Tatsunori Hashimoto is an Assistant Professor of Computer Science at Stanford University, specializing in artificial intelligence, machine learning, and natural language processing. His research focuses on developing robust and ethical language models, addressing challenges in bias mitigation, fairness, and transparency. He leads projects like the Stanford Alpaca, exploring instruction-following models and their societal impacts. Key research interests include generative models, AI ethics, and privacy-preserving techniques. His recent work examines language model behaviors, security risks, and the societal implications of AI systems. Notable contributions include frameworks for auditing language models, improving factual accuracy, and reducing disparities in speech recognition. His publications highlight advancements in long-context processing, few-shot learning, and automated benchmarking. He emphasizes practical applications of AI while addressing dual-use concerns and ensuring alignment with human values.
Bruno Felisberto Martins Ribeiro is an Associate Professor of Computer Science at Purdue University, joining the department in Fall 2015. His research focuses on endowing machine learning algorithms with robust invariant representations for relational and temporal data, emphasizing causal and associational tasks. Key research areas include Networking and Operating Systems, Artificial Intelligence, Machine Learning, and Natural Language Processing. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2010). Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2010 Research Interests: Explores invariances in mathematics and machine learning to improve model robustness. Key topics include graph and tensor invariances, causal relationships, adversarial robustness, and applications in recommendation systems, robotics, and drug discovery. His lab’s work has advanced counterfactual task frameworks and causal reasoning in machine learning. Recent Contributions: Recent publications address zero-shot generalization in graph neural networks, causal discovery methods, and defenses against adversarial attacks. His work spans conferences like ICML, NeurIPS, and SIGCOMM. Awards: Best Paper Award at ACM CODASPY 2021 Best Paper Award at SIGMETRICS 2016 Best Paper Award at IEEE NetSciCom 2014 Advising & Students: Supervises current PhD students Beatrice Bevilacqua, Jincheng Zhou, and Yucheng Zhang, along with MSc student Ipsit Mantri. Notable former students include S Chandra Mouli (Meta), Yangze Zhou (Spotify), and Jianfei Gao (Vector Institute). Labs & Teams: Leads research in invariant representations and causal ML, collaborating with institutions like Stanford during his sabbatical. His work bridges theory and practice, impacting areas like network analysis and AI-driven healthcare.
Laura E. Brumariu is a Professor and Associate Dean for Professional Programs and Student Advancement at the Gordon F. Derner School of Psychology, Adelphi University. She holds a Ph.D. from Kent State University and completed postdoctoral research fellowships at Harvard Medical School. Her office is located in the Hy Weinberg Center, and she is licensed as a psychologist in New York State. Education: Postdoctoral Research Fellow, Harvard Medical School/CHA/MGA (2013) Postdoctoral Research Fellow with Clinical Attributions, Cambridge Health Alliance/Harvard Medical School (2012) Ph.D., Kent State University (2010) Her research adopts a developmental psychopathology perspective, focusing on how parent-child attachment influences emotional and social development, particularly in middle childhood. She investigates emotion regulation, anxiety, disorganized attachment, role-confusion, and family processes related to borderline personality features and dissociation. She developed the Middle Childhood Attachment Strategies Coding System and leads the Child and Adolescent Research (CARE) Lab. Her recent work emphasizes meta-analytic reviews on emotion socialization, attachment, anxiety, and empathy. Her most recent publications reflect trends in attachment theory, emotion regulation, parenting, and internalizing disorders across development, often using meta-analytic and longitudinal methods. Much of her work integrates international collaboration, especially with Romanian researchers. Scientific Awards and Honors: Clinical Research Training Program, Harvard Medical School, Research Fellow (2010–2012) The Jeanette and Louis Reuter Fellowship in Developmental Sciences, Kent State University (2008–2009) She has mentored numerous doctoral students whose research spans topics such as gratitude, empathy, emotion socialization, and trauma. She teaches courses in psychological research, family therapy, and doctoral thesis supervision. She has secured research funding and collaborates extensively on studies related to child and adolescent mental health, though specific grants are not detailed in the text. Her professional activities include frequent presentations at major psychological conferences such as the Association for Psychological Science and the International Attachment Conference. Labs and Research Teams: She directs the Child and Adolescent Research (CARE) Lab at Adelphi University, which focuses on attachment, emotion regulation, and psychopathology in children and adolescents. The lab conducts both observational and survey-based research and trains graduate students in developmental and clinical methods.
Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .
