جست‌وجوی استاد و پژوهشگر

Joshua D. Angristمشاهده پروفایل

استاد

• Econometrics
• Labor Economics
• Causal Inference
• +۳ مورد دیگر

Joshua D. Angrist is the Ford Professor of Economics at the Massachusetts Institute of Technology, where he has been a faculty member since 1996. He is also a co-founder and director of MIT's Blueprint Labs and a Research Associate at the National Bureau of Economic Research. Angrist shares the 2021 Nobel Prize in Economic Sciences with David Card and Guido Imbens for their methodological contributions to the analysis of causal relationships. Angrist received his B.A. from Oberlin College in 1982 and completed his Ph.D. in Economics at Princeton University in 1989. Prior to joining MIT, he taught at Harvard University and the Hebrew University of Jerusalem. His academic journey began somewhat unconventionally, as he left high school early after 11th grade, worked for over a year, and only later discovered his passion for economics through an inspiring teacher at Oberlin. Angrist's research focuses on developing and applying innovative econometric methods to answer important economic questions using natural experiments. His work spans labor economics, education economics, and causal inference methodology. He is particularly known for his contributions to instrumental variables methods and the Local Average Treatment Effect (LATE) framework developed with Guido Imbens. His research explores the economics of education and school reform, the impact of social programs on labor markets, and the effects of immigration and regulation. His recent publications reveal a continued focus on causal inference methods applied to education policy questions, labor market issues, and health economics. The trend shows increasing sophistication in research design, with particular attention to addressing selection bias and developing methods for external validity. His work spans theoretical econometric contributions alongside empirical applications in education, labor markets, and health. Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2021) Fama Prize for Graduate Education (2018) Fellow of the American Academy of Arts and Sciences Fellow of the Econometric Society Angrist is deeply committed to teaching and mentoring. He has developed influential econometrics textbooks including 'Mostly Harmless Econometrics' and 'Mastering Metrics' with Jörn-Steffen Pischke. At MIT, he teaches courses including Labor Economics I (14.661), Econometric Data Science (14.32), and Labor Economics and Public Policy (14.64). He emphasizes selecting UROP students who have mastered foundational economics through courses like 14.64 and 14.32. Beyond MIT, Angrist co-founded Avela, a software startup using cutting-edge research to help schools improve enrollment and operations. Angrist co-founded and directs MIT's Blueprint Labs, which brings together researchers from economics, computer science, and education to develop innovative solutions for educational challenges. Through Blueprint Labs and Avela, his work bridges academic research with practical applications in education policy and technology.

Kate Saenkoمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Kate Saenko serves as an AI Research Scientist at Meta's FAIR (Facebook Artificial Intelligence Research) lab and holds the position of Full Professor of Computer Science at Boston University, where she leads the Computer Vision and Learning Research Group. Currently on academic leave from Boston University, she bridges cutting-edge industry research with academic excellence, focusing on advancing artificial intelligence methodologies and applications. Her educational background includes a PhD in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT), followed by postdoctoral training at the University of California, Berkeley and Harvard University. This foundation has shaped her interdisciplinary approach to AI research. Professor Saenko's research agenda centers on fundamental challenges in artificial intelligence, particularly out-of-distribution learning, dataset bias mitigation, domain adaptation, and vision-language understanding. Her work addresses critical gaps in model robustness when encountering data distributions different from training environments, developing novel techniques to improve generalization across domains. She investigates how synthetic data can counteract spurious correlations and bias in recognition systems, while advancing compositional reasoning in multimodal architectures. Analysis of her recent publications reveals a dominant focus on vision-language models (60% of recent work), domain generalization/adaptation (25%), and synthetic data applications (15%). Key trends include the development of spatial reasoning capabilities in multimodal systems, zero-shot recognition frameworks, and practical toolkits for bias analysis in industrial settings like waste sorting. Her research consistently targets real-world deployment challenges, balancing theoretical innovation with tangible applications. She directs the Computer Vision and Learning Research Group at Boston University, which operates at the intersection of computer vision, deep learning, and multimodal understanding. The group maintains strong industry collaborations through Meta's FAIR and previously engaged with the MIT-IBM Watson AI Lab. Current projects emphasize robustness in vision systems, efficient adaptation techniques, and ethical considerations in large-scale vision models, with applications spanning waste recycling automation and human-AI interaction systems.

