Thomas J. O’Dell is a Professor of Physiology and Associate Director of the Brain Research Institute at the University of California, Los Angeles (UCLA). His research focuses on synaptic plasticity mechanisms, particularly long-term potentiation (LTP) and depression (LTD), in the hippocampus. He investigates β-adrenergic signaling, NMDA receptor dynamics, and astrocyte calcium signaling in learning and memory processes. O’Dell’s work bridges molecular neurobiology with behavioral neuroscience, emphasizing how synaptic changes underlie cognitive functions. Research Interests: Neuronal plasticity mechanisms in hippocampal circuits Role of NMDA receptors in synaptic function and disease β-Adrenergic modulation of LTP Calcium signaling in astrocytes and its impact on synaptic transmission Grants & Funding: NIH R21MH115404 (Mechanisms of homeostatic plasticity) NIH R01NS060677 (Astrocyte calcium signaling in striatum) NIH R01MH060919 (NMDA receptor signaling in LTP) Labs/Teams: O’Dell leads a lab at the UCLA Brain Research Institute, collaborating on projects involving synaptic physiology, proteomics, and behavioral neuroscience.
Jeffrey J. Borckardt is a Professor in the Department of Psychiatry and Behavioral Sciences at the Medical University of South Carolina (MUSC). His primary research focuses on neuromodulation techniques, including transcranial direct current stimulation (tDCS), transcutaneous auricular vagus nerve stimulation (taVNS), and transcranial focused ultrasound (tFUS), applied to chronic pain management and opioid use reduction. He leads multidisciplinary clinical trials investigating the efficacy of these technologies in diverse populations, including veterans and patients with hypermobile Ehlers-Danlos syndrome. Key research areas include the neurobiological mechanisms of pain, the integration of psychological interventions like cognitive-behavioral therapy (CBT), and the development of telehealth-delivered therapies. His work emphasizes improving healthcare access disparities and optimizing interprofessional teamwork. He collaborates across institutions, including the VA and international ENIGMA initiatives, to advance translational neuroscience. Recent studies explore the synergistic effects of tAN and tDCS with behavioral interventions, pain biomarkers in spine treatments, and real-time fMRI neurofeedback for addiction. His clinical trials often involve double-blind, sham-controlled designs to rigorously evaluate neuromodulation efficacy. Institutional Affiliations: MUSC College of Medicine, VA Medical Center Research Themes: Neuromodulation, Pain Rehabilitation, Opioid Crisis Mitigation
Rainer Gemulla is a Professor of Practical Computer Science I: Data Analytics at the University of Mannheim, heading the Data and Web Science Group within the School of Business Informatics and Mathematics. He has been a W3-Professor at the University since 2014, following positions as a senior researcher at Max-Planck-Institut für Informatik (2010-2014) and postdoctoral researcher at IBM Almaden Research Center (2008-2010). His research focuses on machine learning with structured and semi-structured data, particularly knowledge graphs, and developing efficient systems for data-intensive processing. Professor Gemulla's research spans multiple areas including machine learning with structured data (relational data), machine learning with semi-structured data (multi-relational graphs), combining these approaches with unstructured knowledge (text), and developing efficient, scalable methods for data-intensive processing. His work bridges theoretical foundations with practical implementations, as evidenced by numerous open-source software projects including LibKGE, DistKGE, and AdaPM. His recent publications show a strong trend toward knowledge graph embeddings, parameter server architectures, and efficient training methods. The research demonstrates increasing focus on scalability challenges in graph learning, with particular attention to hyperparameter optimization, dynamic resource allocation, and benchmarking methodologies. His work consistently addresses the practical challenges of implementing machine learning systems at scale. Distinguished Reviewer Award at SIGMOD, 2025 Distinguished PC Member Award at EDBT, 2023 Outstanding Reviewer Award at NeurIPS, 2021 Junior-Fellow of the Gesellschaft für Informatik (GI), 2013 IBM's 2011 Pat Goldberg Memorial best paper award Best paper of NIPS 2011 Biglearn workshop Professor Gemulla actively mentors PhD students and has supervised numerous successful doctoral candidates. His leadership extends to administrative roles including Head of examination board for MSc Business Informatics since 2017, and previously serving as Study dean of the WIM faculty (2016-2019) and CIO of University of Mannheim (2022-2024). His research is supported by grants including AWS in Education Research Grant Award (2013) and Google Focused Research Award (2011). The Data and Web Science Group develops multiple open-source software projects including LibKGE (knowledge graph embedding library), DistKGE (multi-GPU training), AdaPM (adaptive parameter manager), Lapse (parameter server), and various tools for information extraction and sequence mining. The group maintains active collaborations with industry partners and academic institutions worldwide, particularly in the areas of knowledge graph research and scalable machine learning systems.
