James R. Eagan is an Associate Professor in the Computer Science and Networks Department (Infres) at Télécom Paris , part of the Institut Polytechnique de Paris. He is also a Visiting Professor at the University of Colorado, Boulder for the 2024–25 academic year. His research focuses on making computers more expressive tools for human interaction, emphasizing malleable software, collaborative dynamic media, and multi-surface environments. His work spans Human-Computer Interaction , Data Visualization , and User Interface Programming . A key theme involves adapting software for user-driven customization, exemplified by projects like Webstrates (shareable dynamic media) and SchemeLens (semantic zoom for technical diagrams). He also explores uncertainty in data analytics and gesture-based interfaces for experts. Recent publications from 2020–2024 address Explainable AI (XAI) , Financial Crime Detection , and Interactive Data Analysis . His tools Tarantula and SchemeLens have received acclaim, including the 2015 ACM SIGSOFT Impact Award and Best Paper at UIST 2015. Scientific accolades include: Prix de l’Impact 2015 d’ACM SIGSOFT Best Paper Award at UIST 2015 Honorable Mention at CHI 2017 He teaches courses in Mobile Application Development , Human-Computer Interaction , and Data Visualization . His lab, DIVA (Design, Interaction, Visualization & Applications), operates within the Information Processing and Communication Laboratory (LTCI). He actively recruits PhD candidates and postdocs for research in these domains.
Najim Dehak is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, part of the Whiting School of Engineering. His research focuses on machine learning applied to speech processing, audio classification, and health applications. He is renowned for developing the I-vector representation for speaker recognition, introduced in 2008 during a workshop at Johns Hopkins’ Center for Language and Speech Processing. Prior to this role, he was a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory. Dehak holds a PhD from the School of Advanced Technology in Montreal (2009). He is a Senior Member of IEEE and contributes to the IEEE Speech and Language Technical Committee. His work bridges AI, healthcare, and signal processing, with notable contributions to neurodegenerative disease detection via speech and handwriting analysis. Research interests include adversarial attacks on speech systems, multimodal biomarker discovery, and robust speech processing across demographics. His lab’s tools, like the Hermespeech Recorder, enable scalable data collection for clinical and research applications. Education: PhD in Advanced Technology (2009), Montreal Affiliations: Johns Hopkins University, IEEE Labs/Teams: Center for Language and Speech Processing (CLSP) His recent work explores AI’s role in aging research, including Alzheimer’s and Parkinson’s disease detection through speech, eye tracking, and handwriting analysis. Ongoing projects address fairness in speaker verification and robustness against adversarial attacks in ASR systems.
Joe Kable, PhD, serves as the Baird Term Associate Professor of Psychology at the University of Pennsylvania, where his research investigates the neurophysiological basis of human decision-making through integrative approaches from experimental economics, cognitive neuroscience, and judgment psychology. His laboratory specializes in fMRI studies examining how subjective value representations guide choices involving immediate versus delayed rewards. Education: B.S. in Chemistry, Emory University PhD in Neuroscience, University of Pennsylvania Dr. Kable's research program centers on neural mechanisms of temporal discounting, risk assessment, and individual differences in choice behavior. His work demonstrates how socioeconomic status, aging, and clinical conditions modulate decision processes, with particular emphasis on hippocampal-prefrontal interactions during value computation. Recent studies reveal how time perception alterations affect neural activity in reward circuits and how social factors influence trust decisions across the lifespan. Analysis of his 15 most recent publications shows a strong methodological focus on fMRI and lesion studies, with growing clinical translation in depression, addiction, and dementia. Key thematic trends include the neural encoding of effort costs in social contexts, structural brain markers for impulsivity, and the dissociable roles of frontal subregions in persistence behaviors. His work consistently bridges basic decision neuroscience with real-world applications in mental health. Scientific Awards: No scientific awards mentioned in source material Dr. Kable leads an active research laboratory at Penn but the source text provides no details about graduate student advising or specific grant funding. His publications indicate collaboration with clinical researchers at the Penn Memory Center, particularly in aging-related decision studies. The laboratory employs multimodal neuroimaging techniques including resting-state fMRI, TMS, and lesion mapping to investigate decision circuits, with recent work extending to computational modeling of value representation and social cognition mechanisms.
