Quanquan C. Liu is an Assistant Professor in the Department of Computer Science at Yale University, part of the School of Engineering & Applied Science. His research focuses on algorithms for large data, dynamic/distributed/parallel graph algorithms, and differential privacy. He holds a PhD from MIT's Theory Group, advised by Erik Demaine and Julian Shun, with postdoctoral experience at Northwestern University and MIT. Liu has authored over 50 publications in top venues like FOCS, SPAA, and STACS, and received a Best Paper Award at SPAA 2022. He advises a team of 12+ students, including PhD and undergraduate researchers. His service roles include PC membership for PPoPP, ESA, and SPAA, and coaching for the USA Computing Olympiad (USACO) and ICPC teams. Notable research contributions include advancements in parallel algorithms for graph problems and privacy-preserving techniques.
Professor Jennifer Whitty is an applied health economist at the University of East Anglia , affiliated with the Norwich Medical School and the Centre of Research Excellence in Telehealth . Her research bridges health economics, patient preferences, and health policy, with a focus on quality of life valuation and economic evaluation. Education: Doctor of Philosophy (Griffith University, 2008), Graduate Diploma in Clinical Pharmacy (University of Queensland, 2002), Bachelor of Pharmacy (University of Wales, 1992) Her work emphasizes person-centered methodologies , particularly discrete choice experiments, to evaluate patient and public preferences for healthcare delivery and outcomes. Key research areas include: Treatment burden and quality of life in chronic diseases like cystic fibrosis Economic evaluation of pharmaceuticals and health technologies Asset-based approaches to community health Priority-setting frameworks in health policy Prof. Whitty has secured over AU$27 million in research funding from organizations including the NHMRC and National Institute for Health and Care Research , and serves on editorial boards for Medical Decision Making and Applied Health Economics and Health Policy .
Bernadka Dubicka is a Clinical Professor and Honorary Clinical Chair in the Division of Neuroscience at the University of Manchester. She holds dual roles as a research lead at Pennine Care NHS Foundation Trust and as the child and adolescent mental health research lead for Health Innovation Manchester. She is the Editor-in-Chief of the Journal of Child and Adolescent Mental Health and previously served as Chair of the Child and Adolescent Faculty at the Royal College of Psychiatrists (2017–2021). Her academic qualifications include a BSc in Psychology and MBBs from University College London, followed by FRCPsych accreditation in child psychiatry. She earned a gold medal for her MD thesis on behavioral disorder implications in depression. Dubicka’s research focuses on adolescent depression, mood disorders, and brief interventions, including the IMPACT and STADIA trials evaluating psychological treatments and standardized diagnostic assessments. Her work also addresses technology’s role in youth mental health and the eco-crisis’s mental health impacts. Education: BSc Psychology, University College London MBBs, University of London MD Thesis (Gold Medal), University College London FRCPsych, Royal College of Psychiatrists Her research spans depression treatment efficacy, behavioral activation, and cross-national studies on pediatric bipolar disorder. She collaborates internationally and has led over 49 publications. Awards include a gold medal for her thesis and recognition as an RCPsych Fellow. Her work also emphasizes policy advocacy, such as addressing digital rights for children and mental health responses to climate change (e.g., COP26 contributions). She trains clinicians in brief psychosocial interventions and behavioral activation through academic programs and national courses. Grants & Funding: Co-investigator on the £1.1M HTA-funded STADIA trial evaluating remote mental health assessments. Principal investigator on the IMPACT trial, one of the largest adolescent depression trials globally. Labs/Teams: Collaborations include the University of Cambridge (IMPACT trial), University of Nottingham (STADIA), and international taskforces on treatment-resistant depression.