Hima Lakkaraju is an Assistant Professor at Harvard University with dual appointments in the Harvard Business School and the Department of Computer Science. Her research focuses on trustworthy AI, including machine learning interpretability, fairness, privacy, and safety. She holds a PhD from Stanford University and has received accolades such as the Alfred P. Sloan Fellowship and NSF CAREER Award. Her work bridges algorithmic foundations and societal implications of AI, with applications in healthcare, policy, and business. Education: PhD in Computer Science from Stanford University (2013-2017). Academic background includes roles at IBM Research, Microsoft Research, and Adobe. Research Interests: Algorithmic Foundations of AI Interpretability and Explainable AI Fairness and Bias Mitigation Privacy-Preserving ML Generative Models and LLMs Ethical AI Policy and Regulation Key Achievements: Over 100 publications in top venues like NeurIPS and ICML; co-founder of the Trustworthy ML Initiative; featured in MIT Tech Review, Forbes, and Harvard Business Review. Current projects include the AI4LIFE research group and work on regulatory frameworks for AI. Advising and Grants: Supervises over 30 students across PhD, master's, and postdoc levels. Research supported by NSF, Sloan Foundation, Schmidt Sciences, Google, Amazon, and others. Initiatives include the Regulatable ML workshop and NeurIPS ethics co-chair roles. Labs and Collaborations: Leads Harvard's AI4LIFE group and collaborates with industry partners like Fiddler AI. Active in policy discussions on AI regulation and societal impact.
Esteban G. Tabak is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. in Mathematics from MIT (1992) and a Hydraulic Engineer degree from the University of Buenos Aires (1988). His research spans fluid dynamics, data science, and optimization, with notable contributions to optimal transport theory, atmospheric and ocean modeling, and machine learning methodologies. He leads the Research and Training Group in Mathematical Modeling and Simulation at NYU. Research Interests include Data Analysis, Optimal Transport, Applied Mathematics, and Physics, particularly in fluid dynamics and geophysical flows. His work bridges theoretical advancements with practical applications, such as sea ice dynamics, internal waves, and turbulence modeling. Publications highlight innovations in density estimation, constrained optimization, and energy spectrum analysis of oceanic internal waves. Collaborations span disciplines, including biomedical applications (e.g., heart transplant diagnostics) and climate science. His methodologies, such as dual ascent algorithms and prototypal analysis, emphasize data-driven solutions to complex systems. Teaching includes courses on partial differential equations, fluid dynamics, and mathematical modeling. His work has been supported by grants addressing stratified flows, internal wave energy spectra, and turbulent mixing.
Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.
Ruth Fong is a Teaching Professor at the Department of Computer Science, Princeton University , where she teaches foundational and advanced AI/ML courses (COS324, COS126) while leading the Looking Glass Lab in explainable AI research. She collaborates closely with the Visual AI Lab and Professor Olga Russakovsky . Education: PhD in Visual Geometry Group, University of Oxford (advised by Andrea Vedaldi , funded by Rhodes Trust and Open Philanthropy ) MSc in Neuroscience, University of Oxford (with Rafal Bogacz , Ben Willmore , and Nicol Harper ) AB in Computer Science, Harvard University (with David Cox and Walter Scheirer ) Research Focus: Pioneering Explainable AI and ML Fairness , with emphasis on post-hoc model understanding, interpretable-by-design architectures, and human-AI interaction frameworks. Her work spans computer vision, self-supervised learning, and neuroscience-inspired methodologies. Publication Trends: Recent papers (2023-2025) analyze interactive explanations , concept salience , and gender artifacts in vision datasets . Earlier work (2017-2020) established foundational techniques in extremal perturbations , backpropagation saliency , and neural network interpretability . Scientific Awards: Princeton Engineering Council Teaching Award (2025) Keller Center Summer Course Development Grant (2025) CHI Honorable Mention Paper Award (2023) Open Philanthropy AI Fellowship (2018) Rhodes Scholarship (2015) Advising: Directly mentored 10 Princeton undergraduates on IW/senior theses projects spanning generative AI , medical imaging fairness , and interactive visualization tools . Grants include Princeton SEAS and Open Philanthropy funding for the Looking Glass Lab. Lab & Team: Leads the Looking Glass Lab with 6 graduate/postgraduate members including Rawand Aziz , Matthew Barrett , and Ben Wachspress . Collaborates with faculty across Princeton and Oxford.
Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.
Robert Östling is a Professor of Economics at the Stockholm School of Economics (SSE), affiliated with the Department of Economics and the Karl-Adam Bonnier Center for Governance within the House of Governance and Public Policy. He holds a PhD from SSE (2008) and previously worked at Stockholm University's Institute for International Economic Studies until 2018. His research focuses on applied microeconomics and behavioral economics, particularly examining how wealth impacts attitudes, behavior, and life outcomes using Swedish lottery data. He teaches econometrics, behavioral economics, and economics for MBA students, and serves as program director for SSE's MSc in Economics. Key roles include board membership of the Swedish Economics Association and the Expert Group on Public Economics (ESO), and membership in the scientific advisory board of the Swedish Consumer Protection Agency. He co-founded the popular economics blog Ekonomistas , recognized as 'Swede of the Year' in 2017. Awards include the Assar Lindbeck Medal (2021) and the Arnberg Prize (2009). His research spans wealth effects on labor supply, health, child development, and political behavior. Notable projects include studying lottery winners' long-term well-being and portfolio decisions. Teaching innovations include using animated GIFs for econometrics instruction. He has contributed to policy debates on economic reforms, consumer protection, and pandemic responses. Outreach activities include public writing and policy advisory roles. His research has been featured in outlets like the New York Times and Swedish media. He remains active in academic networks, including the Network for Evidence-Based Policy.
