Adam Bonica is an Associate Professor in the Department of Political Science at Stanford University . His research integrates data science and politics , focusing on money in politics , campaigns and elections , judicial politics , and political methodology . Research Interests : Data Science, Political Ideology, Campaign Finance, Judicial Polarization, Quantitative Modeling Contact : Encina Hall West, Room 307, Stanford, CA 94305-6044 | Email : bonica@stanford.edu | Phone : (650) 725-7987 His work has been published in leading journals such as the American Journal of Political Science , Political Analysis , Journal of Economic Perspectives , Journal of Law, Economics, and Organization , and JAMA Internal Medicine . Bonica co-authored The Judicial Tug of War , which explores the politicization of the American judiciary. He has developed comprehensive databases like DIME (Database on Ideology, Money in Politics, and Elections) to advance research on political ideology and campaign finance. Recent publications include analyses of ideological persuasion in U.S. elections, judicial reform dynamics, and supervised machine learning applications in political methodology. His research spans topics such as clerk hiring as ideological indicators , professional networks in fundraising , and polarization trends among physicians and legal professionals .
Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.
Shaonan Tian is an Associate Professor in the Department of Marketing and Business Analytics at San José State University's Lucas College and Graduate School of Business. Her office is located in BT 752, and she can be contacted via email at shaonan.tian@sjsu.edu or phone at (408) 924-7245. Her Fall 2019 office hours were scheduled for Tuesdays from 11:45AM to 12:30PM and by appointment. Her research focuses on quantitative business methodologies including Marketing Analytics , Business Analytics , Statistical Analysis , and Data Mining , as evidenced by her teaching portfolio and departmental affiliation. Professor Tian has taught courses including: BUS2 194A: Statistical Analysis (Spring 2018) focusing on analytical techniques BUS2 193: Data Mining (Spring 2018) covering data extraction and pattern recognition methodologies
Cristian Gómez Canela is a Full Professor in the Department of Analytical and Applied Chemistry at the School of Engineering, Ramon Llull University (IQS). He serves as Coordinator of the Master's Degree in Analytical Chemistry and is an active member of the Catalan Chemical Society (SCQ), representing SCQ in EuChems-EYCN. His academic journey includes a PhD in Chemistry from the University of Barcelona (2014), followed by postdoctoral research at IDAEA-CSIC and King's College University. Dr. Gómez Canela's research focuses on environmental analytical chemistry, particularly the optimization and validation of analytical methods based on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and high-resolution mass spectrometry (HRMS) for determining organic pollutants in environmental samples. His work extends to metabolomics applied to aquatic organisms and the analysis of neurotoxic compounds in water systems. His research fingerprint reveals strong expertise in zebrafish models (100%), neurotransmitter analysis (66%), Daphnia magna studies (64%), and neurotoxicity assessment (21%). His recent publications (2024-2025) demonstrate a clear trend toward environmental neurotoxicology, with emphasis on the effects of pharmaceuticals and industrial pollutants on aquatic organisms. His work integrates advanced analytical techniques with biological endpoints to assess environmental risks, particularly focusing on neurological and cardiovascular impacts. The research spans method development for pollutant detection, environmental monitoring, and mechanistic studies of neurotoxic effects. Dr. Gómez Canela leads multiple significant research projects including CHEMIPARK (2024-2027) on passive sampling methodologies for environmental pollutants, GESPA (2022-2025) as part of the Environmental Process Engineering and Simulation Group, and several projects on neuroactive compounds in water systems. He has an impressive research output with 91 scientific publications from 2011-2025 and an h-index of 27 with over 2,000 citations. As a dedicated educator, he contributes to multiple academic programs including the Master in Analytical Chemistry, Master in Pharmaceutical Chemistry, and undergraduate degrees in Chemistry and Chemical Engineering. His research group GESPA represents a multidisciplinary team combining chemical engineering, biotechnology, and chemical analysis to advance environmental sustainability through theoretical and experimental approaches.
