Find Academic Supervisors

Eva Hammar ChiriacView profile

Professor

• Social Psychology
• Group Processes
• Education Systems
• +4 more

Eva Hammar Chiriac is a Professor in the Department of Behavioural Sciences and Learning at Linköping University, specializing in the Division of Psychology. Her research focuses on group processes within educational systems, emphasizing cooperative learning, problem-based learning (PBL), and interprofessional problem-based learning (iPBL). She explores how group dynamics influence learning outcomes, particularly in contexts like classrooms and higher education settings. Her work also addresses challenges in assessing knowledge and abilities developed through group collaboration, as well as fostering positive school climates. Her academic journey includes a PhD in Psychology (2003) and becoming a full Professor at Linköping University in 2020. She leads research projects on group work assessment, classroom management, and school climate improvement, often employing qualitative methods like group observations and focus groups. Collaborations include national and international researchers in social psychology, pedagogy, and medical fields. Key research areas include: Assessment strategies in cooperative learning Interprofessional education dynamics Student and teacher perspectives on group work fairness School climate and teacher-student relationships Her teaching includes courses on group psychology, social psychology, and group observation techniques. She actively supervises PhD students, including Johan Forsell, and contributes to academic networks like the International Association for the Study of Cooperation in Education (IASCE).

• Computer Science
• Machine Learning
• Data Science
• +4 more

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

Katerina SotirakiView profile

Assistant Professor

• Theoretical Cryptography
• Post-Quantum Cryptography
• Complexity Theory
• +4 more

Katerina Sotiraki is an Assistant Professor in the Department of Computer Science at Yale University, associated with the Archimedes AI research group. Her work focuses on theoretical cryptography, post-quantum cryptography, complexity theory, and secure computation. She holds a Ph.D. (2020) and M.S. (2016) in Computer Science from MIT, and a B.S. in Applied Mathematics (2013) from the National Technical University of Athens. Prior to Yale, she was a postdoc at UC Berkeley working with Raluca Ada Popa and Alessandro Chiesa, under the guidance of Vinod Vaikuntanathan at MIT. Research Interests : Design and analysis of cryptographic protocols resilient to quantum computing Efficient zero-knowledge proofs and succinct arguments Algorithmic game theory and fair division problems Lattice-based cryptography and post-quantum security Awards & Honors : Winner of 7th iDASH Competition (Track 3) - 2018 Chateaubriand Fellowship - 2018 Paris C. Kanellakis Fellowship (MIT EECS) - 2013 Key Contributions : Developed HOLMES - a system for secure collaborative learning Pioneered lattice-based succinct arguments with polylogarithmic verification Advanced understanding of modulo-p arguments through topological methods Labs & Teams : Active member of the Archimedes AI group at Yale, focusing on secure computation and cryptographic protocols.

• Human-Robot Interaction
• Social Robotics
• Group Dynamics
• +3 more

Sarah Gillet is a Postdoctoral Researcher at the Division of Robotics, Perception, and Learning at KTH Royal Institute of Technology, where she focuses on developing social robot behaviors to foster collaboration and inclusion in human groups. Her research addresses challenges like in-group favoritism through computational approaches to shape group interactions. She holds a Doctoral Thesis (2024) titled Computational Approaches to Interaction-Shaping Robotics . Her work emphasizes group dynamics , robot-mediated inclusion , and pedagogical robotics , particularly in children and adolescent populations. Key areas include gaze behavior analysis, equitable participation promotion, and social robot roles such as mediators in educational settings. Dr. Gillet teaches the Social Robotics (DD2413) course and supervises master theses. Her recent publications explore robot gaze behaviors for participation balance, socially appropriate listening, and influence prediction models like RoSI. She actively participates in conferences like ACM/IEEE HRI and IEEE RO-MAN. Her research integrates computational methods with social science insights to design robots that actively improve human group interactions, with applications in education, collaboration, and bias mitigation.