Jean-Philippe Brantut is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB), the Institute of Physics (IPHYS), and the School of Physics (SPH-ENS). He leads the Laboratory for Quantum Gases (LQG), a research group focused on quantum simulation with ultracold atomic systems. He also serves as a PhD program committee member for the Doctoral Program in Physics at EPFL. Research Interests: His work lies at the intersection of quantum optics, atomic physics, and condensed matter physics. He investigates strongly correlated fermionic systems, cavity quantum electrodynamics, mesoscopic physics, and quantum transport. His group pioneers the integration of Fermi gases with high-finesse optical cavities to simulate quantum devices and explore novel quantum matter. Recent Research Trends: His recent publications, appearing in Nature , Science , and Nature Physics , demonstrate a strong focus on engineering quantum many-body systems using photon-mediated interactions. Key themes include the realization of random spin models, observation of density-wave ordering, and the investigation of universal pair polaritons in strongly interacting Fermi gases. His earlier work laid foundations in quantum thermoelectricity and quantized transport in neutral matter. Scientific Awards: Latsis University Prize (2023) Physics Teaching Award at EPFL (2023) ERC Consolidator Grant (2022): Driven and Dissipative Quantum Simulators ERC Starting Grant (2016): Devices, engines and circuits: quantum engineering with cold atoms Fondation Sandoz Chair (2016) SNSF Ambizione Fellowship (2013) Advising and Grants: Brantut actively supervises multiple PhD students, including current students Gaia Bolognini, Tabea Bühler, Ekaterina Fedotova, Francesca Orsi, and Zeyang Xue, and has advised several successful graduates such as Victor Helson, Kevin Roux, Nick Sauerwein, and Timo Zwettler. His research is supported by major grants, most notably two European Research Council (ERC) grants, underscoring the significance and innovation of his work in quantum simulation and quantum engineering. Laboratories and Teams: He leads the Laboratory for Quantum Gases (LQG) at EPFL, which operates two main experimental setups: the Fermi gas experiment and the microscope experiment. The team includes post-doctoral researchers, PhD students, and visiting scientists, fostering a collaborative environment for advancing quantum science with ultracold atoms.
Emma Brunskill is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Education. She holds a PhD in Computer Science from MIT (2009). Her research focuses on reinforcement learning, educational technology, and healthcare applications, aiming to develop AI systems that support human learning and decision-making. Notable projects include AI tutoring systems, policy evaluation methods, and behavior change interventions using large language models. Her work bridges theory and practice, addressing challenges in off-policy evaluation, fairness-aware decision making, and scalable educational tools. Brunskill has contributed to foundational research in reinforcement learning algorithms and their applications in real-world scenarios such as healthcare, education, and human-AI collaboration. She also leads initiatives to improve equity and efficiency in educational technologies through data-driven approaches. Brunskill's research has been supported by grants such as the NSF RI: Small grant for data-efficient reinforcement learning. She actively explores the ethical implications of AI systems, particularly in healthcare and education settings. Her recent work emphasizes leveraging large language models (LLMs) for personalized feedback and simulated training environments, as seen in studies like GPTCoach and LLM-based counselor upskilling.
Susan A. Murphy is the Mallinckrodt Professor of Statistics and of Computer Science at Harvard University, with affiliations to the Kempner Institute. She leads the Statistical Reinforcement Learning Lab, focusing on developing algorithms to inform sequential decision-making in health, particularly for Just-in-Time Adaptive Interventions (JITAIs) and micro-randomized trials (MRTs). Her work is funded by NIH institutes, including NIDA, NHLBI, and NIBIB. Dr. Murphy has been awarded a MacArthur Fellowship (2013) and is a member of the National Academy of Medicine (2014) and the National Academy of Sciences (2016). Her research integrates statistical methods with computer science techniques to optimize mobile health interventions. She collaborates with d3Lab and mDOT on projects like HeartSteps and Sense2Stop, evaluating real-time treatment policies. Notable contributions include advancing MRT designs, sample size calculations, and reinforcement learning algorithms for personalized healthcare. Dr. Murphy advises a large team of postdocs, graduate students, and undergraduates, many of whom hold academic and industry roles globally. She emphasizes engagement in digital interventions, balancing personalization with ethical considerations. Her lab’s work spans algorithm development, clinical trial design, and causal inference, aiming to improve health outcomes through adaptive interventions.
