Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.
Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award
Connor Coley is the Henri Slezynger (1957) Career Development Assistant Professor at the Massachusetts Institute of Technology (MIT) School of Engineering. His research bridges chemistry and machine learning, focusing on autonomous molecular discovery, predictive chemistry, and laboratory automation. Education: Ph.D., MIT (2019) M.S.CEP., MIT (2016) B.S., Caltech (2014) Research Interests: Dr. Coley’s work centers on domain-informed machine learning for chemistry, computer-aided molecular design, and autonomous laboratories. Key themes include predictive modeling of chemical reactivity, optimization of synthesis pathways, and integration of AI with experimental data for drug discovery and materials science. Publications: His recent articles highlight advancements in AI-driven reaction prediction, molecular representation learning, and laboratory automation. Trends include applications of Bayesian optimization, contrastive learning, and diffusion models to chemical discovery. Scientific Awards: Camille Dreyfus Teacher-Scholar Award (2025) James W. Swan Outstanding Faculty (2025) Schmidt Futures AI2050 Early Career Fellow (2022) NSF CAREER Award (2021) Forbes 30 Under 30: Healthcare (2019) Software & Tools: He leads the open-source ASKCOS software suite for synthesis planning, adopted by 35,000+ chemists and deployed at 15+ pharmaceutical companies. His team also develops tools for metabolomics and molecular representation learning.
Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.
Senior Lecturer Outi Salo-Ahen is affiliated with Åbo Akademi University's Faculty of Natural Sciences and Engineering , Department of Pharmacy. Her research focuses on computational pharmacology, drug design, and pharmaceutical chemistry, particularly targeting chemokine receptors (CCR5/CXCR4) and transient receptor potential channels (TRPA1) for therapeutic applications. Doctor of Pharmacy (2006, University of Kuopio/UEF) MSc in Pharmaceutical Chemistry (2001, UEF) BSc in Pharmacy (1999, UEF) University Pedagogy Modules 1-5 (2012-2015) Her work contributes to UN Sustainable Development Goals through education and pharmaceutical innovation . Recent research trends include: Antimicrobial resistance solutions TRPA1 channel modulation Nanotechnology-enabled drug delivery Multi-target HIV-1 inhibitors 3D printing of biocompatible materials Computational analysis of nucleic acid frameworks She actively supervises doctoral projects, serves on assessment panels, and leads collaborations like Nordic Pharmaceutical Translation and Innovation. Her 60+ publications demonstrate expertise in molecular modeling and drug discovery.
Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.
Antti Poso is a Professor of Drug Design at the University of Eastern Finland (Kuopio), affiliated with the School of Pharmacy under the Faculty of Health Sciences. His research focuses on computer-aided molecular design, particularly targeting anti-cancer drugs and anti-microbials. Key projects include the EDCMET project (2019–2024) and the GeneCellNano Flagship (2020–2028). He leads the Molecular Modeling and Drug Design Research Group, specializing in QSAR analysis, kinase inhibition profiling, and systems-level drug response modeling. Recent work includes studies on SARS-CoV-2 inhibitors, endocrine disruptors, and bacterial pathogenesis. His findings bridge chemical structure with biological outcomes, leveraging computational tools like CCA and molecular dynamics simulations. Collaborations span medicinal chemistry, pharmacology, and systems biology, contributing to both academic and applied drug discovery efforts. Education: Not explicitly stated in texts; assumed to hold advanced degrees in pharmacy or chemistry. Research Themes: Drug design, molecular modeling, QSAR, computational biology, and anti-infective agents. Key Contributions: Over 150+ publications, including influential works on chemoinformatics-driven drug response analysis and structure-based inhibitor design. Publications highlight advancements in kinase inhibitors, anti-microbial strategies, and viral hijacking mechanisms. His work emphasizes translating computational insights into therapeutic solutions for cancer, infectious diseases, and metabolic disorders.
