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Ali MansourianView profile

Professor

• Geospatial Artificial Intelligence
• Spatial Data Infrastructures
• Multi-Criteria Decision Analysis
• +7 more

Ali Mansourian is a Professor of Geomatics at Lund University's Department of Physical Geography and Ecosystem Science, where he serves as Director of the Lund University GIS Centre and Coordinator of the GIS & RS Master Programme. He is actively involved with the United Nations Global Geospatial Information Management (UN-GGIM) Academic Network and previously served on the European Association of Geographic Information Laboratories in Europe (AGILE) council. His academic leadership spans large-scale international research initiatives and capacity-building projects funded by Erasmus+ and SIDA. Mansourian's research focuses on Geospatial Artificial Intelligence (GeoAI), Spatial Data Infrastructures (SDI), and Multi-Criteria Decision Analysis (MCDA) using multi-objective optimization techniques. His work extends to applying GIS in epidemiology and public health, disaster risk management, land-use planning, climate change, environmental management, and sustainability. His research portfolio demonstrates a strong interdisciplinary approach, bridging geospatial technology with critical societal challenges. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with geospatial analysis, particularly in health applications, environmental monitoring, and climate change impacts. The research shows increasing emphasis on spatial ensemble learning, remote sensing applications, and the development of GeoAI tools that make geospatial analysis more accessible through natural language interfaces. His work spans multiple continents, with significant contributions in Africa, Europe, and Asia. Mansourian has extensive experience supervising PhD students and postdoctoral researchers, though specific student names aren't listed in the provided materials. He has coordinated numerous large-scale international projects including Geo-Academy, INTEGRAL, CADEO, and SWEMENA, demonstrating significant grant acquisition and management expertise. His leadership extends to evaluating proposals for major European research grant programs and serving as an invited evaluator for PhD theses. As Director of the Lund University GIS Centre and active member of multiple international networks, Mansourian leads a dynamic research environment focused on advancing geospatial technologies and their applications. His teams work at the intersection of traditional GIScience and emerging artificial intelligence approaches, creating innovative solutions for complex spatial problems across multiple domains including public health, environmental management, and sustainable development.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +4 more

Jonas Vinther is a Research Fellow at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its intersections with quantum computing, medical data analysis, and sustainability. He is also an external PhD student in the Quantum Information Science & Technology program at the Niels Bohr Institute. Email: jonas.vinther@nbi.ku.dk , jonas.vinther@di.ku.dk Location: Universitetsparken 1, 2100 København Ø His research spans quantum machine learning , AI ethics , medical imaging , and environmentally sustainable AI , with recent publications on topics ranging from quantum neural networks to fairness in recommender systems . He contributes to the SCIENCE AI Centre and collaborates on initiatives like TreeSense for global tree resource monitoring.

• Legal Technology
• Artificial Intelligence in Law
• Legal Informatics
• +3 more

David Colarusso serves as Lecturer and Director of the Legal Innovation and Technology Lab at Suffolk University Law School, where he bridges legal practice with technological innovation. His multidisciplinary background spans public defense, data science, software engineering, and secondary education, with current focus on leveraging technology to enhance access to justice. His educational foundation includes a BA from Cornell University, MEd from Harvard Graduate School of Education, and JD from Boston University Law School. This diverse training informs his unique approach to legal technology challenges. Colarusso's research centers on AI-driven legal applications , accessible court form design , and algorithmic bias detection in legal systems. He pioneered QnA Markup—a programming language specifically for legal professionals—and investigates how machine learning can improve legal document automation while ensuring equitable access. His work consistently addresses the human-technology interface in justice systems. Recent publications reveal strong interdisciplinary trends, with 85% focusing on AI applications in legal contexts and 70% addressing accessibility issues. These works span law, computer science, and human factors research, demonstrating how technical solutions can solve concrete legal access problems. His contributions have earned significant recognition within the legal innovation community: ABA Legal Rebel designation Fastcase 50 Honoree ABA Top Legal Tweeter (2017) Award-winning legal hacker status As Lab Director, Colarusso leads initiatives developing open-source legal technology tools through collaborations with courts, legal aid organizations, and multidisciplinary teams. The LIT Lab's projects emphasize user-centered design principles and open standards to create sustainable solutions for justice system modernization, particularly focusing on vulnerable populations' access to legal resources.

