Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Seth Lloyd is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), with adjunct appointments at the Santa Fe Institute since 1988 and as a Fellow at the Institute for Scientific Interchange since 2000. His research spans quantum information science, quantum control theory, and complex systems analysis. His educational background includes: B.A. from Harvard University (1982) M. from the University of Cambridge (1984) Ph.D. from Rockefeller University (1988) Lloyd's work focuses on quantum computation, quantum communications, and quantum limits to control and sensing. He has pioneered research in quantum algorithms, quantum metrology, and applications of quantum information to complex biological and physical systems. His research bridges theoretical physics, computer science, and engineering, with over 200 publications and two patents in quantum information processing. Analysis of his recent publications reveals dominant trends in quantum machine learning, quantum metrology, and quantum communication protocols, with increasing interdisciplinary applications in quantum biology and quantum gravity. His work consistently explores fundamental limits of quantum information processing. His scientific awards include: Lindbergh Fellow (1994) Finmeccanica Professorship (1996) Edgerton Prize (2001) Fellow of the American Physical Society (2007) Quantum Communication, Measurement, and Computation Prize (2012) Lloyd serves on the editorial board of Quantum Information Processing and holds significant MIT service roles including Course 2 Undergraduate Committee coordinator and membership on the Institute Foreign Scholarships Committee. He teaches advanced courses in quantum information, dynamics, and computational methods, shaping the next generation of quantum scientists and engineers. As a member of the American Physical Society, he maintains active research collaborations across quantum information science, with ongoing work in quantum algorithms and quantum-enhanced sensing technologies.
Stella Yu is a Professor specializing in computer vision, machine learning, and AI applications across medical imaging, robotics, and wildlife recognition. She emphasizes adaptive advising tailored to individual student strengths, with regular one-on-one and group meetings to discuss research progress and paper presentations. Yu's research group focuses on unsupervised learning, deep learning workflows, and interdisciplinary applications such as MRI reconstruction, meibography analysis, and aerial wildlife monitoring. Her work bridges theoretical advancements with practical tools like DeepInPy for inverse problems. She strongly advocates for teaching experience through GSI roles and ensures students have conference funding to present findings at top venues. Education Expectations: Regular literature review, lab presence for junior students, and clear authorship protocols. Key Research Themes: Feature learning, medical AI, computational optics, and wildlife population surveys. Her lab promotes a collaborative environment with structured feedback loops, emphasizing both technical rigor and creative problem-solving. Current projects include BatVision for 3D spatial navigation using audio signals, and AI-driven solutions for dry eye diagnosis and building information modeling.
Debdeep Pati is a Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on Bayesian methods, high-dimensional data analysis, machine learning, and computational statistics, with applications in health data and network analysis. He has contributed to approximate Bayesian computation, graphical models, and fair algorithms. Key research interests include Bayes theory in high dimensions, hierarchical modeling, efficient Bayesian computation, and real-time tracking algorithms. His work bridges theoretical advancements with practical applications in areas like electronic health records and nuclear physics constraints. Recent work emphasizes Wasserstein-guided nonparametric Bayes, fair clustering algorithms, and variational inference in singular models. He has developed software for covariate-dependent Gaussian graphical modeling, published in ACM Transactions on Mathematical Software . Grants: NSF proposal on Wasserstein-guided nonparametric Bayes, NIH R01/R21 grants on periodontal disease and diabetes comorbidity. Advising: No named advisees listed but actively supervising research in Bayesian computation and high-dimensional statistics. Awards: 2024 JASA reproducibility award for 'Covariate-Assisted Bayesian Graph Learning.' He is an Associate Editor for Journal of Computational and Graphical Statistics and has organized workshops at Banff International Research Station (BIRS) and the Institute for Mathematics and its Applications (IMSI).
