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Elana SchlenkerView profile

Lecturer

• Graphic Design
• Visual Communication
• Typography
• +4 more

Elana Schlenker is a Lecturer in the Image Text Ithaca low-residency MFA program at Cornell University's College of Architecture, Art, and Planning since 2016. As principal of Studio Elana Schlenker (founded 2011), she leads a medium-agnostic design practice while co-running the cooperative studio Out of Office with Mark Pernice. Her work bridges academia and professional design through initiatives like Gratuitous Type magazine and the gender wage parity project Less Than 100. Schlenker's research centers on graphic design as a vehicle for social commentary, particularly gender equity in creative industries. She explores typographic innovation, visual identity systems for cultural institutions, and the intersection of image/text in digital and print media. Her practice demonstrates how design methodologies can address political and wellness themes through Visual identity , Branding , and Art direction projects. Recent publications (2021-2024) reveal a focus on modular design systems for art institutions and publishers, with heavy emphasis on interactive web experiences and context-specific typography . She frequently collaborates with photographers and galleries, translating complex narratives into visual communication frameworks that challenge traditional publishing formats. Awards received: Young Gun by The One Club for Creativity (2015) Print Magazine New Visual Artist (2013) In her academic role, Schlenker mentors MFA students in visual communication and design research. Her studio practice operates as a de facto research lab funded through high-profile client contracts with cultural institutions including Walker Art Center, Carnegie Museum of Art, and The New York Times. These projects serve as live case studies for her pedagogy. Schlenker directs Studio Elana Schlenker as her primary creative laboratory, with Out of Office functioning as a collaborative platform. She maintains long-term partnerships with specialists like Maria Adelaide (interactive design) and Élise Rigollet (editorial design), forming interdisciplinary teams for projects spanning academia, art, and technology sectors.

Irene Ye YuanView profile

Assistant Professor

• Human-Computer Interaction
• Social Computing
• Computer-Supported Cooperative Work
• +3 more

Dr. Irene Ye Yuan is an Assistant Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering, with a joint appointment as Associate Member of the McMaster School of Biomedical Engineering. Her research focuses on Human-Computer Interaction (HCI) and Social Computing , specifically designing technology for collaborative social experiences like intergenerational communication and remote play. Education Ph.D. in Computer Science, University of Minnesota M.S. in Human-Computer Interaction and Design, University of Washington Research Focus Dr. Yuan's work integrates ethnographic, design, and mixed methods to develop frameworks for multimodal interactions that foster meaningful social connections. Key domains include: Technology for intergenerational relationships Remote collaboration tools Embodied interaction systems Educational technology (VR/AR pedagogy) Publication Trends Her recent publications (2019-2024) in top-tier venues like CHI and CSCW emphasize socially connected experiences , with recurring themes of remote collaboration, intergenerational communication, and tangible interfaces. Methodologies blend qualitative analysis with system prototyping. Awards Three CHI Honorable Mentions IMWUT Distinguished Paper Award IDC Best Paper Award Academic Activities Dr. Yuan leads the ESI Lab at McMaster, focusing on Engaging Social Interactions. She teaches CAS 731: Social and Collaborative Computing and actively recruits graduate students for projects involving qualitative methods, system building, and empowerment of vulnerable groups (older adults/children).

• Distributed Systems
• Cryptography
• Electronic Voting
• +3 more

Michael J. Fischer is a Professor of Computer Science at Yale University, renowned for foundational contributions to distributed systems theory, cryptography, and parallel algorithms. His work includes the seminal impossibility result for distributed consensus with faulty processes and the development of the parallel prefix algorithm fundamental to modern parallel computing. His educational background: B.S. in Mathematics, University of Michigan (1963) M.A. in Applied Mathematics, Harvard University (1965) Ph.D. in Applied Mathematics, Harvard University (1968) Fischer's research spans theoretical and applied computer science with emphasis on distributed systems (consensus protocols, fault tolerance), cryptography (information-theoretic security, card-based protocols), and electronic voting systems . His work bridges abstract theory with practical security applications, particularly in trust modeling for e-commerce. Current investigations focus on algorithmic approaches to establishing trust relationships in distributed environments. His publication trajectory reveals evolving expertise from early parallel algorithms (1970-80s) to distributed consensus breakthroughs (mid-1980s), then cryptographic protocols and secure e-voting systems (1985-2000s), with recent work synthesizing these domains through trust frameworks. Key thematic threads include fault tolerance in unreliable networks and verifiable security mechanisms. Scientific recognition: ACM Fellow Fischer has held significant leadership roles including Editor-in-Chief of the Journal of the ACM , chair of the Max-Planck-Institute for Computer Science International Scientific Advisory Board, and founding member of the Computing Research Association's subcommittee on Women in Computer Science. His advisory work extends to the National Science Foundation and Wuhan University's State Key Laboratory on Software Engineering. Outside academia, he actively participates in the Yale Figure Skating Club, having served multiple terms as president. He maintains international collaborations as Guest Professor at Wuhan University and through editorial roles including Acta Informatica , while continuing to teach core computer science courses from data structures to cryptography at Yale.

