Filipa Melo Lopes is a Lecturer in Social and Political Philosophy at the University of Edinburgh, affiliated with the School of Philosophy, Psychology and Language Sciences. She currently directs the Scottish Feminist Philosophy Network. Her research focuses on feminist philosophy, social theory, sexual ethics, and the work of Simone de Beauvoir, with recent attention to incel extremism, fashion ethics, and gender-based violence. Education: B.A. from Simon Fraser University (Canada), Ph.D. from University of Michigan (USA). Teaching includes undergraduate courses like 'Advanced Topics in Feminist Philosophy: Gender and Beauty' and PhD supervision in feminist ethics and political theory. She has supervised several doctoral students, including Lilith W. Lee (now Assistant Professor at Vrije Universiteit Amsterdam). Publications analyze themes such as misogynistic dehumanization, feminist fashion ethics, and Beauvoirian critiques of incel ideology. Her work bridges academic philosophy with public discourse on topics like sugar dating and incel violence. Active in feminist knowledge networks, she also writes for broader audiences on fashion, misogyny, and cultural criticism.
WonSook Lee is a tenured Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa’s Faculty of Engineering. Her expertise spans medical imaging, machine/deep learning, computer graphics, and computer vision. She earned her Ph.D. in Computer Science from the University of Geneva (Switzerland) and holds degrees from POSTECH (Korea) and NUS (Singapore). Before academia, she worked at Korea Telecom, Samsung Advanced Institute of Technology, and Eyematic Interfaces Inc. (USA). Her research focuses on applications such as virtual/augmented reality, MRI/CT/Ultrasound analysis, and 3D mesh modeling. She has authored over 130 publications, including 30+ journal papers, and serves on conference committees and editorial boards. Lee has secured major grants (NSERC, CFI, ORF) as Principal Investigator and contributed to global initiatives like South Korea’s National Research Foundation. Her lab explores cutting-edge techniques in medical imaging, AI-driven object detection, and multimodal systems. Notable projects include adversarial perturbation analysis for model robustness, cross-domain GANs for semantic segmentation, and real-time ultrasound-enhanced pronunciation training. She actively promotes interdisciplinary research in healthcare technology and autonomous systems.
Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.
Julia Kempe is a Silver Professor of Computer Science, Mathematics, and Data Science at New York University (NYU), holding joint appointments at the Courant Institute and the Center for Data Science (CDS). She serves as Director of the CDS and is on research leave at the CSD, ENS, Paris (2023–24). Her expertise spans interdisciplinary research in quantum computing, machine learning, and data science. She holds PhDs in Mathematics (UC Berkeley, 2001) and Computer Science (École Nationale Supérieure des Télécommunications, Paris, 2001), alongside advanced degrees in theoretical physics and mathematics from prestigious institutions in France and Austria. Research Interests: Data Science, Machine Learning (theoretical foundations and applications to physics), and past contributions to quantum computing. She focuses on robustness in machine learning models, adversarial examples, and interdisciplinary applications of physics-informed AI. Awards and Honors: Knight of the National Order of Merit (France, 2010), Femme en Or de la Recherche (France, 2010), ERC Starting Grant (2007, top-ranked in Europe), and numerous academic fellowships. She is a member of Academia Europaea (2018) and a Fellow of the Asia-Pacific Artificial Intelligence Association (2022). Grants and Leadership: Principal investigator of NSF NRT grants for CDS PhD programs, co-PI on NASA TCAN grants, and leader in NYU’s Senior Leadership Team. She designed NYU’s Data Science undergraduate programs and expanded interdisciplinary collaborations in machine learning and quantum computing. Labs and Teams: Directs the CDS, collaborates with the Courant Institute, and leads research initiatives in Paris. Her work bridges theoretical computer science, physics, and applied data science, emphasizing interdisciplinary innovation.
