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Gert SmolkaView profile

Professor

• Computational Logic
• Interactive Theorem Proving
• Type Theory
• +3 more

Gert Smolka is a Professor of Computer Science at Saarland University, affiliated with the Saarland Informatics Campus. He has established himself as a leading researcher in theoretical computer science with significant contributions to computational logic, type theory, and programming languages. Professor Smolka's research focuses on Computational Logic , Interactive Theorem Proving , Type Theory , and Programming Languages . His work bridges theoretical foundations with practical applications in formal verification. He has developed influential educational materials including Modeling and Proving in Computational Type Theory Using the Coq Proof Assistant , Introduction to Functional Programming and the Structure of Programming Languages using OCaml , and Programmierung - eine Einführung in die Informatik mit Standard ML , which have shaped teaching approaches in these specialized areas. Analysis of Professor Smolka's recent publications reveals a sustained focus on formal verification using the Coq proof assistant, with particular emphasis on mechanizing fundamental results in type theory, lambda calculus, and automata theory. His work consistently demonstrates how theoretical computer science concepts can be practically implemented and verified, creating a strong connection between abstract mathematical foundations and concrete verification techniques. Scientific recognition associated with Professor Smolka's academic lineage includes: ACP Doctoral Research Award 2010 (awarded to Guido Tack for his thesis) E.W. Beth Dissertation Prize 2008 (awarded to Marco Kuhlmann for his thesis) Professor Smolka has maintained an exceptionally productive doctoral supervision record spanning over 30 years, with students working on diverse but interconnected theoretical topics. His research group, the Programming Systems Lab at Saarland University, has been instrumental in advancing programming language theory and formal methods, particularly through their extensive work with the Coq proof assistant and related verification technologies. His Erdős number of 4 (via F. Baader -> K. Schlechta -> M. Magidor -> P. Erdös) reflects connections to broader mathematical research communities.

Arkaitz ZubiagaView profile

Senior Lecturer

• Natural Language Processing
• Computational Social Science
• Misinformation Detection
• +7 more

Arkaitz Zubiaga is a Senior Lecturer (Associate Professor) at Queen Mary University of London, where he co-leads the Social Data Science lab and serves as Director of Graduate Studies. He is also part of the leadership team of the Centre for Human-Centred Computing. His research sits at the intersection of Computational Social Science and Natural Language Processing, focusing on developing NLP and LLM methods for processing social media and Web data to tackle societal harms. Zubiaga's research interests concentrate on addressing problematic issues with damaging societal effects, including hate speech, misinformation, inequality, biases, and other forms of online harm. He investigates how LLMs can be misused for malicious purposes such as spreading misinformation, generating abusive content, or exacerbating societal biases. His work emphasizes detecting and addressing irresponsible AI use where content is falsely claimed to be human-generated. His publication record shows a clear trend toward addressing bias in detection systems, particularly in cyberbullying detection where swearing bias has been identified as a critical issue. His recent work explores zero-shot and few-shot learning approaches for cross-lingual applications, stance detection, and claim verification. The research spans multiple disciplines including computational linguistics, social computing, and AI ethics, with a growing focus on multimodal approaches and longitudinal model evaluation. 2024 OSNEM best survey award for work on session-based cyberbullying detection Zubiaga actively mentors PhD students, with Peiling Yi recently passing her viva (April 2025) and welcoming new PhD students Alaa Bazaid and Ali Khairallah. He serves as senior area chair for ACL 2025 and leads the HYBRIDS MSCA Doctoral Network. His work demonstrates a strong commitment to developing responsible AI systems that can detect and mitigate online harms while addressing critical issues of bias and fairness in computational approaches.