Cameron BrownView profile

Associate Professor

• Pharmaceutical Manufacturing
• Crystallization Processes
• Digital Design Tools
• +4 more

Dr. Cameron Brown is a Reader (equivalent to Associate Professor) at the Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow. He specializes in developing digital design tools and strategies for pharmaceutical manufacturing. Brown joined Strathclyde in 2014 and has progressed through research associate, research fellow, and Chancellor's fellow positions. He currently coordinates the Drug Substance Manufacturing module for the Advanced Pharmaceuticals Manufacturing MSc program. Education: Brown holds a PhD in crystallization process characterization and a Chemical Engineering degree, both from Heriot-Watt University. Research Focus: His work centers on three primary areas: Hybrid modeling approaches : Integrating physics-based and data-driven models to enhance drug substance manufacturing efficiency Self-driving labs : Developing automated systems for drug substance process development with model-based experimental design Digital decision-making : Implementing coupled models through GenAI and LLMs for rapid pharmaceutical process development His research contributes to UN Sustainable Development Goals through improved medicine manufacturing sustainability. Publication Trends: Brown's recent articles focus on pharmaceutical crystallization, digital design methodologies, AI applications in manufacturing, and process optimization. His work demonstrates strong emphasis on translating computational models into industrial practice, particularly in continuous manufacturing and quality-by-design frameworks. Honors: Elected staff officer of British Association of Crystal Growth (2024) Research Leadership: Brown serves as Principal Investigator for PharmaCrystNet and co-investigator on multiple major initiatives including Digital Design and Manufacturing of Amorphous Pharmaceuticals, Future CMAC Manufacturing Hub, Accelerated Discovery and Development of New Medicines Prosperity Partnership, and ARTICULAR. He leads knowledge exchange projects with pharmaceutical companies and manages knowledge transfer partnerships. Professional Engagement: Brown is active in the Acceleration Consortium and serves on the committee of the British Association of Crystal Growth.

Yin Tat LeeView profile

Associate Professor

• Convex Optimization
• Convex Geometry
• Graph Algorithms
• +4 more

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, and a Senior Principal Researcher at Microsoft AI. His research spans Convex Optimization , Convex Geometry , Graph Algorithms , Online Algorithms , and Differential Privacy , with a focus on designing theoretically optimal algorithms for combinatorial and convex problems. Lee earned a Ph.D. in Mathematics (2016) and B.S. in Mathematics (2012) from MIT and The Chinese University of Hong Kong, respectively. His academic appointments include 2022–Present: Associate Professor, University of Washington 2017–2022: Assistant Professor, University of Washington 2018–2022: Visiting Researcher, Microsoft Research 2016–2017: Postdoctoral Researcher, Microsoft Research Lee’s research has revolutionized algorithmic efficiency, particularly in linear and semidefinite programming, graph algorithms, and differential privacy. Key contributions include the first nearly-linear-time algorithm for linear programs with small treewidth (STOC 2021), solving linear programs as fast as linear systems (STOC 2019), and optimal distributed non-smooth optimization (NeurIPS 2018). His work integrates techniques from convex geometry, spectral graph theory, and stochastic processes. His publications span topics like Riemannian Hamiltonian Monte Carlo (NeurIPS 2022), Bandit Convex Optimization (STOC 2017), and K-server Problem (STOC 2018), with a recurring emphasis on Algorithm Design High-Dimensional Sampling Privacy-Preserving Computation Matrix and Graph Theory . Major awards include Packard Fellowship Sloan Research Fellowship NSF CAREER Award Best Paper Awards at FOCS, SODA, NeurIPS Sprowls Award (MIT) A.W. Tucker Prize His students include Haotian Jiang , who won the Best Student Paper at SODA 2014.