Prof. Dr. Mirko Kremer is a Professor of Supply Chain Management at the Frankfurt School of Finance & Management since July 2014. Prior to this, he was an Assistant Professor at Pennsylvania State University (2008-2014) and held visiting roles at London Business School, Kellogg School of Management, and INSEAD. He earned a Ph.D. in Business Administration from the University of Mannheim (2008), with prior studies in Business Administration at Mannheim (1999-2003) and Economics at Ruhr-Universität Bochum (1997-1999). Research Focus: Micro-behavioral foundations of supply chains, inventory management, sales forecasting, and service systems. Editorial Roles: Department Editor for Production and Operations Management , Associate Editor for Operations Research . Teaching: Courses in Operations Management, Supply Chain Strategy, and quantitative decision modeling at Frankfurt School, INSEAD, London Business School, and Kellogg School of Management. Research Trends: His work bridges behavioral economics with supply chain operations, emphasizing how human decision-making (e.g., managerial biases, consumer behavior) impacts inventory systems, pricing strategies, and forecasting accuracy. Recent articles focus on diagnostic errors under congestion, strategic sourcing biases, and dynamic pricing experiments.
Brendan O'Connor is an Associate Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on computational social science and natural language processing (NLP), particularly exploring how social factors influence language technologies and using text analysis to understand societal trends. His work includes studies on racial bias in NLP, political event analysis, and social media linguistics. He holds a PhD in Machine Learning from Carnegie Mellon University (2014) and dual MS/BS in Symbolic Systems from Stanford University (2006). Education: PhD in Machine Learning, Carnegie Mellon University (2014) MS in Symbolic Systems, Stanford University (2006) BS in Symbolic Systems, Stanford University (2006) Research interests span AI ethics, social media analysis, and computational methods for studying language and society. Notably, he investigates racial disparities in NLP systems, linguistic variation in African American English, and event detection in news and social media. His work has been recognized with NSF CAREER and Google Faculty awards, and his research has been cited thousands of times. His lab, the Statistical Social Language Analysis Lab, develops tools for analyzing large-scale text data. He is affiliated with the Center for Data Science, Center for Intelligent Information Retrieval, and Computational Social Science Institute. Recent projects include analyzing global news coverage of critical events and developing frameworks for zero-shot argument explication. Awards and Honors: NSF CAREER Award Google Faculty Research Award Best Paper Award Advising and Grants: O'Connor has advised projects on social media polling representativeness and demographic analysis. His grants include collaborative research on sociopolitical event extraction and bias mitigation in AI systems. He has also contributed to platforms like Rookie for news archive exploration and ezCoref for coreference resolution. Labs/Teams: Leads the Statistical Social Language Analysis Lab and collaborates with the UMass NLP Group and Harvard Institute for Quantitative Social Science. His work bridges NLP with social science methodologies, emphasizing transparency in algorithms and causal inference using text data.
Dr. Mary Elisabeth Cox holds dual academic appointments. She is a Research Associate at the Oxford Centre for Economic and Social History and the Global History of Capitalism Project at the University of Oxford, and an Assistant Professor in International Relations at the Central European University in Vienna, Austria. Her work bridges historical analysis with contemporary policy considerations, particularly regarding civilian suffering during conflicts. Education: Doctor of Philosophy (DPhil) in History from the University of Oxford Research interests center on the socio-economic and health impacts of war on civilians, focusing on gender and social inequality. She investigates food security, international aid institutions, anthropometric data applications, and 20th-century warfare. Her approach combines quantitative methods with traditional qualitative sources to explore deprivation timelines and recovery patterns across social groups. Her scholarly work analyses wartime blockades' nutritional consequences on German civilians, highlighted in her award-winning 2015 article and 2019 monograph. These studies demonstrate the interdisciplinary potential of blending anthropometric data with institutional reports, personal letters, and other sources. Awards include the Dev Book Prize for her groundbreaking DPhil dissertation and the 2016 Alexander Prize for the aforementioned 2015 article. She is also a shortlisted candidate for the Economic History Society's best doctoral dissertation. In advising and grants, she supervises Masters students at Brasenose College, Oxford, and leads the Leverhulme Trust-funded ‘Hunger Draws the Map’ project . This initiative traces hunger’s geopolitical dimensions across Europe and the Ottoman Empire during 1914–1922, culminating in a co-edited monograph expected in 2020. Collaboration with Dr. Claire Morelon and Sir Hew Strachan is central to this work. Labs/teams: She co-leads the ‘Hunger Draws the Map’ international network, which includes workshops at institutions like the Hoover Institution and partnerships with scholars worldwide. Her research also draws on lived experiences from her childhood in the South Pacific and Scandinavia, informing her cultural and regional perspectives.