Marco L. Della VedovaView profile

Senior Lecturer

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +7 more

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

Albin ErlansonView profile

Lecturer

• Economic Theory
• Game Theory
• Mechanism Design
• +3 more

Dr. Albin Erlanson is a Lecturer in the Department of Economics at the University of Essex since July 2019. His research focuses on economic theory, game theory, and mechanism design, with notable contributions to topics like resource allocation, epidemic modeling, and matching markets. He currently teaches Microeconomics courses and supervises PhD students Tejpal Singh Sandhu and Yun Xu. His work explores the intersection of economic systems and real-world policy challenges, such as optimizing personal assistance services and analyzing production-health trade-offs during epidemics. Dr. Erlanson’s research spans theoretical and applied economics, including studies on costly verification in collective decisions, time exchange systems, and multi-item auction mechanisms. His articles frequently address practical applications of economic theory to public policy and market design. He has secured significant funding, including a 2024 grant for exploring the role of interviews in labor market matching. Academic support hours are held Tuesdays 10-12 at 5B.325, Colchester Campus. Grants: 2024 British Academy grant for “Matching with Interviews: the importance of interviews in labor markets” Advising: Supervising two PhD candidates in Economics Contact: albin.erlanson@essex.ac.uk | +44 (0) 1206 873351

Shi LiView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Combinatorial Optimization
• +11 more

Shi Li is a Professor in the Theory Group at the Department of Computer Science and Technology, School of Computer Science, Nanjing University. He previously held faculty positions at the University at Buffalo (2015–2023) as Assistant and Associate Professor, and was a Research Assistant Professor at Toyota Technological Institute at Chicago (2013–2015). He earned his Ph.D. from Princeton University in 2014 under Moses Charikar and completed his B.S. in Computer Science and Technology at Tsinghua University, where he was part of Andrew Chi-Chih Yao’s Special Pilot Class. His research lies at the intersection of theoretical computer science and combinatorial optimization, with a focus on the design and analysis of algorithms for problems in clustering, scheduling, network design, facility location, and online algorithms. He also explores learning-augmented algorithms and differential privacy in algorithmic contexts. His work combines deep theoretical insights with practical algorithmic frameworks, often leveraging linear programming relaxations, iterative rounding, and randomized techniques. The recent publications highlight a consistent trend in approximation algorithms, particularly in clustering (e.g., correlation clustering, fair k-set selection), scheduling (e.g., unrelated machine scheduling, load balancing), and robust optimization. His work frequently appears in top-tier theoretical venues such as STOC, FOCS, SODA, and ICALP, with increasing emphasis on fairness, privacy, and efficiency in algorithm design. Best Paper Award of Track A, ICALP 2011 Co-winner of Best Paper Award, FOCS 2012 Invited to Special Issue of SICOMP (FOCS 2017 paper) Best Paper Award, COCOON 2018 Invited to Special Issue of SICOMP (STOC 2019 paper) Best Paper Award of Track A, ICALP 2024 Outstanding Paper Award, SPAA 2024 Shi Li has advised several PhD and master’s students, including Yuda Feng, Han Dai, Zihao Liang, and Jia Ye, and has mentored postdoctoral researcher Ruilong Zhang. He has served on numerous program committees (e.g., STOC, SODA, ICALP) and is an Editorial Board Member of ACM Transactions on Algorithms . He teaches core algorithm courses such as Design and Analysis of Algorithms and Advanced Algorithms , and actively collaborates with researchers worldwide. His lab focuses on theoretical foundations of efficient and fair algorithm design, with applications in large-scale data analysis and distributed systems.

Dorothea HerreinerView profile

Associate Professor

• Microeconomics
• Game Theory
• Experimental Economics
• +3 more

Dr. Dorothea Herreiner is an Associate Professor of Economics at Loyola Marymount University (LMU), affiliated with the Bellarmine College of Liberal Arts. She specializes in microeconomics, game theory, and experimental economics, with a focus on fairness, cooperation, and STEM education. Her research explores topics such as fair division, policy analysis, and interdisciplinary collaboration in education. She holds a Ph.D. from the European University Institute and a Master’s from the London School of Economics and Political Science. Her teaching philosophy emphasizes analytical rigor and practical problem-solving, reflected in courses like Environmental Economics , Game Theory , and Mathematical Economics . She has been recognized with LMU’s Economics Department Teacher of the Year Award for her dedication to student mentorship. Her recent work includes studies on concealed carry weapon laws and STEM education equity, as well as experimental investigations into fairness and cooperation in resource allocation. She has contributed to policy discussions through her research on economic decision-making and fairness criteria. Dr. Herreiner has led initiatives such as LMU’s Academic Honesty Policy development and served as Director of the Center for Teaching Excellence. She advocates for close student-faculty collaboration and encourages undergraduates to engage in challenging coursework and utilize office hours.

