Fabian Fagerholm is an Assistant Professor in the Department of Computer Science at Aalto University. His work bridges software engineering, human-computer interaction, and empirical research methodologies. He actively participates in research groups such as Software and Service Engineering (SSE) and Human-Computer Interaction and Design (HCID). Fagerholm's research explores: Continuous experimentation in software development Developer cognition and mental models Agile methodologies and team dynamics Low-code platforms and end-user programming Software engineering education and pedagogy His publications reflect a strong empirical focus, with recurring themes of human factors in technical systems and educational innovation. He has received notable awards including: Journal of Systems and Software Best Paper Award (2018) EUROMICRO SEAA Distinguished Paper Award (2017) Teacher of the Year (2013) Nokia Foundation Scholarship (2013) Fagerholm contributes to software engineering infrastructure through tools for experimentation and boundary artifacts, enhancing collaboration in distributed teams.
Dr. Muhammad Umar Farooq is a Lecturer and Module Director at York St John University's London Campus , specializing in Business, Marketing, and Project Management education for postgraduate students. He joined the university in 2022 and previously held roles as an Associate Lecturer at the University of West London, while also contributing to teaching and supervision at the University of East London and Regent’s University London. He holds a Doctorate in Business Administration (DBA), an MBA, and a Postgraduate Diploma in Leadership and Strategy, coupled with the prestigious Fellow of the Higher Education Academy (FHEA) distinction. His research focuses on digital marketing, SME innovation, AI applications in content creation, and project management strategies. Notable areas include generative AI for SME competitiveness, sustainable lean practices in construction, and agile methodologies in software development. He actively contributes to academic governance as an external examiner at Northumbria University and the University of Huddersfield. Teaching modules include Marketing Communication , Digital Marketing Strategy , Strategic Project Management , and Applied Research Project . His work bridges academic rigor with industry insights, emphasizing practical applications for organizational success. Research Awards: Recognized as FHEA. Professional Contributions: External examiner roles, LinkedIn engagement, and RaY Postgraduate Research supervision.
Joseph Eremondi is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science, Canada. He began his tenure in 2024 after serving as a Royal Society Newton International Fellow at the University of Edinburgh, where he conducted postdoctoral research with Ohad Kammar in the Laboratory for Foundations of Computer Science. He earned his PhD from the University of British Columbia (UBC) under the supervision of Ron Garcia at the UBC Software Practices Laboratory. His research is centered on programming languages theory, with a strong focus on type systems that enhance software reliability and usability. He is particularly known for his work in dependent types, gradual typing, and the integration of both paradigms. His research interests include: Dependent pattern matching and its semantic foundations Gradual dependent types and approximate normalization Error message generation and usability in dependently typed languages Static analysis using set constraints and SMT solvers Theoretical properties of reversal-bounded counter automata and shuffle operations His recent publications, appearing in premier venues like POPL, ICFP, and CPP, reflect a consistent trajectory toward making advanced type systems more accessible and practical. Key themes include coverage semantics for dependent pattern matching, formal models of gradual dependent typing, and improving the developer experience through better tooling and error diagnostics. Notable scientific recognitions include the NSERC Discovery Grant (awarded in 2025) and the prestigious Royal Society Newton International Fellowship. These awards underscore the impact and promise of his research program on the usability of dependently typed programming languages. Joseph is actively mentoring and recruiting graduate students, particularly in areas such as dependently typed programming (Lean, Agda, Idris, Coq), gradual typing, live programming environments, and static analysis. He emphasizes close collaboration within a small, focused research group. He has also served on program committees, including for TyDe and POPL Artifact Evaluation, demonstrating active engagement in the programming languages community. His work bridges theoretical rigor with practical implementation, evident in his artifact releases on GitHub and integration with tools like Ott and DrRacket. He maintains a personal website and open-source repositories that support reproducibility and community involvement.
Zohreh Pourzolfaghar is an Assistant Professor in Management Information Systems (MIS) at the School of Business, Maynooth University (MU). She serves as Director of the IT-Enable Innovation Programme and cluster lead for the Digital Construction cluster at the Innovation Value Institute (IVI). She is also a member of Lero, the SFI Research Centre for Software. Education: PhD in Project Management (University Putra Malaysia, 2011); MSc in Management Systems and Productivity; BSc in Software Engineering. Research Interests : Focuses on digital transformation for SMEs, information management in construction, and smart city architectures. Leads EU-funded projects like EnTrust (agri-data governance), DIGI+ (sustainable digital transformation), and DigiFABs (food & beverage SMEs). Co-PI for projects such as GDI-IE (genomic data infrastructure) and PERFORM (digital retail innovation). Grants & Awards : Secured funding from SFI, Enterprise Ireland, and EU. Notable awards include the BIM Hero Award (2022) and Research Engaged Award (2022). Teaching : Courses include Digital Business, Digital Technology Governance, and Enterprise Architecture. Advises PhD students in MIS and digital transformation. Industry Collaboration : Works with firms like ArcDox and Corballis Technologies on BIM standards and data governance. Active in national and EU policy networks, including NSAI committees.