Stefan SperlichView profile

Professor

• Nonparametric Statistics
• Semiparametric Methods
• Small Area Statistics
• +7 more

Stefan Sperlich is a Full Professor and Director of the Research Institute for Statistics and Information Science at the University of Geneva's Geneva School of Economics and Management. He holds dual appointments in the Department of Econometrics and Statistics, with affiliations in both the Research Institute for Statistics and Information Science and the Institute of Economics and Econometrics. Dr. Sperlich earned his diploma in mathematics from the University of Göttingen and completed his PhD in economics at Humboldt University of Berlin. His academic career includes professorships at University Carlos III de Madrid (1998-2006) and the University of Göttingen (2006-2010), before joining the University of Geneva in 2010. Professor Sperlich's research spans nonparametric and semiparametric statistics , small area estimation , and impact evaluation methods . His work bridges theoretical econometrics with practical applications in development economics, policy evaluation, and poverty measurement. He has made significant contributions to specification testing, causal inference methodologies, and the development of robust statistical techniques for small area estimation. His research often addresses real-world problems through collaborations with international institutions and development programs. His recent publications reveal a strong focus on advancing methodological frameworks for small area statistics, causal inference, and nonparametric estimation. Key themes include developing robust inference techniques for linear mixed models, improving bandwidth selection methods, and creating model-free approaches to difference-in-differences estimation. His work increasingly integrates computational statistics with traditional econometric methods to address challenges in big data analysis and distributed data environments. Professor Sperlich has received numerous accolades including: Tjalling C. Koopmans Econometric Theory Prize (2000-2002) Augusto Gonzalez Linares award (2014) for attracting international talent Elected member of the International Statistical Institute (since 2025) Special rewards from the Economics Department at University Carlos III de Madrid (2004-2005) Grants from the Institute Flores de Lemus (2001-2003) As an advisor and researcher, Professor Sperlich has supervised numerous graduate students and led significant research initiatives. He co-founded the research center 'Poverty, Equity and Growth in Developing Countries' at the University of Göttingen and serves as a research fellow at the Center for Evaluation and Development in Mannheim, Germany. His consultancy work spans regional, national, and international institutions, with participation in development programs like EUROSOCIAL and UN assessment reports. He has secured multiple research grants supporting his work in statistical methodology and economic applications. Professor Sperlich leads research teams focused on nonparametric methods, small area statistics, and impact evaluation. His research group develops innovative statistical approaches for poverty mapping, causal inference, and composite indicator construction. The team maintains strong connections with statistical offices and international organizations, ensuring their methodological advances have practical applications in policy development and evaluation.

• Abdominal Radiology
• Artificial Intelligence in Medical Imaging
• Gastrointestinal Oncologic Imaging
• +5 more

Dr. Ashish P Wasnik serves as Clinical Professor and Associate Chair of Faculty Affairs in the Department of Radiology at the University of Michigan Medical School. He previously held leadership roles as Division Director of Abdominal and Cross-sectional Interventional Radiology and Medical Director of Abdominal Ultrasound. His affiliations span clinical academia, national radiology societies, and research consortia including the OPRA Consortium for rectal cancer studies. Dr. Wasnik's research focuses on artificial intelligence applications in abdominal imaging , with specialized expertise in Crohn's disease severity quantification, ovarian mass characterization, and gastrointestinal oncologic imaging. His work integrates morphomics and machine learning to develop objective disease assessment tools, particularly for bowel imaging and tumor response evaluation. Recent publications demonstrate consistent output in high-impact journals, with 8 articles from 2024-2025 covering AI in body imaging, appropriateness criteria development, and quantitative disease assessment. His work shows strong methodological diversity across CT enterography, MRI prediction modeling, and ultrasound diagnostics. Fellow of the American College of Radiology (FACR) Fellow of the Society of Abdominal Radiology (FSAR) Fellow of the Society of Radiologists in Ultrasound (FSRU) As a funded researcher with over 100 peer-reviewed manuscripts and 150 scientific presentations, Dr. Wasnik maintains active grant-supported projects. His leadership in national committees (ACR Appropriateness Criteria, RSNA Educational Exhibits) demonstrates sustained research impact. He has mentored through extensive educational contributions including two co-authored books and 100+ invited lectures. Dr. Wasnik contributes to academic medicine through his Deputy Editorship of Academic Radiology journal and service on the University of Michigan Medical School Advisory Committee for Clinical Track Appointments. His Weil Institute for Critical Care Research membership connects his imaging expertise to broader critical care applications.

