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Eva AscarzaView profile

Professor

• customer retention
• personalization and targeting
• marketing AI
• +12 more

Eva Ascarza is a Professor of Business Administration in the Marketing Unit at Harvard Business School (HBS) . She co-founded the Customer Intelligence Lab at HBS's D 3 Institute, focusing on responsible and effective customer data utilization. Research Interests include: Customer retention and churn analysis Algorithmic bias in marketing AI Field experimentation (A/B testing) for targeting optimization Customer lifetime value (CLV) modeling Dynamic personalization strategies Scientific Awards and Recognitions: 2023 Weitz-Winer-O'Dell Award (winner) 2022 Paul E. Green Award (PNAS publication) 2020 Marketing Science Institute (MSI) Scholar 2019 Erin Anderson Award for Emerging Female Scholar 2018 Paul E. Green Award (JMR publication) 2014 Frank M. Bass Outstanding Dissertation Award Her articles demonstrate cutting-edge applications of statistical modeling , Bayesian methods , and fair AI frameworks in modern marketing challenges.

Anthony KucheraView profile

Associate Professor

• Nuclear Structure
• Nuclear Reactions
• Neutron-rich Nuclei
• +3 more

Anthony Kuchera is an Associate Professor of Physics at Davidson College , specializing in the study of nuclear structure and reactions. His research focuses on exotic neutron-rich nuclei through radioactive beam experiments and radiation detection. Education : Ph.D. from Florida State University, B.S. from University of Wisconsin at Oshkosh Research : Kuchera investigates short-lived nuclei like 10 He and 26 O at the National Superconducting Cyclotron Laboratory (MoNA collaboration). His work uses high-speed rare isotope beams and neutron detection arrays to explore proton-neutron interactions. Experiments involve superconducting magnets to separate decay fragments for analysis. Teaching & Collaboration : He emphasizes undergraduate student involvement in cutting-edge research, preparing them for future careers through hands-on learning and interactive methods.

Knut ReinertView profile

Professor

• Algorithmic Bioinformatics
• Genomic Sequence Analysis
• Proteomics
• +7 more

Professor Knut Reinert is a leading figure in algorithmic bioinformatics at the Free University of Berlin, where he holds a professorship in the Department of Mathematics and Computer Science. He also maintains a significant affiliation with the Max Planck Institute for Molecular Genetics in Berlin, where he leads the Efficient Algorithms for Omics Data group. His research spans both institutions through the Reinert Lab, which focuses on developing novel computational approaches for biological data analysis. Reinert's educational background includes a Diploma in Computer Science (1994) and a Doctorate (Dr. Ing./Ph.D., 1999, with honors) from the Max-Planck-Institut for Computer Science and Universität des Saarlandes in Saarbrücken. Prior to his professorship, he worked as a computer scientist under Prof. Gene Myers at Celera Genomics in Rockville, USA (1999-2002). His primary research interests center on algorithmic bioinformatics with specific focus on developing novel algorithms and data structures for biomedical mass data analysis. This includes creating mathematical models for genomic sequence analysis and algorithms for mass spectrometry data to detect differential protein expression between normal and diseased samples. His work bridges the gap between computational tool development and practical biological applications, with particular emphasis on NGS and proteomics data. The publications and projects led by Prof. Reinert demonstrate a consistent focus on advancing computational methods in bioinformatics. His research spans genomic sequence analysis, RNA research (particularly long non-coding RNAs), parallel computing applications, and GPU acceleration for biological data processing. The work shows increasing sophistication in handling large-scale biological datasets through innovative algorithmic approaches. Intel® Parallel Computing Center designation for his lab CUDA Research Center status DFG funding of 530 thousand Euros for RNA research de.NBI funding of 2 million Euros BMBF funded projects 'LIVE-DREAM' and 'EssBar' Prof. Reinert leads multiple significant research projects and has established strong collaborations with international partners including Texas A&M, Kings College London, Eberhardt-Karls Universität Tübingen, Robert-Koch-Institute, and various Turkish institutions. His lab receives funding from major organizations including DFG, BMBF, and Intel. The Reinert Lab maintains active teaching responsibilities at FU Berlin, offering courses at BSc, MSc, and PhD levels using both traditional and innovative learning concepts like e-learning and inverted classrooms. The Reinert Lab consists of two interconnected research groups that work closely with experimental biologists and medical researchers to develop practical computational solutions for real-world biological problems. The lab has established itself as a key player in the German and international bioinformatics community through its development of the widely-used SeqAn library and participation in national infrastructure initiatives.

