Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Complex Systems
• +7 more

Jordi Delgado Pin is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Computer Science (FIB) and the Department of Computer Science. He is an active researcher in the IDEAI-UPC and SOCO (Soft Computing) research groups, contributing extensively to artificial intelligence, machine learning, and complex systems. His research interests include artificial intelligence, machine learning, computational complexity, neural networks, and data science, with applications in biological and social networks. He has made significant contributions to unsupervised learning methods, particularly in contrastive divergence and community detection in complex networks. The analysis of his recent publications reveals a strong focus on machine learning optimization, neural network training, and theoretical foundations of computing. His work spans both theoretical and applied domains, including educational technology and bio-inspired algorithms. Jordi Delgado Pin has participated in multiple competitive R&D+i projects, such as 'Gestió i Anàlisi de Dades Complexes' and 'Mineria en datos biológicos y sociales', demonstrating sustained research activity. He has also contributed to educational initiatives in programming and data science. He is involved in key research laboratories including IDEAI-UPC, SOCO, and LARCA, fostering interdisciplinary collaboration in data science and AI.

Shuchin AeronView profile

Associate Professor

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +8 more

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

• Data-Driven Salesforce Management
• Targeted Marketing
• Matching Platforms
• +5 more

Dr. Yuting Zhu is an Assistant Professor at the Department of Marketing, NUS Business School, National University of Singapore. Her research bridges data-driven marketing strategy with machine learning and behavioral economics, supported by grants from Singapore’s Ministry of Education and the National Natural Science Foundation of China. She holds a PhD in Management from MIT (2022), an MA in Economics from the University of Rochester (2017), and dual degrees in Economics and Mathematics from Renmin University of China (2015). Research Interests: Data-Driven Salesforce Management Targeted Marketing and Matching Platforms Human-AI Interaction in Marketing Machine Learning and Game Theory Field Experiments in Real-World Platforms Recent Publications (2019–2025): Her work spans algorithm aversion in ridesharing platforms, group search theory, targeted marketing optimization, and platform design. She employs field experiments with 2 million customers and linear programming advances to solve large-scale marketing constraints. Collaborations: Co-authored studies with researchers like Huachen Lu (NUS), Xiaoyang Cao (NUS), and D. Simester (MIT) highlight cross-institutional partnerships.

Min XuView profile

Assistant Professor

• Machine Learning
• High-dimensional Statistics
• Network Data Analysis
• +2 more

Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.

• E-Commerce Search Engines
• Recommender Systems
• Machine Learning
• +4 more

Anxiang Zeng is a researcher affiliated with the University of Kansas School of Business , Department of Management and Leadership. His work focuses on machine learning applications in e-commerce , particularly in search engines, recommender systems, and preference optimization.

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

Wenjie Li is a Professor at the Department of Computing, Hong Kong Polytechnic University, and holds a PhD from the Chinese University of Hong Kong (1997). His research spans Natural Language Processing, Artificial Intelligence, and Machine Learning , focusing on Large Language Models (LLMs) Multimodal Systems Dialogue and Recommender Systems Speculative Decoding and Inference Optimization Chain-of-Thought Reasoning Recent work emphasizes generative retrieval , personalized web agents , and error-resilient LLM frameworks . Key contributions include the JobFormer for skill-aware recommendations, STeCa for trajectory calibration, and TokenSkip for controllable reasoning compression. Publications reveal a trend toward enhancing multimodal alignment and safety mechanisms in aligned LLMs. Li actively collaborates with institutions like Queen's University , Tsinghua University , and Nanjing University , working on projects such as text-image interleaved retrieval and speculative decoding surveys . His 2024-2025 output includes 15+ papers at venues like ACL, CVPR, ICLR , and journals like IEEE Transactions on Neural Networks .

• Database Systems
• Data Management
• Recommender Systems
• +4 more

Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.

Nachiketa SahooView profile

Associate Professor

• Machine Learning
• Information Systems
• Personalized Recommender Systems
• +5 more

Nachiketa Sahoo is an Associate Professor in the Information Systems Department at Boston University's Questrom School of Business, where he has been faculty since 2011. His research sits at the intersection of Machine Learning and Information Systems, with particular focus on personalized recommender systems and user preference modeling. Research Focus: Dr. Sahoo's work centers on learning user preferences and decision-making processes from large disaggregate datasets. His primary projects include: Personalization and Matching: Developing systems to match items to users while examining stability and fairness implications Retail Analytics: Analyzing customer activity data to reduce product expiration through salesforce incentive alignment Data-driven Healthcare: Estimating value of genomic tests and physician decision modeling Content Recommendation: Studying multi-category utility models for diversified content consumption His recent publications demonstrate strong trends toward ethical implications of recommender systems, particularly regarding fairness and market stability, while maintaining technical depth in machine learning applications. Scientific Contributions: Developed novel methods for uncovering population response paths using time-series data Created approaches to match donors with philanthropic causes on crowdfunding platforms Designed salesforce compensation schemes reducing $15B+ in annual product waste Pioneered multi-category utility models for content recommendation Academic Leadership: Dr. Sahoo advises PhD students in Information Systems, with graduates securing tenure-track positions at institutions like University of Minnesota and University of South Florida. His teaching portfolio includes advanced machine learning courses and doctoral seminars on ML methods for social science research. He previously served as a Visiting Assistant Professor at Carnegie Mellon's Tepper School of Business. Education: He holds a PhD in Information Systems from Carnegie Mellon University's Heinz College, an MS in Knowledge Discovery and Data Mining from CMU's Machine Learning Department, and a B.Tech in Industrial Engineering from IIT Kharagpur. His industry experience includes software engineering at i2 Technologies (now JDA Software).