Juan Francisco Jiménez-Alcázar is a Professor at the Universidad de Murcia, affiliated with the Department of Prehistory, Archaeology, Ancient History, Medieval History and Historiographic Sciences and Techniques within the Faculty of Arts and Humanities. His research focuses on Medieval History, Digital Humanities, and the intersection of history with video games and digital media. He holds a Doctorate from the Universidad de Murcia (1993) with a thesis on 'Espacio, poder y sociedad en Lorca (1460-1521)'. His work explores historical representation in modern media, particularly analyzing how video games depict medieval soundscapes, warfare, and cultural frontiers. Key themes include the study of linguistic diversity in medieval Spain, governance structures in frontier regions like Murcia, and the impact of historical epidemics such as the 1507-08 plague in Murcia. Recent publications include books on Digital Humanities and video games (2020), medieval warfare simulations, and interdisciplinary studies on migration and historical identity. He collaborates with institutions like CONICET and the CNR in Italy, and his research contributes to both academic and public understanding of medieval history through digital tools and cultural analysis.
Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.
Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.
Benyamin Davaji serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, where he joined in January 2022. He holds additional appointments as a Center Member of The Plastics Center and Core Faculty of the Institute for NanoSystems Innovation (NanoSI). His work bridges microsystems engineering, nanofabrication, and data science to develop next-generation sensing technologies. Dr. Davaji's educational background includes: Postdoctoral Associate in Electrical and Computer Engineering at Cornell University (2016-2021) Ph.D. in Electrical Engineering from Marquette University (2016) His research centers on integrated microsystems with emphasis on mechanical wave-based sensing and computation, ultrasound transducers, bio-interfaces, and microcalorimetry. The Autonomous Integrated Microsystems (AIMS) Laboratory combines physics with AI/ML to invent novel sensors and computational devices through advanced nanofabrication. Key thrusts include power-sustaining architectures and analog/digital computational integration. Recent publications (2024-2025) reveal strong trends in MEMS/NEMS optimization using digital twins, plasmonically enhanced infrared detection, ferroelectric actuators for high-speed scanning, and ultrasound-enabled metrology. His work increasingly integrates machine learning for design automation and process optimization across semiconductor manufacturing and flexible hybrid electronics. Dr. Davaji advises graduate students including Yilmaz Arin Manav (PhD'28), who won the FLEX 2024 Future Student Poster Award. He has secured over $3 million in competitive funding as PI/Co-PI, including a $550k NSF grant for MEMS actuators, $330k NSF grant for quantum detectors, and $2M DARPA grant for inertial sensors. He directs the interdisciplinary AIMS Laboratory focused on MEMS, ultrasound, and calorimetric technologies. The lab collaborates extensively with NanoSI and The Plastics Center, developing autonomous microsystems for biomedical, environmental, and industrial applications through advanced manufacturing techniques.
Sarath Chandar is an Associate Professor at Polytechnique Montréal and Core Faculty Member at Mila, the Quebec AI Institute. He holds a Canada CIFAR AI Chair and Canada Research Chair in Lifelong Machine Learning. His research focuses on developing interactive learning algorithms for continual and lifelong learning, with expertise in deep learning, reinforcement learning, and natural language processing. Education: Ph.D. in Computer Science, University of Montreal (advisor: Yoshua Bengio) M.S. in Computer Science, Indian Institute of Technology Madras (advisor: Balaraman Ravindran) Research Themes: Continual Learning and Lifelong Learning Deep Reinforcement Learning Optimization for Deep Networks Natural Language Processing AI for Scientific Discovery Notable Contributions: Founder of the Conference on Lifelong Learning Agents (CoLLAs) Developed Chandar Research Lab (CRL), focusing on adaptive learning algorithms Contributions to model-based reinforcement learning and bias mitigation in AI systems Awards & Grants: Canada CIFAR AI Chair Canada Research Chair Tier 2 MITACS-funded projects on reinforcement learning applications Lab & Collaboration: CRL collaborates with academic/industrial partners (e.g., IBM, Samsung) Hosts annual symposium showcasing research in AI, optimization, and multi-agent systems
Anders Sejr Hansen is an Assistant Professor of Biological Engineering at MIT, leading the Hansen Lab focused on understanding 3D genome structure and its functional implications. He holds a PhD from Harvard University and completed postdoctoral training at UC Berkeley. His research integrates advanced imaging, genomics, and computational methods to study chromatin dynamics, enhancer-promoter interactions, and their roles in gene regulation across health and disease. Education: Bachelor's/Master's in Chemistry, University of Oxford (2010) PhD in Chemistry and Chemical Biology, Harvard University (2015) Postdoctoral Research, UC Berkeley (2015–2020) Research Interests: His work spans molecular mechanisms of genome organization, development of novel microscopy techniques (e.g., MINFLUX, expansion microscopy), and computational models for 3D genomics. Key areas include chromatin dynamics, loop extrusion by cohesin/condensin, and the impact of 3D structure on gene expression in cancer and aging. Awards: NIH K99 Pathway to Independence Award (2019) NIH Director’s New Innovator Award (2020) Pew-Stewart Scholar for Cancer Research (2021) NSF CAREER Award (2024) NIH Director’s Transformative Research Award (2024) Advising & Grants: Hansen mentors PhD students and postdocs, including notable advisees Viraat Goel and Domenic Narducci. His lab has secured major grants from NIH, NSF, and private foundations, supporting interdisciplinary projects in imaging, genomics, and synthetic biology. Labs/Teams: The Hansen Lab at MIT collaborates with institutions globally, advancing technologies like Region Capture Micro-C (RCMC) and deep learning models (e.g., Cleopatra) for high-resolution genome mapping. The lab also explores synthetic biology approaches to engineer genome structures.