Jakob SchoefferView profile

Assistant Professor

• Responsible AI
• Explainable AI
• Human-Centered AI
• +6 more

Jakob Schoeffer is a tenure-track Assistant Professor in the Artificial Intelligence department at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen (Netherlands). His work focuses on the intersection of human decision-making and artificial intelligence, particularly in high-stakes contexts where fairness, transparency, and appropriate human-AI collaboration are critical. Dr. Schoeffer's research interests center on responsible and explainable AI, with specific focus areas including: Human-AI collaboration dynamics in decision-making processes Fairness perceptions and interventions in AI systems Appropriate reliance on AI recommendations Explainable AI techniques for high-stakes domains Transparency mechanisms that improve human-AI team performance Label indeterminacy issues in medical AI applications His recent publications (2023-2025) reveal a strong trend toward applying AI research in critical domains like healthcare (particularly neurological recovery prediction), while maintaining a rigorous focus on the human aspects of AI deployment. His work spans both theoretical foundations of human-AI interaction and practical implementations, often employing mixed-methods approaches that combine technical AI development with behavioral studies. Dr. Schoeffer actively collaborates with researchers across institutions including the University of Texas at Austin and has made significant contributions to top conferences in AI ethics, fairness, and human-computer interaction. His research has been featured in multiple news outlets and policy discussions, indicating real-world impact of his work on responsible AI development. Prior to his current appointment, Dr. Schoeffer was a Postdoctoral Research Fellow at the University of Texas at Austin. He received his PhD from the Karlsruhe Institute of Technology (KIT) in Germany with a dissertation titled "On the Interplay of Transparency and Fairness in AI-Informed Decision-Making." He also holds a master's degree in Operations Research from Georgia Tech and industry experience as a Senior Data Scientist at IBM.

Thomas HackerView profile

Professor

• Cloud Computing
• High-Performance Computing
• Operating Systems
• +3 more

Thomas Hacker is a Professor in the Department of Computer and Information Technology at Purdue Polytechnic Institute, Purdue University. His research focuses on cloud computing, high-performance computing, operating systems, computer networking, and cyber infrastructure . He holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Michigan, along with dual B.S. degrees in Computer Science and Physics from Oakland University. Education: PhD (Computer Science & Engineering), University of Michigan (2004) MS (Computer Science & Engineering), University of Michigan (1993) BS (Computer Science, Mathematics Minor), Oakland University (1989) BS (Physics), Oakland University (1989) Dr. Hacker's research spans cloud and grid computing, operating systems, and distributed systems , with applications in earthquake engineering data systems and AI-driven infrastructure analysis. His recent work explores extended layer 2 networking for bare-metal provisioning ( 2023 IEEE Cloud Summit ) and machine-supported bridge inspection using artificial intelligence ( Transportation Research Record, 2023 ). Notable scientific contributions include 15+ publications on topics like cyberinfrastructure for earthquake engineering, container-based virtualization, and data-intensive systems. His work has been recognized with awards such as the NSF CAREER Award (2010) and multiple Purdue Seed for Success Awards . Key Scientific Awards: NSF CAREER Award (2010) Purdue Seed for Success Awards (2008-2013) ASEE Information Systems Division Best Paper Award (2012) College of Technology Outstanding Faculty in Discovery Award (2010) He has held leadership roles at Purdue, including Department Head (2018-2021) and Interim Department Head (2011-2016) . His career spans academic positions at Indiana University, University of Michigan, and industry roles at Storage Technology Corporation.