Lerrel Pinto is an Assistant Professor of Computer Science at New York University's Courant Institute, where he leads the General-purpose Robotics and AI Lab (GRAIL). His research focuses on enabling robots to generalize and adapt in unstructured environments through advancements in robot learning, decision making, and multimodal sensing. Before joining NYU, he completed a postdoc at UC Berkeley, a PhD in Robotics at Carnegie Mellon University, and an undergraduate degree in Mechanical Engineering at IIT Guwahati. Key research areas include large-scale robot learning, representation learning for sensory data, reinforcement learning for adaptability, and open-source robotics hardware. Notable achievements include the Sloan Fellowship (2025), NSF CAREER Award (2024), and Best Paper Awards at multiple robotics conferences. Pinto's lab has developed influential systems such as the AnySkin tactile sensing framework and the OPEN TEACH teleoperation system. Education highlights include a PhD from CMU (2019) under Abhinav Gupta, a postdoctoral stint with Alexei Efros and Pieter Abbeel at Berkeley, and undergraduate studies at IIT Guwahati. He has authored over 65 publications in top conferences like ICRA, NeurIPS, and CVPR. Pinto teaches courses on robotics, reinforcement learning, and AI at NYU. His service contributions include roles on program committees for ICML, NeurIPS, and IROS, as well as organizing workshops on topics like Dexterous Manipulation and Vision-Language Models for Robotics. His team actively collaborates through the GRAIL lab, with current projects exploring tactile sensing, zero-shot policy deployment, and multimodal robot learning systems. Ongoing research emphasizes bridging the gap between human and robotic dexterity through novel reward structures and adaptive control frameworks.
Prof Andrew Peacock holds the Bishop Wardlaw Professorship in the School of History at the University of St Andrews. His research focuses on Islamic and Middle Eastern history, with a particular emphasis on medieval Anatolia, Persian manuscripts, and the cultural exchanges in Southeast Asia and the Indian Ocean. He has supervised PhD students including Hebah Alheem, Ali Shapouran, and Weijie Xu. His recent publications explore Arabic literary culture in 17th-18th century Southeast Asia, Melaka’s historical sources, and the memoirs of Safavid ruler Shah Tahmasp I. Key projects include the ERC-funded 'IslamAnatolia' initiative (2012-2017) and a British Academy grant for studying Arabic language in early modern Southeast Asia. His work often bridges historical analysis with material culture, such as his edited volume on medieval Islamic inscriptions (2023). Prof Peacock’s research also engages with transnational themes, including Ottoman-Darfur diplomacy and the role of urban notables in medieval Anatolia.
Ryo Suzuki is an Assistant Professor in the Department of Computer Science at the University of Calgary's Faculty of Science. His research focuses on Human-Computer Interaction (HCI) and robotics, particularly in tangible user interfaces, swarm robotics, and shape-changing interfaces. He holds a PhD in Computer Science from the University of Colorado Boulder (2020) and has conducted research internships at Stanford University, UC Berkeley, the University of Tokyo, and Adobe Research. His educational background includes advanced coursework in robotics and HCI, with a strong emphasis on interdisciplinary innovation. He teaches courses such as Human-Computer Interaction II, Human-Robot Interaction, and Special Topics in Mixed Reality Application Design. Research interests include designing novel interfaces for AR/VR/MR systems, haptic feedback mechanisms, and collaborative robotics. His work often bridges physical and digital spaces, such as through projects like LiftTiles (shape-changing building blocks) and RoomShift (room-scale haptic environments). Key awards include the UIST 2020 Honorable Mention Paper Award, DIS 2019 Best Paper Award, and the Ministry of Internal Affairs and Communications in Japan Innovation Award. His research has been featured in IEEE Computer Graphics and Applications, TechXplore, and Wired. He actively explores future work in symbiotic AI systems, AI-in-the-loop AR applications, and scalable shape-changing robotics. His lab emphasizes hands-on prototyping and user-centered design principles.
Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.
David Doty is a Professor in the Department of Computer Science at the University of California, Davis . His research focuses on the intersection of molecular systems and computation , exploring how natural processes like chemical reactions , DNA nanotechnology , and self-assembly can perform computation. He also investigates connections to theoretical computer science, including distributed computing and algorithmic information theory . Doty's work bridges physics , chemistry , and biology through rigorous computational models like the Tile Assembly Model and Population Protocols . His research program includes software development ( scadnano , ppsim ), theoretical analysis, and collaborations with experimentalists. He teaches courses on theory of computation and molecular computing , and has advised numerous students in his research group. His publications cover topics such as algorithmic self-assembly , chemical reaction networks , and thermodynamic binding networks , with a focus on understanding fundamental computational and physical limits. Key software tools developed by his group include scadnano for DNA design and ppsim for population protocol simulations. Doty's recent research trends explore rate-independent chemical computing , error correction , and stochastic modeling in molecular systems. Current academic activity includes teaching ECS 120 (Undergraduate Theory of Computation), ECS 220 (Graduate Theory of Computation), and ECS 232 (Theory of Molecular Computation). He maintains a research lab in 2306 Academic Surge and continues to publish in leading conferences like DNA Computing and CMSB .