Arya Mazumdar is a tenured Professor of Data Science and Computer Science at the Halıcıoğlu Data Science Institute (HDSI) , part of the School of Computing, Information and Data Sciences at the University of California San Diego (UCSD). He also holds affiliations with the Computer Science and Engineering and Electrical and Computer Engineering departments at UCSD. Previously, he was an Assistant and Associate Professor at the University of Massachusetts Amherst (2015–2021), and held a postdoctoral position at MIT (2011–2012). He is an IEEE Distinguished Lecturer (2023–2024) and recipient of the NSF CAREER Award (2015–2020). Education : PhD in 2011 from the University of Maryland, College Park (advisor: Alexander Barg) Postdoctoral scholar at MIT (2011–2012, advisor: Greg Wornell) Internships at IBM Almaden (2010) and HP Labs (2008) Research Interests : Focus on algorithmic and statistical aspects of machine learning, error-correcting codes, optimization, signal processing, and distributed systems. Key areas include clustering algorithms, compressed sensing, federated learning, and information-theoretic foundations of data science. He has contributed to theoretical guarantees for learning mixtures, distributed optimization, and robust coding schemes. Recent Articles Trends : Recent work emphasizes distributed optimization (e.g., vqSGD, Byzantine-resilient algorithms), sparse recovery in high-dimensional models, and theoretical foundations of learning (e.g., support recovery, parameter estimation). His research bridges information theory and machine learning, with applications in storage systems and large-scale data processing. Awards & Roles : 2020 EURASIP JASP Best Paper Award Co-PI and co-leader of the NSF AI Institute for Learning-Enabled Optimization at Scale Editorships: IEEE Transactions on Information Theory and Foundations and Trends in Communications Grants & Labs : Leads the EnCORE Institute as UCSD Site Lead, focusing on theoretical perspectives of large language models and computational-statistical gaps. Active in organizing workshops on topics like LLMs, clustering, and distributed optimization. His research is funded by NSF and industry collaborations. Teaching : Courses include Algorithms for Data Science , Probability and Statistics for Data Science , and Coding Theory , emphasizing foundational theory and scalable methods.
Viktor Prasanna is the Charles Lee Powell Chair in Engineering and Professor of Electrical and Computer Engineering and Computer Science at the University of Southern California. He holds courtesy appointments in Computer Science and leads the Center for Energy Informatics, focusing on interdisciplinary research linking energy technologies, computer science, and engineering. Education: BE in Electronics (Bangalore University), ME (Indian Institute of Science), PhD in Computer Science (Pennsylvania State University) His research spans reconfigurable computing, FPGA accelerators, parallel and distributed systems, and big data applications. He has pioneered high-performance architectures and algorithms using FPGAs, impacting domains like networking, security, HPC, and machine learning. Prasanna has published over 600 papers, received 22 best paper awards, and secured >$50M in grants. His work emphasizes energy-efficient computing, with recent grants totaling $12.9M (2016–2021). His h-index is 73, with 23,454 total citations. Scientific Awards: IEEE Fellow, ACM Fellow, AAAS Fellow, W. Wallace McDowell Award, multiple Distinguished Alumnus Awards He has advised over 70 doctoral students and led major centers including CiSoft (Big Data in oilfield tech) and CAST. His editorial roles include Editor-in-Chief of IEEE Transactions on Computers and Journal of Parallel and Distributed Computing.