Matt Vidal is Reader in Sociology and Political Economy at Loughborough University London's Institute for International Management and Entrepreneurship. He holds a PhD in Sociology from the University of Wisconsin-Madison and has previously been a postdoctoral fellow at the UCLA Institute for Research on Labor and Employment, a research fellow at the Weizenbaum Institute for the Networked Society, and a visiting researcher at Paris Dauphine University and the Max Planck Institute for the Study of Societies. His research focuses on work, organizations, employment relations, labor markets, capitalist growth regimes, and capitalist crises. Vidal has developed an original theoretical framework called 'organizational political economy,' which integrates concepts from organization theory into a classical Marxist framework. His work emphasizes how contradictory developments in labor management lead to divided management approaches, where some managers adopt best practices while others settle for 'good enough' solutions, making capitalist management increasingly a source of organizational inefficiency. Vidal's research methodology combines rich empirical fieldwork (including interviews with 109 individuals across 31 firms and 169 hours of direct observation) with rigorous theoretical synthesis. His work examines not only manufacturing but also extends to education, healthcare, software development, and other sectors where managers face contradictory pressures between standardization versus discretion and deskilling versus upskilling. His 2022 book Management Divided: Contradictions of Labor Management published by Oxford University Press has received significant acclaim from prominent scholars across multiple institutions, establishing him as an important voice in critical management studies and labor sociology. The book presents a synthetic theory explaining why capitalist management often adopts relatively inefficient practices despite the existence of better alternatives. Vidal has also edited The Oxford Handbook of Karl Marx and Comparative Political Economy of Work , and has published over twenty articles or book chapters on work, human resource management, employment relations, labor markets, comparative political economy, and social theory. His contributions have been praised for their theoretical depth, empirical richness, and interdisciplinary approach that bridges sociology of work, organization theory, institutional theory, and Marxist analysis. His work has been recognized through extensive scholarly reviews and endorsements from leading academics including Paul Adler (University of Southern California), Ruth Milkman (CUNY Graduate Center), and Nathan Wilmers (MIT), who commend his ability to develop complex theoretical frameworks grounded in rich empirical evidence from American manufacturing.
Henrique O'Neill is an Associate Professor (with Habilitation) in the Department of Marketing, Operations and General Management at ISCTE - University Institute of Lisbon, Portugal. He is an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture. His research focuses on information systems adoption, organizational strategy, and process optimization in healthcare, finance, and public administration. Education: PhD in Business Organization and Management - University of Cranfield (1995) Master's in Electrical and Computer Engineering - Higher Technical Institute (1987) Bachelor's in Electrical Engineering - Higher Technical Institute (1983) Research Interests: His work spans business/IT strategy, systems modeling, process analysis, and technology implementation. Key domains include healthcare informatics, banking systems, and Industry 4.0 applications. He emphasizes practical solutions for organizational performance through technology integration. Publication Trends: Recent articles explore intelligent business systems, telemedicine, design science methodologies, and supply chain innovation. His work consistently bridges theoretical frameworks with sector-specific applications in healthcare, education, and logistics. Professional Engagement: Member: Portuguese Telemedicine Association, Order of Engineers Commissioner: INEM (National Institute of Medical Emergency) reform study Director: Center for IT Development (2010-2014) Advising & Projects: Supervises 7 graduate students (1 PhD, 6 Master's). Leads EU-funded projects including: Atlantic Crossing (2024-2025): US-Portugal academic collaboration in AI/cybersecurity AAL4ALL (2011-2015): Ambient Assisted Living ecosystem UNITE (2000-2002): Ubiquitous teamwork platforms
Neelakantan R. Krishnaswami is a Professor of Computer Science at the University of Cambridge's Computer Laboratory , and a Fellow of Trinity College . His research focuses on the intersection of program verification, programming language design, and foundational topics like type theory and semantics. His work spans areas such as refinement types, parser design, separation logic for systems software, and the semantics of reactive programming. Notable contributions include the Datafun language for higher-order Datalog and the λert type theory for explicit refinement types. He has also developed foundational frameworks for verifying imperative programs using advanced type systems and logical relations. Key publications include 'Explicit Refinement Types' (ICFP 2023), 'flap: A Deterministic Parser with Fused Lexing' (PLDI 2023), and 'CN: Verifying Systems C Code' (POPL 2023). His work frequently addresses challenges in efficiency, correctness, and modularity for both functional and imperative systems. His awards include Distinguished Paper Awards at PLDI 2019 and POPL 2020. His research integrates theoretical rigor with practical tooling, exemplified by contributions to languages like Coq, Lean, and Haskell.