Terry C. Lansdown is an Associate Professor in the School of Social Sciences and Department of Psychology at Heriot-Watt University. His expertise lies in Human Factors, particularly focusing on driver safety, workload management, and attentional distraction in transportation contexts. Prior to his academic role, he worked as a senior researcher at the Transport Research Laboratory, contributing to numerous EU-funded and government-sponsored projects. His research integrates ergonomics principles with real-world applications, addressing issues like automated driving systems, social distractions in vehicles, and occupational health in construction industries. Key research interests include: Driver behavior and road safety Human factors in automated systems Workload and attentional demands in complex tasks Health and safety in small-to-medium enterprises (SMEs) He has led studies on pandemic response strategies, such as the CHARIS project analyzing behavioral adherence during the COVID-19 crisis. Lansdown’s work frequently bridges psychological theory with practical interventions, including text messaging campaigns to improve construction worker safety and experimental designs to evaluate automation failure responses. Collaborations span academic and industrial sectors, with publications addressing topics ranging from UV exposure in outdoor workers to the cognitive impacts of in-vehicle distractions. His contributions align with UN Sustainable Development Goals related to health (SDG 3) and sustainable cities (SDG 11).
Gail E. Kaiser is a Professor of Computer Science and the Director of the Programming Systems Laboratory (PSL) in the Computer Science Department at Columbia University. She has been with Columbia University since 1985, becoming a full Professor in 1998. Prof. Kaiser's research spans software engineering, program analysis, software testing, and software security, with recent focus on addressing challenges in AI/ML systems testing and security. Prof. Kaiser received her PhD in Computer Science from Carnegie Mellon University in 1985 and her ScB in Computer Science and Engineering from MIT in 1979. Her dissertation at CMU was titled "Semantics for Structure Editing Environments" under advisor Nico Habermann, and at MIT she completed "Automatic Extension of an Augmented Transition Network Grammar for Morse Code Conversations" under advisor Al Vezza. Prof. Kaiser's research interests primarily focus on software engineering following a systems building approach, with recent emphasis on static and dynamic program analysis techniques to improve software reliability and security. Since 2005, she has investigated testing "non-testable" programs, particularly in machine learning, data mining, and scientific computing applications where traditional testing oracles are insufficient. She has developed novel techniques and tools for detecting bugs and verifying repairs in complex systems. Concurrently, she has worked on collaboration environments for computational scientists, creating knowledge sharing and domain-aware environments to support scientific workflows. Prof. Kaiser's recent publications demonstrate a strong focus on the intersection of software engineering and artificial intelligence. Her work addresses critical challenges in testing AI systems, code understanding through deep learning, vulnerability detection, and educational tools for computational thinking. There's a clear evolution from traditional software engineering topics toward AI/ML applications, with particular emphasis on metamorphic testing for non-testable systems, code similarity analysis, and educational applications. Prof. Kaiser has received numerous prestigious awards throughout her career: Distinguished Journal Award (10 Years) from 18th IEEE International Conference on Software Testing, Verification and Validation (ICST), April 2025 Best Research Paper Award at 24th IEEE International Conference on Source Code Analysis & Manipulation (SCAM), October 2024 Distinguished Reviewer Awards for ASE 2024 and FSE 2024 ACM SIGSOFT Distinguished Paper Award for "CONCORD: Clone-aware Contrastive Learning for Source Code", July 2023 Best Student Paper Award at ICCE 2021 Multiple ACM SIGSOFT Distinguished Paper Awards dating back to 2014 Presidential Young Investigator in Software Engineering and Software Systems from NSF (1988-1993) Prof. Kaiser has chaired Columbia's doctoral program since 1997 and served on editorial boards including IEEE Internet Computing and as a founding associate editor of ACM Transactions on Software Engineering and Methodology. Her lab has been continuously funded by major agencies including NSF, NIH, DARPA, ONR, NASA, and numerous companies. Current grants include significant NSF funding for secure containers architecture, learning semantics of code for software assurance, and finding semantic security bugs. As Director of the Programming Systems Laboratory (PSL), Prof. Kaiser leads research in software systems, program analysis, and software testing. The lab has developed numerous tools and techniques for software reliability and security, with recent focus on challenges in AI/ML systems. Her work bridges theoretical foundations with practical applications, often resulting in deployable tools that address real-world software engineering challenges.