Charles RahalView profile

Associate Professor

• Social Science Methodology
• Applied Social Data Science
• High-dimensional Econometrics
• +8 more

Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.

• Electromagnetic Compatibility (EMC)
• Lightning Electromagnetics
• Electromagnetic Time Reversal
• +4 more

Farhad Rachidi-Haeri is a Titular Professor and Head of the Electromagnetic Compatibility (EMC) Group at EPFL. His expertise spans EMC research, lightning electromagnetics, time reversal techniques, and fault location in power systems. He has led the EMC Group since the 1980s, with funding from the Swiss National Science Foundation, European Union, and private sector collaborations. His work involves international partnerships with institutions like the University of Toronto and KTH. Education: PhD in Electrical Engineering from EPFL (1991), M.S. from EPFL (1986). Roles: President of Swiss National Committee of URSI (2012–2020), Editor-in-Chief of IEEE Transactions on EMC (2013–2015), and member of the Academy of Sciences of Bologna Institute (2019). Research Focus: Lightning interaction with infrastructure, electromagnetic field modeling, time reversal applications for fault detection, and high-frequency transient analysis. His work bridges theoretical physics and engineering, addressing challenges in power systems, lightning protection, and aerospace. Awards: IEEE EMC Technical Achievement Award (2005), Berger Award (2016), and Distinguished Honorary Professor at Tsinghua University (2024). Over 400 peer-reviewed papers and 500 conference contributions reflect his prolific research output. Labs/Teams: Leads the EMC Laboratory at EPFL, focusing on experimental and numerical studies of electromagnetic phenomena. Collaborates with global networks on projects like Laser Lightning Control and structural lightning protection for wind turbines.

Emma PiersonView profile

Assistant Professor

• Artificial Intelligence
• Healthcare
• Health Equity
• +3 more

Emma Pierson is an Assistant Professor of Computer Science at the University of California, Berkeley, affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) , Computational Precision Health , and the Center for Human-Compatible AI . She focuses on developing data science and machine learning methods to address issues in healthcare equity and social inequality . Her work includes studies on race adjustments in clinical algorithms, migration patterns, and leveraging LLMs for health equity. Education: Ph.D. in Computer Science from Stanford University (2020), Master’s in Statistics from the University of Oxford. Prior roles include Assistant Professor at Cornell Tech, Senior Researcher at Microsoft Research, and data scientist at 23andMe and Coursera. Research Interests: Her research spans fair clinical prediction , sparse autoencoders , health disparities , and algorithmic fairness . Notable projects include the MIGRATE dataset for granular migration analysis and studies on policing disparities. Awards: NSF CAREER Award, Rhodes Scholarship, Hertz Fellowship, MIT Technology Review 35 Innovators Under 35, and Samsung AI Researcher of the Year. She writes a statistics blog ( Obsession with Regression ) and contributes to media outlets like The New York Times and FiveThirtyEight . Labs/Teams: Leads the MIGRATE project, a collaboration to analyze fine-grained migration data. Engages in interdisciplinary work across AI, healthcare, and social science.

Sarah MarsdenView profile

Researcher

• Mental Health
• Marginalized Populations
• Suicidality
• +4 more

Dr. Sarah Marsden is a Researcher affiliated with Lancaster University's Health Research Department within the Lancaster Medical School, part of the Faculty of Health and Medicine. Her role involves a Scoping Study on Suicide, Self-harm and Learning Disability. She focuses on qualitative research addressing mental health challenges in marginalized groups, particularly suicidality and self-harm behaviors among autistic individuals, neurodiverse populations, and those with learning disabilities. Her research emphasizes understanding systemic barriers in healthcare access through studies like 'Diversifying medicine by widening participation' and exploring self-harm dynamics in autistic adults. Publications reflect interdisciplinary approaches bridging medical sociology and clinical psychiatry. She holds no listed awards but contributes actively to public health discourse through blogs and peer-reviewed journals. As part of the Health Research team, her work aligns with broader institutional priorities in advancing equitable healthcare practices and improving outcomes for vulnerable groups.

