Negin Golrezaei is the W. Maurice Young Career Development Associate Professor of Management and Associate Professor of Operations Management at MIT Sloan School of Management. Her research focuses on machine learning, mechanism design, and optimization algorithms applied to online markets, revenue management, and pricing. She holds a PhD in Operations Research from the University of Southern California and a postdoctoral fellowship at Google Research. Education: BSc and MSc in Electrical Engineering (Sharif University of Technology, 2007/2009), PhD in Operations Research (USC, 2017). Research interests include algorithmic fairness in recommendation systems, auction design, and data-driven strategies for digital platforms. Her work emphasizes equitable and sustainable market mechanisms. Awards: 2018 Google Faculty Award, 2017 George B. Dantzig Dissertation Award, 2021 ONR Young Investigator Award. Grants: Supported by ONR, MIT-IBM Watson AI Lab, Liberty Mutual Insurance, and MIT RSC. Teaching: Courses include Real-Time Tools for Digital Platforms and Introduction to Operations Management. Labs/Teams: Collaborates with MIT-IBM Watson AI Lab and industry partners like Google and Meta.
Grant Schoenebeck is an Associate Professor of Information at the School of Information , University of Michigan. His research focuses on machine learning, economic mechanisms (game theory, mechanism design), and multi-agent systems, applied to scenarios like peer grading, content moderation, and misinformation detection. He holds a PhD from UC Berkeley and was a Simons Postdoctoral Fellow at Princeton. Education: Bachelor’s in Mathematics (Harvard, Highest Honors) MPhil in Theology (Oxford) PhD in Computer Science (UC Berkeley, advised by Luca Trevisan) Research Interests: Combines ML tools with economic approaches to design systems for eliciting information from diverse agents. Key areas include peer prediction, crowd-sourcing, algorithmic game theory, and data economics. Awards: NSF CAREER Award Google Faculty Award Facebook Faculty Award NSF Algorithms in the Field Grant Advising & Grants: Advised PhD students (e.g., Md Sanzeed Anwar, Shengwei Xu) and postdocs (e.g., Bo Li). Funded by NSF, Google, and Facebook grants. Focuses on nonsubmodular influence maximization, prediction markets, and sybil detection. Teaching: Courses include Applied Machine Learning , Incentives in Computational Systems , and Data Science Foundations .
Augustin Chaintreau is an Associate Professor of Computer Science at Columbia University's Columbia Engineering and a member of the Data Science Institute (DSI). He directs the Mobile Social Lab, focusing on balancing data utility with privacy and fairness in social networks. His work explores transparency in personalization systems, mobility privacy, crowdsourced content curation, and fair data-sharing incentives. Education: Ecole Normale Supérieure, Paris (Undergraduate) Ph.D. in Mathematics and Computer Science (2006) Research Interests: Algorithmic fairness and transparency Social network dynamics and privacy Data market mechanisms Human mobility and exposure modeling Key Contributions: Top paper awards at ACM CoNEXT, SIGMETRICS, USENIX IMC, IEEE MASS, and Algotel Influential work cited in The New York Times, Washington Post, and The Guardian Awards: NSF CAREER Award (2013) ACM SIGMETRICS Rising Star Award (2013) Professional Activities: Program chair for ACM SIGMETRICS and CoNEXT Organizer of the Data Transparency Lab Conference Editorial roles at IEEE TMC, ACM SIGCOMM CCR, and ACM SIGMOBILE MC2R Labs/Teams: Mobile Social Lab at Columbia University
Adrian Marius DEACONU is an Associate Professor at the Department of Mathematics and Computer Science, Faculty of Mathematics and Computer Science, University of Brașov. His research focuses on combinatorial optimization, network flow analysis, inverse problems in networks, graph theory, and algorithm design with applications in transportation networks, photovoltaic systems, disaster logistics, and computational epidemiology. Recent work includes developing GPU-accelerated algorithms for maximum flow problems, fuzzy optimization approaches for disaster relief logistics, and parameter estimation methods for photovoltaic cells. He has contributed to theoretical advancements in inverse flow problems and pathfinding algorithms under constraints such as network losses and adversarial disruptions. His publications span diverse applications including vaccination efficacy modeling, robust vehicle routing under attacks, and geomagnetic map construction using inverse methods. He maintains an active research agenda in network optimization with a focus on bridging theoretical mathematics and practical engineering solutions.