Marina Agranov is Professor of Economics at the California Institute of Technology (Caltech), affiliated with the Division of Humanities and Social Sciences. She directs research through the Ronald and Maxine Linde Institute of Economic and Management Sciences, Center for Social Information Sciences (CSIS), and Center for Theoretical and Experimental Social Sciences (CTESS), and serves as Research Associate at the National Bureau of Economic Research (NBER). Her academic credentials include a B.A. from St. Petersburg State Technical University (1999), M.A. from Tel Aviv University (2004), and Ph.D. from New York University (2010). She joined Caltech as Assistant Professor in 2010 and was promoted to full Professor in 2017. Agranov's research pioneers experimental and behavioral economics, focusing on strategic decision-making in bargaining games, social learning environments, network interactions, and information dynamics. Her work examines how individuals form beliefs and navigate tensions between personal goals and collective outcomes, often using controlled laboratory experiments to test theoretical predictions about human behavior under uncertainty. Her recent publications reveal a consistent methodological approach: blending game-theoretic models with experimental validation to investigate communication effects, randomization preferences, and institutional design. Key trends include analyzing how uncertainty impacts committee negotiations, how complexity influences egalitarian outcomes in legislative bargaining, and how information structures shape social learning on networks. Her scientific recognition includes: Associated Students of Caltech (ASCIT) Teaching Award (2017-18) Professor Agranov's research has secured significant institutional support through Caltech centers and NBER affiliation, with findings featured in major economics journals and Caltech news coverage including "Decision by Committee: How Uncertainty Shapes Negotiations" (December 2024) and "Experimental Economics in Theory and Practice" (July 2023). Her work on committee decision-making under uncertainty has direct implications for institutional design in political and corporate governance. She actively contributes to Caltech's research ecosystem through CSIS and CTESS, which facilitate interdisciplinary collaborations in social sciences and experimental methodology development.
Maksym Andriushchenko is an incoming faculty member at the ELLIS Institute Tübingen and Max Planck Institute for Intelligent Systems , where he will lead the AI Safety and Alignment group as a principal investigator. Currently, he is a Doctoral Assistant at the Theory of Machine Learning Laboratory (TML) within the Department of Computer Science (IINFCOM) at Swiss Federal Institute of Technology in Lausanne (EPFL) . His work focuses on AI safety, adversarial robustness, and alignment of large language models (LLMs) with societal values.
Albert Presto is a Research Professor in the Department of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Civil and Environmental Engineering. He serves as Director of the Center for Atmospheric Particle Studies (CAPS) and is a Faculty Affiliate at the Steinbrenner Institute for Environmental Education and Research. His work spans multiple interdisciplinary collaborations across environmental science, engineering, and public health domains. Department of Mechanical Engineering, College of Engineering Center for Atmospheric Particle Studies (CAPS) Steinbrenner Institute for Environmental Education and Research Africa Clean Air Network Advisory Council Health Effect Institute's Energy Review Committee Presto's research focuses on pollutant emissions from energy extraction and consumption and their subsequent atmospheric transformations. His work examines how gas and oil wells emit methane, how vehicles emit carbon monoxide and particulate matter, and how these pollutants undergo oxidation in the atmosphere to create secondary pollutants like ozone and secondary particulate matter. His approach combines ambient measurements, laboratory experiments, source testing of pollution sources, and atmospheric models to provide a holistic view of pollutant emissions and transformations. His recent publications reveal a strong emphasis on air quality monitoring technologies, particularly low-cost sensor networks deployed internationally. There's a clear trend toward studying air pollution in African cities (especially Nairobi, Kampala, and Addis Ababa), examining spatial and temporal variability of particulate matter, and investigating the health impacts of air pollution exposure. His work increasingly integrates machine learning approaches for pollution forecasting and leverages international collaborations to address global air quality challenges. Member of Africa Clean Air Network Advisory Council Collaborator with US embassies worldwide on air quality data collection Quoted in The Guardian on inequality of air pollution exposure, specifically noting how people of color are more likely to be exposed to harmful air pollution Collaborated on research regarding chemicals released during the Ohio train derailment Presto actively collaborates with medical professionals to develop neighborhood-by-neighborhood studies of pollutant exposure and to understand relationships between emissions and health effects like childhood asthma. His work includes investigating racial-ethnic exposure disparities to airborne pollutants and examining how proximity to pollution sources like fast-food restaurants affects pediatric asthma outcomes. He has received seed funding from the Scott Institute for Energy Innovation for research in clean hydrogen production, AI hardware efficiency, and decarbonizing transportation. As Director of CAPS, Presto leads a research team focused on atmospheric particle studies with applications ranging from local Pittsburgh air quality to global monitoring initiatives. His lab develops and deploys advanced air quality monitoring technologies, including low-cost sensor networks that have been implemented in multiple cities across Africa. The CAPS research group works at the intersection of environmental engineering, atmospheric science, and public health to address critical air quality challenges worldwide.
