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Robert WestView profile

Associate Professor

• Natural Language Processing (NLP)
• Machine Learning
• Computational Social Science
• +4 more

Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.

Amitabha BagchiView profile

Professor

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +7 more

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

Anna KorhonenView profile

Professor

• Natural Language Processing
• Multilingual NLP
• Low Resource NLP
• +9 more

Professor Anna Korhonen is a leading academic at the University of Cambridge, holding positions as Professor of Natural Language Processing, Co-Director of the Language Technology Laboratory (LTL), Director of the Centre for Human-Inspired Artificial Intelligence (CHIA), and Fellow of Churchill College. Her work bridges computational linguistics, artificial intelligence, and interdisciplinary applications. Her research focuses on human-centric NLP with core interests in multilingual/low-resource systems, conversational AI, and responsible technology development. She emphasizes applications for social and global good, including healthcare, climate science, and equitable language technologies. Her methodology integrates cognitive modeling with machine learning to create interpretable, fair NLP systems. Key projects include ERC-funded initiatives like MultiConvAI (multilingual conversational AI) and Towards Globally Equitable Language Technologies , alongside Innovate UK's ESG RoboFactory and EPSRC's Modeling Idiomaticity project. Her work spans biomedical text mining ( PheneBank , LION ), educational technology ( EF Education First Research Lab ), and cross-lingual transfer learning. Fellow of the Association for Computational Linguistics (ACL) Fellow of ELLIS (European Laboratory for Learning and Intelligent Systems) Royal Society University Research Fellow (2005-2014) Google Faculty Award recipient EACL Chair Elect Korhonen actively supervises PhD/MPhil students in Computation, Cognition and Language programs and leads interdisciplinary collaborations across computer science, linguistics, and domain sciences. Her lab (LTL) develops foundational NLP techniques while addressing real-world challenges through partnerships with healthcare, environmental science, and education sectors. Current strategic initiatives include the Institute for Technology and Humanity and CHIA's human-inspired AI framework.

• Operations Research
• Machine Learning
• Optimization under Uncertainty
• +4 more

Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.

Dean EcklesView profile

Associate Professor

• Social Influence
• Network Analysis
• Causal Inference
• +4 more

Dean Eckles is an Associate Professor of Marketing at MIT Sloan School of Management and serves as an Associate Director of the MIT Institute for Data, Systems, and Society (IDSS). He is also affiliated with the MIT Schwarzman College of Computing through the Institute for Data, Systems & Society and its Statistics and Data Science Center. Additionally, he leads the analytics research area at the Initiative on the Digital Economy and organizes the annual Conference on Digital Experimentation (CODE@MIT). His educational background includes a BA in philosophy, BS and MS in cognitive science, MS in statistics, and PhD in communication, all from Stanford University. Prior to joining MIT, Eckles worked as a scientist at Facebook, where he contributed to areas including News Feed, messaging, advertising, tools for randomized experiments, and survey methods. He previously held research positions at Nokia and Yahoo. Eckles's research primarily focuses on social influence mediated by interactive technologies, examining how communication technologies mediate, amplify, and direct social influence. His work spans multiple specific areas including social interactions, contagion, and interventions in networks; experimental design and inference in networks; and methods for causal inference. His research often combines social science with advanced statistical methods. His notable publications include research on long ties in social networks and their relationship to economic prosperity, how network structure affects social contagions, and algorithmic transparency in social media platforms. His work has appeared in prestigious journals including PNAS and Nature Human Behaviour, and he has provided expert testimony before the US Senate on algorithmic ranking. Long ties, disruptive life events and economic prosperity (PNAS) Long ties accelerate noisy threshold-based contagions (Nature Human Behaviour) Algorithmic transparency and assessing effects of algorithmic ranking (Senate testimony) Eckles actively shares his research through social media platforms including Bluesky, Twitter, and Mastodon, as well as through his blog and contributions to the Gelman et al. blog. His work bridges academic research with practical applications in technology and policy.