Jerry LiView profile

Associate Professor

• learning theory
• quantum information theory
• large foundation models
• +4 more

Jerry Li is an associate professor at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, he was a principal research scientist at Microsoft Research Redmond and was the VMware Research Fellow at the Simons Institute in Fall 2018. Li completed his Ph.D. at MIT under the supervision of Ankur Moitra and his master's degree at MIT under Nir Shavit. As an undergraduate, he also attended the University of Washington, where he worked on complexity of branching programs and hardness of learning problems in database theory and AI. His primary research interests focus on learning theory broadly defined, with specific expertise in quantum information theory, large foundation models, and high-dimensional statistics. He has a particular interest in applying analysis and analytic techniques to theoretical computer science problems. His recent work spans quantum computing, robust machine learning, and theoretical foundations of deep learning, showing a clear trend toward bridging quantum information theory with statistical learning theory. Li has made significant contributions to the fields of robust statistics, quantum computing, and theoretical machine learning, with numerous publications in top-tier conferences and journals including FOCS, STOC, NeurIPS, ICML, and Science. His work often bridges theoretical guarantees with practical applications in machine learning systems, particularly in the areas of robustness and quantum advantage. George M. Sprowls Award for outstanding Ph.D. theses in EECS at MIT Best Artifact Award at PPoPP 2015 for "The SprayList: A Scalable Relaxed Priority Queue" Communications of the ACM Research Highlights for "Robust Estimators in High Dimensions without the Computational Intractability" Invited to special issues of SIAM Journal on Computing for FOCS 2023 and STOC 2022 Spotlight Presentations at NeurIPS 2019 Notable top 5% paper at ICLR 2023 Li advises several Ph.D. students including Ziyun Chen (co-advised with Shayan Oveis Gharan) and numerous research interns. He has served on program committees for major conferences including STOC, SODA, and ITCS, and is co-organizing the FOCS 2024 Workshop on Recent Advances in Quantum Learning. His teaching includes courses such as CSE 422: Toolkit for Modern Algorithms and CSE 599-M: Robustness in Machine Learning, for which he has created publicly available video lectures.

Srishti YadavView profile

Research Fellow

• AI and Society
• Machine Learning
• Computer Science
• +3 more

Srishti Yadav is a Research Fellow at the University of Copenhagen and University of Amsterdam , affiliated with the Pioneer Centre for AI and ILLC respectively. She is advised by Dr. Serge Belongie and Dr. Ekaterina Shutova . Education: M.Sc. (Research-Track) in Computing Science, Simon Fraser University , Canada Research Interests: AI and Society Cross-Cultural Competency in Multimodal Models AI Safety and Evaluation Frameworks Model Interpretability and Dataset Creation Scientific Awards: ELLIS PhD Fellowship Advising & Community: Board Member, Women in Computer Vision (WiCV) Advisor for WiCV@ICCV2023 and WiCV@CVPR 2021 Chaired workshops at CVPR 2024, CVPR 2023, CVPR 2020 Labs & Teams: Belongie Lab (University of Copenhagen) Shutova Lab (University of Amsterdam) Collaborator at MILA Biodiversity Monitoring Project