Benedikt Bünz is an Assistant Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. He is also a co-founder and chief scientist of Espresso Systems, where he applies his research expertise to real-world blockchain solutions. His academic work bridges theoretical cryptography with practical blockchain implementations, focusing on enhancing privacy, security, and usability of decentralized systems. Dr. Bünz's research centers around applied cryptography, consensus mechanisms, and game theory as they relate to cryptocurrencies. His work spans zero-knowledge proofs, verifiable delay functions, secure multi-party computation, and privacy-preserving protocols. He has made significant contributions to Bulletproofs, a zero-knowledge proof system deployed on blockchains like Monero, and pioneered research in verifiable delay functions which are now part of Ethereum 2.0's design. His recent work focuses on recursive proof systems, accumulation schemes, and efficient verification techniques for blockchain scalability. His publication record shows a consistent progression from foundational cryptographic primitives to practical blockchain implementations. Recent work demonstrates increasing sophistication in recursive proof systems (ProtoStar, HyperPlonk), novel accumulation techniques (ARC, DewTwo), and foundational work on randomness generation (VDFs). His research consistently bridges theoretical cryptography with real-world blockchain applications, resulting in protocols that are both theoretically sound and practically implementable across multiple blockchain platforms. Dr. Bünz actively contributes to the academic community through teaching and mentorship. He teaches courses on cryptography of blockchains and computer security at NYU, providing students with hands-on experience in blockchain security and cryptographic protocols. His industry engagement through Espresso Systems demonstrates his commitment to translating academic research into practical solutions for the blockchain ecosystem.
Peter Aronow is a Professor at Yale School of Public Health , with appointments in the Department of Statistics and Data Science , Economics Department , and the Institute for Social and Policy Studies . His interdisciplinary work bridges political science, biostatistics, and epidemiology. Professor of Public Health (Biostatistics) Secondary appointments in Political Science and Economics Associate Professor in the Institute for Social and Policy Studies Dr. Aronow specializes in causal inference and statistical methodology, particularly in non-traditional field research contexts. His research encompasses: Design-based approaches to causal inference Complex experimental designs Social network analysis Survey methodology with incomplete data His recent publications focus on spatial experiments under unknown interference, bias correction in RCTs, and temporal validity challenges. While no formal awards are listed, his work is cited across disciplines including: Political Analysis Econometrics Biostatistical Modeling Observational Study Design
Roberta Sinatra is a Professor in Social Data Science at the University of Copenhagen, with part-time affiliations at ITU Copenhagen, ISI Foundation (Italy), and CSH (Austria). She co-founded the NERDS Research Group at ITU and co-leads the Pioneer Centre for AI in Copenhagen. Research Interests: Her work spans computational social science, network science, and data science, focusing on fairness in AI, scientific careers, and human mobility. Recent projects include analyzing child protection algorithms and modeling urban bicycle networks. Scientific Awards: ERC Consolidator Grant Villum Young Investigator Grant Complex Systems Society Junior Prize DPG Young Scientist Award for Socio- and Econophysics Sapere Aude: Starting Grant Publications: Her research, often in top-tier venues like Nature and Science , explores interdisciplinary themes, including AI ethics, gender disparities in science, and social network dynamics. Labs & Teams: She leads research initiatives at the Center for Social Data Science (KU) and co-founded the NERDS Research Group at ITU, fostering international collaboration in network science and AI ethics.