Samual AmponsahView profile

Professor

• Development Economics
• Labor Economics
• Quantitative Research Methods
• +4 more

Samual Amponsah serves as Professor of Economics and Statistics at Tokyo International University's School of Economics, where he has held academic appointments since 2012 including Visiting Scholar, Lecturer, Associate Professor, and current Professor (2023–present). He also held a Part-time Lecturer position at Saitama University (2016). His educational background includes: Ph.D. in Economics from Tokyo International University (2013) M.A. in Economics from Tokyo International University (2007) Teacher’s Certificate ‘A’ from University of Cape Coast (1996) Amponsah's research centers on Development Economics and Labor Economics with strong methodological emphasis on Quantitative Research Methods and Statistics. His work investigates socio-economic disparities, health insurance systems, child labor dynamics, and inclusive business models, primarily through empirical analysis of Ghanaian and Japanese contexts. Key recurring themes include rural-urban income gaps, health shock impacts, and cocoa sector economics in West Africa. His 15 most recent publications reveal strong regional focus on Ghana (12/15) with emerging work on Japan (3/15), spanning journal articles, book chapters, and conference presentations. Methodologically, 87% employ quantitative approaches analyzing household surveys, financial diaries, and cross-sectional data, while 60% examine agricultural economies—particularly cocoa value chains. Thematic clusters include health economics (27%), labor dynamics (33%), and inclusive business models (20%). Scientific recognition includes: JSPS Postdoctoral Fellowship for Foreign Researchers (2016–2019) JSPS KAKEHI Grant as Coinvestigator (2016–2019) JSPS KAKEHI Grant as Lead Investigator (2019–2022) His grant activity demonstrates sustained research funding through Japan's premier scientific funding body, with progression from coinvestigator to lead investigator roles. While no formal students are listed in available records, his conference presentations frequently acknowledge collaborators including Koga Kyoko, Munier N., and Okada Yoshitaka. His work bridges academic research with practical development challenges through fieldwork in Ghanaian cocoa communities and policy-relevant analysis of Japanese socio-economic disparities. Amponsah maintains active research engagement through TIU Global Dialogues and international conferences, with no institutional lab or team affiliations explicitly documented.

Christian WilmsView profile

Researcher

• Computer Vision
• Object Recognition
• Image Segmentation
• +6 more

Christian Wilms is a Researcher at the University of Hamburg's Department of Informatics within the Faculty of Mathematics, Informatics and Natural Sciences, where he contributes to the Computer Vision Research Group's cutting-edge work in visual recognition systems. His research focuses on: Object recognition techniques including proposal generation, instance segmentation, and detection Foundation models for segmentation and superpixel-based algorithms Applications in agricultural monitoring, medical imaging, and industrial inspection Multi-modal signal processing for audio-visual perception Analysis of his recent publications reveals a strong emphasis on solving small object detection challenges through semi-supervised learning and attention mechanisms, particularly evident in orchard monitoring applications. His work consistently bridges theoretical advances in segmentation topology with practical implementations in drone-based systems and industrial anomaly detection. As part of the Computer Vision Research Group led by Prof. Simone Frintrop, Wilms contributes to major projects including InteGreatDrones, Crossmodal Learning, and NEUROBOTICS, which explore active perception, object pose estimation, and simultaneous localization in complex environments.