Sean F. Reardon is the Professor of Poverty and Inequality in Education and a Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR) at Stanford University . He also holds a Professor (by courtesy) of Sociology . His work focuses on social and educational inequality, including residential and school segregation, racial/ethnic and socioeconomic disparities in academic achievement, and causal inference methods. Education: Ed.D., Harvard University, Educational Administration, Planning, and Social Policy (1997) M.Ed., Harvard University, Educational Administration, Planning and Social Policy (1992) M.A., University of Notre Dame, International Peace Studies (1991) B.A., University of Notre Dame, Program of Liberal Studies; Minor in Honors Mathematics (1986) Research Interests center on the causes and patterns of educational and racial inequality, the effects of school integration policies, and income inequality's social consequences. His Stanford Educational Opportunity Research Lab analyzes achievement gaps and segregation trends across U.S. school districts using large-scale datasets. Recent Publications highlight cross-national achievement gap comparisons, pandemic recovery disparities, and methodological advancements in segregation measurement. His work often integrates Data Sciences and Sociology to address policy-relevant questions. Awards Elected Member, American Academy of Arts and Sciences Elected Member, National Academy of Education Advising includes mentoring Doctoral Students like Tyler McDaniel and Postdoctoral Researchers like Farah Mallah. His Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis (2009–2021) trained future scholars in rigorous policy methods.
Aleksander Madsen is a Researcher at the University of Oslo (UiO), affiliated with the Department of Sociology and Human Geography under the Faculty of Social Sciences. His primary research interests include social stratification, immigrant assimilation in labor markets, welfare state policies, and statistical methods. He holds a PhD from Oslo Metropolitan University and an MA from UiO. Education: PhD in Sociology, Centre for the Study of Professions, Oslo Metropolitan University (2014–2018) MA in Sociology, University of Oslo (2013) His research explores labor market integration of immigrants, gender disparities in male-dominated workplaces, and the impact of education-occupation mismatch on health outcomes. He has conducted longitudinal studies on long-term sickness absence and labor market trajectories using sequence analysis. Dr. Madsen has held postdoctoral and research positions at UiO and NIFU, and was a visiting scholar at Stanford University's SCANCOR (2024) funded by the Fulbright Foundation. His work spans interdisciplinary collaborations, including projects on workplace diversity, ethnic stratification, and policy evaluations. Awards: U.S. - Norway Fulbright Foundation Grant (2023–2024) His research group affiliations include the Social Inequalities and Population Dynamics team and the ORGMIGRANT project, investigating work organizations and immigrant assimilation. Recent publications address residential networks among immigrants, workplace ethnic diversity, and women’s attrition from male-dominated sectors.
Daniele Caramani is a Part-time Professor at the Robert Schuman Centre for Advanced Studies at the European University Institute, where he holds the Ernst B. Haas Chair in European Governance and Politics and co-directs the European Governance and Politics Programme. He also serves as a professor and Head of Department at the University of Zurich, holding the Chair of Comparative Politics. His academic journey began with BA and MA degrees from the University of Geneva, followed by a PhD from the European University Institute. Dr. Caramani's research focuses on comparative politics, European integration, and globalization, with particular emphasis on political cleavages, electoral systems, and democratization processes. His work bridges historical analysis with contemporary political challenges, examining how territorial and functional divisions shape political landscapes across different eras and regions. His approach combines theoretical innovation with empirical rigor, drawing connections between 19th century mass democratization and current debates about European integration and global governance. Caramani's publications reveal a consistent trajectory of increasingly complex analyses, moving from national political systems to European integration and then to global comparative frameworks. His work demonstrates strong methodological expertise in comparative and quantitative analysis, with a particular focus on large cross-national datasets and historical electoral results. The evolution of his research shows a clear progression from national-level analysis to transnational and global comparative frameworks. Stein Rokkan Prize for Comparative Social Science Research (for The Nationalization of Politics ) APSA Lijphart-Przeworski-Verba Dataset Award (for Constituency-Level Elections Archive) As an educator and academic leader, Caramani has supervised numerous students and edited influential textbooks in comparative politics. His leadership extends to directing major research projects, including an ERC Advanced Grant on global cleavages across world regions. At the University of Zurich, he serves as Head of Department, demonstrating administrative leadership alongside his research and teaching responsibilities. His Constituency-Level Elections Archive (CLEA) represents a significant contribution to the field, providing essential data for comparative electoral research.