Jose WalterosView profile

Associate Professor

• Large-Scale Optimization
• Integer Programming
• Multilevel Optimization
• +3 more

Jose Walteros is an Associate Professor in the Department of Industrial and Systems Engineering at the School of Engineering and Applied Sciences, University at Buffalo. His research focuses on Large-Scale Optimization, Integer Programming, Multilevel Optimization, Logistics, Interdiction, and Defense Applications. PhD, Industrial and Systems Engineering (2014), University of Florida MSc, Industrial Engineering (2007), Universidad de los Andes, Bogotá BS, Industrial Engineering (2005), Universidad de los Andes, Bogotá Dr. Walteros' work spans critical applications in security, energy systems, and humanitarian logistics. His research includes interdiction games, stochastic programming for infrastructure resilience, and graph partitioning algorithms. His group GAMMA has mentored numerous graduate students who have successfully defended dissertations and moved to prestigious postdoctoral positions at institutions like Carnegie Mellon University and University of Pittsburgh. As part of the University at Buffalo faculty, Dr. Walteros contributes to seminar series and student mentoring programs. His group has received recognition for student service and teaching excellence, with advisees receiving multiple UB ISE Student Service and Leadership Awards and Teaching Awards.

Rajnish KumarView profile

Professor

• Mechanism Design
• Game Theory
• Network Economics
• +7 more

Rajnish Kumar is a Professor of Economics at Queen's Business School (QBS), Queen's University Belfast, with a focus on Mechanism Design, Game Theory, and Network Economics. His research integrates theoretical frameworks with practical applications in welfare economics, development economics, and health policy. He teaches courses including Public Economics (Eco 3012), Industrial Organizations (Eco 2012), and Economic Policy for Business (Eco 2002). His work contributes to UN Sustainable Development Goals related to good health and well-being, quality education, and sustainable cities. Research Highlights: Leading the MECHANISMS study on adolescent smoking norms using game theory and network analysis Pioneering work on probabilistic network values and cooperative game solutions Analysis of social networks in health behaviors and policy design He has secured significant grants including the AGILITY Fund (2023), UKIERI-UGC Partnership (2017), and Newton Mobility Grant (2018-2019). His research outputs span 44 peer-reviewed articles and 5 funded projects. Recent awards include the Promising Academic International Fellowship (2014) and Government of India Scholarship (2002-2004).

Arash AsadpourView profile

Associate Professor

• Data Science/Analytics
• Operations Management
• Operations Research
• +7 more

Arash Asadpour is an Associate Professor at the Narendra Paul Loomba Department of Management, Zicklin School of Business, Baruch College, City University of New York (CUNY). His research focuses on operations research, optimization, and marketplace dynamics, particularly in gig economy platforms and online retailing. PhD in Management Science and Engineering, Stanford University 2010 B.Sc. in Computer Engineering, Sharif University of Technology, Tehran, Iran 2005 His work spans data science , dynamic pricing , stochastic optimization , and matching markets , with applications to ridesharing, online advertising, and resource allocation. Recent publications emphasize platform economics, algorithmic game theory, and submodular function optimization. Arash has received prestigious awards including the Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA) in 2010, top honors in Iran’s national graduate entrance exam (2004), and silver medals in national informatics olympiads (1999-2000).

Robert D. KleinbergView profile

Professor

• Algorithms
• Theoretical Computer Science
• Economic Aspects of Algorithms
• +5 more

Robert D. Kleinberg is a Professor of Computer Science at Cornell University's Department of Computer Science and a member of the field of Information Science. He received his Ph.D. from MIT in 2005 and previously worked at Akamai Technologies designing internet content delivery networks. His research bridges theoretical computer science and practical applications in electronic commerce, networking, and information systems. Research Interests: Kleinberg's work focuses on the design and analysis of algorithms, with special emphasis on economic aspects of algorithms, online learning, random processes in networks, and applications to electronic commerce and information retrieval. His research combines theoretical rigor with real-world impact, exploring how algorithmic principles can optimize complex systems. Publication Trends: Kleinberg's recent publications demonstrate a consistent focus on online optimization, algorithmic fairness, network design, and learning algorithms. His work frequently appears in top-tier venues spanning theoretical computer science (STOC, FOCS), machine learning (NeurIPS, COLT), and networking (NSDI). The publications reflect interdisciplinary approaches combining computer science, economics, and applied mathematics. Awards and Honors: Microsoft Research New Faculty Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Best Paper Award at ACM EC 2014 Advising and Academic Leadership: Kleinberg actively mentors Ph.D. students and postdoctoral researchers, with current advisees including Raunak Kumar, Princewill Okoroafor, and Tegan Wilson. He has supervised numerous graduates who now hold academic positions worldwide. His teaching includes core algorithm courses (CS 4820, CS 6820) and specialized topics at both undergraduate and graduate levels.