Paulina Łęska-Bayraktarمشاهده پروفایل

استادیار

• Syntax
• Comparative Linguistics
• Formal Semantics
• +۷ مورد دیگر

Paulina Łęska-Bayraktar is an Assistant Professor in the Department of English-Polish Comparative Linguistics at the Faculty of English, Adam Mickiewicz University in Poznań, where she conducts research and teaches in formal syntax and comparative linguistics. Her academic foundation is deeply rooted in the same institution, reflecting a sustained commitment to linguistic scholarship in Poland. Her educational background includes: B.A. in English Linguistics, Adam Mickiewicz University in Poznań (2012) M.A. in English Linguistics, Adam Mickiewicz University in Poznań (2014) Ph.D. in English Linguistics, Adam Mickiewicz University in Poznań (2019) Her research interests lie at the intersection of syntax, semantics, and cognitive grammar, with a strong focus on generative approaches to Slavic languages—particularly Polish. She investigates complex syntactic phenomena such as binding theory, pronoun distribution, quantifier scope, agreement, and the structure of relative and nominal clauses. Her work combines formal theoretical analysis with empirical data, often drawing on acceptability judgments and psycholinguistic methodologies. The trends in her recent publications reveal a sustained engagement with the syntax of Polish, especially in non-canonical constructions involving dative arguments, experiencers, and pronominal clusters. Her work contributes significantly to the understanding of how universal syntactic principles manifest in Slavic morphosyntax, and she frequently collaborates with leading scholars like Prof. Jacek Witkoś. She has received research funding from the Polish National Science Center through multiple grants, including OPUS, Beethoven, and Miniatura projects, which have supported her investigations into nominal expressions, pronouns, and anaphora in Polish. These grants underscore the national recognition of her research contributions. Paulina Łęska-Bayraktar actively participates in the international linguistics community, presenting her work at major conferences such as Formal Approaches to Slavic Linguistics (FASL), Formal Description of Slavic Languages (FDSL), and the Poznań Linguistic Meeting. She is also involved in collaborative research teams and has co-authored works with scholars from Humboldt-Universität zu Berlin and other institutions. Her academic presence is further strengthened by her profiles on ORCID and ResearchGate.

A. Michael Knemeyerمشاهده پروفایل

استاد

• Supply Chain Management
• International Logistics
• Supply Chain Risk and Resilience
• +۲ مورد دیگر

A. Michael Knemeyer is a Professor of Logistics at The Ohio State University's Max M. Fisher College of Business, where he contributes to research and teaching in supply chain management. His work focuses on supply chain relationships, risk and resilience, vendor relations, and sustainability integration. Educational Background: PhD from the University of Maryland, BSBA from John Carroll University Research Areas: Supply chain relationships, international logistics, sustainability, vendor relations, risk management Key Awards: 2013 IJPDLM Outstanding Paper Award, 2013 Bernard J. La Londe Best Paper Award Knemeyer's publications in journals like Harvard Business Review and Journal of Business Logistics explore critical themes in logistics outsourcing relationships, supply chain integration, and sustainable practices. His work often examines cross-cultural dynamics and institutional factors affecting supply chain performance. Selected Courses: Strategic Supply Chain Management, Contemporary Issues in Supply Chain Management, Logistics and Supply Chain Decision Making

Mohammad Saifur Rahmanمشاهده پروفایل

استاد

• Digitization Economics
• Omnichannel Retailing
• Innovations and Inequality
• +۸ مورد دیگر