Univ-Prof. Dr. med. Malek Bajbouj serves as Director of the Institute for Affective Neuroscience and Emotion Modulation at Charité – University Medicine Berlin's Campus Benjamin Franklin (CBF), operating within the Department of Neurology, Neurosurgery and Psychiatry (CC 15). His position integrates clinical leadership with translational neuroscience research focused on severe mental illnesses. Dr. Bajbouj's research program centers on affective neuroscience and emotion dysregulation mechanisms in psychiatric disorders, particularly schizophrenia spectrum conditions and depression. He pioneers multimodal intervention approaches combining neuromodulation (tDCS), oxytocin augmentation, mindfulness therapies, and digital health tools. His work emphasizes translational biomarker development using neuroimaging, machine learning, and physiological stress parameter analysis to personalize treatment for treatment-resistant populations. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) novel treatment combinations for negative symptoms in schizophrenia (oxytocin + mindfulness, yoga therapy); (2) real-world implementation of neuromodulation (at-home tDCS protocols, technical efficacy monitoring); and (3) global mental health responses to crises (pandemic impacts on vulnerable groups, culturally adapted refugee interventions). His methodology consistently employs rigorous randomized controlled trials with embedded biomarker studies. As director of his eponymous institute, Dr. Bajbouj leads a multidisciplinary team conducting neuroimaging studies, clinical trials, and international collaborations focused on emotion modulation pathways. The institute coordinates research across CC 15's clinical infrastructure at CBF Building V, with particular emphasis on bridging laboratory neuroscience with clinical psychiatry through the DepressionDC and OXYMIND trial frameworks.
Dr. Frances Chen is a Professor and Area Coordinator in the Department of Psychology at the University of British Columbia (UBC), located on the traditional, ancestral, and unceded territory of the Musqueam People. She holds a PhD from Stanford University (2009). Her research integrates health psychology, social psychology, and neuroendocrinology to explore how social experiences influence mental and physical health. Key areas include the physiological impacts of loneliness, social support, and hormonal changes during puberty on adolescent development. Education: PhD, Psychology, Stanford University, 2009 Research Focus: Dr. Chen investigates how social interactions 'get under the skin' through studies on loneliness, stress, conflict negotiation, and hormonal mechanisms. Her work emphasizes interventions to enhance social connection and reduce health disparities. Recent Article Trends: Recent publications highlight interdisciplinary approaches, including genetic influences on depression, effects of near-infrared lighting on cognition, and longitudinal studies on adolescent hormonal contraceptive use. Her work bridges basic science and applied health outcomes. Awards & Grants: Michael Smith Health Research BC C2 Award (2022) Killam Faculty Research Fellowship (2019) Teaching & Learning Enhancement Fund Grant (2025) SSHRC Prosociality Project Funding (2023) Lab & Mentorship: Director of the Social Health Lab, she mentors graduate and undergraduate students, prioritizing equity and inclusion. Recent lab achievements include studies on teen health development and interventions to improve student success in psychology programs. Lab Initiatives: UBC Teen Health and Development Study (longitudinal hormonal/mental health tracking) NIR lighting health impact research (collaborative interdisciplinary project) Prosociality 'in the Wild' SSHRC project
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Prof. Baker Mohammad serves as Professor and Director of the System on Chip Lab in the Department of Computer and Information Engineering at Khalifa University. With over 15 years of industrial experience at Intel and Qualcomm designing microprocessors and DSP chips, he bridges academic research with real-world engineering challenges in high-performance computing and low-power systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin (2008) M.S. in Electrical and Computer Engineering, Arizona State University B.S. in Electrical Engineering, University of New Mexico Dr. Mohammad's research spans cutting-edge domains where VLSI design converges with AI acceleration and emerging memory technologies . His work pioneers Memristor applications in environmental sensing (radiation, vacuum, glucose) and neuromorphic computing, while advancing energy harvesting systems for wearable electronics. The integration of in-memory computing with security primitives represents a paradigm shift in hardware design, moving beyond traditional CMOS limitations. His publication trajectory reveals accelerating focus on self-powered neuromorphic systems and RRAM-based architectures, with recent work (2021-2023) emphasizing hardware-software co-design for edge AI. Over 75% of his recent publications involve cross-disciplinary collaborations spanning materials science, chemistry, and biomedical engineering. Notable scientific recognition includes: IEEE TVLSI Best Paper Award 2016 IEEE MWSCAS Myrill B. Reed Best Paper Award Qualcomm Qstar Award for Performance Leadership KUSTAR IP Excellence Award Multiple SRC Techon Best Session Papers As a dedicated mentor, he has supervised over 15 graduate students while securing competitive funding from Khalifa University, ADEK, Qualcomm, Tii, and UAE space agencies. His grant portfolio demonstrates exceptional translational impact, converting fundamental research in memristive devices into drone flight computers and medical sensors. Current projects integrate academic rigor with industrial deployment timelines. The System on Chip Lab operates as a multidisciplinary hub where semiconductor physicists collaborate with AI researchers to develop RISC-V-based secure processors and piezoelectric nanogenerator systems. Recent expansions include partnerships with Tii for aerospace applications and medical device startups for glucose monitoring technology.