Niels da Vitoria LoboView profile

Associate Professor

• Computational Vision
• Active Vision
• Mobile Robotics
• +6 more

Niels da Vitoria Lobo is an Associate Professor in the Department of Computer Science at the University of Central Florida. His research spans computational vision, mobile robotics, and user interface design, with a focus on real-world applications including object detection, person tracking, and obstacle avoidance systems. Education: Ph.D. in Computer Science – University of Toronto Research Interests: Dr. Lobo’s work in computational vision addresses challenges like integral image-based curve detection and object detection in cluttered environments. He has developed systems for hand and person tracking, automobile lane following, and optical flow integration, alongside exploring graphical modeling for wristband trackers and educational games. Scientific Contributions: UCF Millionaire’s Club (2008) – recognizing significant research contributions Teaching Incentive Program Award (1996) – honoring excellence in academic instruction As an Associate Editor for computer vision journals, he has played a key role in advancing scholarly discourse in artificial intelligence and robotics.

Arlen C. MollerView profile

Associate Professor

• Motivation Science
• Intrinsic Motivation
• Behavioral Science
• +5 more

Arlen C. Moller is an Associate Professor and Director of Undergraduate Programs in the Department of Psychology at Illinois Institute of Technology since 2013. He leads the Motivation Science and Digital Health Lab , supervising students across multiple degree programs including Clinical Psychology (Ph.D.) and Game Design. Education: Ph.D. in Social Psychology (2007) - University of Rochester B.A. in Psychology (2000) - Cornell University His research focuses on digital health interventions that promote physical and mental wellness through motivation science and self-determination theory , particularly examining how digital technologies like wearable monitors, video games, and extended reality can be designed to support intrinsic motivation and behavioral change . This work spans multiple domains including occupational health , human-computer interaction , and game design . Current research trends include: Developing digital tools that balance competition mechanics with psychological needs Understanding social identity influences on health behaviors Examining long-term effects of extrinsic rewards on motivation Implementing self-determination theory in healthcare settings Creating peer-to-peer mental health support systems Optimizing digital behavior change through autonomy support Dr. Moller collaborates with institutions such as the Center for Self-Determination Theory , Society of Behavioral Medicine , and University of Lisbon's PANOS*SR Research Group . His lab team includes three Ph.D. candidates investigating specific applications of their research.

• Immersive virtual reality in education
• AI ethics in educational technology
• Digital learning and equity
• +3 more

Associate Professor Erica Southgate is a leading researcher at the University of Newcastle , specializing in emerging educational technologies with a focus on virtual reality and AI ethics . Her work bridges digital learning and sociology of education , emphasizing equity in education and technology accessibility for all learners. PhD and BEd from University of Newcastle 2016 Equity Fellow for national research 2017 ASCILITE Innovation Award Southgate's research explores how immersive technologies can transform education through student content creation , ethical frameworks , and curriculum redesign . Recent studies focus on generative AI policy , VR learning environments , and digital footprint management . Her VR School Study demonstrates practical implementation strategies. Key publications include the first VR education book and AI ethics guidelines for Australian Government. She leads the Digital Identity, Curation and Education (DICE) research network and advocates for human rights in educational technology .