Steve Whittaker is Professor of Human-Computer Interaction at the University of California at Santa Cruz. He conducts interdisciplinary research at the intersection of social science and computer science, focusing on how technology affects human memory, communication, and personal information management. His current research explores human-centric AI systems, mental health technologies, and digital identity. His research interests center on designing interactive systems that support human needs in digital environments. He investigates how people manage digital information, remember personal experiences through lifelogging, and interact with conversational agents and social robots. His work emphasizes computational well-being, affective computing, and the social implications of technology use. He has made foundational contributions to the fields of personal information management (PIM), computer-mediated communication (CMC), and human-robot interaction. The recent publications reflect a strong trend toward mental health technology, human-AI interaction, and digital well-being. His work spans from theoretical models of emotion and memory to practical systems for mental health apps, chatbots, and immersive visualization. He frequently publishes in top-tier venues such as CHI, CSCW, and IUI, often in collaboration with researchers across disciplines. Lifetime Research Achievement Award from SIGCHI Fellow of the Association for Computational Machinery (ACM) Member of the CHI Academy Lasting Impact Award from ACM CSCW Best Paper Award at CSCW10 Best Paper Award at CHI07 Honourable Mention at ACM CHI 2020 Multiple best paper nominations at CHI, CSCW, and IUI MIT Siegel Prize Steve Whittaker has supervised numerous PhD and Master’s students, though specific names are not listed in the provided text. His research has been funded by major grants from NSF, NIH, and industry partners, enabling long-term studies on digital behavior and system development. He is Editor of the journal Human Computer Interaction and has authored over 200 peer-reviewed publications. His most recent book, The Science of Managing Our Digital Stuff (MIT Press), co-authored with Ofer Bergman, synthesizes decades of research on personal information management. He leads a vibrant research lab at UC Santa Cruz that focuses on human-centered computing, where students and collaborators work on projects involving AI, mental health, digital memory, and social interaction. The lab has produced influential work on lifelogging, email management, telepresence robots, and algorithmic transparency. The team employs mixed methods, combining qualitative studies with system design and evaluation.
Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.
Danyang Zhuo is an Assistant Professor of Computer Science at Duke University, Trinity College of Arts & Sciences, with expertise in datacenter/cloud computing and machine learning systems. He joined Duke in 2020 after postdoctoral research at UC Berkeley under Ion Stoica and a PhD at the University of Washington advised by Tom Anderson and Arvind Krishnamurthy. Education: PhD in Computer Science (University of Washington, 2019) His research focuses on improving cloud infrastructure through systems like Phoenix (application-level abstractions) and Phantora (GPU cluster simulation). Recent work explores LLM verification, tensor compression via video codecs, and fairness in LLM serving. His 15 most recent publications span operating systems, machine learning, and networked systems conferences like HOTOS, NSDI, SIGCOMM, and OSDI. Scientific honors include NSF CAREER Award (2023), USENIX Security Distinguished Paper (2023), and multiple industry research awards. He has secured major NSF grants for projects including "OS-Managed Remote Procedure Call" and "Campus-level RDMA Networking." At Duke, he advises PhD students and teaches courses such as Introduction to Operating Systems (CompSci 310) and Systems for Machine Learning (CompSci 590.05). His work appears in leading conferences and journals, with collaborations across institutions including UC Berkeley, University of Washington, and industry partners.
Marco Pedersoli serves as an Assistant Professor at École de technologie supérieure (ETS) in Montreal since February 2017, where he leads research in computer vision and machine learning. His work focuses on reducing computational costs and annotation requirements for deploying vision algorithms on embedded devices, positioning ETS at the forefront of Montreal's AI ecosystem. His academic journey includes: Ph.D. from Autonomous University of Barcelona (UAB) under Jordi Gonzàlez and Juan José Villanueva Post-doctoral research at INRIA Grenoble with Cordelia Schmid and Jakob Verbeek (2015-2016) Research at KU Leuven with Tinne Tuytelaars (2012-2015) Dr. Pedersoli's research tackles deep learning bottlenecks through weakly-supervised methodologies and computational efficiency innovations . His three core projects address: Reduced Supervision : Developing weakly/semi-supervised learning for images, video, audio and text Exploration Learning : Optimizing data selection in unstructured environments Efficient Computation : Accelerating deep learning training and inference These efforts enable vision algorithms to run on resource-constrained portable devices. Publication trends (2014-2022) reveal consistent focus on weak supervision (60% of works) and computational efficiency (30%), with recent expansion into medical imaging and multimodal emotion recognition. Key venues include CVPR, ICCV, NeurIPS and ECCV. His accolades include: Best Paper Award at ICIAR 2019 NVIDIA Titan X Pascal hardware donation Dr. Pedersoli actively mentors 18 graduate students across PhD and MSc programs, with notable placements at Huawei and Radio Canada. His lab secures competitive tax-free funding for projects with international collaborations, including Element AI and European institutions. Current openings emphasize Python/C++ proficiency and deep learning expertise. He leads a dynamic research group at ETS developing open-source tools for Roi-Pooling, weakly-supervised detection, and 3D object recognition, maintaining active GitHub repositories with community contributions. Recent WACV 2023 acceptances demonstrate ongoing productivity following medical leave.