Antonio RangelView profile

Professor

• Neuroeconomics
• Decision Neuroscience
• Behavioral Economics
• +4 more

Antonio Rangel is the Bing Professor of Neuroscience, Behavioral Biology, and Economics at the California Institute of Technology (Caltech), where he also serves as Head Faculty in Residence. He is a faculty member in the Division of Humanities and Social Sciences (HSS) with research focusing on the computational and neurobiological basis of value-based decision-making. Dr. Rangel received his educational training at prestigious institutions: B.Sc. from Caltech in 1993 M.S. from Harvard University in 1996 Ph.D. in 1998 Professor Rangel's research lies at the intersection of neuroscience, economics, and psychology, with a focus on understanding how the brain makes decisions. His work investigates the neural mechanisms underlying value computation, choice processes, self-control, and social decision-making. Using a multidisciplinary approach that combines functional magnetic resonance imaging (fMRI), eye-tracking, computational modeling, and behavioral experiments, his lab has made significant contributions to the field of neuroeconomics. His research has revealed how value signals are represented in the brain, how attention influences choice, and the neural basis of self-control failures. Professor Rangel has pioneered methods for studying decision processes with high temporal resolution using eye-tracking data, demonstrating how fixation patterns relate to value computations and choice outcomes. His work spans from theoretical frameworks of value-based decision-making to practical applications in behavioral public economics. Professor Rangel's work has been recognized with prestigious awards: 2019 NOMIS Distinguished Scientist Award 2018 Fellow of the Association for Psychological Science As an academic leader, Professor Rangel has mentored numerous students and researchers who have gone on to make their own contributions to neuroscience and economics. His lab, the Rangel Neuroeconomics Laboratory at Caltech, serves as a hub for interdisciplinary research, bringing together students and scholars from neuroscience, economics, psychology, and computer science. The lab has received significant funding to support its research on the neural basis of decision-making, including support from the NOMIS Foundation. The Rangel Neuroeconomics Laboratory is equipped with state-of-the-art facilities including fMRI analysis capabilities, eye-tracking systems, and computational resources for modeling decision processes. The lab fosters a collaborative environment where researchers apply methods from experimental economics and cognitive neuroscience to unravel the complexities of human decision-making.

Fei FangView profile

Associate Professor

• Artificial Intelligence
• Multi-Agent Systems
• Machine Learning
• +5 more

Fei Fang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University (CMU) , where she explores the intersection of artificial intelligence and multi-agent systems . Her work integrates machine learning with game theory to address challenges in security , sustainability , and mobility , aligning with the AI for Social Good mission. Ph.D. in Computer Science, University of Southern California (2016) B.Eng. in Electronic Engineering, Tsinghua University (2011) Recent research focuses on reinforcement learning , large language models (LLMs) , and human-AI collaboration . Her team’s work has been recognized with 15+ awards across prestigious venues like IAAI, AAAI, and IJCAI. Notable accolades include the 2023 Allen Newell Award , 2022 Sloan Fellowship , and NSF CAREER Award (2021) . She actively contributes to educational initiatives , including teaching "Demystifying AI for Everyone" at CMU, and has sought part-time teaching assistants for course development. Her research spans 15+ domains , including AI ethics , cyber defense , traffic optimization , and public health .

Flavio Vasconcellos ComimView profile

Professor

• Sustainability
• Economics
• Ethics
• +11 more

Flavio Vasconcellos Comim is a Full Professor at IQS School of Management in Barcelona, Spain, holding the Chair of Ethics and Christian Thought (intercenter). His research focuses on the intersections of Sustainability, Economics, and Ethics. Fields of Interest : Human Development, Capability Approach, Social Exclusion, Regional Development, AI Ethics, Poverty Stigma (Aporophobia), Sustainable Development, Social Welfare, Justice, and Happiness. Recent research includes publications on youth HDI in Spain (2025), AI bias against the poor (2024), and critiques of Sen's social choice theory (2024). He leads projects like Big data applied to the study of aporophobia and Population Medicine and Sustainable Development collaborations with China. His academic output spans journals like Regional Studies , AI and Society , and Cambridge Journal of Regions, Economy and Society , with keywords spanning Economics, Machine Learning, Human Development, and Social Justice. No formal students or awards are listed in available records.