Dr. Ahmet Acar is an Associate Professor at the Department of Biological Sciences, Middle East Technical University (METU), Ankara, Turkey. He leads the Cancer Precision Medicine and Drug Resistance Laboratory, focusing on understanding mechanisms of drug resistance in cancer. His research integrates experimental models, next-generation sequencing, and deep learning to address clinical challenges in cancer therapy. Dr. Acar holds a B.Sc. from METU's Biological Sciences department and a Ph.D. from the Cancer Research UK Manchester Institute. He completed postdoctoral training at the Institute of Cancer Research, London, and the University of Manchester. Research Interests: Drug resistance mechanisms, precision oncology, tumor microenvironment modeling, patient-derived organoids, computational pathology, and evolutionary cancer biology. His lab develops 2D/3D co-culture systems, PDO biobanks, and AI-driven histopathology tools to improve treatment strategies. Recent Work Trends: Recent publications emphasize tumor evolution modeling, matrix mechanics in drug resistance, and AI applications in histopathology. Collaborations with hospitals in Turkey and Europe support PDO biobank initiatives. His team explores evolutionary steering strategies to exploit collateral drug sensitivities. Labs/Teams: Precision Medicine and Drug Resistance Lab at METU focuses on interdisciplinary approaches combining wet-lab experiments with computational methods. Current projects include ex vivo tumor modeling and AI-driven diagnostic tools for oncology.
Dr. Primoz Skraba is a Professor in Applied and Computational Topology at the School of Mathematical Sciences, Queen Mary University of London. As Deputy Head of the Centre for Probability, Statistics and Data Science, he bridges theoretical topology with practical applications in data analysis, machine learning, and optimization. Education : PhD in Electrical Engineering from Stanford University (2009) Prior Roles : Positions at INRIA, France; Jozef Stefan Institute, Slovenia; University of Primorska; University of Nova Gorica His research focuses on applying topological methods to analyze complex data. Key areas include: Stability of persistence diagrams for quantitative control in finite sampling Variants of persistence (zig-zag, robustness, multiparameter) Algorithmic Complexity in computational topology Stochastic Topology for random geometric models (Poisson, Boolean) Recent publications emphasize persistent homology in random geometric complexes, universality theorems, and integrating topological methods into machine learning. He received grants from the Leverhulme Trust, EPSRC, and Alan Turing Institute for projects on topological universality and AI foundations. His advisee Gabryel Mason-Williams explores wireless sensor network applications of homology.
Jessica Lin is an Associate Professor in the Department of Computer Science at George Mason University, with a focus on data mining and time series analysis. She has published extensively on topics including motif discovery, anomaly detection, clustering, and symbolic representation of time series data. Ph.D., M.S., and B.S. in Computer Science from UC Riverside (2005, 2002, 1999) Her research spans efficient algorithms for mining massive time series datasets, extending to multimedia data like images and texts. She has developed tools such as GrammarViz and SAX for pattern visualization and symbolic analysis. Recent publications highlight advancements in variable-length motif discovery, interpretable classification frameworks, and anomaly detection. Her work appears in top conferences like AAAI, ICDM, and SDM, as well as journals including Knowledge and Information Systems and Data Mining and Knowledge Discovery . Dr. Lin has advised numerous Ph.D. students, many of whom have taken academic or industry positions. She has served on editorial boards and program committees for conferences such as KDD, ICDM, and ECML-PKDD.