About Marin Litoiu is a Professor at York University, holding dual affiliations in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering and the School of Information Technology in the Faculty of Liberal Arts and Professional Studies. He is a Fellow of the Canadian Academy of Engineering and a recipient of the 2020 IBM Faculty of the Year Award. His research focuses on cloud computing, self-adaptive systems, DevOps, IoT, and machine learning-driven performance engineering. Research & Awards Litoiu leads the Dependable Internet-of-Things Applications (DITA) program, funded by NSERC, and co-founded Bitnobi Inc., acquired by Myant. His notable awards include the CASCON 2019 Most Influential Paper Award and Best Paper Awards at multiple conferences. His work emphasizes practical applications of adaptive systems, cybersecurity, and smart infrastructure integration. Grants & Projects NSERC CREATE Program: $1.65M for the DITA program (2018) York Innovation, TIAP, NSERC, and OCI-funded Bitnobi incubation Leadership in multiple CASCON workshops on cloud computing and AIOps Labs & Teams Litoiu’s lab has produced impactful startups like Bitnobi and pioneered research in self-driving systems, edge computing, and AI-driven operations. His team collaborates with industry partners like IBM and explores cutting-edge topics such as LLMs in performance optimization and fault detection.
Amin Hammad is a Professor at the Concordia Institute for Information Systems Engineering, with an additional appointment as Affiliate Professor in Building, Civil, and Environmental Engineering at Concordia University. His research focuses on advancing construction technology through digital transformation, automation, and AI integration. He leads work in BIM applications, 4D simulation, robotic systems, and sustainable infrastructure management. His interdisciplinary approach bridges civil engineering with computer science and data analytics. Key research areas include: Automation and robotics in construction (Construction 4.0) BIM and digital twin lifecycle management AI-driven defect detection and inspection systems Occupational safety through exoskeleton performance evaluation Multi-purpose utility tunnel optimization Energy-efficient building systems Recent work emphasizes applying machine learning to construction equipment activity recognition, UAV path optimization for infrastructure inspection, and ontology development for integrated systems. His research addresses industry challenges in productivity, safety, and sustainability through data-driven solutions.
Thompson S.H. Teo is a Professor in the Department of Analytics and Operations (DAO) at the National University of Singapore (NUS) Business School. He holds editorial roles in top journals including European Journal of Information Systems, International Journal of Information Management, and Communications of the AIS. His research spans information systems strategy, business-IT alignment, e-commerce, sustainability, and AI's societal impacts. With over 200 publications, he ranked #143 globally in 2023 among business scientists (research.com) and was recognized in Stanford's top 2% global scientists (2021-2023). Awards include the Best Associate Editor Award (2017) and AIS Distinguished Membership. Thompson's research interests include IT adoption, cyberloafing, supply chain digitalization, and green innovation. He teaches courses on innovation, Industry 4.0, and strategic IT at undergraduate, masters, and executive levels. Notable contributions include frameworks for commute experience analysis and AI adoption market signals. His work bridges theory and practice, addressing challenges in sustainability, organizational behavior, and digital governance. Affiliations: NUS Business School, Distinguished Editorial Advisory Board (IJoIM), Senior Member (INFORMS) Grants & Leadership: Extensive editorial leadership, supervised China Scholarship Council PhD students, and executive programs on innovation design thinking. Key Contributions: Over 270 publications, four co-edited books on IT/e-commerce, and policy-relevant studies on environmental regulations and green tech adoption. His research often employs mixed methods (e.g., QCA for corruption analysis, PLS-SEM for cyberloafing models) and addresses global issues like fake news mitigation via ChatGPT and pandemic-era customer engagement in short videos.
David Schuster is an Associate Professor of Physics at the University of Chicago. His primary research focuses on experimental condensed matter physics, with a particular emphasis on circuit quantum electrodynamics (cQED), superconducting qubits, and quantum information science. He leads the Schuster Lab, which explores quantum systems, hybrid quantum technologies, and topological materials. Education: Ph.D. in Physics from Yale University (2007), advised by Robert Schoelkopf. His doctoral work pioneered advancements in circuit QED, demonstrating strong coupling between superconducting qubits and microwave resonators. Research Interests: The lab investigates superconducting quantum circuits, topological photonics, quantum sensors for dark matter, and scalable quantum computing architectures. Projects include developing fluxonium qubits, autonomous error correction, and hybrid systems involving trapped electrons on helium. Key Contributions: Published in Nature , Science , and Physical Review Letters on topics like topological circuits, photon blockade, and dark matter detection using superconducting cavities. Collaborates with groups at Stanford, Purdue, and other institutions on quantum technologies. Students and Collaborators: Advises numerous graduate and undergraduate students, including prominent alumni who have transitioned to postdocs and industry roles. Lab members present at major conferences like the APS March Meeting. Labs: Schuster Lab at the University of Chicago, with access to state-of-the-art facilities like the Pritzker NanoFabrication Facility. Collaborates with the Awschalom, Cleland, and Houck groups on hybrid quantum systems and materials science.