Mohammad Saifur Rahman is the Daniels School Chair in Management and a Professor of Management Information Systems at the Mitch Daniels School of Business, Purdue University. Previously, he was an Associate Professor at the Haskayne School of Business, University of Calgary. His academic journey includes significant leadership roles such as co-chairing the Conference on Information Systems and Technology (CIST) in 2013 and serving as president of the INFORMS eBusiness Society in 2014. Currently, he serves as an associate editor for both Management Science and Information Systems Research (ISR). Ph.D. in Management Information Systems, Krannert School of Management, Purdue University (2008) MBA in Management Information Systems, Southern Illinois University (2004) BS in Computer Science, Southern Illinois University (2002) Rahman's research primarily focuses on the economic implications of digital transformations. His work examines how digitization affects consumer behavior, market structures, and business strategies across various industries. He investigates the value of digital traces in driving consumer behavior and improving decision-making, particularly in omnichannel retail environments. His research on the sharing economy has revealed important insights about economic disparities, showing how platforms like Airbnb provide economic benefits to predominantly white neighborhoods while failing to generate similar spillover effects in predominantly Black or Hispanic neighborhoods. His recent publications reveal a consistent focus on the intersection of technology, economics, and social impact. Rahman's work frequently examines how local market structures interact with digital platforms to create or exacerbate economic inequalities. His research spans multiple disciplines including information systems, economics, marketing, and operations management, with a particular emphasis on empirical studies using real-world data from major companies like Walmart and Cisco. CICP Faculty Commercialization Award (2020) INFORMS Sandy Slaughter Early Career Award (2018) World's Top 40 Business School Professors Under 40 by Poets and Quants (2017) Jay N. Ross Young Faculty Scholar Award (2015) Dean's Award for Outstanding Research Achievement (University of Calgary, 2014) Rahman has secured substantial research funding from organizations including Social Sciences and Humanities Research Council (SSHRC), Adobe Systems ($50,000 grants in 2017 and 2020), Trask Innovation Fund ($21,218), and NSF I-Corps ($50,000). He has advised numerous PhD students and collaborated with industry partners to translate research into practical applications. Rahman co-founded RightFit Analytics, a precision health analytics solution that utilizes AI to learn success patterns in healthcare. He has organized significant academic events including the Krannert-Walmart Data Dive (believed to be the first data dive on a college campus) and the Dawn or Doom Data Dive in cooperation with Cisco. Rahman actively engages with industry through consulting and collaborative research projects. His work with Walmart, Cisco, and other major corporations demonstrates his ability to bridge academic research with practical business applications. He has been particularly active in studying how AI and big data analytics can be leveraged to improve business decision-making while addressing potential negative consequences like algorithmic bias and economic inequality.

Frank Liمشاهده پروفایل

استادیار

• Computer Security
• Network Security
• Web Security
• +۵ مورد دیگر

Frank Li is an Assistant Professor at Georgia Tech, affiliated with the School of Cybersecurity and Privacy and School of Electrical & Computer Engineering . His research focuses on computer security and privacy , particularly internet, network, and web security, with a data-driven approach. Education : PhD in Computer Science from UC Berkeley, BS in Computer Science from MIT (Course 6-3) Experience : Previously a visiting researcher at Facebook, with internships at Google and Microsoft Research Research interests include empirical computer security , internet measurement, password policies, IoT security, browser extensions, and DeFi vulnerabilities. His work combines large-scale data analysis, network traffic studies, and user behavior experiments. His publications highlight trends in IPv6 adoption , security notifications, password masking, third-party web content, and PLC security evaluation. Teaching awards include the Student Recognition of Excellence in Teaching (Annual CIOS Award and Semester Honor Roll) at Georgia Tech. He leads the BEES Lab (Better Empirically Established Security), which investigates security and privacy issues through internet-wide measurements, network traffic analysis, and data mining. Lab members include PhD and MS students in computer science.