Yrd. Doç. Dr. Ghazaal Sheikhi is an Assistant Professor in the Department of Artificial Intelligence Engineering at Final University, within the Faculty of Engineering. Her research focuses on machine learning applications in healthcare, biomedical engineering, and natural language processing. She holds a Ph.D. in Computer Engineering from Eastern Mediterranean University (2020), an M.Sc. in Biomedical Engineering from Amirkabir University of Technology (2007), and a B.Sc. in Biomedical Engineering from the University of Isfahan (2003). Education: Doctoral Degree in Computer Engineering, Eastern Mediterranean University, North Cyprus (2020) Master's Degree in Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran (2007) Bachelor's Degree in Biomedical Engineering, University of Isfahan, Isfahan, Iran (2003) Her research interests span Machine Learning, Biomedical Engineering, and Natural Language Processing. She specializes in applying deep learning techniques to medical image analysis, developing explainable AI models for healthcare diagnostics, and enhancing fact-checking systems using NLP. Her work in biomedical data analysis focuses on feature selection methods and predictive modeling for diseases like diabetes. Additionally, she explores speech processing techniques for language-specific challenges, such as Farsi syllable segmentation using signal processing and fuzzy logic approaches. Recent contributions include breast tumor segmentation (2025), NLP-based claim detection (2023), and novel feature selection methods (2021). Earlier work addressed speech signal analysis (2011–2013) and diabetes cost analysis (2016). Scientific Awards: No awards explicitly mentioned in the provided text. Advising & Grants: Information on students or grants is not provided in the text. Administrative tasks are listed but no details are given. Labs/Teams: No specific lab affiliations or teams mentioned.
Prabhani Kuruppumullage Don is an Associate Research Professor and Director of Online Programs in the Department of Statistics at Penn State University. She holds a B.Sc. (First Class Honors) in Statistics from the University of Colombo (2005), an M.S. in Statistics (2011), and a Ph.D. in Statistics (2014), all from Penn State. Her research focuses on computational statistics, bioinformatics, statistical genetics, and statistical education. She has held roles including Assistant Professor at the University of Rhode Island (2016–2018) and Postdoctoral Research Fellow at Dana-Farber Cancer Institute and Harvard School of Public Health (2014–2016). Her teaching portfolio includes online courses such as STAT500 (Applied Statistics), STAT501 (Regression Methods), and STAT555 (Genomics Data Analysis). She has led or contributed to grants including the Penn State Schreyer Institute Teaching Grant (2019) and the Center for Human Evolution and Diversity Seed Grant (2019). Her work spans collaborations with the Clinical and Translational Science Institute and the Schreyer Institute for Teaching Excellence. Her publications address topics like post-treatment bias in healthcare analytics, menstrual health recovery in athletes, and algorithmic advancements in statistics. She also serves as a reviewer for the American Journal of Distance Education .
Kamesh Munagala is a Professor in the Computer Science Department at Duke University's Pratt School of Engineering. His academic career spans theoretical computer science with a focus on approximation algorithms, online algorithms, and computational economics. He has made significant contributions to resource allocation, decision making, and provisioning problems across various applications including data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Professor Munagala's research interests span several key areas in theoretical computer science: Theoretical foundations of approximation algorithms and online algorithms Computational economics and market design Resource allocation with fairness constraints Algorithmic game theory and mechanism design Persuasion and information revelation in optimization contexts Group fairness based on proportionality and stability His recent publications demonstrate a strong focus on fairness in algorithmic decision-making, particularly in societal contexts like school assignment and participatory budgeting. He has also made significant contributions to the theory of Bayesian persuasion and information disclosure in competitive settings. His work bridges theoretical computer science with practical applications in social choice, economics, and policy-making. Notable scientific achievements include: Best paper award at WINE 2018 for 'A simple mechanism for a budget constrained buyer' Multiple publications in top theoretical computer science conferences including STOC, SODA, and FOCS Significant contributions to the understanding of fairness in resource allocation Innovative work on metric distortion in social choice Professor Munagala has advised numerous students and collaborators, with recent work involving researchers such as Govind S. Sankar, Yiheng Shen, and Kangning Wang. His research has been supported by various grants, though specific grant details aren't provided in the available information. He teaches advanced courses in algorithms, including Algorithm Design, Randomized Algorithms, and Algorithmic Game Theory, shaping the next generation of theoretical computer scientists. His work has implications for real-world systems requiring fair and efficient decision-making, from school assignment algorithms to data exchange markets and civic budgeting platforms. He is actively engaged in both theoretical advancements and practical implementations of his research.