Kathryn E. SpierView profile

Professor

• Law and Economics
• Industrial Organization
• Litigation
• +9 more

Kathryn E. Spier is the Domenico De Sole Professor of Law at Harvard Law School and a Research Associate at the National Bureau of Economic Research (NBER). She previously held faculty positions at Northwestern University’s Kellogg School of Management and Harvard University’s Economics Department. She served as President of the American Law and Economics Association (2015–2016) and has held editorial roles at the RAND Journal of Economics and the American Economic Review. Education: Ph.D. in Economics, Massachusetts Institute of Technology, 1989 B.A. in Mathematics and Economics, Yale University, 1985 Her research centers on law and economics, with a focus on litigation, contracts, tort law, antitrust, and business organizations. She employs theoretical and experimental methods to analyze settlement bargaining, platform liability, class actions, and product safety. Her work integrates insights from industrial organization and game theory to address legal and regulatory challenges. Recent publications explore the liability of digital platforms, class action waivers, IPO disclosure, and litigation finance. Her articles frequently appear in top journals such as the American Economic Review, RAND Journal of Economics, and Journal of Law, Economics, and Organization. The research demonstrates a consistent focus on incentive design, strategic behavior, and institutional mechanisms in legal contexts. Scientific Awards and Honors: Honorary Doctorate, BI Norwegian Business School, 2023 Garvin Prize in Law & Economics, Berkeley, 2001 National Science Foundation Graduate Fellowship, 1985–1988 Sidney J. Levy Teaching Award (twice), Kellogg, 2002 & 2006 Multiple Yale University prizes for academic excellence, 1984–1985 Olin Fellowship, University of Chicago Law School, 1993–1994 She has advised numerous doctoral students and collaborated extensively with scholars such as Albert H. Choi, Claudia M. Landeo, and J.J. Prescott. Her research has been supported by the National Science Foundation and the Searle Foundation. She is actively engaged in academic service, including editorial board memberships and leadership in professional associations. Professor Spier leads research initiatives on platform regulation and civil litigation reform. She teaches courses in analytical methods for lawyers, business strategy, and graduate workshops in contracts and organizations. Her work continues to shape policy debates on liability, antitrust, and consumer protection.

• Complex Systems
• Computational Modeling
• Network Science
• +7 more

Jorge Louçã is a Full Professor in the Department of Information Science and Technology at ISCTE-IUL, where he has been a faculty member since 2000. He is also an Integrated Researcher at ISTAR-Iscte, the Research Center in Information Sciences, Technologies and Architecture, and leads the research group The Observatorium . He holds a PhD in Computer Science and Artificial Intelligence from Université Paris Dauphine and the University of Lisbon, and completed his Aggregation in Complexity Sciences in 2019. PhD in Computing – University of Lisbon & Université Paris-Dauphine (2000) Master’s in Informatique: Intelligent Systems – Université Paris-Dauphine (1995) Aggregation in Complexity Sciences – ISCTE-IUL (2019) His research centers on computational modeling of social systems, focusing on data-intensive analysis of human communication, knowledge generation in large networks, and the dynamics of complex systems. He founded the Doctoral Program in Complexity Sciences and has been instrumental in advancing the field through international collaborations such as the UNESCO Unitwin network for the Complex Systems Digital Campus and participation in the Conference on Complex Systems (CCS/ECCS). The recent publications highlight a strong interdisciplinary focus, combining network science, data analysis, and social theory. Key themes include the modeling of malaria transmission, information diffusion in social media, structural inequality in education, and the dynamics of opinion and popularity. His work often employs agent-based models, temporal network analysis, and entropy-based measures, reflecting a deep integration of computational and theoretical approaches. Research Methods for Doctorate in Complexity Sciences Advanced Topics in Complexity Sciences Data Science Fundamentals Development for the Internet and Mobile Applications Web Interfaces for Data Management Advanced Network Analysis Jorge Louçã has supervised over a dozen doctoral and master’s students, with completed theses on topics such as malaria modeling, information diffusion, temporal networks, and social inequality. His research has been supported by projects like NESS (Non-Equilibrium Social Science in ICT and Economics), reflecting his leadership in interdisciplinary science. He has held significant academic management roles, including Director of the Department of Information Science and Technology and head of multiple degree programs. His work continues to bridge computer science, social science, and policy, positioning him as a key figure in the global complexity science community.

• Game Theory
• Epistemic Game Theory
• Quantum Game Theory
• +7 more

Adam M. Brandenburger is the J.P. Valles Professor at the Leonard N. Stern School of Business, New York University, with additional appointments as Distinguished Professor at the Tandon School of Engineering, Faculty Director of the NYU Shanghai Program on Creativity + Innovation, and Global Network Professor. He previously served as a professor at Harvard Business School from 1987 to 2002. His academic foundation includes a B.A., M.Phil., and Ph.D. from the University of Cambridge. His research focuses on game theory , epistemic game theory , quantum game theory , and business strategy . He has made foundational contributions to the understanding of rationality, belief hierarchies, and the intersection of quantum mechanics with decision theory. His work spans economics, philosophy, computer science, and cognitive neuroscience, reflecting a deeply interdisciplinary approach. The recent publications show a strong trend toward integrating quantum foundations , information theory , and behavioral economics . Themes include epistemic reasoning in non-classical systems, the role of symmetry in strategic interactions, and the neural basis of decision-making. His work increasingly explores the limits of classical probability and cognition, using tools from quantum information and sheaf theory. While no formal scientific awards are listed, his publications in Nature Communications , Econometrica , and Philosophical Transactions of the Royal Society indicate high scholarly impact. His collaborations with leading researchers across disciplines suggest a central role in advancing interdisciplinary science. He advises and collaborates extensively, though specific students are not listed. His leadership in the NYU Shanghai Program on Creativity + Innovation highlights his commitment to global education and innovation. He has not received any grants explicitly mentioned, but his sustained publication output suggests active funding support. He is associated with research initiatives bridging game theory, quantum information, and cognitive science. His recent unpublished work on quantum-assisted observatories and large language models points to forward-looking research at the intersection of AI, quantum technology, and strategic reasoning.

