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FOONG Pin SymView profile

Academic

• Patient decision aids
• Mobile health applications
• Lifestyle change and motivation
• +4 more

FOONG Pin Sym is a Senior Research Fellow at the Saw Swee Hock School of Public Health, National University of Singapore. She leads the Telehealth Core, a research center focused on developing and studying eHealth projects. Her academic background includes a Master's in Human Computer Interaction Design and a PhD in Interactive Media. Her research emphasizes patient-centered technologies, with a focus on chronic disease prevention, caregiver support, and mobile health applications. Her work addresses challenges in healthcare decision-making, end-of-life care, and lifestyle interventions. Notable projects include developing digital tools for diabetes risk communication, caregiver decision support systems, and studies on telehealth during the pandemic. She teaches courses in Human-Computer Interaction, Mobile Interaction Design, and Technology for Older Adults. Her research spans qualitative and quantitative methodologies, including randomized controlled trials and participatory action research. She actively participates in interdisciplinary collaborations across health, technology, and design sectors to improve patient and caregiver outcomes. Education: PhD in Interactive Media, Master's in Human Computer Interaction Design Labs/Teams: Leads the Telehealth Core research team Grants/Projects: SMART-GDM Study, Vita project for dementia care

Anoop SarkarView profile

Professor

• Multilingual Natural Language Processing
• Machine Learning for Natural Language Processing
• Statistical Machine Translation
• +3 more

Anoop Sarkar is a Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Natural Language Lab. His research focuses on multilingual natural language processing, machine learning applications in NLP, and machine reading with visualization techniques. He has held significant grants including NSERC Discovery Accelerator Supplements (2012–2023) and multiple Google/IBM Faculty Awards. His teaching includes advanced NLP courses and compiler design. Over 38 students have graduated under his supervision, with a strong emphasis on computational linguistics and machine translation. His research explores areas such as statistical machine translation, decipherment of ancient scripts, and semi-supervised learning. Key software contributions include the TroFi toolkit and metaphor dataset. He has served as area chair for EMNLP (2023) and co-chair of NAACL (2015). The Natural Language Lab hosts weekly meetings open to collaboration. His work bridges theory and application, addressing challenges in low-resource language processing. Recent publications (2023–2024) highlight advancements in entity linking, cognate alignment, and structured prediction. Grants span foundational and applied NLP research, supported by NSERC and industry partnerships. His advising spans 12 PhD and 26 MSc graduates, reflecting a sustained impact on the next generation of NLP researchers.

• Information Systems
• Healthcare IT
• Knowledge Management
• +5 more

Atreyi Kankanhalli is a Professor at the National University of Singapore, specializing in Information Systems with a focus on knowledge management, healthcare IT, and digital innovation. Their research spans over three decades, with prolific contributions in top journals like MIS Quarterly, Journal of AIS, and Information & Management. They have co-authored over 150 papers addressing topics such as crowdsourcing, online communities, and the impact of AI on scholarly practices. Notable work includes studies on user adherence to health apps, innovation in public sector data utilization, and the ethical challenges of generative AI in peer review. Kankanhalli has also led research on global virtual teams and digital technologies' role in social justice, reflecting a commitment to both technical and societal dimensions of information systems. Education & Background: While specific degree details are not provided, their extensive publication history and academic roles imply advanced qualifications in Information Systems or related fields. They have collaborated with global researchers across institutions like NUS, University of Illinois, and Singapore Management University. Research Themes: Core areas include digital health interventions (e.g., fitness app adherence), organizational innovation via open data and crowdsourcing, and the socio-technical challenges of AI in academia. Their work often bridges theoretical frameworks with practical applications, such as healthcare decision support systems and policy-driven technology adoption. Impact & Influence: As an editorial board member and frequent conference contributor (e.g., ICIS, PACIS), Kankanhalli shapes the field's research agenda. Their recent focus on generative AI's implications highlights proactive engagement with emerging technologies' ethical and methodological challenges.