Seyedali Mirjalili is a Professor of Artificial Intelligence and Director of the Centre for Artificial Intelligence Research and Optimization (AIRO) at Torrens University Australia. He holds distinguished professorships at VSB Technical University of Ostrava, Óbuda University, and De La Salle University, and is an adjunct researcher at Griffith University and Yonsei University. His research focuses on optimization algorithms, swarm intelligence, evolutionary computation, and machine learning. Education: PhD in Computer Science (Griffith University, 2016). Research interests include optimization techniques, swarm intelligence algorithms (e.g., Grey Wolf Optimizer, Whale Optimization Algorithm), machine learning applications, and robust optimization frameworks. He has published over 600 papers with an H-index of 110 and is among the most cited researchers in optimization fields globally. Awards include the Pro-Vice Chancellor Research Special Award (2019) and recognition as a top 1% highly cited researcher (since 2019). He serves as an associate editor for journals like Neurocomputing and Applied Soft Computing. Labs/Teams: Leads the AIRO Centre at Torrens University, focusing on AI-driven solutions for complex optimization problems.
Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.
Kishore Gopalakrishnan is a Researcher in the Department of Biological Sciences at Wayne State University's College of Liberal Arts and Sciences. His work focuses on bioprocess development, microbial physiology, and sustainable biotechnology. He holds a Ph.D. in Chemical and Process Engineering from the University of Canterbury (New Zealand) and has expertise in fermentation optimization, scale-up, and interdisciplinary environmental research. Education: Ph.D., Chemical and Process Engineering, University of Canterbury (2015) MTech, Industrial Biotechnology, Annamalai University (2008) BTech, Industrial Biotechnology, Anna University (2006) Research Interests: Dr. Gopalakrishnan specializes in bioprocess innovation for industrial applications, including microbial cultivation strategies, wastewater bioremediation, and microplastic pollution analysis. His work integrates advanced technologies like IoT-based environmental monitoring with traditional fermentation techniques. Current projects explore biofilm-based biofuel production, microplastic cycling dynamics, and algal-bacterial co-cultivation systems. Scientific Impact: Key contributions include optimizing algal biofuel systems, defining microplastic-organism interactions, and developing predictive models for invasive species management. His research has been supported by grants such as the One Health Pilot Project Initiative ($30,000) and the Anderson Engineering Ventures Institute Grant. Grants & Awards: One Health Pilot Project Initiative Award (2024-2025) Early Career Travel Grant, ASLO (2018) Doctoral Scholarship, University of Canterbury (2011-2014) Professional Focus: Dr. Gopalakrishnan collaborates with cross-functional teams to address environmental and industrial challenges. His recent work bridges biotechnology, ecological science, and digital technologies to advance sustainable solutions for resource recovery and pollution mitigation.
Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.
Dr. Victor Hugo Lachos is a Professor in the Department of Statistics at the University of Connecticut. His research focuses on advanced statistical methodologies for handling complex data structures, including censored regression models, mixed-effects models, and heavy-tailed distributions. He has contributed extensively to Bayesian inference, EM algorithms, and software development for statistical analysis. Multivariate Student-t and skew-normal distributions Longitudinal and spatial data modeling Regularization techniques for high-dimensional data Software packages for censored data analysis His recent publications emphasize robust modeling of censored and irregularly observed data, with applications in medical research (e.g., HIV longitudinal studies) and environmental modeling (e.g., acid rain analysis). His work integrates theoretical advances in distribution theory with practical computational tools in R packages like ‘StempCens’ and ‘mixsmsn’. These contributions are complemented by methodological innovations in EM algorithm applications, influence diagnostics, and semiparametric regression. Dr. Lachos' research has been applied to diverse fields such as medical data analysis, environmental science, and educational measurement. While no explicit awards or student advisement details are listed, his prolific output in top-tier journals and software development underscores his active academic engagement.