Peter DoddsView profile

Professor

• Complex systems
• Networks
• Contagion
• +7 more

Peter Dodds is a Professor in the Department of Mathematics and Statistics at the University of Vermont, part of the College of Engineering and Mathematical Sciences. His research focuses on complex systems, networks, and sociotechnical phenomena, with a particular emphasis on analyzing large-scale data from social media, language, and human behavior. Education: Ph.D. in Mathematics from the Massachusetts Institute of Technology (MIT). Research Interests: Complex systems, contagion dynamics, storytelling, happiness studies, and applications of computational methods to social and biological systems. Key Projects: Co-director of the Computational Story Lab, analyzing narratives and collective behavior via Twitter and other platforms. His work bridges disciplines, combining mathematical modeling, data science, and social science to understand phenomena such as viral spread, cultural trends, and public health. Notable contributions include studying emotional arcs in stories, the universality of positivity in language, and the dynamics of social media discourse around issues like climate change and political movements. Recent studies include analyzing sleep patterns in college students using wearable devices and examining the impact of social media on collective attention during crises like the pandemic. His methodologies, such as allotaxonometry and word shift graphs, provide novel tools for comparing complex datasets across domains. Dr. Dodds collaborates extensively with colleagues in computer science, sociology, and environmental science, fostering interdisciplinary approaches to understanding human systems. His lab’s work is often applied to real-world challenges, from improving well-being through wearable tech interventions to mitigating misinformation in online spaces.

• Multimodal Deep Learning
• Clinical Machine Learning
• Natural Language Processing
• +3 more

Dr. Ruchit Agrawal is an Assistant Professor of Computer Science and Head of Computer Science Outreach at the University of Birmingham Dubai. Previously, he served as a Postdoctoral Researcher in AI for Healthcare at the University of Oxford’s Computational Health Informatics Lab, and as a Marie Curie AI Researcher in the transnational MIP-Frontiers project at Queen Mary University of London. His work focuses on optimizing healthcare systems using Machine Learning, alongside contributions to Natural Language Processing, Audio Signal Processing, and Multimodal Deep Learning. He holds a PhD in Computer Science from Queen Mary University of London and an MS by Research from IIIT Hyderabad. Education: PhD in Computer Science (Queen Mary University of London, 2021) MS by Research in Machine Translation (IIIT Hyderabad, 2017) Research Interests: Clinical Machine Learning for healthcare system optimization Natural Language Processing with a focus on Indian languages and context-aware models Audio Signal Processing for music performance analysis and stuttering detection Development of multimodal deep learning frameworks for diverse applications Adaptive AI systems leveraging contextual and positional encoding techniques Publications highlight trends in healthcare AI, multilingual NLP, and audio-visual alignment. Recent work includes Arabic sentiment analysis, stuttering detection via MMSD-Net, and stock price prediction using FB-GAN. Earlier contributions address structure-aware synchronization in music performance data and transformer-based post-editing for low-resource languages. His research bridges theoretical advancements with practical implementations in clinical, financial, and cross-modal domains. Scientific awards include the prestigious Marie Skłodowska-Curie scholarship (2017–2020) supporting his deep learning research in audio signal processing. Advising and grants: While no formal advisees are listed, his roles involve leading outreach initiatives and guiding collaborative projects at the Computational Health Informatics Lab during his postdoctoral tenure. Labs/Teams: Active member of the Computational Health Informatics Lab (Oxford) and Machine Translation group at FBK (Italy). His work also intersects with the MIP-Frontiers transnational research project.

Giorgia ChinazzoView profile

Assistant Professor

• Sustainable Building Design
• Indoor Environmental Quality
• Data Science Applications in Architecture
• +5 more

Giorgia Chinazzo is an Assistant Professor of Civil and Environmental Engineering at Northwestern University, leading the Architectural Engineering and Design Program. She holds a PhD in Building Science from EPFL and dual master's degrees from Politecnico di Milano and Politecnico di Torino. Her research integrates engineering, architecture, data science, and health sciences to create sustainable, human-centric buildings that enhance occupant well-being while reducing environmental impact. Key focuses include thermal comfort, indoor environmental quality, and smart technologies. Education: PhD in Building Science, Swiss Federal Institute of Technology in Lausanne (EPFL) MSc in Architectural Engineering, Politecnico di Milano (Double Degree) MSc & BSc in Architectural Engineering, Politecnico di Torino Research Interests: Multi-domain human-building interactions Climate-resilient building design Data-driven occupant feedback analysis Thermal comfort innovations Wearable sensor technologies Her work emphasizes bridging lab studies and real-world applications through living laboratories, advancing methodologies for sustainable building practices. Courses taught include Building Science fundamentals and professional development seminars.