Neil SmalheiserView profile

Professor

• Neuroscience
• RNA Biology
• Synaptic Plasticity
• +5 more

Neil Smalheiser is a Professor in Psychiatry at the University of Illinois Chicago , with dual expertise in neuroscience and medical informatics . He directs two laboratories: one focused on small RNAs in brain function and disease, and another dedicated to informatics tools for hypothesis generation and data analysis. His research spans RNA biology , synaptic plasticity , pathogenesis of neuropsychiatric disorders , and text mining . Smalheiser has developed innovative tools like Anne O'Tate and Arrowsmith for biomedical literature analysis and leads NIH-funded projects on data synthesis and systematic reviews. Recent work explores exosomal RNA transfer , microRNA regulation , and automated indexing of scientific articles . He was named an ACMI Fellow (2023) and holds multiple NIH grants for medical informatics research. Smalheiser's lab investigates how RNA molecules influence synaptic plasticity and mental health, while advancing computational methods for evidence synthesis. Scientific Awards : ACMI Fellow (2023) NIH R01 Grants (2016–2020, 2023) NIH NIA P01 AG039347 Grant (2017–2018) Fellow of the American Medical Informatics Association (2021)

ChengXiang ZhaiView profile

Professor

• Intelligent information systems
• Information retrieval
• Data mining
• +4 more

ChengXiang Zhai is the Donald Biggar Willett Professor in Engineering at the University of Illinois at Urbana-Champaign , holding appointments in the Department of Computer Science , Carl R. Woese Institute for Genomic Biology , and the Department of Statistics . He leads research in intelligent information systems, with a focus on information retrieval, data mining, NLP, and machine learning. His TIMAN research group and DAIS explore applications in healthcare, education, and scientific discovery. He develops MOOCs on text retrieval and mining, and has published extensively on topics including LLM alignment, user simulation, and multimodal systems. Key Research Areas : Intelligent search engines, explainable AI, human-AI collaboration, biomedical informatics Recent Trends : LLM economics, knowledge overshadowing, just-in-time recommendation systems He has received the ACM Fellow title, SIGIR Salton Award , and multiple teaching honors including Rose Award and Graduate Mentoring Award . He serves as series editor for Springer Information Retrieval Book Series .

Chris TanasescuView profile

Professor

• Digital Humanities
• Computational Linguistics
• Natural Language Processing
• +5 more

Chris Tanasescu is Professor in the Faculty of Arts and an Adjunct Professor in both the Faculty of Arts and the Faculty of Engineering at the University of Ottawa, where he also serves as Senior Researcher in the School of Engineering and Computer Science. Since 2019 he additionally holds the Altissia Chair in Digital Humanities at the Université Catholique de Louvain (Belgium) within the Institut des civilisations, arts et lettres . Known artistically under the alias Margento , he is a poet, translator, and the Editor-at-Large for Romania and Moldova at Asymptote Journal . Education Doctor of Philology in American Studies (2008) Research Focus Tanasescu’s scholarship and creative practice intertwine computational methods with literary and cultural studies. His core interests include digital humanities, graph-theoretic approaches to poetry, natural-language processing for metaphor detection, and algorithmically assembled literature. Working at the intersection of code and verse, he investigates how network science can illuminate translation dynamics, poetic form, and cross-cultural circulation of texts. Recent Publications & Trends Across his 2016-2018 output, Tanasescu has advanced data-intensive approaches to poetry analysis, combining deep-learning models with humanistic inquiry. His work on metaphor detection leverages large poetry corpora and neural networks, while his anthology US Poets Foreign Poets demonstrates algorithmic translation and generative editing. Collectively, these projects expand digital literary studies into new computational territories. Teaching & Mentorship At the University of Ottawa he currently teaches courses such as DHN 1500 – Arts et sciences humaines numériques DHN 4100 – Digital Humanities Capstone Project DHN 4500 – Projet de fin d’études en sciences humaines numériques Office hours are held in Simard Hall, Room 327. International Appointments He has held visiting professorships at Université Paris 10 (2013-2019) and was a Senior Fulbright Postdoctoral Fellow and Visiting Professor in San Diego, CA (2010-2011). Creative & Editorial Work Beyond academia, Tanasescu curates and edits literary translation projects, fosters Romanian contemporary writing on the global stage, and actively participates in digital and performance poetry scenes worldwide.