Yusuf SayedView profile

Professor

• Global Education Policy
• Teacher Education
• Social Justice in Education
• +3 more

Yusuf Sayed is a Professor of Education at the University of Cambridge 's Faculty of Education. His expertise spans global education policy, equity, inclusion, and social justice, with over 40 years of international experience as a scholar, policy practitioner, and education rights activist. He holds qualifications including a PhD in Comparative Education and Policy from the University of Bristol and an MBA from the University of Brighton. Former roles include Professor at the University of Sussex, South African Research Chair in Teacher Education, and Founding Director of the Centre for International Teacher Education (CITE) at CPUT, South Africa. Professional memberships: Visiting Professor at Sussex, Honorary Professor at CITE, Editorial Board Member of key journals, and active in BAICE, CESI, SACHES, and SAERA. His research focuses on equity in education systems , particularly in the Global South, analyzing barriers to social and cognitive justice. Key areas include teacher education, public-private partnerships, inclusive pedagogy, and decolonizing education. Current projects address teacher pedagogy in South Africa, equity in PPP schooling, and the role of international development agencies. Yusuf has authored/co-authored over 20 books and 200+ academic works, engaging with diverse contexts across Africa, Asia, and beyond. His work critiques policy implementation gaps and advocates for marginalized voices in educational decision-making. Advising & Grants: Supervised over 40 doctoral students to completion, currently guiding postgraduates on education governance and policy. Teaches MPhil modules on globalization and education, and leads the Research Methods program. Labs/Teams: Affiliated with the Research in Education, Assessment, and Learning (REAL) Centre at Cambridge and the Centre for International Teacher Education (CITE).

Sean F. ReardonView profile

Professor

• Educational Inequality
• School Segregation
• Socioeconomic Disparities
• +6 more

Sean F. Reardon is the Professor of Poverty and Inequality in Education and a Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR) at Stanford University . He also holds a Professor (by courtesy) of Sociology . His work focuses on social and educational inequality, including residential and school segregation, racial/ethnic and socioeconomic disparities in academic achievement, and causal inference methods. Education: Ed.D., Harvard University, Educational Administration, Planning, and Social Policy (1997) M.Ed., Harvard University, Educational Administration, Planning and Social Policy (1992) M.A., University of Notre Dame, International Peace Studies (1991) B.A., University of Notre Dame, Program of Liberal Studies; Minor in Honors Mathematics (1986) Research Interests center on the causes and patterns of educational and racial inequality, the effects of school integration policies, and income inequality's social consequences. His Stanford Educational Opportunity Research Lab analyzes achievement gaps and segregation trends across U.S. school districts using large-scale datasets. Recent Publications highlight cross-national achievement gap comparisons, pandemic recovery disparities, and methodological advancements in segregation measurement. His work often integrates Data Sciences and Sociology to address policy-relevant questions. Awards Elected Member, American Academy of Arts and Sciences Elected Member, National Academy of Education Advising includes mentoring Doctoral Students like Tyler McDaniel and Postdoctoral Researchers like Farah Mallah. His Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis (2009–2021) trained future scholars in rigorous policy methods.

Ming YinView profile

Associate Professor

• Social Computing
• Crowdsourcing
• Human-AI Interaction
• +6 more

Ming Yin is an Associate Professor in the Department of Computer Science at Purdue University. Her research bridges human-computer interaction, applied artificial intelligence, computational social science, and behavioral sciences. She focuses on leveraging human behavior data to design intelligent systems that balance machine efficiency with human understanding, trust, and engagement. Education : PhD in Computer Science (Harvard University, 2017), B.E. in Computer Software (Tsinghua University, 2011) Previous Roles : Postdoctoral researcher at Microsoft Research New York City (2017–2018) Teaching : Courses on AI, Human-AI Interaction, Data Mining, and Human-Centered Computing Research Interests center on social computing, crowdsourcing, human-AI interaction, and ethical AI. She employs experimental and computational methods to study how human behavior can improve AI systems' design, fairness, and user trust. Her work has significant implications for gig economy platforms, decision support systems, and algorithmic accountability. Scientific Contributions include over 15 recent articles in top venues like CHI, IJCAI, and ACL. These works explore topics such as LLM-driven trust calibration, adversarial social influence, and ethical AI design. Her research has been recognized with the NSF CAREER Award Siebel Scholar (Class of 2017) Multiple Best Paper and Honorable Mention Awards at CHI, CSCW, and HCOMP Teaching Expertise spans courses like Introduction to Artificial Intelligence (CS 471), Human-AI Interaction (CS 592-HAI), and Data Mining (CS 573). She emphasizes project-based learning and designing systems for real-world problems, such as "learning in a new era" in her 2025 HCI course.