Professor Rainer Böhme is a prominent academic in the field of IT security and privacy at the University of Münster's School of Business and Economics, Department of Information Systems. He also holds a professorship for Security and Privacy at the University of Innsbruck's Institute of Computer Science. His research spans multiple dimensions of digital security, with particular expertise in steganography, digital forensics, privacy-enhancing technologies, and the economic aspects of cybersecurity. Dr. Böhme earned his Doctoral Degree from TU Dresden's Faculty of Computer Science in 2008, following studies in Communications, Economics and Computer Science at Technische Universität Dresden and Université Paris XII. His academic journey includes positions as Assistant Professor at the University of Münster (2010-2015), Postdoctoral Fellow at the International Computer Science Institute in Berkeley (2009-2010), and Scientific Staff at TU Dresden's Chair of Privacy and Data Security. His research interests focus on steganography and steganalysis, digital forensics, privacy-enhancing technologies, security and usability, economic and behavioral aspects of information privacy, virtual currencies, and the economics of information security. His work bridges technical security mechanisms with economic and behavioral considerations, creating a unique interdisciplinary approach to cybersecurity challenges. Analysis of his recent publications reveals a strong trend toward the economics of security, particularly regarding blockchain technologies, cryptocurrency, and privacy valuation. His work increasingly examines the intersection of technical security mechanisms with market dynamics, regulatory frameworks, and user behavior. The research spans theoretical game-theoretic models, empirical studies of user behavior, and practical applications in digital forensics and privacy protection. Program Co-Chair, Financial Cryptography and Data Security (2015) Program Co-Chair, New Security Paradigms Workshop (NSPW) (2015) General Chair, 8th International Conference on IT Security Incident Management & IT Forensics (2014) Organizer and Program Co-Chair, 1st Workshop on Bitcoin Research (2014) Associate Editor, Electronic Commerce Research (since 2013) Associate Editor, EURASIP Journal on Information Security (since 2013) Professor Böhme has supervised multiple Master's theses in Information Systems, including work on static code analysis for smart contracts, remote maintenance protocol security, and application protection requirements in retail banking. His research is supported by significant grants from the German Federal Ministry of Research, Technology and Space, the DFG, and the EU FP7 program, with projects focusing on digital forensics, privacy-enhancing technologies, and cryptocurrency crime prevention. He leads the IT Security Research Group at the University of Münster, which investigates theoretical and applied aspects of information security with particular emphasis on the economic dimensions of security decisions.
Song Kim is an Associate Professor of Political Science at the Massachusetts Institute of Technology (MIT) and a Faculty Affiliate at the Institute for Data, Systems, and Society (IDSS). He holds a Ph.D. in Politics from Princeton University, where he was awarded the Harold W. Dodds Fellowship (2012-2013). His research focuses on International Political Economy, Formal and Quantitative Methodology, and Big Data analysis of international trade. He is particularly known for his work on firm-level political incentives in trade liberalization, which earned him the 2015 Mancur Olson Award and the 2018 Michael Wallerstein Award for best published article in political economy. Kim develops computational methods for analyzing trade data, including dimension reduction and visualization techniques. He maintains two key databases: LobbyView (tracking firm lobbying efforts) and TradeLab (for trade policy analysis). His research has been published in top journals such as the American Political Science Review, American Journal of Political Science, and International Organization. Educations : Ph.D. in Politics (Princeton University), B.A. not explicitly stated. His research interests include the dynamical evolution of lobbying networks, strategic links between political donations and lobbying, and the political origins of trade regulations. He also contributes methodological innovations, such as two-way fixed effects models and matching methods for causal inference with panel data. Awards : Mancur Olson Award (2015) Michael Wallerstein Award (2018) Harold W. Dodds Fellowship (2012-2013) Advising & Grants : No listed advisees. His work is supported by MIT’s IDSS and institutional funding. He collaborates on software tools like the 'wfe' and 'concordance' R packages, advancing computational social science. Labs/Teams : Associated with MIT’s Political Science Department and IDSS, focusing on interdisciplinary projects in trade, lobbying, and quantitative methods.