Peter BuiView profile

Teaching Professor

• Systems Programming
• Operating Systems
• Parallel Computing
• +6 more

Peter Bui is a Teaching Professor in the Computer Science and Engineering department at the University of Notre Dame , located within the College of Engineering. He teaches courses such as Data Structures, Systems Programming, and Ethical and Professional Issues, while also managing the core Elements of Computing programming sequence for the Computing & Digital Technologies minor. Education: Ph.D. in Computer Science and Engineering from University of Notre Dame (2012) His research interests span systems programming, operating systems, parallel computing, cloud computing, distributed computing, programming languages, compilers, and web services . He actively integrates these domains into his teaching and extracurricular work with the Linux Users Group. Recent publications highlight his work in distributed computing frameworks , including the development of tools like WorkQueue and Madeup for scalable scientific workflows and 3D printing integration. Projects such as ROARS and Weaver demonstrate his focus on robust data management and workflow automation. Outside academia, he stewards the Linux Users Group , engages with open-source communities, and balances personal interests like gaming in RuneScape with family time.

Sameer DeshpandeView profile

Assistant Professor

• Bayesian hierarchical modeling
• Bayesian regression trees
• Causal inference
• +3 more

Sameer Deshpande is an Assistant Professor in the Department of Statistics at the University of Wisconsin–Madison. His research bridges Bayesian methodology development with applications in public health and sports analytics. Prior to joining UW–Madison, he completed a postdoctoral fellowship with Professor Tamara Broderick at MIT and earned his Ph.D. in Statistics from the Wharton School under Professors Ed George and Veronika Rockova. His educational background includes undergraduate studies in mathematics at MIT and a year at Jesus College, Cambridge through the Cambridge-MIT Exchange program. His research focuses on advancing Bayesian hierarchical modeling, treed regression, and causal inference techniques, with particular emphasis on flexible tree-based methods like BART variants for complex data structures. Deshpande's recent publications reveal a strong trend toward developing scalable Bayesian methods for high-dimensional data while maintaining rigorous uncertainty quantification. His work frequently applies these techniques to sports analytics (particularly baseball and football) and public health studies examining long-term effects of adolescent sports participation. The consistent focus on methodological innovation paired with substantive applications demonstrates his dual commitment to statistical theory and real-world impact. He actively mentors graduate students at UW–Madison, requiring STAT 775 as preparation for research collaboration. His Deshpande Lab focuses on Bayesian computation and causal inference, though specific grant details are not publicly listed. Notable projects include the NFL Big Data Bowl submission analyzing quarterback decision-making using Expected Hypothetical Completion Probability. Outside academia, Deshpande maintains interests in cooking, cocktail making, and photography, while remaining a devoted fan of Dallas sports teams – often seen wearing a Texas belt buckle.

Ulrich SchroedersView profile

Professor

• Psychological Diagnostics
• Technology-based Competency Diagnostics
• Measurement of Cognitive Abilities
• +5 more