Michael GröschelView profile

Professor

• Business Process Management
• Digital Transformation
• Business Models
• +3 more

Prof. Dr. Michael Gröschel is a Professor of Business Informatics at the Faculty of Computer Science, Mannheim University of Technology. His expertise spans Business Process Management (BPMN, Process Mining), Digital Transformation, and innovative Business Models. He teaches courses on BPM, project management, and e-business, often collaborating with industry clients through student projects. As a consultant, he specializes in BPMN training and IT-driven business strategy. His work emphasizes practical applications of IT tools like RPA and low-code platforms. Recent publications focus on RPA bot performance evaluation, AI in automotive trade, and business model-IT alignment. Prof. Gröschel’s research bridges academic insights with real-world challenges, particularly in leveraging technology for business innovation. He has authored books and articles on business intelligence tools, mobile business strategies, and digital customer care. His consulting services include executive coaching for academic career advancement and enterprise IT management best practices. Office: Building A, Room 007c | Phone: +49 621 292-6764 | Professional website available for further engagement opportunities.

Silvia SantosView profile

Researcher

• Developmental Biology
• Cell Biology
• Cell Cycle & Chromosomes
• +3 more

Silvia Santos is a Group Leader at the Francis Crick Institute, leading the Quantitative Stem Cell Biology Lab since January 2018. Her research focuses on understanding cell decision-making during transitions, specifically cell division and differentiation in early development using human embryonic stem cells. She combines experimental techniques with theoretical approaches, including advanced microscopy, genomics, and computational modeling. Education and Career: PhD in Molecular and Cell Biology from EMBL-Heidelberg (2008), followed by postdoctoral training at Stanford University (2009-2014). She held an MRC Career Development Award at Imperial College London (2014-2017) before joining the Crick. Her work emphasizes interdisciplinary methods to study cellular processes in health and disease. Research Interests: Spatial-temporal control in cell decisions, stem cell differentiation, cell cycle regulation, and modeling embryonic development. She advocates for women in science and mentorship programs for early-career researchers. Key Achievements: Recipient of Marie Curie E-Star, EMBO, and HFSP fellowships. Recognized with the BioModels’ Model of the Year 2023 for contributions to systems biology. Her lab develops models like gastruloids to study embryonic development. Grants and Mentorship: Supported by MRC and other grants. Committed to fostering excellence in training and mentorship, previously chairing mentorship initiatives at Imperial College London. Labs and Teams: Quantitative Stem Cell Biology Lab at the Crick, collaborating with interdisciplinary teams on projects involving proteomics, genomics, and high-throughput screening.

• Urban Sustainability
• Transport Accessibility Analysis
• Urban Visualization
• +7 more

Dr Duncan Smith is an Associate Professor in GIS and Visualisation at the Centre for Advanced Spatial Analysis (CASA) at University College London (UCL). He serves as Programme Co-Director for the MSc Urban Spatial Science, focusing on digital visualisation and Geographical Information Systems (GIS) education. His research emphasizes urban sustainability, transport accessibility, and interactive urban visualisation, with a particular interest in global mega-cities like London, Singapore, and São Paulo. Dr Smith holds a PhD from UCL (2011), an MSc from the University of Edinburgh (2005), and a BA (2003). His recent projects include leading the ESRC-funded Driving Urban Transitions project (2024) exploring sustainable travel in small cities and outer metropolitan areas. Previously, he was Co-Investigator on the SIMETRI project (NSFC JPI Urban Europe) analyzing urban sustainability in London, the Randstad, and China's Greater Bay Area. His research interests span urban visualisation techniques, transport equity analysis, and polycentric urban development. Key areas include GIS-driven urban modelling, accessibility metrics, and the intersection of housing policy with sustainable urban form. His interactive visualisations, available on citygeographics.org , showcase innovative approaches to spatial data presentation. Dr Smith's work consistently addresses global sustainability challenges through geospatial methodologies. Recent studies highlight inequalities in transport accessibility and the spatial implications of urban policies. Upcoming projects will further investigate small-city mobility dynamics using multi-national collaborations with Amsterdam and Lisbon researchers.

