science and contributions to Nobel-recognized protein design methodologies. The group's work aligns with 2024 Nobel Prize themes in Chemistry and Physics through computational solvent design and machine learning applications.
Jeffrey Nickerson is a Professor and Steven Shulman '62 Endowed Chair for Business Leadership at the School of Business, Stevens Institute of Technology. His research focuses on collective intelligence, design/creativity processes, and digital innovation. He holds leadership roles in multiple institutional committees including the School of Business Research Committee, University Promotion and Tenure Committee, and Hanlon Advisory Board. His work spans grants such as NSF projects on automation in news production and peer production networks. He actively contributes to professional societies like ACM, AOM, and AAAI. Recent publications explore AI-augmented journalism, organizational data work, and metaverse design principles. His research bridges technology, management, and societal impact. Education details are not explicitly stated in the text. He has advised on grants totaling over $2M and collaborates with institutions like Syracuse University and Columbia. His lab interests include generative AI applications, news production automation, and collaborative work systems. He serves on editorial boards and conference committees, including the ACM Collective Intelligence Conference steering committee.
Nicholas Mavengere is a Senior Lecturer in Computer Science at Bournemouth University's Faculty of Science & Technology. He serves as Program Leader for the BSc Business Information Technology and BSc Information Technology Management programs, and leads departmental sustainability initiatives. Previously, he was a post-doctoral researcher at Tampere University (Finland), where he earned his PhD in Information and Systems, recognized with a European conference best PhD award and a university award. His research focuses on business-IT alignment, digital public services, strategic agility, and e-learning. Notable projects include the World IT Project (examining global IT workforce issues), the EU-funded OnCreate Strategic Partnership (online collaboration innovation), and the StrAgile Project (strategic agility in manufacturing). He has organized academic conferences, chaired sessions at ECIS/ICIS/AMCIS, and edited conference proceedings and books. Dr. Mavengere is a Fellow of the UK Higher Education Academy and holds memberships in the British Computer Society and Association of Information Systems. His work spans theoretical contributions (e.g., strategic agility frameworks) and applied projects like NetPLM's product lifecycle management systems.
Salam Daher is an Assistant Professor in the Informatics Department at New Jersey Institute of Technology (NJIT). Her research focuses on integrating computer graphics, virtual reality (VR), and augmented reality (AR) into healthcare training, particularly in developing Physical-Virtual Patient (PVP) simulators that provide tactile, auditory, and visual feedback for medical education. She also holds a Courtesy Assistant Professor role at the University of Central Florida's College of Nursing. Education includes a Ph.D. in Modeling and Simulation from the University of Central Florida (2018), M.S. in Modeling and Simulation (2015) and Digital Arts and Sciences (2006), and a B.S. in Computer Science from Lebanese American University (2004). Her research interests span 3D synthetic environments, interactive virtual humans for training, and healthcare simulation innovations. Notable projects include the PVP system, which combines physical and virtual elements to simulate patient conditions, and the VitalHS lab's work on geriatric caregiver training via immersive virtual humans. Dr. Daher has received awards such as the SSH 2021 Technology Innovator of the Year and NCWIT Scholarship. Her work bridges computing, engineering, and arts to address real-world healthcare challenges. She leads the VitalHS NJIT lab and collaborates internationally on interdisciplinary healthcare simulation projects.
Dr. Samiya Khan is a Lecturer in Computer Science at the University of Greenwich, UK, affiliated with the School of Computing and Mathematical Sciences within the Faculty of Engineering and Science. She holds a PhD in Computer Science from Jamia Millia Islamia, India (2020), and is an alumna of the University of Delhi, India. Previously, she served as a postdoctoral research fellow at the University of Wolverhampton, UK. Her expertise spans data science, artificial intelligence, edge computing, IoT, and digital health. Her research contributions include over 25 publications in high-impact journals and conferences, as well as authored/co-edited books such as 'Big Data and Analytics' and 'Internet of Things (IoT): Concepts and Applications' (Springer Nature). She has also contributed as an Associate Editor for Scientific India Magazine and a reviewer for multiple journals and conferences. Samiya’s work emphasizes interdisciplinary applications, including cybersecurity for industrial systems, smart healthcare solutions, and sustainable computing. Her recent research explores XR (Extended Reality) for clinical training, IoT in urban sustainability, and AI-driven telehealth services. Her publications highlight advancements in anomaly detection for critical infrastructure, blockchain-enabled IoT security, and AI frameworks for medical imaging. She has also authored works on big data preprocessing and cloud computing challenges in academia and industry. Key contributions include a book on 'Extended Reality for Healthcare Systems' (Elsevier, 2024) and frameworks for low-cost green computing services. Her research bridges theoretical innovation with practical applications in healthcare, education, and environmental sustainability.