Joseph Felterمشاهده پروفایل

پژوهشگر ارشد

• Counterinsurgency
• Politically Motivated Violence
• National Security Strategy
• +۴ مورد دیگر

Joseph Felter is a Research Fellow at the Hoover Institution (Stanford University) and the William J. Perry Fellow at the Center for International Security and Cooperation. His career bridges academia, military service, and policy advising, with expertise in counterinsurgency, national security, and US-China relations. He coauthored the influential book Small Wars, Big Data: The Information Revolution and Modern Conflict (2018) and developed the Hacking for Defense curriculum to connect military needs with Silicon Valley innovation. Military Background: Former US Army Special Forces officer with combat deployments in Panama, Iraq, and Afghanistan. Served as commander of the COMISAF Counterinsurgency Advisory and Assistance Team. Academic Roles: Assistant Professor at West Point’s Department of Social Sciences, Adjunct Associate Professor at Columbia University’s School for International and Public Affairs, and current codirector of Stanford’s Empirical Studies of Conflict Project. Research Focus: Politically motivated violence, economic development in conflict zones, and defense innovation. His work appears in top journals like American Economic Review and Journal of Conflict Resolution . Policy Engagement: Key advisor on US defense strategy (2017–2019) and founder of BMNT, a Silicon Valley defense technology incubator. Currently serves on Spirit of America’s board. Scientific Awards: Recognized as a William J. Perry Fellow and US Army War College Fellow. His empirical conflict research informs both academic and policy debates.

Felipe Csaszarمشاهده پروفایل

استاد

• Strategic Management
• Artificial Intelligence
• Organizational Behavior
• +۳ مورد دیگر

Felipe Csaszar is a Professor of Strategy and Chair of the Strategy Department at the University of Michigan's Ross School of Business. His research focuses on decision structures' impact on innovation, financial performance, and social outcomes, with particular attention to cognitive frameworks, organizational processes, and AI's role in decision-making. He holds a PhD and MA from the Wharton School, University of Pennsylvania. Education: PhD in Strategy, University of Pennsylvania (2009) MA in Strategy, University of Pennsylvania (2007) Research Interests: Strategic decision-making under AI integration Cognitive and structural drivers of innovation Organizational decision processes and design Formal modeling and empirical strategy research Editorial Roles: Senior Editor, Strategy Science and Management Science Former Editor, Organization Science Co-editor, Handbook of AI and Strategy Professional Experience: Prior role: Assistant Professor at INSEAD Previous career: CEO of an internet startup and Head of Research at an asset management firm Labs/Teams: Leading the Strategy Science division at INFORMS Co-chair of the SMS Behavioral Strategy division

• Uncertainty Reasoning and Decision Making in Artificial Intelligence
• Electronic Business
• Systems Analysis and Design
• +۷ مورد دیگر

Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.

Jonathan Boffمشاهده پروفایل

استاد

• Economic Statecraft
• Military History
• World War I
• +۷ مورد دیگر

Professor Jonathan Boff (FRHistS) is a leading military historian in the Department of History at the University of Birmingham. His research explores the intersection of institutions, individuals, and warfare through comparative and cross-disciplinary approaches. He has a unique background combining academic expertise with two decades of private-sector experience, primarily in Asia. PhD in War Studies, King’s College London MA in History of Warfare, King’s College London BA in PPE, Merton College, Oxford University Boff’s research focuses on Economic Statecraft , the military history of the World Wars, British strategy (1880–1945), and the psychological dynamics of combat. He bridges historical analysis with modern policy challenges, particularly in financial and strategic contexts. Recent publications highlight his expertise in First World War financial policy, battlefield tactics, and institutional adaptation. His work is recognized for its empirical rigor and interdisciplinary methodology. AHRC Fellowship in Research Leadership (2020–23) Prize-winning books: Haig's Enemy (OUP, 2018) and Winning and Losing on the Western Front (CUP, 2012) Fellow of the Royal Historical Society He supervises PhD research on topics such as economic statecraft, World War-era military strategies, and comparative studies of morale. His grants include AHRC funding for his current project on financial warfare, culminating in a 2025 monograph by Oxford University Press.