• Requirements Engineering
• Goal Modeling
• Conceptual Modeling
• +7 more

Sotirios Liaskos is an Associate Professor in the School of Information Technology at the Faculty of Science, York University. He is a leading researcher in requirements engineering and conceptual modeling, with a focus on goal models, empirical evaluation, and model-driven engineering. Research Interests: Requirements Engineering Goal and Conceptual Modeling Empirical Software Engineering Model-Driven Design and Automation Uncertainty and Decision-Theoretic Reasoning in Models Applications in Blockchain and Reinforcement Learning His recent work emphasizes the empirical validation of modeling constructs, the integration of decision theory into goal models, and the automated generation of secure workflows and AI training environments. He has led and co-authored numerous experimental studies on model comprehensibility and semantic quality. Publication Trends: His publications consistently appear in top venues like ER, RE, and iStar. The last five years show a strong trend toward empirical evaluation of modeling languages, decision-theoretic goal models, and applications of goal modeling in emerging domains such as blockchain and reinforcement learning simulation design. Scientific Contributions: Developed frameworks for empirical evaluation of modeling language ontologies (Peira framework). Advanced decision-theoretic approaches to goal modeling under uncertainty. Pioneered model-driven methods for generating blockchain simulators and reinforcement learning environments. Conducted foundational empirical studies on the comprehensibility of contribution links and visualization alternatives in goal models. Advising and Collaboration: He has advised several researchers including Ibrahim Jaouhar, Wisal Tambosi, Mehrnaz Zhian, and Saba Zarbaf. He maintains a highly collaborative research profile with frequent co-authorship with John Mylopoulos, Shakil M. Khan, and other international researchers. He has served as a co-editor for multiple iStar workshop proceedings, indicating leadership in the goal-oriented requirements engineering community. Labs and Teams: While no specific lab name is mentioned, his work is closely associated with research groups focused on requirements engineering and conceptual modeling, likely within York University’s software engineering research cluster. His collaborations span institutions in Canada, Europe, and beyond.

• Adaptive structures
• Mechanical metamaterials
• Robotic materials
• +14 more

Andres Arrieta is an Associate Professor in the School of Mechanical Engineering at Purdue University. His research focuses on adaptive structures, mechanical metamaterials, and programmable systems. He holds a PhD from the University of Bristol and conducted postdoctoral research at ETH Zurich. Education: Mechanical Engineer, Universidad de los Andes, 2006 PhD in Mechanical Engineering, University of Bristol, 2010 Postdoctoral Research Fellow, ETH Zurich, 2012 Research Interests: Adaptive Structures Multistable Systems Structural Nonlinearity Robotics & Mechanosensing Origami Engineering Awards: 2019 ASME Best Paper Award 2018 Gary Anderson Early Achievement Award 2012 ETH Postdoctoral Fellowship Labs: Directs the Programmable Structures Lab , exploring smart materials and morphing systems.

Joseph A. CampbellView profile

Assistant Professor

• Explainable AI
• Robotics
• Machine Learning
• +3 more

Joseph A. Campbell is an Assistant Professor in the Department of Computer Science at Purdue University, leading the Collaborative AI for Machines and People (CAMP) Lab. He holds a Ph.D., M.S., and B.S. in Computer Science and Computer Engineering from Arizona State University. Before academia, he worked as a software engineer for five years. Prior to Purdue, he was a Postdoctoral Fellow at Carnegie Mellon University's Robotics Institute. His research focuses on explainable machine learning and robotics, particularly how agents use explanations for self-improvement and decision-making. Key areas include theory of mind in multi-agent systems, lifelong learning, and interpretable transfer learning. His work bridges robotics and AI, with applications in human-robot interaction and prosthetic control. Notable publications include advancements in reinforcement learning with language models, multi-agent collaboration frameworks, and methods for enhancing state estimation in robots. His research has been presented at top conferences like NeurIPS, EMNLP, and CoRL. Dr. Campbell maintains an active GitHub profile (joe-campbell) with repositories such as Interaction Primitives for robotics applications. His lab, CAMP, explores AI systems that collaborate effectively with humans and other machines.

Bruno Felisberto Martins RibeiroView profile

Associate Professor

• Networking and Operating Systems
• Artificial Intelligence
• Machine Learning
• +4 more