Frank MollicaView profile

Lecturer

• Computational Cognitive Science
• Language Acquisition
• Conceptual Development
• +7 more

Frank Mollica is a Lecturer in Computational Cognitive Science at the Complex Human Data Hub , School of Psychological Sciences, University of Melbourne. He is also an Honorary Fellow at the Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh, and an associate member of the Center for Language Evolution at the University of Edinburgh. University: University of Melbourne , University of Edinburgh School: School of Psychological Sciences , School of Informatics Academic Rank: Lecturer Research Interests Frank’s research explores how humans construct conceptual systems supporting cognition and language, focusing on computational modeling and experimental methods. He investigates: Development of rich conceptual systems in children and adults Cognitive efficiency, diversity, and transmission Context-building in language processing Information-theoretic models of communication Publication Trends His recent work spans computational models of: Memory retrieval costs in semi-compositional systems Color perception and referential overinformativeness Lexicalization frequency across languages Context-sensitive word frequency effects Legal language accessibility Kinship term acquisition Labs & Affiliations Frank is affiliated with the Complex Human Data Hub (Melbourne), Institute for Language, Cognition and Computation (Edinburgh), and the Center for Language Evolution (Edinburgh).

Dmitriy (Tim) KuniskyView profile

Assistant Professor

• Probability
• Statistics
• Mathematical Data Science
• +7 more

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.

Luca VerginerView profile

Lecturer

• Network Science
• Empirical Economics
• Supply Chain Management
• +4 more

Luca Verginer is a Lecturer at the Department of Management, Technology, and Economics within ETH Zurich. His work bridges empirical economics and network science, focusing on pharmaceutical supply chains, patent dynamics, and social network impacts. Current affiliation: ETH Zurich (School of Management and Social Sciences) Academic rank: Lecturer His research integrates causal inference, advanced econometrics, and deep learning methods like Graph Neural Networks (GNN) and Natural Language Processing (NLP) to address policy and strategic challenges in healthcare and innovation. Key trends in his publications include: Quantifying supply chain resilience through reroute flexibility Analyzing scientist mobility networks and global cities' dominance Studying social dynamics in online radicalization and migration Methodologically, he employs agent-based modeling, big data analytics, and empirical validation across diverse domains from opioid distribution to academic collaboration. His work demonstrates strong interdisciplinary focus, combining economics, computational methods, and policy analysis to tackle real-world challenges in healthcare systems and knowledge production networks.

• Machine Learning
• Artificial Intelligence
• Ethics
• +7 more

Dr. ir. Cynthia C.S. Liem is an Associate Professor in the Multimedia Computing department at Delft University of Technology, specializing in ethical and reliable AI frameworks. Her work bridges machine learning, human-computer interaction, and societal impact. Academic Rank: Associate Professor Department: Multimedia Computing School: Electrical Engineering, Mathematics and Computer Science Liem’s research focuses on Explainable AI , Algorithmic Transparency , and AI Ethics , emphasizing fairness in machine learning and societal implications of autonomous systems. Recent publications explore adversarial testing, energy-constrained counterfactuals, and critiques of unscientific AGI claims. Her most recent articles highlight trends in autonomous vehicle testing , AI explainability , and ethical algorithmic design , leveraging techniques like conformal prediction and differential evolution . Scientific Awards : SBFT 2023 Best Paper Award TU Delft Education Fellow WWW 2018 Challenge Winner 2024 Women in AI Netherlands Diversity Leader Award Liem serves as an advisor for DUO’s research ethics audits and frequently contributes to public discourse on AI’s societal role, appearing in national media outlets like Delta and Trouw . She also leads datasets on music representation learning and algorithmic recourse.