• Radar System Design
• Signal Processing
• MIMO Radar
• +6 more

Professor Kim Eun-hee is a faculty member in the Department of Defense Systems Engineering at Sejong University, specializing in advanced radar technologies and signal processing. Her work bridges theoretical research and practical applications in defense systems. Ph.D. in Mechanical Engineering (2004), KAIST M.Sc. in Engineering (1996), KAIST B.Sc. in Precision Engineering (1994), KAIST Her research focuses on radar system design, including airborne active phased array radar, automotive radar, broadband noise radar, and over-the-horizon radar. She explores waveform optimization, MIMO architectures, and signal processing algorithms to enhance radar performance in complex environments. Publications highlight her expertise in MIMO radar configurations, Doppler-insensitive waveforms, and machine learning integration for signal analysis. She leads industry-academic collaborations with organizations like Hanwha Systems and LIG Nex1. She contributes to technical committees, including the Sensor and Signal Processing Division of the Korean Society of Military Science and Technology. Her laboratory (Defense Radar Technology Laboratory) focuses on radar design, signal processing, and sensor integration.

Aria FallahView profile

Assistant Professor

• Evidence-Based Surgery
• Clinical Trials
• Pediatric Epilepsy Surgery
• +7 more

Dr. Aria Fallah, MD, is an Assistant Professor in Neurosurgery at the University of California, Los Angeles (UCLA), with secondary appointments in Health Policy and Management. His research focuses on Evidence-Based Surgery , Clinical Trials , and Meta-Analyses for pediatric epilepsy surgery outcomes, including prognostic tools and comparative effectiveness studies . He actively participates in international collaborations like the CTSI and has published extensively in journals such as Epilepsia , Neurology , and Clinical Neurophysiology . University: University of California, Los Angeles Department: Neurosurgery Email: afallah@mednet.ucla.edu Dr. Fallah’s research integrates machine learning and deep learning to analyze high-frequency oscillations in epilepsy, aiming to refine diagnostic and therapeutic strategies. His recent work includes developing tools like PyHFO for oscillation detection and HOPS calculator for predicting seizure freedom after hemispherectomy. His publications highlight collaborations across institutions in pediatric epilepsy surgery , hemimegalencephaly , and thalamic neuromodulation . While no explicit awards or student advisement are listed, his contributions span clinical trials , neuroimmunology , and global neurosurgery initiatives , including work in Haiti.

• Computational Creativity
• Music Intelligence and Personalisation
• Semantic Music Web

Dr Jacopo de Berardinis is a Lecturer (Assistant Professor) in Music AI, focusing on computational methods for music analysis and information retrieval. His work spans three key areas: Computational Creativity , Music Intelligence and Personalisation , and the Semantic Music Web , aiming to enhance musical understanding and education through AI-driven agents. Prior to his current role, he held positions at the University of Manchester (S+T+ARTS MUSAE project) and King's College London (EU H2020 Polifonia project, where he led the INTERLINK pilot). He earned a PhD in Machine Learning from the University of Manchester, specializing in the automatic evaluation of generative models for music composition. Scientific awards include the AHRC Innovation Accelerator Grant (2022-2026), supporting interdisciplinary research at the intersection of artificial intelligence and musicology. His work emphasizes personalized music experiences for well-being and advancing creative applications of AI in music.