Bruno Felisberto Martins Ribeiro is an Associate Professor of Computer Science at Purdue University, joining the department in Fall 2015. His research focuses on endowing machine learning algorithms with robust invariant representations for relational and temporal data, emphasizing causal and associational tasks. Key research areas include Networking and Operating Systems, Artificial Intelligence, Machine Learning, and Natural Language Processing. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2010). Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2010 Research Interests: Explores invariances in mathematics and machine learning to improve model robustness. Key topics include graph and tensor invariances, causal relationships, adversarial robustness, and applications in recommendation systems, robotics, and drug discovery. His lab’s work has advanced counterfactual task frameworks and causal reasoning in machine learning. Recent Contributions: Recent publications address zero-shot generalization in graph neural networks, causal discovery methods, and defenses against adversarial attacks. His work spans conferences like ICML, NeurIPS, and SIGCOMM. Awards: Best Paper Award at ACM CODASPY 2021 Best Paper Award at SIGMETRICS 2016 Best Paper Award at IEEE NetSciCom 2014 Advising & Students: Supervises current PhD students Beatrice Bevilacqua, Jincheng Zhou, and Yucheng Zhang, along with MSc student Ipsit Mantri. Notable former students include S Chandra Mouli (Meta), Yangze Zhou (Spotify), and Jianfei Gao (Vector Institute). Labs & Teams: Leads research in invariant representations and causal ML, collaborating with institutions like Stanford during his sabbatical. His work bridges theory and practice, impacting areas like network analysis and AI-driven healthcare.

Nichole Denise PinkardView profile

Professor

• Learning Sciences
• Educational Technology
• Community-Level Learning Ecosystems
• +3 more

Nichole Denise Pinkard is the Alice Hamilton Professor of Learning Sciences and Faculty Director of the Office of Community Education Partnerships at Northwestern University's School of Education and Social Policy. She founded the Digital Youth Network (DYN) and developed L3, a social learning platform connecting youth's learning across multiple environments. Education: PhD, Learning Sciences, Northwestern University, 1998 MS, Computer Science, Northwestern University BS, Computer Science, Stanford University Research Focus: Designs socio-technical systems to support ecological models of learning, emphasizing community-level learning ecosystems and pedagogical-based social networks. Her work bridges technology and urban education contexts to reimagine learning documentation and opportunities. Awards: 2010 Common Sense Media Award 2004 Jan Hawkins Award 2000 NSF Early Career Fellowship Projects & Initiatives: Leads Chicago City of Learning and Digital Youth Divas programs, advancing digital equity and youth empowerment through technology. Collaborates with city agencies to create existence proofs for innovative urban learning models.

Carla P. GomesView profile

Professor

• Artificial Intelligence
• Constraint Reasoning
• Optimization
• +3 more

Carla P. Gomes is a Professor of Computer Science at Cornell University with joint appointments in the Department of Computer Science and the Dyson School of Applied Economics and Management. She holds a PhD in computer science from the University of Edinburgh and an M.Sc. in applied mathematics from the University of Lisbon. Her research focuses on artificial intelligence, constraint reasoning, optimization, and computational sustainability. As Director of the Institute for Computational Sustainability (ICS) and co-director of the Cornell University AI for Science Institute, she leads efforts to integrate AI with sustainability challenges. Her research themes include the integration of constraint reasoning, machine learning, and operations research to solve large-scale problems. She pioneered the field of Computational Sustainability, addressing environmental, economic, and societal challenges through AI. Gomes directed two NSF Expeditions in Computing awards and established CompSustNet, a large-scale sustainability research network. Key awards include the 2021 ACM–AAAI Allen Newell Award, AAAI Feigenbaum Prize, and fellowships from AAAI, ACM, and AAAS. Her work spans over 200 publications, with contributions to AI, sustainability, and materials discovery. She advises numerous PhD students and oversees postdocs in AI, sustainability, and interdisciplinary projects. Gomes' lab focuses on AI for scientific discovery, including autonomous materials synthesis and crystal-structure phase mapping. She collaborates with institutions like JCAP and the Materials Project, advancing AI-driven solutions for energy and environmental challenges. Current projects include Schmidt AI in Science postdoc initiatives and AI-driven materials discovery platforms like DRNets and SARA.