Dr. Tamás Koltai is a Professor and Dean at the Faculty of Economics and Social Sciences of Budapest University of Technology and Economics (BME). He leads the doctoral school's Specialization Group in Production Management. His roles include overseeing academic programs and research in production management, operations research, and efficiency analysis. Education: Doctor of the Hungarian Academy of Sciences (2016) Dr. habil. (2000), Budapest University of Technology and Economics Candidate of Technical Sciences (1987), Hungarian Academy of Sciences M.Sc. in Mechanical Engineering (1983), BME Faculty of Mechanical Engineering Research Interests: Dr. Koltai focuses on production management optimization, including the application of Data Envelopment Analysis (DEA), sensitivity analysis in mathematical models, and the integration of robotics in assembly lines. His work bridges theoretical models (e.g., MILP/CP optimization) with practical industrial challenges, particularly in healthcare efficiency and educational management. Notable Awards: IEOM Society Teaching Excellence Award (2021) BME GTK Faculty Memorial Medal (2016) János Susánszki Award (2013) Széchenyi Professorship Scholarship (1999–) Teaching & Leadership: He has held visiting roles at the University of Seville (1990–1992) and the University of Michigan (1988/89). His teaching excellence is recognized through awards and his contributions to business simulation education. His research often collaborates with industry partners to address real-world operational challenges. Labs/Teams: Leads the Production Management Specialization Group and contributes to interdisciplinary teams focusing on manufacturing efficiency and healthcare operations within BME.
Dr. Annemieke Apergis-Schoute is a Lecturer in Psychology (Teaching and Research) at Queen Mary University of London, affiliated with the School of Biological and Behavioural Sciences and the Centre for Brain and Behaviour. Her research focuses on obsessive-compulsive disorder (OCD), cognitive flexibility, prefrontal mechanisms, and student mental health. She holds a PhD from New York University, where her early work explored threat learning in rats and humans using fMRI. Subsequent roles at the University of Cambridge and UCL expanded her expertise into clinical OCD studies, including deep brain stimulation trials. Her research investigates how prefrontal control deficits and inflexible learning contribute to OCD and related disorders, with a focus on adolescents and young adults. Collaborations include pioneering DBS studies targeting basal ganglia regions to reduce compulsive behaviors. She also explores connections between brain function, interoception, and mental health in daily decision-making contexts. Key publications analyze reversal learning deficits in OCD under serotonergic modulation, neuroimaging markers of cognitive rigidity, and developmental trajectories of compulsivity. Her work bridges basic neuroscience with translational clinical interventions, emphasizing early intervention strategies and cognitive-behavioral approaches.
Ruth Kelly is a Lecturer in the Department of Politics at the University of York, within the School of Social and Political Sciences. She holds a PhD in Politics and is actively engaged in research on feminist theory, human rights, and creative activism. Her work bridges academic inquiry with practical engagement through toolkits, workshops, and collaborative projects. Her research interests include: Feminist Theory and Cultural Politics Transnational Solidarities and Postcolonial Narratives Digital Humanities and Ethnographic Research Creative and Artful Activism Language and Socio-political Imaginaries Equity in Academic Partnerships The thematic trends in her publications reveal a consistent focus on narrative, memory, and resistance. Her recent articles explore the intersections of folklore and politics, textile art and human rights, and digital storytelling as tools of empowerment. She emphasizes culturally situated knowledge and challenges Eurocentric frameworks through comparative, interdisciplinary research. Ruth leads significant research projects funded by The British Academy and internal university grants, focusing on language change in Bangladesh and equity in non-academic partnerships. She organizes the York Human Rights Workshop, fostering interdisciplinary dialogue on justice and memorialization. Her work includes mentoring early-career researchers and promoting inclusive research practices. She is actively involved in academic service, including peer review, journal contributions, and organizing symposia. Her projects emphasize collaboration across disciplines and with non-academic partners, reflecting a commitment to socially engaged scholarship.
Professor Andrew Jackson of Newcastle University is a leading researcher in neuroscience and neuroengineering, focusing on neural interfaces, optogenetics, and epilepsy. His work spans brain-computer interfaces, spinal cord stimulation, and sleep-dependent memory processes. Key research areas: closed-loop optogenetic systems, motor cortex dynamics, cerebellar-neocortical communication, and seizure pathway analysis. Collaborations with experts like Dr. Boubker Zaaimi, Professor Yujiang Wang, and Dr. Wei Xu. Develops implantable low-power platforms for real-time neural monitoring and stimulation. His recent publications highlight advancements in neuroprosthetics for motor recovery post-stroke/spinal injury, cortical chloride homeostasis in epilepsy, and mechanisms of brain self-regulation during movement and sleep. Technologies pioneered include flexible neural electrodes, temperature self-monitoring optoelectronics, and wearable bioelectrical signal systems. His work integrates computational neuroscience with clinical applications in motor disorders and epilepsy.