Tim AlthoffView profile

Assistant Professor

• Data Science
• Natural Language Processing
• Mental Health Informatics
• +3 more

Tim Althoff is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he directs the Behavioral Data Science Group. His research combines data science, social network analysis, and natural language processing to develop computational methods for understanding human behavior, mental health, and public health through digital traces.

• Visual Object Recognition
• Learning from Small and Imbalanced Data
• Fine-grained Recognition
• +5 more

Dr. Paul Bodesheim serves as a Postdoctoral Researcher and Lecturer in the Computer Vision Group at Friedrich Schiller University Jena's Department of Mathematics and Computer Science. He is also a member of ELLIS (European Laboratory for Learning and Intelligent Systems) and ELLIS Unit Jena. His academic journey began with Computer Science studies at Friedrich Schiller University Jena, focusing on Digital Image Processing and Computer Vision, followed by PhD research on "Discovering unknown visual objects with novelty detection techniques." Dr. Bodesheim's research interests center on bridging computer vision with biodiversity applications. His work spans visual object recognition, fine-grained recognition techniques specifically applied to biodiversity research, novelty detection and open set recognition, and active learning approaches for lifelong learning systems. He has developed approaches that address challenges of learning from small and imbalanced datasets, which are particularly valuable in ecological monitoring where data collection can be challenging. His publication record reveals a strong trend connecting computer vision methodologies with biodiversity monitoring applications. A significant portion of his recent work focuses on automated visual monitoring systems for insects and mammals, particularly using camera traps for biodiversity assessment. His research demonstrates how techniques like deep learning, few-shot learning, and uncertainty quantification can be effectively applied to ecological challenges such as species identification and population monitoring. Best Paper Award at DeLTA 2024 (Few-Shot Learning with Novelty Detection) Best Paper Award at Anomaly Detection Workshop of ICML 2016 Best Poster Award at FEAST Workshop of ICPR 2014 Best Paper Honorable Mention Award at ACCV 2012 Dr. Bodesheim has supervised numerous theses across bachelor's, master's, and diploma levels, focusing on applications of computer vision to biodiversity monitoring. His research is supported by various projects including LEPMON (Monitoring Biodiversity of Moths), InsectAI (EU COST Action Network), and previously BACI and AMMOD projects. He serves as a reviewer for prestigious journals and conferences including IEEE TPAMI, JMLR, and CVPR workshops. He leads the Computer Vision and Machine Learning team within the Computer Vision Group and contributes to the development of the AMMOD (Automated Multisensor station for Monitoring of species Diversity) system, which integrates multiple sensor technologies for comprehensive biodiversity monitoring. His work demonstrates a strong commitment to developing practical computer vision solutions for pressing ecological challenges.

• Media Studies
• Journalism
• Alternative Media
• +7 more

Tine Ustad Figenschou is a Professor at Oslo Metropolitan University in the Department of Journalism and Media Studies within the Faculty of Social Sciences. She is actively engaged in multiple research groups including Digital Journalism, Media Industries, Platforms and Politics (MIPP), and the Media Narrative Group (MNG). Her office is located at Pilestredet 48, 0167 Oslo (Office S454) and she can be reached at +47 924 36 104 (mobile) or +47 672 38 438 (office). Professor Figenschou's research focuses on alternative media, journalism studies, and political communication, particularly examining the relationship between mainstream journalism and its challengers. Her work explores how alternative media navigate the journalistic field, establish legitimacy, and interact with traditional media institutions, governance units, and the state. She has made significant contributions to understanding boundary work in the Nordic media model and the strategic action field of journalism. Her recent publications reveal a strong focus on media contestation, with particular attention to far-right media dynamics, alternative news ecosystems, and the evolving nature of journalistic authority in digital environments. The publications demonstrate a consistent analytical framework examining how media actors position themselves within broader media ecosystems and how they construct legitimacy through discourse and practice. Professor Figenschou leads and participates in significant research projects including 'Unpacking the modern working class: Life chances, social cohesion and recognition in an age of migration' and 'Far-right online and societal resilience (FREXO)', which examines how digital media has created new opportunities for far-right groups to produce and distribute content. She maintains an active research agenda with 73 scientific publications, 5 research reports, and 65 dissemination activities. Her collaborative work, particularly with Karoline Andrea Ihlebæk, represents a substantial portion of her output and demonstrates her strong position within the Nordic media studies research community.