Gerard A. Ateshian is the Andrew Walz Professor of Mechanical Engineering and Professor of Biomedical Engineering at Columbia University, where he has been a faculty member since 1991. He also served as Chair of the Department of Mechanical Engineering from 2011 to 2014 and is the founding director of the Musculoskeletal Biomechanics Laboratory (MBL), established in 1996. Education: BS (1986), MS (1987), MPhil (1990), and PhD (1991) in Mechanical Engineering from Columbia University. Research Interests: Professor Ateshian's research focuses on the mechanics of soft biological tissues , particularly articular cartilage . His work integrates theoretical, experimental, and computational approaches to understand cartilage lubrication, tissue engineering, and growth and remodeling in living tissues. He has extended continuum mechanics frameworks to model complex biological phenomena such as mass transport, osmotic effects, and reactive mechanics in tissue mixtures. His recent efforts include developing open-source computational tools (FEBio) and translating research into clinical treatments for osteoarthritis . Scientific Awards: H.R. Lissner Medal, ASME (2017) OARSI Basic Science Award (2013) Columbia Engineering Alumni Association Distinguished Faculty Teaching Award (2012) Great Teacher Award, Society of Columbia Graduates (2002) YC Fung Young Investigator Award, ASME (1997) Fellow: ASME, BMES, AIMBE Advising and Mentorship: Professor Ateshian has advised numerous PhD students, including recent graduates Vince Sise and Katherine Spack , who completed their dissertations on cartilage fatigue and osteochondral allografts, respectively. His lab actively mentors students from both Mechanical and Biomedical Engineering programs. Laboratory and Collaborations: He directs the Musculoskeletal Biomechanics Laboratory (MBL) , which collaborates closely with the Cellular Engineering Laboratory of Prof. Clark Hung and with Dr. Jeffrey Weiss at the University of Utah on computational modeling (FEBio). He also collaborates with Columbia University Medical Center faculty on clinical translation of cartilage research.
Charalampos Papamanthou is an Associate Professor of Computer Science at Yale University, where he also serves as Co-director of the Yale Applied Cryptography Laboratory and a member of the Yale Institute for Foundations of Data Science. He holds affiliations with the Yale Center for Algorithms, Data, and Market Design. Additionally, he is Chief Scientist at Lagrange Labs. His research focuses on computer security and applied cryptography, particularly verifiable and privacy-preserving computations, leakage-abuse attacks on searchable encryption, and scalable blockchains/cryptocurrencies. He has advised numerous students and postdocs, and his work is supported by NSF, Protocol Labs, and JP Morgan. Research Interests: His primary areas include cryptographic protocols, privacy-preserving systems, blockchain infrastructure, secure cloud computing, and distributed consensus mechanisms. He has pioneered advancements in zero-knowledge proofs, private information retrieval, and dynamic searchable encryption. Awards: He has received prestigious awards such as the CCS Test-of-Time Award (2022), JP Morgan Faculty Research Award (2022), and NSF CAREER Award (2017). His contributions span over 140 publications in top venues like CRYPTO, CCS, and SODA. Teaching: He has taught advanced courses in cryptography, algorithms, and computer systems security at Yale and previously at the University of Maryland and Brown University. Recently, he chairs Yale’s PhD admissions in Computer Science. Labs & Teams: Leads the Yale Applied Cryptography Lab, focusing on real-world applications of cryptographic research. Collaborates with industry partners like Lagrange Labs to develop privacy-preserving technologies.
Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.
Ron H.J. Peerlings is Associate Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e) , where he leads the Mechanics of Materials research group. Promoted to Associate Professor in 2007 after joining as Assistant Professor in 2000, he has built an extensive portfolio in theoretical and computational mechanics of materials. Education: PhD (1999) – Eindhoven University of Technology, thesis: Enhanced damage modelling for fracture and fatigue Post-doctoral research (1999–2000) – University of Cambridge, Engineering Department Research interests revolve around micromechanics , micro-plasticity , multiscale modelling , homogenisation , damage and fracture , and enriched continuum theories . His work spans advanced high-strength steels, composites, paper and fibrous networks, with strong emphasis on coupling rigorous theoretical developments to industrially motivated problems. His recent publications (2023-2025) demonstrate a clear trajectory towards integrating advanced experimental techniques (e.g., digital image correlation, micro-mechanical testing) with high-fidelity computational frameworks such as crystal-plasticity finite-element modelling, FFT-based solvers and micromorphic homogenisation. Dominant themes include: Deformation and fracture in lath martensite and dual-phase steels Hygro-mechanics of paper and fibrous networks Pattern-transforming mechanical metamaterials Discrete-to-continuum scale bridging methods Scientific awards are not explicitly listed in the provided material; however, his prolific output (294 research items, >6500 citations) attests to significant peer recognition. Teaching & supervision: He delivers courses on Computational Mechanics – Numerical Methods for Fluids and Solids and Fracture Mechanics – Theory and Application , and has supervised >80 student works and numerous PhD candidates whose names appear on joint publications. Laboratory & teams: He heads the Group Peerlings within the Mechanics of Materials cluster, maintaining close collaboration with the Mechanics of Materials Group Geers and extensive national/international experimental and computational networks.
Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.
Charles D. Sprenger is a Professor of Economics at the California Institute of Technology (Caltech), where he has served since 2020 and held the position of Executive Officer from 2022 to 2025. He is affiliated with Caltech's Division of the Humanities and Social Sciences (HSS) and holds key roles at the Ronald and Maxine Linde Institute of Economic and Management Sciences and the Center for Theoretical and Experimental Social Sciences (CTESS). His external appointments include Board of Editors for the American Economic Review and Associate Editor roles for the Journal of the European Economic Association and Quantitative Economics . His educational background includes a B.A. from Stanford University (2002), an M.Sc. from University College London (2005), and a Ph.D. from the University of California, San Diego (2011). These credentials established his foundation in economic theory and experimental methodology. Sprenger is a leading behavioral and experimental economist specializing in intertemporal decision making and choices under uncertainty. His research designs innovative experiments across diverse contexts—from food deserts in the United States to polio vaccination drives in Pakistan—to test the validity of standard economic models. His work consistently reveals significant deviations from rational choice theory, particularly regarding time inconsistency, risk preferences, and reference-dependent behaviors. He has pioneered methods for measuring time preferences and testing cumulative prospect theory, with implications for public policy and behavioral interventions. Analysis of his 15 most recent publications (2015-2024) shows a cohesive research program centered on behavioral anomalies in decision making. His work bridges laboratory precision with real-world field applications, demonstrating how psychological factors like procrastination and loss aversion manifest in high-stakes environments. Key trends include the development of tailored incentive structures, validation of rank-dependent utility models, and exploration of dynamic inconsistency across domains including health, finance, and public policy. His notable recognition includes: Sloan Foundation Fellowship (2016-2018) Sprenger actively contributes to the academic community through editorial leadership and collaborative research. His work has been featured in Caltech news for projects like "Reducing Procrastination with Tailored Incentives" (2023) and the graduate summer program "Experimental Economics in Theory and Practice" (2023). Though specific advisees aren't listed, his teaching of advanced courses like Experimental Economics (SS 212 abc) indicates mentorship of graduate researchers. He secures significant research funding through fellowships and institutional support, enabling large-scale field experiments. As a core member of CTESS, Sprenger leads a multidisciplinary team conducting cutting-edge experimental economics research. His lab integrates theoretical modeling with empirical validation, focusing on how behavioral insights can improve policy design in areas like tax compliance, vaccination programs, and financial decision making. Current work emphasizes scalable interventions for procrastination and preference-based incentive customization.
Thomas R Overton serves as Department Chair and Professor of Dairy Nutrition and Management in the Department of Animal Science at Cornell University's College of Agriculture and Life Sciences (CALS). He also directs PRO-DAIRY, an extension program focused on enhancing dairy farm profitability and efficiency in New York through research-based strategies for transition cow management. Education: Doctorate, University of Illinois, 1998 Master of Science, University of Illinois, 1994 Bachelor of Science, Cornell University, 1991 Dr. Overton's research centers on the transition period (late pregnancy to early lactation) in dairy cows—the most critical phase for health and productivity. His work integrates nutritional physiology with practical farm applications, targeting hypocalcemia management, protein supplementation strategies, inflammation control, and biomarker validation for metabolic health. This research directly informs his extension efforts through PRO-DAIRY, which delivers actionable knowledge to dairy producers and industry professionals. Analysis of his 15 most recent publications (2022-2024) reveals consistent emphasis on transition cow nutrition, with growing focus on metabolic analytics, precision feeding, and real-world herd management. His work bridges controlled studies at Cornell's Ruminant Center with observational research on commercial New York dairy farms. Scientific Awards: 2024 Nutrition Professionals Applied Dairy Research Award (American Dairy Science Association) 2010 CALS Extension/Outreach Team Award (PRO-DAIRY) 2007 Foundation Scholar Award (American Dairy Science Association) 2007 CALS Extension/Outreach Team Award Dr. Overton advises undergraduate and graduate students in Animal Science while leading PRO-DAIRY's extension initiatives. His program secures industry partnerships and grants focused on improving dairy farm sustainability through nutritional science. The July 2025 'Farm of the Future' showcase at Cornell's Ruminant Center exemplifies his commitment to translating research into practical innovation. He operates within Cornell's PRO-DAIRY program and the Cornell University Ruminant Center—a living laboratory for dairy technology testing—collaborating closely with industry stakeholders to address emerging challenges in dairy production.