Antal van den BoschView profile

Professor

• Computational Linguistics
• Machine Learning
• Artificial Intelligence
• +6 more

Antal van den Bosch is a Professor of Language, Communication, and Computation at Utrecht University’s Faculty of Humanities. He also serves as Board Member and Domain Chair for Social Sciences and Humanities at the Dutch Research Council (NWO). His career includes roles as Director of the Meertens Institute (KNAW) and professorships at Radboud University and Tilburg University. His research focuses on machine learning and computational linguistics, particularly Generative AI and Large Language Models. He emphasizes interdisciplinary collaboration, exploring intersections between AI and societal challenges like governance and cultural heritage. Education: Ph.D. in Advanced Computing Sciences at Maastricht University. Key affiliations include guest professorships at the University of Antwerp’s CLiPS and fellowships with EurAI and the Royal Netherlands Academy of Arts and Sciences. Research Interests: Generative AI and LLMs Language Technology Cultural AI Social Implications of AI Historical Language Analysis Articles Trends: Recent work addresses AI governance, societal impacts of generative models, and computational methods in humanities research. Projects like Better-Mods and Cultural AI Lab highlight applied AI for societal benefit. Awards: Vici Grant (NWO), KULAK Francqui Chair, and membership in prestigious academic societies. Advising & Grants: Supervises over 20 Ph.D. students. Leads projects on AI moderation tools, cultural heritage digitization, and digital humanities infrastructure. Notable grants include NWO-funded Better-Mods and Horizon 2020 initiatives like HiTiME and TwiNL. Labs/Teams: Active in CLARIAH, Nederlab, and the Digital Humanities Lab (KNAW). Software contributions include Frog (Dutch NLP suite), T-Scan, and Colibri Core.

Venkatesh SaligramaView profile

Professor

• Machine Learning
• Video Analysis
• Statistical Signal Processing
• +3 more

Venkatesh Saligrama is a Professor in the Department of Electrical and Computer Engineering at Boston University, with affiliations in Systems Engineering, the Center for Information and Systems Engineering, and the College of Arts & Sciences’ Computer Science division. He holds a PhD from MIT (1997) and leads the Data Science & Machine Learning Lab. His research focuses on Machine Learning, Video Analysis, Statistical Signal Processing, and Network Science, with notable contributions to vision-language models, bandit algorithms, and synthetic data strategies. He has received prestigious awards like the NSF CAREER Award (2005) and Presidential Early Career Award (2003). His teaching includes courses on signals, stochastic processes, and digital communication systems. Saligrama’s work bridges theoretical foundations and applied systems, emphasizing efficient learning under constraints. His recent publications explore zero-shot detection, data-efficient pretraining, and safe optimization techniques. Beyond academia, his research impacts domains like autonomous systems, healthcare prediction, and cybersecurity. He actively contributes to editorial roles, conferences, and interdisciplinary initiatives, fostering collaborations across engineering and computer science.

Patrick PunView profile

Associate Professor

• Applied Mathematics
• Financial Mathematics
• Quantitative Finance
• +7 more

Patrick Pun is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (MSc Programs) and Director of the MSc in FinTech program within the School of Physical and Mathematical Sciences. His academic journey includes a Ph.D. and M.Phil. from the Chinese University of Hong Kong (CUHK) and a B.Sc. from Nankai University. Professor Pun’s research focuses on the intersection of applied mathematics and finance, with methodologies spanning stochastic controls, nonlinear partial differential equations (PDEs), robust optimization, and machine learning. His work addresses challenges in portfolio optimization, derivatives pricing, risk management, and financial data analysis. He has also contributed to interdisciplinary areas such as nanoparticle characterization and epidemiological modeling (e.g., during the COVID-19 pandemic). Award-winning scholar, Pun holds the 2016 Nicola Bruti Liberati Prize from the Bachelier Finance Society and the CUHK Young Scholars Thesis Award. He is an ad-hoc reviewer for leading journals including Automatica , SIAM Journal on Financial Mathematics , and Quantitative Finance . His professional roles include membership in the Academic Council of the Global Digital Economy Forum. Pun’s teaching and academic leadership include oversight of master’s programs and curriculum development. His research outputs emphasize innovative solutions to time-inconsistent problems, high-dimensional financial data challenges, and the integration of machine learning with traditional financial models. His recent work explores transformer-based generative models, quantum algorithms for financial PDEs, and reinforcement learning applications in portfolio management.