• Theoretical Computer Science
• Computational Complexity
• Data Structures
• +3 more

Prof. Michal Koucký is a faculty member at the Institute of Informatics, Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on theoretical computer science, particularly computational complexity, data structures, algorithms, and combinatorics. He leads significant research projects including EPAC (supported by GA CR) and LBCAD (funded by ERC). His educational background includes a Ph.D. from Rutgers University (2003) and a Master's degree from Charles University (1998). Prof. Koucký's research spans multiple areas of theoretical computer science with emphasis on computational complexity , data structures , algorithms , and combinatorics . His work explores fundamental questions about computational efficiency, lower bounds, and algorithmic techniques. He has made significant contributions to edit distance algorithms, string processing, and circuit complexity. His research often bridges theoretical foundations with practical algorithmic applications, particularly in areas like dynamic data structures and approximation algorithms. An analysis of his recent publications (2019-2024) reveals a strong focus on string algorithms, particularly edit distance problems. His work develops novel approximation techniques, sketching methods, and sublinear-time algorithms for pattern matching. There's also significant work on data structure lower bounds and connections to popular complexity conjectures. His research demonstrates a consistent pattern of tackling fundamental problems with innovative approaches that push theoretical boundaries while maintaining practical relevance. Antonin Svoboda award for best dissertation in 2014 (awarded to Jan Bulánek) Best paper award at SAGT 2016 for 'The Big Match in Small Space' Best paper award at FOCS 2018 for 'Approximating Edit Distance Within Constant Factor in Truly Sub-Quadratic Time' He has successfully advised numerous graduate students including Pavel Dvořák, Karel Král, Debarati Das, and Jan Bulánek who have gone on to positions at prestigious institutions like Bristol, Google (Zurich), and Penn State. His research is supported by significant grants including the EPAC project (GA CR) and LBCAD project (ERC), demonstrating the impact and relevance of his work in theoretical computer science. Prof. Koucký is an active member of the theoretical computer science community, regularly presenting at major conferences including STOC, FOCS, ICALP, and SODA. His work often involves collaborations with researchers from institutions worldwide, contributing to a vibrant research environment at Charles University's Institute of Informatics.