Sarah Wiegreffe is an Assistant Professor in the Department of Computer Science at the University of Maryland, affiliated with the CLIP laboratory. She holds a PhD from Georgia Institute of Technology (2022), advised by Prof. Mark Riedl. Previously, she was a postdoctoral researcher at the Allen Institute for AI (Ai2) and a member of H2Lab at the University of Washington, advised by Ashish Sabharwal and Hannaneh Hajishirzi. Her research focuses on mechanistic interpretability and transparency of language models, aiming to improve reliability, safety, and performance through natural language explanations. Notable contributions include the Mechanistic Interpretability Benchmark (MIB) and work on explaining model noncompliance. Awards include the Ai2 Outstanding Intern Award, Rising Star in Machine Learning/EECS/Generative AI, and recognition as an Outstanding Area Chair (ACL 2023, NeurIPS 2023). She has advised students like Alec Bunn and contributed to workshops like the Actionable Interpretability at ICML 2025. Education: PhD in Computer Science, Georgia Tech (2022); Master’s/Undergraduate degrees not explicitly stated. Active in organizing conferences (e.g., NAACL 2021 Publicity Co-Chair) and teaching (e.g., graduate seminar CMSC848R on LM interpretability).
Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.
Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.
Adriana I. Kovashka is an Associate Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. She serves as Chair of the Department of Computer Science. Her research focuses on computer vision, machine learning, and their intersections with human-machine communication and visual rhetoric analysis. Kovashka earned her BA in Computer Science and Media Studies from Pomona College (2008) and her PhD in Computer Science from the University of Texas at Austin (2014). She joined Pitt in 2015. Her work emphasizes improving image retrieval systems through semantic attributes, human-in-the-loop feedback, and crowd-sourced data. Notable projects include analyzing advertisements' persuasive strategies, developing object detection models resilient to domain shifts, and exploring multimodal learning with linguistic and visual inputs. She has secured significant grants, including NSF awards for geographic diversity in object detection (2023), CAREER funding for weak supervision methods (2021), and multiple Google Faculty Research Awards. Kovashka advises PhD students on topics ranging from multimodal intent modeling to domain generalization. She has organized workshops on advertising understanding and subjective attributes in vision conferences. Her lab's datasets, such as the 64,832-image ad repository and video ad collections, are widely used in vision research. Recent efforts include quantifying perceptual diversity in multilingual systems and mitigating bias in CNNs through shape regularization. Awards and recognitions include the NSF CAREER Award, Pitt's CRDF grants, and leadership roles in CVPR and WACV conferences. Her research bridges technical innovation with societal impact, addressing challenges in visual communication, ethical AI, and educational robotics.
Brian Calder is a Research Professor at the Center for Coastal and Ocean Mapping, University of New Hampshire, with a strong affiliation in Ocean Engineering and Earth Sciences. He holds a Ph.D. and M.S. in Image Analysis and Electronics Communications Engineering from Heriot-Watt University. His academic work is centered on advanced methods in seafloor characterization and hydrographic data processing. Ph.D., Image Analysis, Heriot-Watt University M.S., Electronics Communications Eng, Heriot-Watt University His research focuses on the development and application of computational techniques for seabed mapping, bathymetric uncertainty modeling, and autonomous ocean sensing. He integrates machine learning, signal processing, and remote sensing to improve the accuracy and reliability of marine geospatial data. His work supports navigation safety, coastal zone management, and deep-ocean exploration. Recent publications highlight trends in automated nautical chart generalization, trusted community bathymetry systems, and wireless ocean-of-things networks for volunteer data collection. His article portfolio reveals a strong emphasis on data quality, uncertainty quantification, and algorithmic innovation in hydrography and marine geodesy. Brian Calder has received multiple research grants, primarily from NOAA and the U.S. Navy, supporting projects such as IT support for NOAA personnel at UNH, development of bathymetric uncertainty models, and autonomous mapping using Saildrone technology. These grants reflect sustained funding and recognition in the field of hydrographic science. He teaches graduate courses including Seafloor Characterization , Seabed Mapping , and Doctoral Research , indicating active mentorship and academic leadership. His work is conducted within the Center for Coastal and Ocean Mapping, a leading institution in hydrographic research, where he collaborates extensively with experts like Yuri Rzhanov, Larry Mayer, and Christos Kastrisios.