Debabrota BasuView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +6 more

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

• Algorithmic Game Theory
• Market Equilibrium Computation
• Fair Division
• +5 more

Ruta Mehta is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign . Since 2016 she has led a vibrant research program in algorithmic game theory, market design, and fair division, while actively shaping the community through service roles such as Program Co-Chair of WINE 2020 and Area Chair of EC 2021. Education Ph.D. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2012. (Advisors: Prof. Milind Sohoni & Prof. Bharat Adsul; ACM India Doctoral Dissertation Award 2012) M.Tech. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2005 B.E. in Computer Engineering, Maharaja Sayajirao University (MSU) Baroda, 2003 Research Interests Mehta’s work lies at the intersection of theoretical computer science , mathematical economics , and social choice theory . She investigates the computability and complexity of equilibria—both market and Nash—under a variety of utility models, and designs provably efficient algorithms that are practical for real-world resource-allocation tasks. Current themes include: Algorithmic Game Theory: equilibrium computation, smoothed analysis, learning in games Fair Division: envy-freeness up to any good (EFX), mixed manna, competitive equilibrium with chores Interdisciplinary Applications: genetic evolution, machine-learning markets, climate-aware allocation Publication Trends Her 15 most recent works (2017–2025) span Operations Research , Mathematics of Operations Research , STOC, SODA, EC, ITCS, AAMAS, and NeurIPS. The articles cluster around three thrusts: (i) rigorous hardness and approximation results for market equilibrium in Leontief and PLC exchange economies, (ii) algorithmic advances toward guaranteed EFX allocations and competitive equilibrium with mixed manna, and (iii) novel game-theoretic analyses of genetic diversity and strategic resource allocation under budget constraints. Scientific Awards & Honors NSF CAREER Award (2018) Outstanding Post-Doctoral Researcher Award, Georgia Tech (2014) Rising Stars in EECS (2013) ACM India Doctoral Dissertation Award (2012) Google India Anita Borg Memorial Scholarship (2012) IBM PhD Award (2010) IBM PhD Fellowship (2009–2010) Grants, Advising, and Community Leadership Mehta currently mentors a growing group of graduate students and post-docs. She is PI on an NSF CAREER grant and has served on federal panels reviewing NSF CISE proposals. Beyond research, she founded the EC (AGT) Mentoring Workshop , co-located with the ACM Economics & Computation conference, to broaden participation of women and under-represented minorities in algorithmic game theory. Labs & Teams Her research group operates within the Theory & Algorithms cluster at UIUC, leveraging ties with the Decision & Control group and the Social & Algorithmic Thinking initiative. She is an active member of ACM SIGecom and regularly organizes reading groups on algorithmic game theory and fair division.

Marina KnittelView profile

Assistant Professor

• Graph Algorithms
• Fair Machine Learning
• Mechanism Design
• +3 more

Marina Knittel is an Assistant Professor of Computer Science at Reed College in the Division of Mathematical and Natural Sciences, starting Spring 2025. She currently serves as a postdoctoral researcher at UC San Diego working with Profs. Barna Saha and Sanjoy Dasgupta on graph algorithms. She holds a PhD from the University of Maryland (2023) and a BS in Mathematics and Computer Science from Harvey Mudd College. Her research focuses on graph algorithm theory with applications spanning fairness in machine learning, mechanism design, and massively parallel computation. She explores interdisciplinary connections between computer science, economics, and biology, particularly how computational problems increasingly require cross-domain expertise. Knittel's publication portfolio demonstrates consistent focus on scalable algorithms for massive datasets, with recent work emphasizing fairness in clustering and hierarchical methods. Her articles frequently appear in top theoretical computer science venues and show sophisticated mathematical approaches to practical computational problems.