Yannis PaschalidisView profile

Professor

• Optimization
• Control Systems
• Stochastic Systems
• +8 more

Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.

Srijan KumarView profile

Assistant Professor

• Data Science
• Applied Machine Learning
• Network Analysis
• +7 more

Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.

Alexandre JacquillatView profile

Associate Professor

• Data-driven decision-making
• Stochastic optimization
• Integer optimization
• +6 more

Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.

Xiaowei JiaView profile

Assistant Professor

• Machine Learning
• Data Mining
• Physics-Guided Neural Networks
• +5 more

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China (USTC) and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal and environmental challenges, such as climate modeling, hydrology, and fairness in AI. Education: Ph.D., University of Minnesota (2020) M.S., State University of New York at Buffalo B.S., University of Science and Technology of China (USTC) Research Interests: Knowledge-Guided Machine Learning Spatiotemporal Data Mining Fairness in AI for Social Good Applications in Environmental Science and Healthcare Publications showcase his work on physics-integrated neural networks, spatiotemporal modeling (e.g., water temperature prediction), and fairness-aware algorithms. His work has been recognized with Best Paper awards at SIAM SDM (2022, 2023). Awards include the Best Applied Data Science Paper Award at SIAM SDM in 2022 and 2023. He teaches advanced machine learning courses, emphasizing theory integration with real-world applications.

• Operations Management
• Supply Chain Management
• Sharing Economy
• +6 more

Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence Ethics
• +3 more

Anders Søgaard is a Professor at the University of Copenhagen , affiliated with both the Department of Computer Science and the Department of Communication. His research bridges Natural Language Processing and Machine Learning with a focus on AI ethics , explainability , and human-AI interaction . Primary Affiliation: Department of Computer Science, University of Copenhagen Secondary Affiliation: Department of Communication, University of Copenhagen Email: soegaard@di.ku.dk, soegaard@hum.ku.dk Research Interests His work spans Natural Language Processing , Machine Learning , and AI ethics , with recent studies addressing: Trustworthiness in AI systems Explainable AI (XAI) frameworks Multilingual model fairness and alignment Human-AI collaboration in reasoning tasks Ethical implications of social robots Mental health analytics using ML Recent Publications His 2025 output highlights trends in: AI ethics (e.g., fairness metrics, trustworthy systems) Multilingual model analysis (knowledge retention, cross-lingual transfer) Human-centric AI (gaze data, cultural considerations) Applications in healthcare and social good

Alison PowellView profile

Associate Professor

• Technology Design and Policy
• Public Sector Technology
• Digital Futures
• +5 more

Dr Alison Powell is an Associate Professor in the Department of Media and Communications at the London School of Economics and Political Science (LSE), serving as Programme Director for the MSc Media and Communications (Data and Society). Her research focuses on technology design, public sector technology, and digital futures. She is known for her book Undoing Optimization: Civic Action in Smart Cities (2021) and her participatory research method, data walking. From 2019–2023, she directed the JUST AI network, supported by the Ada Lovelace Institute and UKRI, advancing AI ethics research. Her work integrates critical theory, participatory methods, and interdisciplinary collaboration. Education: PhD in Communication from Concordia University (Canada), MA in Media Studies from Toronto Metropolitan University, and postdoctoral research at the Oxford Internet Institute. Prior to academia, she worked in the film and TV industry. Research interests include civic engagement in data-driven governance, ethical AI, and participatory urbanism. She has advised projects like Africa Just AI and Operationalising Ethics for AI, funded by Horizon Europe, AHRC, and the Open Society Foundations. Her articles address topics such as AI governance, urban light ecology, feminist research praxis, and the ethical implications of technology for marginalized communities. She contributes to blogs like Media@LSE and leads initiatives at Polis and the LSE’s Digital Futures for Children project. Grants and collaborations include work with Projects by IF, Open Rights Group, and the Human Data Interactions Network (EPSRC). She explores intersections of technology, justice, and sustainability, emphasizing equitable and inclusive innovation.