Michael TsikerdekisView profile

Associate Professor

• Cybersecurity
• Social Computing
• Online Deception
• +4 more

Michael Tsikerdekis is an Associate Professor of Computer Science at Western Washington University in Bellingham, WA, where he maintains an active research program at the intersection of computer science and social systems. He is also an IEEE Senior Member and a 2024-2025 U.S. Fulbright Scholar. His work focuses on cybersecurity, particularly in detecting and preventing online deception and related social engineering attacks. Dr. Tsikerdekis is the academic lead for PISCES (Public Infrastructure Security Cyber Education System) and is actively involved in cybersecurity education initiatives. Dr. Tsikerdekis received his educational training through the following institutions: Ph.D. in Informatics (Computer Science) from Masaryk University, Czech Republic (2013) BSc/Mgr. in Forestry and Natural Environment from Aristotle University, Greece (2008) Dr. Tsikerdekis's research interests center around cybersecurity, social computing, online deception, and machine learning. His work explores the intersection of computer science and social systems, with a particular focus on detecting and preventing online deception and related social engineering attacks. He has developed methodologies for identity deception detection in social media using behavioral analysis and network data. His research spans multiple domains including misinformation detection, network security, and the psychological effects of online hoaxes. Dr. Tsikerdekis also investigates how automated feedback systems impact learning outcomes in educational technology contexts. His publications reveal a strong focus on practical cybersecurity applications, with particular emphasis on identity deception detection, network anomaly detection, and misinformation analysis. Over the past decade, his research has evolved from theoretical models of online behavior to applied security solutions using machine learning and deep learning techniques. His work bridges the gap between academic research and real-world cybersecurity challenges, with applications in social media security, IoT protection, and network defense systems. Dr. Tsikerdekis has received several notable recognitions for his work: IEEE Senior Member 2024-2025 U.S. Fulbright Scholar Dr. Tsikerdekis is actively involved in mentoring and advising students, particularly those interested in cybersecurity and social computing research. He encourages undergraduate and graduate students to engage in research projects through independent study and graduate studies. His grant funding includes significant projects such as: Public Infrastructure Security Cyber Education System - Northwest (PISCES-NW) with Pacific Northwest National Laboratory (2024) Enhancing Cybersecurity Education and Infrastructure for Small Public Entities in Greece (2024 Fulbright Grant) Mini-Grant Award from Western Washington University (2023) Public Infrastructure Security Cyber Education System - Northwest (PISCES-NW) with Pacific Northwest National Laboratory (2020) Dr. Tsikerdekis leads the PISCES (Public Infrastructure Security Cyber Education System) initiative, which focuses on providing cybersecurity education and infrastructure support for small public entities. Through this program, he works with students and community partners to develop practical cybersecurity solutions. He is also actively involved in book authorship, having written "Overnight Hercules for Network Security: Become a Security Analyst" and "Grokking Relational Database Design," both aimed at making complex technical topics accessible to learners.

Robert PellerinView profile

Professor

• Industrial engineering
• Management information systems
• Logistics
• +7 more

Robert Pellerin is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, where he has established himself as a leading expert in Industry 4.0, digital transformation, and production management systems. With over three decades of academic experience, he holds the Jarislowsky/AtkinsRéalis Research Chair in International Project Management and maintains active affiliations with multiple research centers including SAGIP, CIRODD, Laboratoire Poly-Industries 4.0, IVADO, GMT, and CIRRELT. His research focuses on the industry of the future and digital society, with particular emphasis on Industry 5.0 (human-centered Industry 4.0 practices), real-time production management, project planning and control, management information systems (ERP, MES, PLM), process reengineering, and digital transformation. Professor Pellerin's work bridges theoretical research with practical industrial applications, addressing critical challenges faced by manufacturing SMEs in their digital transformation journeys. His extensive publication record includes 347 scholarly works, with recent research trending toward human-centered AI applications in manufacturing, digital twin technology for warehouse and mining operations, and optimization of production planning under uncertainty. His scholarly impact is evidenced by multiple top-cited and top-viewed papers in leading journals such as the Journal of Intelligent Manufacturing and International Journal of Production Research. Selected Awards and Distinctions: Best Student Paper Award 2024, Administrative Sciences Association of Canada Conference Best paper at the 34th European Modeling & Simulation Symposium (2022) Multiple Top cited/viewed paper awards in leading journals (2018-2020) Excellence in Teaching Award from Polytechnique Montréal (2012) Best Professor of Industrial Engineering for Graduate Studies Award (2011) Majdi Najm Award from the SAP University Alliance (2007) Professor Pellerin has supervised 12 PhD students and 49 Master's students to completion, with recent theses focusing on Industry 4.0 implementation in SMEs, digital transformation project management, and human-AI interaction in manufacturing settings. His research is supported through multiple collaborative projects with industry partners, particularly in the mining, construction, and manufacturing sectors. He has developed innovative frameworks for business process reengineering and project portfolio management that have been adopted by several industrial organizations. As a member of the Laboratoire Poly-Industries 4.0 and other research centers, he leads interdisciplinary teams exploring the integration of emerging technologies in production systems. His current work focuses on developing human-centered AI solutions that enhance rather than replace human capabilities in manufacturing environments, aligning with the emerging Industry 5.0 paradigm.