Cecilia Mascolo is a Professor of Mobile Systems at the University of Cambridge , specifically in the Department of Computer Science and Technology . She co-directs the Centre for Mobile, Wearable System and Augmented Intelligence and is a Fellow of Jesus College, Cambridge . Her research focuses on mobile systems , machine learning for mobile health , and earable technology . She has been awarded prestigious grants such as the ERC Advanced Research Grant (2019-2025) and the EPSRC Open Research Fellowship (2025-2030). Currently on sabbatical at Harvard University , her work bridges systems and machine learning for health applications. Education: PhD in Computer Science from the University of Bologna, Italy. Previous Affiliation: Faculty at University College London before 2008. Her research spans mobile and wearable systems for health and behavior monitoring, focusing on on-device machine learning , uncertainty-aware models , and audio-based diagnostics . Key areas include federated learning , edge computing , and respiratory disease progression analysis via wearables. She explores earable technology for physiological monitoring, gait analysis, and even toothbrushing tracking using in-ear sensors. Her recent publications highlight advancements in earable-based health monitoring , including heart rate estimation , respiratory rate detection , and ECG analysis using machine learning. She emphasizes longitudinal health data from consumer devices, advocating for scalable diagnostics beyond traditional clinical standards. Scientific Awards: ERC Advanced Research Grant EPSRC Open Research Fellowship Best Paper Award - IEEE Percom 10-Year Impact Award - ACM Ubicomp Computer Laboratory Ring Hall of Fame Best Paper Award Student: Andrea Ferlini - ACM SIGMOBILE Doctoral Dissertation Runner-up She leads the Mobile Systems Research Laboratory , mentoring a team of 15 researchers (postdocs and PhD students), and has graduated over 25 PhD students. Her teaching includes Mobile Health courses at the University of Cambridge, and she serves as Director of Studies for Computer Science at Jesus College.
Vasant Dhar is the Robert A Miller Professor of Business and Professor of Data Science at the Leonard N. Stern School of Business at New York University. He serves as Director of Industry Relations and specializes in Technology, Operations, and Statistics. Joining Stern in 1983, Professor Dhar has established himself as a leading expert in artificial intelligence, data science, and financial technology. Professor Dhar's educational background includes: Ph.D. in Artificial Intelligence from the University of Pittsburgh (1984) M.Phil. from the University of Pittsburgh (1982) B.Tech. in Chemical Engineering from the Indian Institute of Technology, Delhi (1978) His research focuses on how risk influences our trust in AI systems, demonstrating the existence of an "automation frontier" that expresses a tradeoff between how often machines will be wrong and the consequences of their errors. Professor Dhar examines how innovations such as Artificial Intelligence impact our lives, and how we can create technology and policy for a better future in a world of increasingly intelligent machines. His work spans financial applications of AI, where he was among the first to bring machine learning to Wall Street in the 1990s, founding the machine-learning-based hedge fund SCT Capital Management. Professor Dhar's recent publications reveal a strong focus on the practical applications and societal implications of AI. His work addresses critical issues including AI reliability in financial document analysis, the governance of AI companies, ethical considerations in biometric payments, and the evolving relationship between humans and increasingly intelligent machines. His research demonstrates how AI is transforming various sectors while raising important questions about trust, accountability, and the future of work. Among his notable recognitions is the Robert A Miller Professorship, an endowed chair position at NYU Stern. His research has been funded by grants from industry and government agencies such as the National Science Foundation. Professor Dhar teaches courses on Systematic Investing, Data Science, Prediction, and Tech Innovation. He has written over 100 research articles and is the host of the "Brave New World" podcast, which explores how technology and virtualization in the post-COVID era is transforming humanity. He publishes fortnightly at vasantdhar.substack.com and is a frequent speaker in academic and industrial forums.