Ben Harwood is a Research Scientist at CSIRO's Collaborative Intelligence Future Science Platform (CINTEL FSP), where they focus on developing next-generation scientific workflows for human-AI collaboration. They work on interdisciplinary projects like enhancing human-technology information sharing for the Australian Square Kilometre Array Pathfinder (ASKAP) and integrating Social Science with Machine Learning expertise to maximize positive societal impact. Education: PhD in Computer Systems Engineering (2019), Monash University Bachelor of Mechatronics Engineering with Honours (2012), Monash University Bachelor of Science (Computer Science and Mathematics) (2012), Monash University Bachelor of Computer Science Honours (2011), Monash University Ben's research spans Machine Learning , Pattern Recognition , and Human-Computer Interaction , with a focus on efficient algorithms for high-dimensional big data. They also contribute to organizational initiatives as an officer in the Data61 Diversity, Inclusion and Belonging Committee and co-founder of the CSIRO Neurodiverse Staff Network. Their professional work intersects with Reinforcement Learning , Indexing and Retrieval , and translational science applications, while maintaining interests in community engagement through roles like national committee member for Queers In Science.
Michele Coscia is an Associate Professor in the Department of Computer Science at the IT University of Copenhagen (ITU), where he conducts research at the intersection of network science, digital humanities, and data analytics. He leads the NERDS research group and supervises PhD students and postdoctoral researchers working on financial crime detection, archaeological networks, and work environment modeling. PhD in Computer Science, University of Pisa (2012) Former researcher at the Center for International Development (CID), Harvard University (6 years) Visiting researcher at Barabási Lab, Northeastern University His research focuses on developing and applying network science methodologies such as noise-corrected backboning , node attribute analysis , and network variance to study complex systems. His work spans diverse domains including: Archaeology : Inferring social and biological relationships from material culture at Neolithic sites like Çatalhöyük. Cultural Analytics : Mapping Italian music networks, analyzing Wikipedia’s gender bias, and studying ideological polarization on social media. Social Media Dynamics : Investigating meritocracy vs. topocracy, intolerance feedback loops, and information virality on platforms like Reddit and Twitter. Sports Analytics : Analyzing predictability trends in team sports and the impact of economic systems on league competitiveness. His publications appear in high-impact journals such as Science Advances , EPJ Data Science , and Applied Network Science . He is the author of The Atlas for the Aspiring Network Scientist , a comprehensive open-access textbook now in its second edition, which covers graph theory, machine learning on graphs, and statistical foundations of network analysis. Recent trends in his work show a growing emphasis on interdisciplinary applications of network science, particularly in archaeology and cultural studies, often in collaboration with institutions such as Aarhus University and the National Research Center for Work Environment. His research consistently promotes open science, with datasets and code publicly shared. Co-PI on a Villum Synergy project applying network analysis to Roman Empire archaeological data Active contributor to the CUDAN (Cultural Data Analytics) community Developing methods for uncertain and incomplete network data Michele Coscia’s work demonstrates a strong commitment to methodological innovation and real-world impact across the humanities, social sciences, and computational domains.
Dr. Le Xu serves as a Lecturer in the Department of Strategy and Policy at the National University of Singapore (NUS) Business School. Her academic appointments include coordination of the Business Economics specialisation and teaching roles in economic strategy, global economy, and managerial economics courses since 2018. Bachelor of Science in Mathematics, Northwestern Polytechnical University (2000-2004) Master of Science, Northwestern Polytechnical University (2004-2007) PhD in Economics, University of Manchester (2007-2011) Dr. Xu's research spans Managerial Economics , Chinese and Southeast Asian Economies , and Behavioral Decision Theory . Her work examines economic strategy through game theory frameworks while investigating consumer behavior, gift-giving psychology, and intertemporal choices. Recent publications reveal a strong focus on framing effects, AI-human interaction in sales contexts, and memory mechanisms in social relationships. Her publication trends indicate a sophisticated integration of traditional economic modeling with behavioral insights, particularly examining how cognitive biases affect market decisions and social interactions. The research demonstrates methodological diversity spanning experimental economics, field studies, and theoretical modeling. 10 Years Award (2021) 5 Years Award (2016) Dr. Xu actively mentors students while serving on the NUS Social Committee (2018-2020) and as Business Economics Coordinator (2021-2025). Her teaching philosophy emphasizes real-world application through contemporary business cases and economic indicators. She has taught courses including Managerial Economics, Global Economy, and Economics of Strategy, consistently receiving high student evaluations for her interactive methods and supportive approach. As a media commentator, she provides economic analysis for ThinkChina, The Straits Times, and CNA, leveraging expertise in China's economy and business environment. Her sixth-edition Chinese textbook on Managerial Economics (co-authored with Prof. I.P.L. Png) demonstrates significant scholarly impact.