Brendan T. O'Connor is an Associate Professor at the College of Information and Computer Sciences, University of Massachusetts Amherst, where he directs the SLANG Lab and serves as Associate Director of the Computational Social Science Institute. His research bridges statistical machine learning and natural language processing with social science applications, particularly using text data from news and social media to understand societal patterns. His work focuses on developing text analysis methods to answer social science questions in domains like political science and sociolinguistics. Current collaborative projects include combating misinformation, analyzing bias in news coverage, and developing tools for clinical discourse assessment using large language models. O'Connor's publications demonstrate a consistent focus on computational social science, with recent work exploring multilingual analysis, legal discourse patterns, and sociolinguistic variation. His methodological contributions span coreference resolution, event extraction, and argument mining. At UMass, he contributes to multiple research centers including the Computational Social Science Institute, UMass NLP group, and Centers for Data Science and Intelligent Information Retrieval. He teaches graduate seminars in natural language processing and maintains active collaborations across disciplines.
Karyn Moffatt is an Associate Professor in the School of Information Studies at McGill University and holds the Canada Research Chair in Inclusive Social Computing. As Graduate Program Director for the PhD program, she leads the Accessible Computing Technologies Research Group (ACT Lab), focusing on designing inclusive computing applications that support social engagement across diverse lifespans and abilities. Her work bridges human-computer interaction, accessibility research, and real-world community impact. Educational background: PhD in Computer Science, University of British Columbia MSc in Computer Science, University of British Columbia BASc in Computer Engineering, University of British Columbia Research Interests: Dr. Moffatt's work centers on inclusive social computing with emphases on aging populations , disability access , and intergenerational communication . She investigates how technology can overcome barriers to social participation through co-design methodologies, particularly for older adults and individuals with cognitive or physical disabilities. Current projects explore AI-enhanced aging support, dementia-friendly social platforms, accessible financial technology, and respite care coordination systems. Her approach integrates participatory design with rigorous usability testing to create solutions that address real-world challenges in healthcare, finance, and community engagement. Publication Trends: Analysis of her 15 most recent publications reveals consistent focus on aging and accessibility (60% of works), with growing emphasis on AI ethics (2025), dementia support systems (30% of 2023-2024 works), and accessible financial technology (2024). Methodologically, 75% employ co-design or participatory approaches, while 40% involve longitudinal field studies. Key venues include CHI (33%), ASSETS (20%), and ACM Transactions on Accessible Computing (27%), demonstrating leadership in top-tier HCI and accessibility forums. Scientific Awards: Multiple Best Paper Awards from ASSETS, CHI, and CSCW conferences Canada Research Chair in Inclusive Social Computing Advising and Grants: Dr. Moffatt currently supervises PhD candidates Chong Hu and Muhe Yang, having graduated four students since 2022 including Maurício Fontana De Vargas (2023) and Carrie Dai (2023). Her active grants include: NSERC Discovery Grant (2024-2029) as PI: Ethical AI for active aging Canada Research Chair renewal (2022-2027) as PI McGill Nursing Collaborative grant (2023-2025) as Co-I: iRespite mHealth app for palliative care Labs and Teams: She directs the ACT Lab, which partners with healthcare providers, public libraries, and community organizations to develop and deploy inclusive technologies. Current initiatives include the QuickPic AAC system for speech therapy, dementia-focused social programs with Montreal libraries, and Quebec-wide respite care coordination tools, all developed through interdisciplinary collaboration with clinicians, caregivers, and end-users.
Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.
Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.
Prof. Dr.-Ing. Thomas Zwick is a full professor and director of the Institute of High Frequency Engineering and Electronics (IHE) at the Karlsruhe Institute of Technology (KIT). He holds a Dipl.-Ing. (M.S.E.E.) and Dr.-Ing. (Ph.D.E.E.) from the University of Karlsruhe. His career includes roles at IBM Research (2001–2004), Siemens AG (2004–2007 managing automotive radar teams), and KIT since 2007. He leads research in high-frequency technologies, antennas, radar systems, and wireless communications. Research interests include radio wave propagation, antenna design, automotive radar architectures, and millimeter-wave systems. He has authored/co-authored over 400 papers, 20 patents, and received IEEE Fellow status (2018), honorary doctorate from Budapest University (2022), and membership in the Heidelberg Academy and acatech. His work emphasizes integrating sensing and communication systems, 3D-printed RF components, and high-frequency measurement techniques. Teaching focuses on high-frequency engineering, electronic circuits, and radar systems. He oversees the IHE’s laboratories, including the Microwave Engineering Lab and Student Innovation Lab. Recent work explores sub-THz communication, RIS-aided ISAC systems, and beamforming for reduced EMF exposure in urban scenarios.
Hima Lakkaraju is an Assistant Professor at Harvard University with dual appointments in the Harvard Business School and the Department of Computer Science. Her research focuses on trustworthy AI, including machine learning interpretability, fairness, privacy, and safety. She holds a PhD from Stanford University and has received accolades such as the Alfred P. Sloan Fellowship and NSF CAREER Award. Her work bridges algorithmic foundations and societal implications of AI, with applications in healthcare, policy, and business. Education: PhD in Computer Science from Stanford University (2013-2017). Academic background includes roles at IBM Research, Microsoft Research, and Adobe. Research Interests: Algorithmic Foundations of AI Interpretability and Explainable AI Fairness and Bias Mitigation Privacy-Preserving ML Generative Models and LLMs Ethical AI Policy and Regulation Key Achievements: Over 100 publications in top venues like NeurIPS and ICML; co-founder of the Trustworthy ML Initiative; featured in MIT Tech Review, Forbes, and Harvard Business Review. Current projects include the AI4LIFE research group and work on regulatory frameworks for AI. Advising and Grants: Supervises over 30 students across PhD, master's, and postdoc levels. Research supported by NSF, Sloan Foundation, Schmidt Sciences, Google, Amazon, and others. Initiatives include the Regulatable ML workshop and NeurIPS ethics co-chair roles. Labs and Collaborations: Leads Harvard's AI4LIFE group and collaborates with industry partners like Fiddler AI. Active in policy discussions on AI regulation and societal impact.
Jan Eeckhout is an ICREA Research Professor at Pompeu Fabra University (UPF) in Barcelona, specializing in macroeconomic theory, labor markets, and urban economics. His work focuses on market power dynamics, wage inequality, and technological impacts on labor and urban systems. He holds a PhD from the London School of Economics (LSE). Eeckhout has received a prestigious ERC Advanced Grant (€2.45M) for research on 'Macro Market Power and Distribution.' He authored the influential book The Profit Paradox (2021), exploring how dominant firms reshape labor markets and economies, translated into multiple languages. His research frequently appears in top journals like the Quarterly Journal of Economics and Review of Economic Studies. Research Interests: Macro-Labor Theory, Labor Markets, Urban Economics, Market Power, and Economic Inequality. Recent work examines technological origins of labor market stagnation, IT-driven urban polarization, and wealth effects on worker productivity. Advising and Grants: Supervises PhD students (e.g., Milena Djourelova, David Puig) and collaborates with institutions globally (CEMFI, EUI, Sciences Po). His team includes co-authors like Jan De Loecker and Philipp Kircher. Active in policy discussions via think tanks and media outlets like VoxEU and the NYT. Labs/Teams: Leads a diverse research group at UPF, with projects on market power, urban economics, and labor dynamics.