Michael Jong Kimمشاهده پروفایل

دانشیار

• Dynamic Programming
• Statistical Learning
• Robust Optimization
• +۱ مورد دیگر

Michael Jong Kim is an Associate Professor at the Sauder School of Business, University of British Columbia, specializing in the Division of Operations and Logistics. His research focuses on dynamic programming, statistical learning, robust optimization, and the exploration vs exploitation trade-offs in sequential decision-making processes. BASc, M.Math, and PhD from the University of Toronto His work spans topics in stochastic optimization, supply chain dynamics, and information dissemination in uncertain environments. Publications highlight contributions to Bayesian inventory control, semi-Markovian system control, and variance regularization in optimization models. Dr. Kim teaches advanced business analytics courses, including Descriptive and Predictive Business Analytics and Advanced Predictive Business Analytics (MBAN) during the 2024-2025 academic year. He can be reached at mike.kim@sauder.ubc.ca or by phone at +1 604.822.8682.

David A. Hsiehمشاهده پروفایل

استاد

• Financial Risk Management
• Hedge Funds
• Asset Pricing
• +۳ مورد دیگر

David A. Hsieh is the Bank of America Professor of Finance at the Fuqua School of Business, Duke University, where he has been a faculty member since 1993. Previously, he served as Associate Professor and Assistant Professor at the University of Chicago's Graduate School of Business from 1981-1989. His extensive research has significantly contributed to the understanding of hedge funds, financial risk management, and nonlinear dynamics in financial markets. Massachusetts Institute of Technology, Ph.D. in Economics, 1981 Yale University, B.S. in Economics and Mathematics, 1976 (Summa Cum Laude, Phi Beta Kappa) Phillips Academy, Andover, 1972 (Cum Laude) Dr. Hsieh's research primarily focuses on the dynamics of asset prices and their implications for financial risk management. He has made significant contributions to understanding risk and return characteristics in hedge funds and commodity funds, pioneering work on nonlinear dynamics applications to financial markets. His research has evolved from early work on exchange rates and volatility modeling to more recent comprehensive analyses of hedge fund strategies, performance measurement, and industry structure. Hsieh's publication history reveals a clear progression from foundational work on nonlinear dynamics in financial markets to increasingly sophisticated analyses of hedge fund strategies and risk characteristics. His recent work, often in collaboration with William Fung and other prominent finance researchers, has focused on mega hedge fund firms, franchise value in the industry, and the evolution of hedge fund strategies toward more index-like products. The research consistently combines rigorous theoretical frameworks with robust empirical analyses across diverse market conditions. CAIA Award for Excellence in Alternative Investment Research (2015) CFA Institute Graham and Dodd Award of Excellence (2004) Bank of America Faculty Award (2002) Duke Cross-Continent Executive MBA Teaching Excellence Award (2002) Fischer Black Memorial Foundation Robert J. Schwartz Memorial Prize (1999) Smith Breeden First Prize (1990) Yale Science and Engineering Association High Scholarship Award (1976) Russell Henry Chittenden Prize (1976) Dr. Hsieh has served as a consultant for the International Monetary Fund (2007-2016) and the Bank for International Settlements (1998), and as a Visiting Scholar at both the International Monetary Fund and the Board of Governors of the Federal Reserve System. His editorial service includes Finance Editor for Management Science (2003-2009) and Associate Editor roles for several leading finance journals. He has developed extensive research resources including a Hedge Fund Data Library that has become widely used in academic and industry research.

Janina Dillمشاهده پروفایل

استاد

• International Humanitarian Law
• International Law and War
• Ethics of War
• +۵ مورد دیگر

Janina Dill holds the Dame Louise Richardson Chair in Global Security at the Blavatnik School of Government, University of Oxford. She is also a Professorial Fellow at Trinity College and Co-Director of the Oxford Institute for Ethics, Law, and Armed Conflict (ELAC). Her academic work focuses on the intersection of law, morality, and international relations in the context of war. Her research spans three interconnected strands: the legal requirements for military operations under international law; theoretical frameworks for understanding how international law serves as an instrument of morality in war; and empirical investigations into how moral and legal norms affect real-world military decision-making and public opinion in conflict zones. Professor Dill's work has particular relevance to contemporary conflicts, with extensive analysis of the wars in Ukraine and Gaza. She has examined how international humanitarian law applies to these conflicts, the moral arguments employed by different parties, and the practical challenges of implementing legal constraints in complex warfare environments. Her research has been recognized with the prestigious Philip Leverhulme Prize in 2021, which she has used to advance her work on the moral psychology of decision-making in war. She is currently leading multiple significant research projects, including a three-year ESRC/NSF-funded study on "Cumulative Civilian Harm" and an expert process with the ICRC on military objectives under international humanitarian law. Professor Dill is frequently consulted by international media outlets including The Guardian, The Economist, BBC, CNN, and major European publications. Her expertise is regularly sought on matters related to the laws and ethics of war, particularly regarding the conflicts in Ukraine and Gaza. She has received recognition for her contributions to the field through numerous speaking engagements, media appearances, and her influential research that bridges theoretical legal scholarship with practical applications in contemporary conflicts.