Jie Bai is an Associate Professor of Public Policy at Harvard Kennedy School (HKS), focusing on firms and markets in developing economies. His research addresses barriers to firm growth, market frictions, and policy design for private sector development in regions like China, East Africa, and Southeast Asia. Methodologically, he combines randomized control trials, quasi-experiments, and structural modeling from industrial organization and international trade. He holds a Ph.D. in Economics from MIT (2016) and previously worked at Microsoft Research New England before joining HKS in 2017. His work emphasizes collaboration with governments and NGOs to evaluate industrial and trade policies. Key research themes include collective reputation in trade, environmental policy impacts, corruption dynamics, and child labor economics. His recent publications analyze China's dairy industry reputation, Vietnam's firm corruption patterns, and Zambia's product choice perceptions. He co-founded initiatives like the China Econ Lab and China and the Global Economy project to foster research on China's role in global economics. Teaching includes advanced microeconomic analysis and game theory. No grants or labs are explicitly listed in the provided texts.
Gustavo J. Bobonis is a Professor in the Department of Economics at the University of Toronto, with affiliations at the Munk School of Global Affairs and Public Policy. He holds a Ph.D. from the University of California, Berkeley (2005) and a B.A. from the University of Puerto Rico at Rio Piedras (2000). He co-directs the Forward Society Lab and is actively involved in research on development, labor, political economy, and economic history. Research Interests: His work focuses on development economics, particularly the impact of public policies on poverty, violence, education, and governance. He employs rigorous empirical methods, including field experiments and quasi-experimental designs, to analyze issues such as intimate partner violence, corruption, clientelism, and human capital accumulation. His research is geographically concentrated in Latin America and Puerto Rico. Recent Research Trends: His recent publications and working papers (2022–2025) demonstrate a strong focus on institutional reform, including anti-corruption audits, domestic violence courts, and education management. He also investigates long-term social impacts of welfare programs and climate adaptation strategies. His interdisciplinary approach bridges economics, public policy, and social science. Scientific Awards: U of T Department of Economics Faculty Award for Excellence in Undergraduate Teaching, 2015 John C. Polanyi Prize in Economic Science, 2009 National Academy of Education / Spencer Foundation Postdoctoral Fellow, 2008 Advising and Grants: While specific student names are not listed, he advises graduate students through the Honours Essay and research workshops. He leads collaborative research projects funded through grants and affiliations with J-PAL and BREAD, often involving large interdisciplinary teams. His work includes randomized evaluations and long-term follow-ups, suggesting sustained funding and research support. Labs and Teams: He co-directs the Forward Society Lab, which likely supports policy-relevant research on social development. He frequently collaborates with economists such as Paul Gertler, Marco Gonzalez-Navarro, Simeon Nichter, and Luis R. Cámara Fuertes. His affiliations with J-PAL and BREAD indicate integration into major global research networks focused on development and poverty alleviation.