Ricardo Valerdi is a Professor and Department Head in the Department of Systems and Industrial Engineering at the University of Arizona's College of Engineering. He is a Distinguished Outreach Professor, Faculty Athletics Representative for the Big 12 Conference and NCAA, and a member of the Graduate Faculty. His academic journey includes positions at MIT (2005–2011) and continuous service at the University of Arizona since 2011, with current roles beginning in 2018 and ongoing leadership since 2020. His educational background includes a PhD in Industrial and Systems Engineering from the University of Southern California, an MS in System Architecture and Engineering from the same institution, and a BS in Electrical Engineering from the University of San Diego. Valerdi's research spans systems engineering, cost estimation, model-based systems engineering (MBSE), digital engineering, sports analytics, and test and evaluation of complex systems. He is renowned for his work on the Constructive Systems Engineering Cost Model (COSYSMO) and has pioneered the integration of virtual reality with MBSE. His recent publications reflect a strong focus on executable modeling, systems thinking education, cost modeling convergence, and applications in space and defense systems. His body of work from 2020 to 2025 shows a consistent trajectory in advancing digital engineering tools, integrating immersive technologies into systems design, refining parametric cost models, and assessing systems thinking competencies in education. The publications emphasize interdisciplinary applications, including space missions, ERP systems, and cyber resiliency, demonstrating a blend of theoretical and applied systems engineering. Best paper award, Journal of Systems Engineering International Council of Systems Engineering, Summer I 2016 Foreign Member, Mexican Academy of Engineering, Summer I 2016 Frank Freiman Award for Lifetime Achievement in Cost Estimation and Parametric Modeling, International Cost Estimating & Analysis Association, Fall 2015 Dr. Valerdi has advised numerous graduate students and led educational initiatives integrating industry-focused projects. He founded and co-edited the Journal of Enterprise Transformation and served as editor-in-chief of the Journal of Cost Analysis and Parametrics. He has received significant recognition and grants supporting research in systems engineering cost modeling, human systems integration, and digital transformation. His leadership extends to service as a Fulbright Scholar, visiting professor at West Point, and visiting fellow of the UK Royal Academy of Engineering. He leads research teams focused on cost estimation, digital engineering, and systems integration, often collaborating with defense and aerospace stakeholders. His labs and initiatives emphasize virtual reality integration, executable modeling, and systems thinking assessment. Future work is expected to further explore AI-driven cost models, digital twins for complex systems, and scalable frameworks for MBSE adoption across domains.
Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.
Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.
Dr Nour Ali is a Reader in the Department of Computer Science at Brunel University London , where she co-heads the Brunel Software Engineering Lab and serves as Vice-Dean of Education for the College of Engineering, Design and Physical Sciences. She holds a PhD in Software Engineering from Universidad Politecnica de Valencia, Spain, and a Major in Computer Science from Bir-Zeit University, Palestine. Research Interests: Software architecture for distributed and adaptive systems, integrating techniques like Model-Driven Engineering, Reverse Engineering, and Machine Learning. Teaching: Module leader for Software Project Management and supervisor of undergraduate group projects and final-year projects. Scientific Contributions: Over 70 publications in journals, conferences, and books. Key research areas include microservice architecture recovery, autonomic healthcare systems, and mobile self-adaptive architecture. Scientific Awards: Fellow of the Higher Education Academy (HEA). Membership: Deputy Editor-in-Chief for IET Software, member of multiple conference program committees, and reviewer for EPSRC, NWO, and other funding bodies.