Hedderik van RijnView profile

Professor

• Temporal Cognition
• Interval Timing
• Cognitive Control
• +5 more

Hedderik van Rijn is a Professor at the Department of Psychology, University of Groningen, and chair of "Cognitive Science and Neurosciences." His research focuses on temporal cognition, exploring how time influences cognitive processing, memory, and decision-making through behavioral, neuroimaging (fMRI, EEG, MEG), and computational modeling approaches. He develops methods like the adaptive learning system SlimStampen / RuggedLearning , which personalizes fact-learning using cognitive models and is used by 800,000+ students in the Netherlands. University: University of Groningen Department: Department of Psychology Academic Rank: Professor Van Rijn's research spans computational models of interval timing, cognitive control mechanisms, and applications in education and public health. He integrates symbolic process models (ACT-R) with neurobiological data, particularly in Drosophila studies, to uncover genetic bases of temporal processing. His work on time perception has theoretical and practical implications, from understanding neural substrates to real-world applications in skill training and language processing. His recent publications highlight interdisciplinary collaborations in temporal cognition, memory dynamics, and educational technology. Key themes include the interplay between time perception and cognitive load, the development of adaptive algorithms for learning, and the neural correlates of timing and decision-making. His lab's focus on deconvolving pupillary responses and modeling multi-tasking scenarios underscores methodological innovation. Scientific Awards: VICI grant (NWO Netherlands Organization for Scientific Research) Van Rijn has supervised numerous PhD students whose work spans pronoun processing, interval timing, Bayesian optimization of learning systems, and clinical applications of timing research. He collaborates with institutions like Noordhoff Publishers and the University Medical Center Groningen, and is a founding director of SlimStampen B.V., a spin-off commercializing adaptive learning principles. His grants include NWO, Horizon 2020, and John Templeton Foundation support.

• Data Visualization
• Human-Centered AI
• Human-Computer Interaction
• +7 more

Huamin Qu is a Chair Professor and founding Dean of the Academy of Interdisciplinary Studies at the Hong Kong University of Science and Technology (HKUST) . He leads the VisLab and coordinates the Human-Computer Interaction (HCI) group within the Department of Computer Science and Engineering. His academic journey began with a BS in Mathematics from Xi'an Jiaotong University , followed by an MS and PhD in Computer Science from Stony Brook University . Founding Head of HKUST's Division of Emerging Interdisciplinary Areas (EMIA) Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ) Director of IPO and senior administration team member As a pioneer in data visualization and human-computer interaction , his research bridges urban computing , explainable AI , social media analysis , and E-learning . Recent work focuses on human-AI teaming , multimodal communication , and augmented reality applications . His publications reveal a trajectory from foundational graph visualization and volume rendering to cutting-edge AI-integrated visual systems . Scientific awards include induction into the IEEE Visualization Academy , IEEE VGTC Technical Achievement Award , and multiple best paper/honorable mention awards at top conferences like IEEE VIS, ACM CHI, and IEEE VAST. His lab has graduated 48 PhDs and 21 MPhil students , with 21 PhDs now faculty members at institutions including UC Davis, University of Minnesota, and Zhejiang University. Technologies developed by his group have been adopted by Microsoft , IBM , and Google . His work on projects like Pulse of HKUST and ATMSeer has received global media coverage from MIT News , IEEE Spectrum , and NHK TV . He has served as Associate Editor of IEEE TVCG and held leadership roles in major conferences including IEEE VIS , PacificVis , and VINCI .

Haluk BingölView profile

Professor

• Complex Networks
• Evolutionary Game Theory
• Social Network Analysis
• +5 more

Prof. Haluk Bingöl serves as a Professor at Yeditepe University's Faculty of Computer and Information Sciences, Department of Computer Science and Information Technologies since 2018. Previously, he held academic positions at Boğaziçi University from 2001 to 2018, progressing from Assistant Professor to full Professor in the Department of Computer Engineering. His educational background includes a PhD (1987) and MS (1985) in Electrical and Computer Engineering from Syracuse University, with doctoral research on Boolean Comparison by Simulation , and a BS in Electrical and Electronics Engineering from Middle East Technical University (1982). His research centers on Complex Networks and Evolutionary Game Theory , with significant contributions to understanding cooperation dynamics in the Iterated Prisoner's Dilemma, community detection algorithms, and epidemic modeling in urban networks. His work integrates computational modeling with real-world applications in social dynamics and information systems, often employing agent-based simulations to explore emergent phenomena. Recent publications demonstrate consistent focus on game-theoretic models of cooperation, network structure analysis, and applications in social media and epidemiology. He maintains strong publication output in high-impact journals including Physical Review E , Advances in Complex Systems , and Journal of Theoretical Biology , with increasing interdisciplinary reach into mental health informatics and computational linguistics. Multiple Publication Incentive Awards from Boğaziçi University and TÜBİTAK Interpro IT Award for industry-academia collaboration Best Poster Award at COST MP0801 United Nations Fellowship (1984-1985) and TEV International Scholarship National competition awards for student thesis supervision Prof. Bingöl has supervised 23 graduate students across master's and PhD programs, with research spanning community detection, evolutionary dynamics, and attention economics. His grant portfolio includes leadership of the TURKCELL m-learning Project ($130,000), participation in EU-funded initiatives (EUMedGrid, ASSYST), and COST actions (TD1210, MP0801) focusing on complex systems research infrastructure. He actively contributes to COST actions and collaborative networks, developing methodologies for complex systems analysis while mentoring next-generation researchers through structured academic programs and industry partnerships.