Ulrich Schroeders is a Professor of Psychological Diagnostics at the University of Kassel, where he has been employed since October 2017. His work focuses on developing and validating psychological assessment tools, with particular expertise in cognitive diagnostics and educational measurement. He teaches various programs for approximately 500 students annually and serves as a supervisor for teacher training students preparing for their oral state examinations in Pedagogy/Psychology. Dr. Schroeders earned his PhD from Humboldt University of Berlin in 2010 with a dissertation titled "Measurement of Cognitive Abilities Using Modern Technologies: Artifacts, Equivalence, and New Constructs." Prior to that, he completed his Diploma in Psychology at Julius-Maximilians-University Würzburg in 2004 with a thesis on diagnosing dyscalculia in first-grade students. His research spans several key areas in psychological assessment. He specializes in technology-based competency diagnostics, developing innovative methods for measuring cognitive abilities and school competencies. A significant portion of his work involves applying Machine Learning and metaheuristics to psychometric problems, particularly in structural equation modeling. His methodological expertise includes advancing techniques in Local Structural Equation Modeling (LSEM) and Meta-Analytic Structural Equation Modeling (MASEM), with applications across educational and clinical psychology contexts. Analysis of Dr. Schroeders' recent publications reveals a strong focus on computational approaches to psychological assessment. His work frequently employs optimization algorithms like Ant Colony Optimization and Bee Swarm Optimization to address challenges in test construction and validation. There's a clear trajectory toward game-based and technology-enhanced assessment methods, as seen in studies using Mastermind and Wordle as assessment tools. His research also demonstrates growing interest in applying machine learning to predict behavioral outcomes, including juvenile delinquency, suicide risk, and psychotherapy outcomes. Dr. Schroeders has secured significant research funding, including projects funded by the German Research Foundation (DFG) and the Hector Foundation. His current projects include "Facing the Replication Crisis in Machine Learning Modeling" (2025-2027) and "PINGUIN: Potenzialidentifikation IN der GrUndschule" (2024-2027), which focuses on identifying elementary students' initial competencies. He leads the development of the BEFKI assessment system (Berliner Test zur Erfassung fluider und kristalliner Intelligenz), which includes versions for different age groups (5-7, 8-10, and 11+). His methodological toolbox includes specialized approaches for test construction and validation, particularly focusing on optimization algorithms applied to psychological measurement problems.

Éva TardosView profile

Professor

• Algorithms
• Algorithmic Game Theory
• Network Games
• +3 more

Éva Tardos is a Professor of Computer Science at Cornell University, holding the prestigious Jacob Gould Schurman Professorship . Her research spans theoretical computer science, with a focus on algorithms and game theory in decentralized systems. Education : DiplMath (1981), PhD (1984) from Eötvös Loránd University under advisor András Frank Her work addresses algorithmic challenges in networks and auctions, ensuring provably optimal outcomes despite user selfishness. She has contributed to programs at Cornell including Learning and Games (Spring 2022) , Real-Time Decision Making (Spring 2018) , and Economics and Computation (Fall 2015) . Key Research Areas : Algorithm design, Game theory, Network optimization, Auction mechanisms, Complexity analysis Scientific Awards : Jacob Gould Schurman Professor of Computer Science

• Big data analytics
• Data analysis
• Data management
• +8 more

Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains

Benjamin LubinView profile

Associate Clinical Professor

• Mechanism Design
• Combinatorial Auctions
• Spectral Graph Theory
• +4 more

Benjamin Lubin is a Clinical Associate Professor in the Information Systems Department at Boston University's Questrom School of Business. He holds office 621A in the Rafik B. Hariri Building at 595 Commonwealth Avenue, Boston, MA 02215. His academic journey began with a Bachelor's degree in Computer Science from Harvard University in 1999, followed by six years working at BBN Technologies, where he contributed to advanced multi-agent modeling, scheduling, and logistics systems. He later returned to Harvard to complete his Ph.D. at the intersection of computer science, game theory, and economics. Dr. Lubin's research spans three primary areas: (1) mechanism design, particularly combinatorial auctions and exchanges that support efficient reallocation of goods with complex participant preferences; (2) application of spectral graph theory to advance social network analysis; and (3) leveraging network science and machine learning to improve healthcare delivery systems. His work demonstrates a consistent pattern of bridging theoretical computer science with practical economic applications, with recent publications showing increasing focus on healthcare applications while maintaining strong contributions to auction theory and network analysis. His research has been supported by significant funding from NIHCM and the Veterans Administration, and he has received prestigious recognition including the Siebel Fellowship and Yahoo Key Technical Challenge award. Dr. Lubin has mentored numerous graduate students including PhD candidates Vatche Ishakian, Marisabel Guevara, and Sarah Zheng, as well as Master's students Benedikt Buenz and Michael Weiss. Siebel Fellowship Yahoo Key Technical Challenge award As an educator, Dr. Lubin teaches several courses including IS 710 (Core MBA Class on Information Systems), IS 716 (Accelerated Part-Time Evening MBA), IS 717 and IS 756 (MSMBA Intensives), and QD601x (Business Experimentation on edX). His teaching materials include innovative approaches like using adventure games to teach web development and creating practical exercises for understanding analytics in business contexts. He has developed several software tools including JOpt for MIP programming, the Iterative Combinatorial Exchange market software, InvEigen for inverse eigenvector problems, SpectralGOF for network model goodness-of-fit testing, and SATS for spectrum auction instance generation.