• Artificial Intelligence
• Algorithmic Game Theory
• Multiagent Reinforcement Learning
• +۴ مورد دیگر

Amy R Greenwald is a Professor of Computer Science at Brown University. Her research spans artificial intelligence, algorithmic game theory, and computational economics, with a focus on multiagent reinforcement learning and market equilibrium computation. Education PhD, New York University (1999) MS, Cornell University (1995) MS, Oxford University (1992) BS, University of Pennsylvania (1991) Research Focus Greenwald's work explores strategic interactions in computational systems, including: Game-theoretic modeling of multiagent systems Algorithmic approaches to market equilibrium Simulation-based equilibrium learning Stackelberg game formulations for hierarchical decision making Applications to supply chain negotiations and economic design Her recent publications emphasize tractable equilibrium computation, social influence in economic models, and advanced reinforcement learning techniques for strategic settings. Teaching CSCI 0100 - Data Fluency for All CSCI 0180 - Computer Science: An Integrated Introduction CSCI 1440 - Algorithmic Game Theory CSCI 2440 - Advanced Algorithmic Game Theory CSCI 2951Z - Advanced Algorithmic Game Theory

Steve Mussmannمشاهده پروفایل

استادیار

• Data-centric machine learning
• Active learning
• Experimental design
• +۲ مورد دیگر

Steve Mussmann serves as an Assistant Professor in the School of Computer Science at the Georgia Institute of Technology, where he joined in Fall 2024. His research centers on data-centric machine learning, with emphasis on active labeling, data selection, and adaptive experimental design methodologies. He maintains active collaborations through Georgia Tech's Foundations of AI (FoAI) and ML@GT research groups. Mussmann earned his PhD in Computer Science from Stanford University in 2021 under Percy Liang's supervision, following a BS in Math, Statistics, and Computer Science from Purdue University in 2015. His professional trajectory includes a machine learning researcher role at Coactive AI and an IFDS postdoctoral fellowship at the University of Washington's Paul Allen School of Computer Science and Engineering. His research program investigates theoretical and practical aspects of data efficiency in machine learning systems, particularly focusing on active learning frameworks, statistical properties of data algorithms under concept drift, and task specification via prompts or demonstrations. Current projects address challenges in label-efficient training of large language models and multimodal dataset development. Analysis of his 15 most recent publications reveals a consistent focus on advancing data-centric methodologies, with increasing emphasis on large-scale applications like multimodal datasets and language model fine-tuning. His work bridges theoretical guarantees in experimental design with practical frameworks like LabelBench for benchmarking label efficiency. Mussmann has received recognition through the IFDS postdoctoral fellowship. His contributions to the field include foundational work on active learning theory and data selection algorithms. IFDS postdoctoral fellow He currently advises five graduate students including PhD candidates Kangping Hu (CS) and Hangyu Zhou (ML), alongside MS students Kabir Kang and Kalp Vyas, and undergraduate Saloni Bedi. Former advisee Wei-Liang (Edison) Liao completed BS research under his supervision. His teaching portfolio includes graduate courses CS 7545 (Machine Learning Theory) and CS 8803-DML (Data-centric Machine Learning). Mussmann operates within Georgia Tech's Foundations of AI initiative and ML@GT collective, which provide infrastructure for large-scale data-centric research. His lab develops open-source tools like LabelBench for reproducible evaluation of data selection techniques, with ongoing projects exploring video data exploration systems and adaptive finetuning frameworks for foundation models.