Pia Vogel is a Professor in the Department of Biological Sciences at Southern Methodist University (SMU), where she leads research on nucleotide-binding proteins using Electron Spin Resonance spectroscopy and molecular modeling. Her work focuses on elucidating structural mechanisms in ATP synthase, multidrug resistance transporters, and calcium channels with biomedical applications in cancer therapy and neurodegenerative diseases. Education: Ph.D., University of Kaiserlautern Dr. Vogel's research program investigates three interconnected domains: the rotary mechanics of FoF1-ATP synthase (particularly the external stalk subunit b-dimer), the structural basis of multidrug resistance in P-glycoprotein and MRPs, and ATP-regulated calcium release via ryanodine receptors. Her laboratory employs site-specific spin labeling, ESR spectroscopy, and computational modeling to resolve protein dynamics and interactions at molecular resolution, contributing to understanding energy transduction in ATP synthase and mechanisms of drug resistance. Analysis of her 15 most recent publications (2020-2025) reveals a dominant focus on developing and characterizing P-glycoprotein and BCRP inhibitors to overcome chemotherapy resistance in cancer. These studies integrate computational screening, ATPase assays, and cell-based models to evaluate inhibitor efficacy, with emerging applications in Alzheimer's research through amyloid-β transport studies. The work demonstrates consistent methodological synergy between biophysical characterization and therapeutic development. Dr. Vogel maintains an active research group supported by sustained funding, evidenced by continuous publication output and laboratory infrastructure. Her team employs multidisciplinary approaches spanning biophysics, biochemistry, and computational biology to address fundamental questions in membrane protein function. Her laboratory facilities in DLSB 221 include specialized Electron Spin Resonance instrumentation and dual Linux computing clusters for molecular dynamics simulations. The research environment supports collaborative projects extending her work into cancer therapeutics and neurodegenerative disease mechanisms through partnerships with clinical and computational researchers.
Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health
Dr. Ed E. Moret is an Associate Professor of Computational Medicinal Chemistry at Utrecht University, where he serves as Managing Director of the Utrecht Institute for Pharmaceutical Sciences. He is a member of the Departmental Executive Board and Chair of the Board of Examiners of the School of Pharmacy. His academic career spans over three decades with significant contributions to pharmaceutical sciences. Utrecht University, Utrecht Institute for Pharmaceutical Sciences School of Pharmacy, Department of Chemical Biology and Drug Discovery Managing Director since January 2010 Dr. Moret's educational background includes completing Gymnasium-b at Gymnasium Camphusianum in Gorinchem in 1979, followed by pharmacy studies at Utrecht University until 1988. He earned his PhD in 1993 with research on calculations and simulations of DNA-alkylating cytostatics under supervision of Prof. L.H.M. Janssen and Prof. J.P.A.E. Tollenaere. He also conducted postdoctoral research at the Scripps Research Institute with Prof. A.J. Olson. His primary research interests focus on molecular recognition, particularly in auto-immune diseases, with expertise spanning computational medicinal chemistry, computer-aided drug discovery, cheminformatics, and bioinformatics. Dr. Moret's work bridges the gap between theoretical calculations and experimental validation in drug design. His research portfolio demonstrates a consistent trajectory from fundamental molecular interactions to applied drug discovery, with particular emphasis on enzyme inhibitors, carbohydrate-protein interactions, and molecular recognition processes. Analysis of his publication record reveals a strong focus on structure-based drug design, with significant contributions to the development of inhibitors for enzymes like β-glucocerebrosidase, NNMT, and neuraminidase. His work spans multiple therapeutic areas including lysosomal storage disorders, cancer metabolism, and infectious diseases. The interdisciplinary nature of his research is evident in the integration of computational approaches with experimental validation across biochemistry, pharmacology, and medicinal chemistry. Teacher of the Year (awarded three times by Pharmacy students) Member of editorial boards for Medicines and Conceptuur journals Secretary of Board of FIGON (2016) Secretary of Raad voor de Farmaceutische Wetenschappen (2024) Member of Board of Stichting Farmaceutische Erfgoed (2024) Dr. Moret has been actively involved in educational innovation, developing and coordinating the master's programme Drug Innovation, the profile Drug Regulatory Sciences, and the Honours programme Pharmaceutical Sciences. He has taught courses for pharmacy, chemistry, UCU and medical sciences students, as well as PhD courses in bioinformatics and computer-aided drug discovery. His educational contributions include developing an inquiry-based elective course on drug discovery, for which he published educational research. He holds BKO and SKO teaching qualifications and participated in the Centre of Excellence in University Teaching program. As Managing Director of the Utrecht Institute for Pharmaceutical Sciences, Dr. Moret leads research initiatives across chemical biology, drug discovery, and pharmaceutical sciences. His leadership extends to multiple advisory and editorial roles within the pharmaceutical research community, reflecting his significant contributions to both academic and professional spheres of pharmaceutical sciences.
Dr. Richard Y. Zhao is a tenured Professor in the Department of Pathology and Microbiology-Immunology at the University of Maryland School of Medicine. His research combines molecular biology, fission yeast genetics, mammalian biology, and virology to study virus-host interactions, particularly for HIV and Zika virus. He previously held academic positions at Northwestern University and Columbia University and has contributed to over 120 peer-reviewed articles. B.S., China Oceanography University (1981) M.S., Oregon State University (1995) Ph.D., Oregon State University (1991) Postdoctoral Training, Columbia University (1991-1992) Dr. Zhao's research focuses on: Virus-host interactions and pathogenicity High-throughput drug screening for antivirals Role of viral proteins in neuroinflammation and cancer Translational genomics in precision medicine His recent publications highlight SARS-CoV-2 ORF3a, Zika envelope proteins, and HIV protease inhibitors, emphasizing host-pathogen mechanisms across species. He has served on NIH panels and editorial boards for journals like Cell Research and Retrovirology . Scientific awards include: Fellow, American Academy of Microbiology (2019) Bernard L Mirkin Endowed Chair (2001-2004) Honorary Director, Shandong Gallo Institute (2009) Distinguished Service from SCBA (2015) Outstanding Service from CBA-USA (2016) Dr. Zhao also contributes to clinical diagnostics and personalized medicine through molecular testing and pharmacogenetics programs.
Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.
Dr. Laura B. Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley. Her work focuses on causal inference, machine learning, and addressing methodological challenges in both randomized trials and observational studies, particularly in global health contexts. She leads collaborations in East Africa, focusing on HIV elimination and community health in rural regions. Her research emphasizes translating academic findings into real-world impact. Education: PhD in Biostatistics, UC Berkeley (2015) MPhil in Computational Biology, University of Cambridge (2009) BS in Applied Mathematics, University of Vermont (2008) Research Interests: Dr. Balzer’s work addresses causal inference in complex settings, including semi-parametric methods, measurement challenges, and dependence structures. Her global health projects target HIV prevention, tuberculosis transmission, and hypertension management in sub-Saharan Africa. She designs interventions like the SEARCH Dynamic Choice model, which offers flexible HIV prevention options, and evaluates community health worker programs. Publications highlight her contributions to HIV/AIDS research, including studies on PrEP uptake, viral suppression in adolescents, and tuberculosis-HIV co-infection. Methodologically, she advances causal inference frameworks to handle missing data and clustered designs. Awards: While no specific awards are listed, her work has been funded by initiatives like the SEARCH trials, reflecting its scientific and public health significance. Advising & Grants: Balzer collaborates with multidisciplinary teams in Uganda and Kenya, focusing on translational research. Her grants support interventions linking statistical innovation to healthcare delivery improvements in resource-limited settings. Labs/Teams: Her research is embedded within global health partnerships, particularly within the SEARCH trials network, which integrates biostatistics with clinical and community-based implementation.
Prof David Harrison is a Professor at the University of St Andrews' School of Medicine, affiliated with the Sir James Mackenzie Institute for Early Diagnosis and the Cellular Medicine Division. His research focuses on oncology, pharmacology, molecular biology, and AI-driven medical diagnostics. Key projects include the NuCana Biomed research agreement and the ICECAP initiative investigating immunopathology in severe COVID-19 cases. He supervises multiple PhD students and has contributed to advancements in drug development, cancer biology, and AI applications in pathology. His work spans clinical trials (e.g., RAMPART, NUC-3373 studies) and innovative imaging techniques (e.g., DESI-MSI for cancer metabolomics). Education: PhD (not explicitly stated in texts, inferred from academic rank). Research Interests: Prof Harrison’s expertise includes cancer drug mechanisms, molecular enzymology (e.g., 4-oxo-L-proline reductase), AI-driven pathology automation (e.g., cervical biopsy analysis), and immunological responses in critical illnesses. His lab develops tools for high-resolution histopathological imaging and explores metabolic-phenotypic links in tumors. Publications: Recent work includes studies on enzyme catalysis, clinical trial adaptations, and AI applications in diagnostics. These highlight his interdisciplinary approach to bridging basic science with translational medicine. Awards: None explicitly listed in the provided texts. Grants & Advising: Leads projects funded by NuCana Biomed (£171k) and Medical Research Scotland (£8k). Supervises eight PhD students focusing on drug mechanisms, imaging, and molecular biology. Labs/Teams: Collaborates within the Sir James Mackenzie Institute and cellular medicine teams, contributing to multidisciplinary research in early diagnosis and treatment strategies.
Professor Thierry Langer is a Full Professor of Pharmaceutical Chemistry at the University of Vienna’s Faculty of Life Sciences (Department of Pharmaceutical Sciences). He leads research in computational drug design, with a focus on pharmacophore modeling, 3D-QSAR analysis, and AI-driven molecular design. His work bridges theoretical and experimental chemistry, addressing targets like viral proteases (e.g., SARS-CoV-2), GABA receptors, and dopamine transporters. Research interests include: Pharmacophore-guided drug discovery for anti-viral and CNS therapies Development of next-generation computational tools (e.g., PharmacoMatch, QPhAR) Protein-ligand interaction modeling using neural networks and graph-based algorithms Recent studies focus on: Inhibitors for herpesvirus nuclear egress complexes, AI-optimized antivirals, and dopamine transporter inhibitors for cognitive enhancement. His lab collaborates on projects like the NeuroDeRisk initiative to de-risk neurotoxic compounds. Publications emphasize drug repurposing, metabolic pathway analysis, and scalable synthesis methods for promising drug candidates.