Travis J. Fuerst is an Assistant Professor of Practice at the School of Engineering Technology within the Purdue Polytechnic Institute at Purdue University, West Lafayette. He has held this position since 2022, previously serving in the Department of Computer Graphics Technology from 2016 to 2022. Before returning to academia, he accumulated over 13 years of industry experience at The Boeing Company as an Engineering Workplace Coach, IT Project Manager, and Continuous Improvement Leader, complemented by 21 years of military service in the U.S. Army Reserves where he retired as a Major from USTRANSCOM in 2017. His academic credentials include: Master of Science in Technology (Product Lifecycle Management) from Purdue University (2002) Bachelor of Science in Computer Graphics Technology from Purdue University (2000) with a minor in Organizational Leadership and Supervision Professor Fuerst specializes in Product Lifecycle Management (PLM), Project Management, Continuous Improvement, and Configuration Management, integrating Lean Manufacturing and Six Sigma methodologies into both industrial applications and educational frameworks. His instruction emphasizes practical skill development for industry readiness, leveraging extensive real-world experience to bridge theoretical concepts with professional practice in engineering technology fields. His publication portfolio demonstrates a clear trajectory toward integrating Product Data Management systems into engineering education, with emphasis on digital enterprise solutions and pedagogical innovation. Key themes include parametric solid modeling applications, collaborative project-based learning frameworks, and curriculum development for PLM implementation across undergraduate programs, reflecting his dual focus on technological advancement and educational transformation. His recognition includes: Purdue Polytechnic 2013 Early Career Award Professor Fuerst actively mentors undergraduate and graduate students through project-based design learning that cultivates higher-order thinking skills, directly applying his industry expertise in risk analysis, resource allocation, and cross-functional team leadership. His curriculum development work demonstrates sustained commitment to advancing engineering education practices through practical, industry-aligned methodologies. His leadership experience spans Boeing's continuous improvement initiatives and U.S. Army cyber operations, providing a robust foundation for developing team-based project management approaches that emphasize operational efficiency and strategic problem-solving in academic settings.
Leslie Breitner is a Senior Faculty Lecturer at McGill University’s Desautels Faculty of Management, serving as Academic Director of the International Masters for Health Leadership (IMHL) and Co-Director of the Graduate Certificate in Healthcare Management (GCHM). She also co-created a GROOC (Global Registered Open Online Course) on social entrepreneurship. Her roles include teaching and academic leadership in healthcare financial management and nonprofit organizations. Affiliated with the Marcel Desautels Institute for Integrated Management, she contributes to interdisciplinary healthcare education and policy initiatives. Dr. Breitner holds a D.B.A. from Boston University, an MBA from Simmons College, and a BA from the University of Wisconsin. Her expertise spans healthcare financial systems, nonprofit management, and performance measurement in healthcare organizations. She has taught at the University of Washington (Evans School of Public Policy) and Harvard University (Kennedy School), where she received teaching excellence awards. Her research focuses on healthcare financial management and cross-sector collaboration, with publications in peer-reviewed journals and textbooks on accounting principles. Notable grants include projects on fiscal planning for children’s services in Bosnia, public management training in Azerbaijan, and disaster response funding analysis with FEMA. Awarded Teacher of the Year (2005) and Dean’s Award for Teaching Excellence (2004), she emphasizes practical financial literacy and leadership training for healthcare professionals. Her current work integrates global healthcare leadership development through executive education programs and online platforms.
Reda Mastouri is an Adjunct Professor in the Department of Data Sciences within the College of Computer and Information Sciences at Saint Peter’s University. He combines academic roles with 12 years of industry experience as a Lead Cyber Security Engineer and Product Consultant, collaborating with Fortune 200 and 500 companies. His teaching includes courses such as DS-520 Data Analysis, DS-530 Big Data, and CS-332 Advanced Computing. Ph.D., AI & Data Sciences, Saint Peter’s University M.Eng., Telecommunication and Network Engineering, ENSA-M Cadi Ayyad University M.S., Data Sciences, Saint Peter’s University B.S., Computer Sciences, New Jersey Institute of Technology B.A., Applied Mathematics, Rutgers University His scholarly work focuses on AI-driven algorithms for truth demystification and cluster computing applications in high-fidelity image/video forgery detection within cybersecurity. Additional expertise spans DevSecOps, enterprise architecture, and software economics, with a dedication to innovation in business strategy and technology integration. Dr. Mastouri’s research trends emphasize heterogeneous ad hoc networks, collaborative honeypot architectures, and blockchain-based security models for IoT. His work addresses distributed attack detection, false positive/negative reduction, and protocol optimization, aligning with his specialization in cybersecurity and artificial intelligence. Certified Splunk Super User Palo Alto Networks Certified Cybersecurity Associate (PCCSA) CyberArk Certified Trustee Certified Scrum Professional SFPC Certified 10-Hr OSHA Training for the Construction Industry Certified Project Management Essentials Certified (PMEC)™ Lean Six Sigma Yellow Belt (ICYB) CPR & AED Certified AWS Certified Developer Associate Scrum Foundation Professional Certificate NSE 1 Network Security Associate NSE2 Fortinet's Network Security Expert
Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.
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