Maarten de BoerView profile

Professor

• Mechanics of Materials
• MEMS (Microelectromechanical Systems)
• Micro/Nano Manufacturing
• +7 more

Maarten de Boer is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Materials Science and Engineering. He joined CMU in 2007 after roles as a process engineer at Hewlett-Packard (1983–1991) and principal member of technical staff at Sandia National Labs (1996–2010). He holds a Ph.D. in Materials Science (University of Minnesota, 1996), an MS in Electrical Engineering (University of Colorado, 1982), and a BS in Electrical Engineering (Cornell University, 1981). His research focuses on nanomechanical behavior of materials, MEMS, and additive manufacturing. Key projects include tantalum-based thermal actuators, high-entropy alloys, and micromachine reliability. His work is funded by the DOE, NSF, NASA, and the Army Research Lab. He has authored over 90 peer-reviewed articles, holds seven US patents, and advises students in the de Boer Group. Research Themes: Micro/Nano Manufacturing, Thin Film Mechanics, Friction & Wear, MEMS Reliability Funding Sources: NSF, DOE, NASA, ARL Courses Taught: Mechanics of Materials, Material Selection, Electronics for Sensing, Thermodynamics Notable collaborations include Gianluca Piazza (NSF LEAP-HI grant), Jack Beuth, and Bryan Webler (high-entropy alloys). Media highlights include breakthroughs in tantalum MEMS and ultra-strong polymer nanofibers. His group operates advanced test facilities for in-situ environmental studies of materials.

Sanjay JainView profile

Professor

• Inductive Inference
• Recursion Theory
• Complexity Theory
• +3 more

Sanjay Jain is a Provost's Chair Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). His research focuses on theoretical computer science with particular emphasis on inductive inference, recursion theory, complexity theory, and computational learning theory. Education: B.Tech. in Computer Science from Indian Institute of Technology Kharagpur, India (1986) M.S. in Computer Science from University of Rochester, USA (1988) Ph.D. in Computer Science from University of Rochester, USA (1990) Professor Jain's research spans multiple areas of theoretical computer science. His primary contributions are in computational learning theory, where he has made significant advances in understanding the intrinsic complexity of language identification and the limits of inductive inference. His work on recursion theory explores fundamental questions about computability and complexity, while his research in complexity theory addresses structural aspects of computational problems. A notable achievement was his work on "Deciding Parity Games in Quasipolynomial Time," which won the prestigious STOC 2017 best paper award and later the EATCS-IPEC Nerode Prize. Professor Jain's publication record shows a consistent focus on theoretical foundations of computer science, particularly in learning theory and computational complexity. His recent work has expanded into automatic structures, semiautomatic models, and connections between computational learning and algebraic structures. There is a clear progression from foundational work on language identification to more complex models involving automatic functions, transducers, and connections to mathematical logic. Scientific Awards: STOC 2017 Best Paper Award for "Deciding Parity Games in Quasipolynomial Time" EATCS-IPEC Nerode Prize (2021) Professor Jain has served on the editorial board of Information and Computation and has been actively involved in the academic community through program committee memberships for major conferences including COLT, ALT, LATA, TAMC, and PRICAI. He has held leadership roles as program co-chair for ALT 2000 and ALT 2013, and conference chair for ALT 2005. His work has been supported by various research grants, though specific details are not provided in the available materials. Professor Jain leads research in theoretical computer science at NUS, where he has built a strong research group focused on computational learning theory and related areas. His work often involves collaborations with researchers from around the world, particularly with Frank Stephan, with whom he has co-authored numerous papers. His research group has made significant contributions to understanding the fundamental limits and possibilities of computational learning models.

Ugur CetintemelView profile

Professor

• Data Management Systems
• Database Systems
• Distributed Systems
• +5 more

Ugur Cetintemel is the Khosrowshahi University Professor of Computer Science at Brown University, where he has been since completing his PhD at the University of Maryland in 2001. His research focuses on data management systems, database systems, distributed systems, and stream processing, with recent work integrating AI techniques into database systems. He teaches courses such as Database Management Systems and Data Science fundamentals. Notable contributions include the Aurora and Borealis stream processing engines, S-Store for transaction processing, and DBPal for natural language interfaces. His work emphasizes scalable, efficient systems for large-scale data challenges. Education: PhD in Computer Science, University of Maryland, 2001 MS in Computer Science, Bilkent University, 1996 BS in Computer Science, Bilkent University, 1994 Research Interests: Data management, stream processing, distributed systems, predictive analytics, and AI integration with databases. Key projects include optimizing database systems for modern hardware, developing real-time stream processing frameworks, and exploring interactive data exploration techniques. Grants & Advising: Extensive contributions to grants and collaborations, though specific grant details are not listed. Supervises graduate students in areas like database systems and machine learning integration. Part of the Brown Data Management Group. Labs/Teams: Leads research within the Brown Data Management Group, focusing on advancing database systems for big data and real-time analytics.