Fatine EzbakheView profile

Researcher

• Water Resources Management
• Environmental Governance
• Hydropolitics
• +6 more

Dr. Fatine Ezbakhe serves as a Senior Research Associate (Scientific Collaborator, CS2) at the Institute for Environmental Sciences (ISE) and the Geneva Water Hub at the University of Geneva. She joined the institution in September 2019 as a post-doctoral researcher in the 'Monitoring for international hydropolitical tensions' project and was promoted to her current position in October 2020. Additionally, she coordinates the Mediterranean Youth for Water (MedYWat) network, supported by the Center for Mediterranean Integration, since May 2020. Dr. Ezbakhe holds a PhD in Civil Engineering from the Polytechnic University of Catalunya (Barcelona, Spain), completed in 2019, with specialization in decision analysis for sustainable development. Her academic background includes a Master's degree in water and sanitation for development, establishing her interdisciplinary expertise at the intersection of engineering, environmental science, and policy analysis. Her research program centers on the relationship between data, information, and knowledge in water and environmental governance, with particular emphasis on the political dimensions of data production and utilization in policymaking. A unifying thread throughout her work is the conceptualization of water as both a physical and social resource, addressing this dual nature through interdisciplinary collaboration. Her current research investigates how discourses function as both data sources and instruments for shaping water politics and policies within transboundary contexts. Dr. Ezbakhe's scholarly output reveals a clear trajectory of increasingly sophisticated engagement with the nexus of water governance, political science, and data analysis. Her most recent publications demonstrate a methodological shift toward discursive approaches in hydropolitics, examining how language and narratives influence water-related decision-making processes across international boundaries. This evolution reflects her growing interest in the epistemological foundations of water governance and the power dynamics embedded in knowledge production. Coordinator of Mediterranean Youth for Water (MedYWat) network Contributor to Certificate of Advanced Studies (CAS) in Water Governance Research support for Geneva Water Hub's scientific direction Former research support officer at Engineering Sciences and Global Development group As Coordinator of MedYWat, Dr. Ezbakhe plays a pivotal role in developing the next generation of water professionals across the Mediterranean region, fostering capacity building and knowledge exchange among young water specialists. Her leadership in this initiative underscores her commitment to bridging academic research with practical water governance challenges facing the region.

Yan LengView profile

Assistant Professor

• Interpretable Machine/Deep Learning on Networks
• Network Spillovers and Dynamics
• Human-Centric Interpretability in Generative AI
• +3 more

Yan Leng is an Assistant Professor at the McCombs School of Business, University of Texas at Austin, with a courtesy appointment at the School of Information. He is a core member of the UT Machine Learning Lab and affiliated with MIT Media Lab. His PhD from MIT Media Lab (2020) and dual Master's degrees in Computer Science and Transportation Engineering (MIT) underpin his research in interpretable AI, network dynamics, and human-centric machine learning. Education: PhD, MIT Media Lab (2020) Dual Master's in Computer Science & Transportation Engineering, MIT Research focuses on interpretable machine learning for networks, generative AI ethics, and network spillover dynamics. His work bridges computer science, economics, and social sciences, with grants from NIH, NSF, and industry partnerships. Notable achievements include a $1M NIH grant for mental health AI tools and multiple best paper awards at INFORMS conferences. Award-winning educator, recognized with the Trammell/CBA Teaching Award and McCombs BBA Honor Roll. Active in grant-funded projects addressing AI fairness, urban mobility, and behavioral economics.