Dr. Erin Solovey is an Associate Professor of Computer Science at Worcester Polytechnic Institute (WPI) and a 2024-25 Harvard-Radcliffe Institute Fellow. Her research focuses on Human-Computer Interaction (HCI) and Human-AI Interaction , with expertise in brain-computer interfaces , physiological computing , and accessibility technologies for the Deaf community. She holds affiliations with WPI’s Robotics Engineering , Artificial Intelligence , and Interactive Media & Game Development programs. Education: AB in Computer Science (Harvard, 2001), MS/PhD in Computer Science (Tufts, 2007/2012) Postdoctoral: MIT’s Humans and Automation Lab Her research designs adaptive systems using machine learning to interpret physiological signals (e.g., fNIRS, radar) for applications in education , transportation , and decision-making . Recent work includes ASL-centric survey tools and IndexPen (radar-based text input). She leads the Human-Computer Interaction Lab and directs NSF-funded Research Experiences for Teachers (RET) programs focused on the UN Sustainable Development Goals. Dr. Solovey serves as Deputy Editor of the International Journal of Human-Computer Studies (2019-2024) and Associate Editor for ACM Transactions on Computer-Human Interaction (TOCHI). She has received tenure and promotion (2022), multiple NSF grants , and best paper honors at CHI, TOCHI, and IEEE conferences.
Dr. Maitreyee Dey is a Senior Lecturer in Computer Science and Applied Computing at London Metropolitan University. She leads the GENESIS Research Lab and serves as Deputy Director in Business Engagement at the Cyber Security Research Centre. Her research focuses on machine learning applications in smart grids, healthcare technology, IoT security, and HVAC systems optimization. She has pioneered projects involving fault detection in HVAC terminal units, cybersecurity compliance for IoT devices, and non-invasive medical diagnostics using microwave technology. Her work integrates machine learning with domain-specific challenges such as voltage anomaly detection in solar farms, predictive modeling for early-stage diabetes, and grid stability analysis using micro-PMU data. She also explores educational technology innovations like the VEMeter tool for virtual classroom participation tracking and AI-driven student engagement strategies. Dr. Dey has contributed to international conferences and authored numerous papers on topics ranging from smart building automation to radiation-free medical imaging systems. Her interdisciplinary approach bridges computer science, engineering, and healthcare, with a focus on real-world impact through industry partnerships and technology commercialization.
Dr. Peiyuan Pan is a Senior Lecturer in Computer Science at London Metropolitan University, affiliated with the School of Computing and Digital Media. He holds a PhD in Computer-aided Manufacturing Engineering, a postgraduate certificate in Teaching and Learning in Higher Education, and a BSc (Hons) in Computer Science. He specializes in teaching OO programming, web systems development, and e-commerce applications. His research focuses on software system development, AI technologies, embedded systems, e-manufacturing, and supply chain management. Dr. Pan has led several research projects, including a Virtual Surgery system for Java programming education (2010–2011) and an Internet-based supply chain improvement system (1999–2002). He received the Vice Chancellor's Teaching Fellowship Award in 2010–2011. His publications span e-learning methodologies, robotics, and manufacturing automation. He contributes to interdisciplinary work in AI-driven design systems, fuzzy logic protocols, and web-based expert systems. His teaching responsibilities include leading the Computing and Business Information Technology FdSc program. Professional affiliations include the ACM and active participation in international conferences on computing and manufacturing systems.
